How do you secure your DevOps environment? Best practices and tools

In the dynamic world of software development, where speed and efficiency are key, security is too often left behind. Enterprises implementing DevOps methodologies face the challenge of integrating security without slowing down the development cycle. This article is aimed at DevOps architects, security engineers and technical team leaders who already have a basic understanding of DevOps practices, but need specific guidance on implementing security in their environments.

We will discuss both the technical aspects and organizational challenges of implementing DevSecOps, paying particular attention to the trade-offs between security and speed of delivery. We will also outline criteria for selecting the right tools for different organizations – from startups to enterprise enterprises. Regardless of your organization’s DevOps maturity level, you’ll find practical approaches to help improve security posture without sacrificing agility.

What is a DevOps environment?

DevOps is an approach to software development that combines development (Development) and operations (Operations) teams, eliminating traditional organizational silos. In a DevOps environment, the software development lifecycle is continuous – from planning, to coding, to building, to testing, to deployment, to monitoring and feedback. Automation and continuous integration/delivery (CI/CD) are the cornerstones of this approach, enabling rapid and reliable change.

A DevOps environment consists of many components: code repositories (such as GitHub, GitLab, Bitbucket), continuous integration tools (Jenkins, CircleCI, GitHub Actions), containerization platforms (Docker), orchestration systems (Kubernetes), monitoring tools (Prometheus, Grafana) and many others. This technical complexity creates significant security challenges – each of these components has its own vulnerabilities and requires appropriate configuration. What’s more, the automation inherent in DevOps can act as a double-edged sword: while it speeds up value delivery, it also allows vulnerabilities to spread rapidly – a security bug can be replicated throughout a production environment within minutes.

Many organizations face a significant barrier to implementing security in DevOps: conflicting priorities. Development teams are primarily judged by how fast they can deliver new features, while implementing security is often seen as a slowing factor. This tension between security and speed is a real organizational challenge that will not be solved by tool deployment alone. An effective DevOps environment is based on a culture of collaboration and shared responsibility, which brings us to the concept of DevSecOps – where security is integrated into the entire development cycle from the very beginning, becoming a shared responsibility of all teams involved.

How to implement the DevSecOps model to integrate security into the software development process?

Once you understand what a DevOps environment is and the security challenges it generates, the natural step is to move to a DevSecOps model. Implementing this approach requires a fundamental shift in the way we think about security. Instead of treating security as the final step in pre-deployment verification, it should be integrated into every step of the manufacturing process.

The first step is to shift security thinking “to the left” (shift-left security) – that is, to introduce it as early as possible in the manufacturing cycle. This means taking security into account as early as the planning, architecture design and coding stages. In practice, this often proves more difficult than in theory. Developers may see security requirements as an obstacle that limits their creativity and slows down the manufacturing process. Product managers may be reluctant to take on additional tasks that don’t translate directly into customer-visible features. These cultural challenges are often more difficult to overcome than technical barriers.

A key component of DevSecOps implementation is security automation. While manual code reviews and penetration testing still have their value, they cannot keep up with the pace of DevOps. Therefore, it is essential to integrate automated security tools into CI/CD pipelines. Choosing the right tools depends on the specifics of your organization:

For small teams and startups, we initially recommend tools with a low entry threshold and minimal configuration, such as OWASP Dependency Check (dependency analysis), SonarQube Community Edition (code analysis) or Trivy (container scanning).
Mid-sized organizations can benefit from more advanced solutions like Checkmarx SAST, Snyk (comprehensive security), or Aqua Security (container security).
Large enterprises, especially in regulated sectors, often need enterprise solutions like Fortify, Veracode or Prisma Cloud, which offer advanced reporting, integration with compliance processes and dedicated support.

An important aspect, often overlooked when implementing DevSecOps, is the issue of false positives. Automated tools can generate a large number of alerts, many of which may be false positives. This leads to “alert fatigue” (alert fatigue), when teams begin to ignore alerts. An effective strategy should include an alert review process and prioritization mechanisms to focus on real threats.

DevSecOps success also requires breaking down barriers between teams, which is probably the most difficult aspect of the transformation. Security professionals must change their mindset from “change-blocking gatekeepers” to “value-delivering enablers.” Programmers and operators, in turn, must take responsibility for the security of the systems they develop. This cultural shift often requires support from top management, clear motivators (e.g., including security in employee evaluations) and long-term educational efforts.

DevOps	DevSecOps	Actual challenges
Focus: Speed and Cooperation	Focus: Balance between speed, collaboration and security	Reconciling conflicting metrics for evaluating development and security teams
Safety at the end	Security from the beginning of the process	Overcoming habits and convincing developers of the new approach
Responsibility for security: Specialists	Security Responsibility: All	Ensure that all team members are competent
Security features can slow down the process	Automated and integrated security	Avoiding “security theater” – safeguards that give a false sense of security but actually slow things down

As we move forward with the implementation of DevSecOps, we must first identify the biggest security threats that could occur in our environment. This will help us target our efforts to the highest risk areas.

What are the biggest security risks in a DevOps environment?

The DevSecOps model addresses a number of specific threats associated with the DevOps environment. Understanding these threats is key to successful security implementation.

One of the main risks is the mismanagement of secrets (credentials, API keys, certificates). In a highly automated DevOps environment, this sensitive information often ends up in code repositories, configuration files or environment variables, where it can be accidentally exposed. This problem, known as “secrets sprawl,” poses a serious risk, as stolen credentials can open the door to the entire infrastructure for attackers. Interestingly, a study by GitGuardian found that 5-10% of corporate repositories contain exposed secrets, and most organizations lack effective processes for detecting and responding to such incidents.

Infrastructure configuration vulnerabilities are another major threat. Automation and infrastructure-as-code (IaC) management enable environments to be created and modified quickly, but configuration errors can lead to serious vulnerabilities. This threat is particularly acute in multi-cloud environments, where each vendor has its own tools and security mechanisms, increasing complexity and risk of error. Technologies like Terraform and CloudFormation, while helping to standardize infrastructure, themselves become a potential attack vector – a malicious Terraform module downloaded from the public registry can introduce backdoors into the infrastructure.

External dependencies represent a third high-risk area, particularly in the context of supply chain attacks. Modern applications can contain hundreds of dependencies, each of which is a potential attack vector. Cases like SolarWinds and event-stream show that even widely used and trusted components can be compromised. Moreover, there is a phenomenon of “dependency blindness” in many organizations – teams are often unaware of all the components used in their applications and do not have processes to verify new dependencies before they are introduced.

Finally, CI/CD pipelines are becoming an increasingly attractive target for attackers. Compared to traditional systems that require many manual steps to deploy, automated CI/CD pipelines offer a “golden ticket.” – Compromising a single point can lead to the automatic deployment of malicious code in production. Attackers can exploit weaknesses in the configuration of CI/CD tools (like Jenkins or GitLab CI), plant malicious code through dependencies used in the build process, or use intercepted credentials to deploy their own changes.

It is worth noting that these threats are interconnected and often cascade – for example, a credential leak from a code repository can lead to a CI/CD pipeline takeover, which in turn enables a backdoor to be introduced into the application. Therefore, an effective security strategy must take into account the overall threat picture, rather than focusing only on individual attack vectors.

Knowing the main risks, we can now move on to implement the basic security principle in DevOps – “Security by Design,” which allows us to systemically address these risks from the design stage.

How do you implement Security by Design in DevOps processes?

Having identified the main risks in a DevOps environment, the next logical step is to implement a “Security by Design” approach that systemically minimizes these risks. This principle assumes that security is not an add-on, but a fundamental element of any solution, considered from the very beginning of the design process.

Implementation of this principle begins with the creation of clear security standards that define requirements for all applications and infrastructure components under development. Unlike traditional, voluminous security policy documents, which often remain unread and ignored by development teams, effective standards in a DevOps environment should take the form of:

Reusable component libraries – instead of mandating “implement secure session management,” provide off-the-shelf components with implemented security mechanisms
Infrastructure templates as code with built-in security features
Automated policy-as-code that can be integrated into CI/CD pipelines
Short “golden path” guides for developers, showing a recommended way to implement typical functionality

An important but often overlooked component of Security by Design is threat modeling. Many organizations avoid this process, perceiving it as too time-consuming or complicated for rapid DevOps cycles. In reality, however, threat modeling can be adapted to an agile approach. Instead of comprehensive STRIDE or DREAD workshops, which can take days, consider:

Short, 30-60 minute threat modeling sessions focused on specific functionalities
Automation of parts of the process through tools like OWASP Threat Dragon or Microsoft Threat Modeling Tool
The “threat modeling as code” approach, where threat models are versioned along with the code

These tools differ significantly in terms of capabilities, cost and learning curve:

Microsoft Threat Modeling Tool – a free tool for Microsoft environments, with a relatively steep learning curve
OWASP Threat Dragon – open source, lighter solution, good for starters
IriusRisk – paid enterprise solution with CI/CD integrations and automation
Threagile – a modern “threat modeling as code” approach, ideal for DevOps environments

A significant barrier to implementing “Security by Design” is the conflict between security and usability. Default security often introduces additional steps or restrictions that can frustrate users and developers. For example, requiring multi-factor authentication increases security but adds friction to the login process. Similarly, restrictive firewall rules can block legitimate use cases. A successful “Security by Design” approach must strike a balance between rigorous security and practicality of use, which it requires:

Design security with user experience in mind
Segmentation of security by risk level (stronger controls for critical systems)
Automate security checks to minimize manual interventions

Implementing “Security by Design” also requires cultural change, which is often the most difficult aspect of the transformation. In many organizations, there is a perception that security is the domain of security specialists, rather than the responsibility of every team member. Breaking this silo requires:

Support from senior management
Incorporate security metrics into the goals of development teams
“security champions” programs, where selected developers receive additional training and become security ambassadors within their teams
Gamification of security aspects through internal contests or bug bounty programs

Security by Design – Key Practices and Challenges

Practice	Business value	Implementation challenges
Security standards as code	Ensure consistency, automate verification	Maintaining a balance between rigorous standards and developer flexibility
Threat modeling	Early identification of risks, reduction of repair costs	Match fast development cycles, avoid “paralysis by analysis”
Secure default settings	Minimize the risk of misconfiguration	Conflict with usability, potential user resistance
Minimum set of rights	Reducing the potential impact of a breach	Increased complexity of identity management, potential delays in operation
Security automation	Consistency of control, elimination of human error	Initial implementation costs, risk of false positives

Once “Security by Design” is implemented as a fundamental principle, proper access and identity management becomes crucial as the first line of defense against unauthorized access.

How do you effectively manage access and identity with the principle of least privilege (PoLP)?

“Security by Design” is a fundamental approach that must be supported by specific controls. One of the most important is proper access and identity management, based on the Principle of Least Privilege (PoLP).

According to this principle, users, processes and systems should have only the level of privilege that is absolutely necessary to perform their tasks. While this concept is intuitive, its proper implementation in a DevOps environment is a major challenge. DevOps environments are characterized by high dynamics of change, frequent deployments and complex dependencies between systems, which complicates the precise definition of the “least necessary” set of permissions.

PoLP implementation requires transformation in three areas: people, processes and technology. Effective IAM (Identity and Access Management) solutions vary significantly depending on the size of the organization and the technologies used:

Small teams and startups can start with solutions like AWS IAM + IAM Roles for cloud access control, HashiCorp Vault for secret management and GitHub RBAC for code access control. These tools offer a good balance of functionality and administrative complexity.
Midsize organizations should consider more advanced solutions like Okta or Auth0 for federated identity management, integrated with cloud-specific tools (AWS Organizations, Azure AD Privileged Identity Management).
Large enterprises often need comprehensive enterprise solutions like CyberArk or BeyondTrust, which offer advanced privileged access management, auditing and regulatory compliance features.

In a DevOps environment, managing non-personal identities – system, service or API accounts – is a particularly difficult challenge. Automation requires permissions that allow operations to be performed without human intervention, which often leads to the creation of accounts with very broad permissions. Typical problems include:

Using one service account for many different automation processes
Give service accounts full administrative rights for simplicity
No rotation of credentials and monitoring mechanisms
Storing credentials in unsecured locations (environment variables, configuration files)

Effective strategies for mitigating these risks include:

Granular service accounts – each automation process should use a dedicated account with a precisely defined set of permissions
Dynamic credentials – instead of static API keys, it makes sense to use technologies like AWS IAM Roles, Azure Managed Identities or temporary token services
Contextual security – permissions dynamically adjusted based on multiple factors (time, location, behavior)

Another key aspect of PoLP is the temporal restriction of privileges. Traditional access models assume a static assignment of privileges – “Alice has access to database X.” In a DevOps environment, a dynamic approach is more appropriate – “Alice has access to database X only for the duration of task Y, after approval by Z.”

Just-In-Time Access (JIT) or Privileged Access Management (PAM) solutions implement this approach, offering:

Temporary upgrading of powers for the duration of the tasks
Multi-level authorization for access to critical systems
Detailed logging of all actions performed with elevated privileges
Automatically revoke access when a task is completed or a specified time has elapsed

Examples of tools in this category include CyberArk Privileged Access Manager, BeyondTrust PAM, HashiCorp Boundary or AWS IAM Access Analyzer.

Even the best-designed PoLP system will be useless without regular review and revision of privileges. In practice, the biggest challenge here is “privilege creep” – the gradual accumulation of more and more privileges by users that are rarely revoked. This problem is particularly acute in organizations with high employee turnover and changing roles. Automation of privilege reviews becomes essential at enterprise scale, where manual verification of thousands of accounts and privileges is virtually impossible.

Once access is properly managed, the next logical step is to implement tools to automatically detect vulnerabilities in code, allowing security issues to be identified and fixed early.

Which automatic code scanning tools (SAST/DAST) are the most effective?

Proper access management is the first line of defense, but even the best identity management practices will not protect against vulnerabilities in the application code itself. Therefore, another key element in a DevSecOps strategy is the implementation of automated code scanning.

Code scanning tools fall into several main categories, each offering different capabilities and suitable for different use cases. The selection of appropriate solutions should take into account the specifics of the organization, the technologies used and the maturity level of DevSecOps.

Static Analysis (SAST)

Static Application Security Testing (SAST) tools analyze source or compiled code without execution, identifying potential security vulnerabilities. There are a number of SAST solutions available on the market, which vary significantly in terms of:

Criterion	Open source solutions	Commercial solutions
Cost	Low initial cost of implementation, but higher cost of maintenance and configuration	Higher license cost, but often lower total cost of ownership (TCO)
Support for languages	Often limited to the most popular technologies	Wider support, especially for older or niche technologies
Accuracy	Variable, often higher false alarm rate	Usually better precision, but still problems with false alarms
Integration	Requires its own integration work	Usually ready-made integrations with popular CI/CD tools
Updates	Dependent on the community, sometimes irregular	Regular updates to vulnerability databases

How to secure CI/CD pipelines from malicious code injection?

CI/CD pipelines are the backbone of modern DevOps practices, but they are also an attractive target for attackers. Compromising a pipeline can be particularly dangerous, as it potentially allows malicious code to spread automatically to all environments, including production. Effectively protecting CI/CD pipelines requires a multi-layered approach.

Security challenges of CI/CD pipelines

Before we move on to recommendations, it is worth understanding the distinctive characteristics of CI/CD pipelines that make them difficult to secure:

High level of automation – limited human intervention means fewer checkpoints
Broad access – pipelines often need access to multiple environments and resources
Numerous integrations – each integration with an external tool increases the attack surface
Complexity of configuration – advanced pipelines can contain hundreds of steps and conditions
Frequent changes – constant modifications to pipelines make it difficult to secure consistently

The most popular CI/CD platforms have different security and vulnerability models:

CI/CD platform	Typical attack vectors	Security distinctions
Jenkins	Vulnerable plug-ins, improper role configuration	Rich ecosystem of security plug-ins, but high level of complexity
GitHub Actions	Malicious shares from marketplace, poorly secured secrets	Well-designed entitlement model, but challenges with controlling activities from marketplace
GitLab CI	Vulnerable base images, dangerous scripts	Built-in scanning features, but risks in self-hosted runners
Azure DevOps	Vulnerabilities in tasks, improper configuration	Strong integration with Azure identities, but complex entitlement model
CircleCI	Compromise account, poorly protected secrets	Limited permissions by default, but challenges in image control

Practices for securing CI/CD streams

The first step to effectively protecting CI/CD pipelines is to implement strict access control. In practice, this means:

Permission segmentation – different roles for different environments (dev, staging, prod)
Multi-level authorization – requiring additional approvals for critical changes
Multi-factor authentication (MFA) – mandatory for all administrative accounts
Dedicated identities – separate accounts for different automation activities

Most security incidents in CI/CD pipelines are related to improper secret management. Instead of storing secrets in environment variables or configuration files, we recommend:

Centralized secret management – using dedicated services like Vault, AWS Secrets Manager or Azure Key Vault
Dynamic credentials – automatic rotation of secrets and short lifespan
Just-In-Time Access – delivering secrets only while the task is being performed
Limiting the range of secrets – availability only for specific tasks, not the entire pipeline

Verifying code integrity and dependencies is critical to preventing supply chain attacks. Effective practices include:

Digital signing of commits – mandatory for all code changes
Verification of signatures – automatic verification in pipelines
Dependency scanning – checking libraries for known vulnerabilities
Whitelisting of sources – allowing only trusted component repositories
Immutable artifacts – building once and promoting through environments, without re-building

Isolation of CI/CD environments further reduces potential risks. Practical approaches:

Disposable build environments – each task executed in a clean, isolated container
Restricting Internet access – minimizing the possibility of downloading malicious code
Network separation – physical or logical separation of CI/CD environments from production
Dedicated agents – separate environments for different types of tasks (e.g., separate for public PR)

Implementing these safeguards can create tension between security and developer performance. Typical challenges include:

Increased build time – additional verification steps lengthen the cycle
Management complexity – more security policies means more administrative burden
Flexibility limitations – stringent safeguards can make it difficult to experiment quickly
User resistance – developers may seek workarounds for overly restrictive controls

It is worth remembering that CI/CD pipeline security must be part of a broader monitoring and response strategy. Once pipeline security is in place, another key element of protection is comprehensive infrastructure monitoring to quickly detect potential incidents.

How do you monitor your infrastructure for anomalies in real time?

Effectively securing a DevOps environment requires not only preventive controls, but also proactive monitoring to quickly detect and respond to incidents. Real-time anomaly monitoring is a critical layer of defense, especially in the face of constantly evolving threats that can bypass static defenses.

A layered approach to monitoring

Comprehensive security monitoring in a DevOps environment requires collecting and analyzing data from different layers of the infrastructure:

Layer	What to monitor	Typical tools	Challenges
Infrastructure	Resource utilization, configuration changes, operating system events	Prometheus, Grafana, Datadog, Nagios	Large amount of data, difficulty in distinguishing anomalies from normal fluctuations
Network	Traffic flow, communication patterns, connection attempts	Zeek, Suricata, ntop, Wireshark	Encrypted traffic limits visibility, high performance requirements
Applications	Application logs, performance metrics, errors	ELK Stack, Splunk, Graylog	Inconsistent log formatting, insufficient logging
User access	Logins, permission changes, unusual behavior	SIEM (Splunk, QRadar), CloudTrail, Azure Monitor	Difficulty in distinguishing legitimate actions from malicious ones
Pipeline CI/CD	Configuration changes, status of tasks, dependencies used	Jenkins Audit Trail, GitHub Audit Log, GitLab Audit Events	High rate of change makes it difficult to identify anomalies

The selection of appropriate monitoring tools should take into account:

The scale and complexity of the environment
Performance and latency requirements
Integration with existing tools
Available resources for management and analysis

Monitoring tools and practices in different types of organizations

Monitoring needs vary significantly depending on the size and maturity of the organization:

Small teams and startups can start with simpler solutions:

Prometheus + Grafana for infrastructure monitoring
ELK Stack (Elasticsearch, Logstash, Kibana) for central log management
Wazuh (open-source SIEM) for basic security analysis
AWS CloudTrail/Azure Activity Logs for auditing cloud activities

These tools offer a reasonable balance between functionality and implementation and maintenance costs. The challenge, however, is the configuration of event correlation between different systems and limited automatic response capabilities.

Mid-sized organizations need more advanced solutions:

Datadog or New Relic for comprehensive application and infrastructure monitoring
Splunk or Sumo Logic for advanced log analysis
AlienVault USM or Rapid7 InsightIDR as more advanced SIEM.
Dedicated cloud monitoring tools like Prisma Cloud (formerly Redlock)

These tools offer better integration, automatic correlation and more advanced analytics, but come with higher licensing costs and greater management requirements.

Large enterprises typically need enterprise solutions:

IBM QRadar, Splunk Enterprise Security or Microsoft Sentinel as a comprehensive SIEM
ServiceNow for incident response automation
Palo Alto Cortex XDR or CrowdStrike Falcon for advanced tip protection
Dedicated vulnerability management tools like Tenable or Qualys

These solutions offer the highest level of sophistication, but require dedicated teams to manage and significant investment.

Challenges in effective monitoring

There are numerous challenges to implementing effective monitoring in a DevOps environment:

Scale and speed of data – DevOps systems can generate huge volumes of logs and metrics, making real-time processing difficult
False alarms – overly sensitive detection rules can lead to overloading the team with irrelevant alerts
Dynamic infrastructure – ephemeral containers and serverless services complicate traditional approaches to monitoring
Lack of business context – difficulty in distinguishing real threats from technical anomalies
Fragmentation of tools – using multiple non-integrated monitoring solutions hinders the full picture

Advanced monitoring techniques

Modern DevOps environments require advanced anomaly detection techniques:

Machine Learning and AI – Machine learning algorithms can identify subtle patterns that indicate potential threats without defining rigid rules. Solutions like Darktrace, Vectra AI and ML functions in Splunk use these techniques.
Behavioral Analytics – monitoring normal behavioral patterns of systems and users, with alerts on deviations. This is particularly valuable in detecting advanced, long-running attacks.
Threat Intelligence – Integration with external sources of threat information allows for earlier identification of potential attacks. Platforms like ThreatConnect, IBM X-Force and Recorded Future provide such data.
Distributed Tracing – tracking requests through various microservices allows better understanding of data flow and detecting anomalies in communication. Tools like Jaeger, Zipkin and Datadog APM offer such capabilities.

Implementing effective security monitoring requires balancing threat detection with operational efficiency. Overly aggressive monitoring can overload systems and generate excess alerts, while an overly light approach can miss critical threats.

Moving from monitoring general infrastructure to more specific areas, let’s now look at how to effectively secure container environments and microservices, which are the foundation of modern DevOps architectures.

How to ensure the security of Docker containers, microservices and images?

Containers and microservices have revolutionized the way applications are built and deployed, but they have also brought new security challenges. Unlike traditional monolithic applications, microservice architecture creates a much larger attack surface and requires a different approach to security.

Major security challenges in container architecture

Security for containers and microservices must address a number of unique challenges:

Ephemerality – containers are created and destroyed dynamically, making traditional monitoring and security difficult
Density of deployments – production environments may contain hundreds or thousands of container instances
Shared infrastructure – containers share the host kernel, creating the risk of “escape-the-container” attacks
Complexity of communication – microservices often communicate over unsecured internal networks
External dependencies – base images and external components increase the risk of attacking the supply chain

Securing Docker images and the build process

The security of a container environment begins with securing the images themselves – the foundation upon which the entire architecture rests. Key practices include:

Minimize image content

The smaller the image, the smaller the potential attack surface. Practical approaches:

Multi-stage images (multi-stage builds) – using one container to build and another, minimal one to run the application
Alpine or distroless base images – much smaller than standard Ubuntu or Debian images
Remove development tools – debuggers, compilers and system tools should not be in production images

It is also a popular practice to use dedicated images for different applications:

Image type	Advantages	Disadvantages	Suitable for
Alpine	Very small (5-8MB), popular	Limited system libraries	Most applications where compactness is a priority
Distroless	Minimal (runtime only), secure	More difficult debugging, no shell	Production applications requiring maximum security
Scratch	Absolute minimum	No tools, libraries or shell	Statically compiled applications (Go, Rust)
Slim variants	Compromise between functionality and size	Still includes basic tools	Applications that require some system tools

Automatic image scanning

Regular scanning of images for vulnerabilities is essential. Key tools in this category:

Trivy – fast, simple and accurate scanning, ideal start for smaller organizations
Clair – open-source, good integration with container registries, but requires more configuration
Aqua Security – a comprehensive enterprise solution with enhanced reporting and compliance features
Snyk Container – scanning both applications and containers, with good integration with CI/CD pipelines

Effective scanning should be integrated at various stages of the container lifecycle:

During the building process in CI/CD
Before adding the image to the registry
Periodically for all images in the registry
Before deployment to production
Continuously for running containers

Signing and verification of images

To ensure the integrity and authenticity of images, it is worth implementing digital signing:

Docker Content Trust – a built-in mechanism in Docker
Cosign – part of Sigstore project, simpler alternative to DCT
Notary – advanced signing and verification system

Securing the container runtime environment

Securing the images themselves is only half the battle – properly configuring the runtime environment is equally important:

Secure container configuration

Core practices include:

Unprivileged access – containers should never run as root. Solutions:
- Explicite settings for non-privileged user in Dockerfile (USER)
- Using SecurityContext in Kubernetes
- Implementation of Pod Security Policies/Admission Controllers.
Resource limits – always define CPU and memory limits to prevent DoS attacks:
- Docker settings: –memory, –cpu-quota
- In Kubernetes: requests and limits in the specification it will state
Read-only filesystem – where possible, mount the container filesystem as read-only:
- Docker: –read-only
- Kubernetes: readOnlyRootFilesystem in SecurityContext
Capability dropping – limiting the kernel permissions available to the container:
- Docker: –cap-drop ALL –cap-add [only necessary].
- Kubernetes: configuring capabilities in SecurityContext

Securing container orchestration

For Kubernetes-based environments, key security practices include:

Network Policies – block all communication by default, allow only necessary connections
Under Security Standards – Baseline or Restricted profile implementation
RBAC – precise control of user and service access
Admission Controllers – automatic verification of compliance with policies before creating resources
Encrypted Secrets – always encrypt secrets in etcd (–encryption-provider-config)
Audit Logging – enable detailed audit logging for all activities in the cluster

Secure communication between microservices

In a microservice architecture, securing communication between components is critical:

Mutual TLS (mTLS) – mutual authentication between services, not just encryption:
- Service mesh like Istio, Linkerd and Consul automate mTLS deployment
- Certificates can be managed automatically by tools like cert-manager
Zero-Trust Networking:
- Assume that the internal network is compromised
- Require authentication for every communication, even within the cluster
- Implement the finest possible granularity of permissions for network communications
API Gateway:
- Centralization of authentication and authorization
- Rate limiting and throttling for preventing DoS attacks
- Input/output validation at the system boundary

Real-time monitoring and security

Even the best-secured container environment requires constant monitoring:

Runtime Security – tools like Falco, Aqua Runtime Protection and Sysdig Secure monitor container behavior, detecting anomalies
Network Monitoring – observing communication patterns between containers
Compliance Scanning – continuous verification of compliance with CIS Benchmarks for Docker and Kubernetes

Compromises and challenges

When implementing container and microservices security, organizations face difficult trade-offs:

Security vs. developer simplicity – Complex security policies can hinder rapid iterations
Security vs. Performance – Some security controls (like encryption of all communications) can affect latency
Security vs. Resources – comprehensive monitoring and scanning require significant resources

The key is to find the right balance for a particular organization, taking into account the level of risk, resources and regulatory requirements.

After securing containers and microservices, the next critical aspect is to ensure proper data encryption, which we will discuss in the next section.

Why is encryption of data in motion and at rest crucial for compliance?

Data encryption is a fundamental component of security in a DevOps environment, serving the dual role of protecting information from unauthorized access and ensuring regulatory compliance. In a dynamic DevOps ecosystem, where data flows between numerous components and is stored in different locations, a comprehensive encryption strategy becomes a necessity.

Regulatory requirements for encryption

Today’s regulations impose increasingly stringent requirements for data protection, with a particular focus on encryption:

Adjustment	Encryption requirements	Consequences of non-compliance
RODO/GDPR	Requires “appropriate” technical measures to protect personal data (Article 32), where encryption is explicitly mentioned	Fines of up to €20 million or 4% of global turnover
PCI DSS	Requires encryption of payment card data both at rest (req. 3) and in motion (req. 4)	Loss of ability to process card payments, financial penalties
HIPAA	Requires electronic health information (ePHI) to be secured when transmitted over networks (§164.312(e))	Fines of up to $1.5 million per year per category of violation
SOC 2	Requires controls for confidentiality and integrity of customer data	Loss of certification, business consequences
ISO 27001	Requires cryptographic controls (A.10) to protect confidentiality, authenticity and integrity	Loss of certification, business consequences

It is worth noting that in many jurisdictions (e.g., in the EU under RODO), properly encrypted data can be exempted from breach notification, providing additional business motivation for implementing robust encryption mechanisms.

Encryption of data in motion

Data-in-motion encryption is concerned with protecting information during its transmission between systems. In a DevOps environment, where components often communicate over unsecured networks, this is particularly important.

Challenges specific to DevOps

Micro-service architectures and multi-cloud environments create unique challenges for encryption in motion:

Increased number of communication points between services
Various communication mechanisms (REST, gRPC, asynchronous queues)
Hybrid environments combining on-premise infrastructure with public clouds
Automating certificate management for ephemeral services

Recommendations and best practices

TLS everywhere – use TLS 1.2+ for all HTTP communications, not just external traffic:
- Automate certificate management with tools like Let’s Encrypt and cert-manager
- Use automatic certificate rotation
- Regularly update TLS versions and cipher suites
mTLS for communication between services:
- Implement service mesh (Istio, Linkerd) for mTLS automation
- Implement zero-trust netwok access (ZTNA) even inside a private network
Secure certificate management:
- Use HSM or Cloud KMS to protect private keys
- Implement Certificate Transparency Log Monitoring
- Implement automatic alerts on expiring certificates
Additional layers of security:
- VPN for access to administrative infrastructure
- Dedicated channels for sensitive communications

Tools and solutions

Choosing the right solutions for encryption in motion depends on the scale and characteristics of the organization:

Small teams: Caddy Server (with automatic HTTPS), Traefik, Let’s Encrypt
Medium-sized organizations: NGINX with ModSecurity, HAProxy with LetsEncrypt, AWS Certificate Manager.
Large enterprises: F5 BIG-IP, Istio with dedicated CA, enterprise PKI solutions

Data encryption at rest

Data encryption at rest protects information stored in databases, file systems and other persistent storage.

Challenges specific to DevOps

DevOps environments introduce specific challenges:

Automated infrastructure provisioning requires automated key management
Ephemeral temporary environments also store sensitive data
Multi-layered environments (dev, staging, prod) require key isolation
Infrastructure as Code may accidentally expose encryption configurations

Data encryption levels at rest

Data encryption at rest can be implemented at different levels, each with its own advantages and limitations:

Encryption level	Advantages	Disadvantages	Typical solutions
Application level	Superior control, independence from infrastructure	Increased application complexity, potential impact on performance	Cryptographic libraries, ORMs with encryption
Database level	Granular control (columns, tables), transparency for applications	Complexity of key management, does not protect against DB administrators	Transparent Data Encryption, field-level encryption
File system level	Transparency for applications, broad protection	Less granularity, vulnerability to attacks on running system	LUKS, BitLocker, eCryptfs
Drive/volume level	Full transparency, ease of implementation	No granularity, data decrypted after installation	EBS encryption, Azure Disk Encryption
Hardware level	No CPU load, high security	High cost, limited flexibility	Self-encrypting drives, HSMs

The ideal approach is often to implement encryption at multiple levels, creating a deep defense.

Cryptographic key management

The biggest challenge in data encryption is secure key management. Problems include:

Generation of cryptographically strong keys
Secure key storage
Key rotation without downtime
Recovery after lost keys
Key access control

How to conduct regular penetration tests and infrastructure security audits?

Regular penetration testing and security audits are an essential part of a DevSecOps strategy to verify the effectiveness of deployed security measures under conditions similar to an actual attack. In a DevOps environment, where changes are frequent and rapid, the traditional approach to penetration testing (once a quarter or a year) proves insufficient. It is necessary to implement a more agile and continuous testing model.

The evolution of penetration testing in a DevOps environment

Traditional penetration testing often fails to keep up with the pace of change in the DevOps environment, leading to new approaches:

Test model	Characteristics	Advantages	Disadvantages
Traditional pentests	Comprehensive tests performed periodically (e.g., once a quarter)	Accuracy, depth of testing	Delayed results, high costs, infrequent frequency
DevSecOps pentests	More frequent, smaller tests integrated into the development cycle	Faster feedback, agility	Smaller scope of tests, potential omissions
Continuous pentesting	Continuous automated safety testing with elements of manual testing	Immediate feedback, constant protection	Limitations of automation, possibility of false alarms
Crowdsourced security	Bug bounty programs, invited experts	Variety of approaches, increased test coverage	Management difficulties, unpredictable results

Modern DevOps environments typically require a combination of these approaches: continuous automated testing supplemented by regular in-depth manual testing and bug bounty programs.

Penetration testing methodology for DevOps environments

Effective penetration testing in a DevOps context should be:

Automated where possible
Integrated with CI/CD processes
Iterative instead of monolithic
Targeted for specific changes
Complete in terms of testing all layers of infrastructure

Phases of successful penetration testing:

Preparation and scope:
- Clearly define the goals and boundaries of the tests
- Identification of critical assets and potential attack vectors
- Establish “rules of engagement” – permitted techniques and limitations
Information gathering:
- Passive reconnaissance (DNS, public information, GitHub)
- Active scanning (infrastructure mapping, service identification)
- Inventory of technologies and potential vulnerabilities
Threat modeling:
- Identification of potential attack vectors
- Prioritization of test areas based on business risk
- Develop test scenarios that simulate real-world threats
Penetration tests:
- Testing the external attack surface (web applications, APIs, VPN)
- CI/CD infrastructure security verification
- Cloud and container configuration tests
- Attacks on the software supply chain
- Simulation of social engineering attacks
Analysis and reporting:
- Prioritization of found vulnerabilities according to actual risk
- Practical repair recommendations
- Integration of results with vulnerability management systems
- Clear metrics and comparisons with previous tests
Remediation and retesting:
- Verification of the effectiveness of repairs
- Include repairs in CI/CD pipelines as automatic checks

Choosing between internal and external teams

The decision to use internal or external testing teams depends on several factors:

Aspect	Internal teams	External experts
Costs	Lower direct costs, higher costs to maintain competence	Higher direct costs, no maintenance costs
Availability	Immediate availability, limited resources	Planning ahead, access to a wider pool of experts
Objectivity	Risk of “blind spots” and architectural habits	Fresh perspective, experience from different organizations
Knowledge of the environment	In-depth knowledge of the systems	Requires time to get to know the environment
Regulatory compliance	May not meet the requirements of some standards	Often required by regulations (e.g. PCI DSS)

The optimal solution is often a hybrid model:

Internal team responsible for continuous automated testing integrated with CI/CD
External experts to conduct periodic, comprehensive tests and audits
Bug bounty programs as a supplement, especially for publicly available applications

Security test automation

In a DevOps environment, automation of security tests and their integration with CI/CD pipelines is key. Popular approaches include:

Automated security scanning:
- OWASP ZAP or Burp Suite for automated testing of web applications
- Metasploit for infrastructure test automation
- Container security scanners (Trivy, Clair) for container images
- Cloud security posture management (Prisma Cloud, CloudSploit) for cloud configuration
Infrastructure as Code Testing:
- Static analysis of IaC templates (Terraform, CloudFormation)
- Automatic verification of compliance with security practices
- Simulation of deployments in isolated test environments
Continuous Security Validation:
- Platforms like Cymulate or AttackIQ for continuous validation of security controls
- Breach and Attack Simulation (BAS) to automate attack scenarios
- “Red team as a service” with elements of automation

Challenges and practical recommendations

Implementing penetration testing in a DevOps environment presents several common challenges:

The pace of change – traditional pentests become obsolete soon after completion
- Solution: Modularize testing, focus on components that have changed
Dynamic infrastructure – ephemeral environments make testing difficult
- Solution: Testing infrastructure “blueprints” instead of specific instances
Automation vs. depth – automated tests won’t find all the problems
- Solution: A layered approach that combines automation with regular manual testing
Integration of results – problems with prioritization and vulnerability tracking
- Solution: Integrated vulnerability management system with API for testing tools
Cyclic discovery of the same problems – recurring vulnerabilities in new components
- Solution: Security templating, training, “Security Champions” in teams

Performance metrics for penetration testing

The effectiveness of a penetration testing program should be measured by appropriate metrics:

Time to detect – how quickly new vulnerabilities are detected
Time to remediate – how quickly problems found are fixed
Test coverage – what percentage of infrastructure/code is being tested
Vulnerability density – number of vulnerabilities per unit of code/infrastructure
Regression rate – frequency of reappearance of repaired vulnerabilities

Proper collection and analysis of these metrics allows for continuous improvement of the testing process and overall security posture.

In addition to penetration testing, securing a DevOps environment also requires effective credential management to prevent uncontrolled credential proliferation, which will be the subject of the next section.

How to combat “secrets sprawl” in credential management?

The problem of “secrets sprawl” – the uncontrolled proliferation of sensitive credentials in an infrastructure – is one of the key security challenges in a DevOps environment. In an ecosystem where automation is the foundation, credentials (passwords, API keys, certificates, tokens) are essential for communication between different components. However, their mismanagement can lead to serious security breaches.

Anatomy of the “secrets sprawl” problem

The problem of the spread of secrets has many dimensions and causes:

Typical causes of “secrets sprawl”:

Time pressure – developers under pressure to deliver functionality often choose the fastest rather than the safest solution
Insufficient awareness – lack of understanding of the consequences of storing secrets in unsecured locations
Infrastructure complexity – multilayered environments with numerous integrations require multiple credentials
Lack of standards – inconsistent secret management practices across teams
Legacy systems – older systems often have permanently encoded credentials

The most common locations of secret leaks:

Location	Risks	Why is this a problem?
Code repositories	Very high	Public access (open source) or broad access within the organization
Configuration files	High	Often stored without encryption, accessible to administrators
Environment variables	Medium-High	Visible in logs, memory dumps, visible to other processes
Automation scripts	High	Often stored without access control, shared between teams
Containers and paintings	Very high	May be publicly distributed, difficult to update once problem detected
Log files	High	Long-term storage, often shared for problem analysis
Backups	Medium	Long-term storage, often with lower levels of security

A strategic approach to managing secrets

Effective secret management requires a comprehensive strategy that includes tools, processes and education.

Centralized management of secrets

The foundation of a successful strategy is the implementation of a dedicated secret management system. The most popular solutions:

Solution	Type	Advantages	Disadvantages	Suitable for
HashiCorp Vault	Self-hosted/Cloud	Versatility, advanced features, open-source	Complexity of configuration, HA management required	Organizations of any size requiring advanced functions
AWS Secrets Manager	Cloud (AWS)	Native integration with AWS, automatic rotation	Vendor lock-in, higher costs at large scale	Organizations primarily using AWS
Azure Key Vault	Cloud (Azure)	Native integration with Azure, HSM	Limited functionality outside the Azure ecosystem	Organizations primarily using Azure
Google Secret Manager	Cloud (GCP)	Simple integration with GCP, versioning	Limited functionality outside of GCP	Organizations primarily using Google Cloud
CyberArk Conjur	Self-hosted/Cloud	Enterprise features, support for legacy systems	High cost, complexity	Large enterprises with complex requirements
Bitwarden Secrets Manager	Cloud/Self-hosted	Ease of use, good value for money features	Limited enterprise integrations	Small/medium organizations

When choosing a solution, consider:

Compatibility with existing ecosystem of tools
Automation and integration capabilities with CI/CD
High availability requirements
Maintenance costs in the long term
Identity and access management functions

Dynamic credential management

The modern approach to secrets in a DevOps environment is based on the concept of dynamic, temporary credentials instead of static, long-lived secrets:

Short-term credentials:
- JWT tokens with short lifespans
- AWS IAM roles for EC2 instances and ECS jobs
- Azure Managed Identities
- Google Cloud Service Account Impersonation
Automatic secret rotation:
- Regular, automatic change of all static credentials
- Immediate rotation when a potential leak is detected
- Synchronized rotation between supplier and consumer secrets
Zero-knowledge authentication:
- Use of protocols that do not require secret storage
- OAuth 2.0 and OIDC implementation for user and system authentication
- Mutual TLS (mTLS) instead of shared secrets where possible

Automatic secret detection

A key element in the fight against secrets sprawl is proactive detection of sensitive data in inappropriate locations:

Pre-commit hooks:
- Local scanning before saving changes to the repository
- Tools like git-secrets, Talisman or Gitleaks
- Blocking commits containing credential patterns
Scanning in the CI/CD pipeline:
- Automatic scanning of each commit
- Blocking the merge of code containing secrets
- Tools like TruffleHog, detect-secrets or SpectralOps
Continuous monitoring:
- Regular scanning of the entire codebase
- Monitoring of public repositories
- Services like GitGuardian, GitHub Secret Scanning
Remediation workflows:
- Automatic notifications to code owners
- Tracking and verification of repairs
- Automatic rotation of detected credentials

Integration with DevOps practices

Effective secret management must be tightly integrated with DevOps processes:

Secrets in Infrastructure as Code:
- Use of tools like Terraform Vault Provider
- Dynamic retrieval of secrets during deployment
- Encryption of sensitive data in Terraform state (e.g., with SOPS)
Secrets in Containers and Kubernetes:
- Use of Kubernetes Secrets (with additional encryption)
- Integration with external secret management systems (Vault Injector, External Secrets Operator)
- Avoid embedding secrets in container images
Secrets in CI/CD:
- Secure CI/CD pipeline variables
- Just-in-time access to secrets only during construction
- Separation of permissions between different stages of the pipeline

Education and organizational policies

Technical solutions alone will not solve the secrets sprawl problem without proper organizational practices:

Education Program:
- Regular training of developers on secure secret management
- Practical workshop demonstrating the effects of leaks
- Clear guidelines and best practices documentation
Security Champions:
- Designate safety ambassadors in each team
- Responsibility for verification of secret management practices
- Promoting a culture of safety
Policies and Standards:
- Clear rules for classifying credentials
- Standards for storage and rotation of secrets
- Spill response procedures
Audit and Enforcement:
- Regular audits of secret management practices
- Policy compliance metrics
- Consequences for repeated violations

After securing the secrets, another critical component of a DevOps security strategy is to properly configure cloud environments, which will be the subject of the next section.

How to secure cloud environments from configuration errors?

Configuration errors in cloud environments are one of the most common attack vectors in today’s cyber security landscape. Unlike traditional vulnerabilities in code, configuration errors often result from misalignment of cloud resources, unawareness of security implications or simple mistakes during rapid deployment.

Typical cloud configuration errors and their consequences

Configuration errors can occur at different levels of cloud infrastructure, with different consequences:

Error category	Examples	Potential consequences
Access control	Public S3 buckets, overly broad security groups rules, redundant IAM permissions	Data leakage, unauthorized access
Managing secrets	Secrets in environment variables, unencrypted keys	Compromise credentials, escalation of privileges
Network	Unnecessarily open ports, no segmentation	Lateral movement, access to internal systems
Monitoring and logging	Disabled audit logs, no alerts	Undetected violations, obstructed investigations
Data encryption	Unencrypted data at rest, no encryption in transit	Data leakage in case of breach
Identity management	Weak authentication, no MFA, shared accounts	Account seizure, unauthorized access

Especially in multi-cloud or hybrid environments, where different tools and practices are used for different vendors, the risk of misconfiguration increases significantly. Teams often focus on functionality while neglecting security aspects, leading to a “security debt” that becomes increasingly difficult to repay over time.

Practices for securing cloud environments

Successfully securing cloud environments requires a combination of technical and organizational practices. Key approaches include:

Infrastructure as code (IaC)

Adopting the IaC approach fundamentally changes the way the cloud is secured, eliminating manual, error-prone resource configuration in favor of repeatable, versioned infrastructure definitions. The most popular IaC tools vary in their characteristics and use cases:

Tool	Characteristics	Best use	Typical challenges
Terraform	Universal, declarative, supplier-independent	Multi-cloud, heterogeneous environments	Complexity in large deployments, state management
AWS CloudFormation	Native to AWS, full integration with AWS services	AWS-only environments	Steep learning curve, limitation to AWS
Azure Resource Manager	Native to Azure, good integration with the rest of the ecosystem	Azure-only environments	Restricted to Azure, complex JSON syntax
Google Cloud Deployment Manager	Native to GCP, support for Python/Jinja	GCP-only environments	Limited adoption, smaller community
Pulumi	Programmatic approach, support for programming languages	Teams with strong programming skills	Higher entry threshold, younger ecosystem

Regardless of the tool chosen, key practices in IaC include:

Infrastructure modularization – building reusable, well-tested components
Infrastructure code versioning – treating IaC with the same care as application code
Code review for infrastructure changes – mandatory second-person review
Automated infrastructure testing – verification of functionality and security before deployment
Immutable infrastructure – instead of updating existing resources, deploy new ones with improved configuration

Automatic configuration scanning

Continuous scanning of IaC definitions and deployed infrastructure for security vulnerabilities is an essential part of security. Solutions in this category can be divided into:

IaC scanning tools:
- Checkov – scans Terraform, CloudFormation, Kubernetes, ARM
- tfsec – dedicated to Terraform, high-performance
- cfn-nag – focuses on AWS CloudFormation templates
- Snyk IaC – a commercial solution with broad support
Tools for scanning deployed infrastructure:
- AWS Config – monitoring the compliance of AWS resources
- Azure Policy – enforcing standards and compliance in Azure
- Google Security Command Center – central security management in GCP.
- Prisma Cloud – a multi-cloud solution with advanced features
Cloud Security Posture Management (CSPM):
- Platforms such as Wiz, Prisma Cloud and Orca Security
- Continuous monitoring of the entire cloud environment
- Prioritization of risks based on exposure and potential impact
- Recommendations and automation of repairs

Identity and access management in the cloud

Effective access management in cloud environments requires a specialized approach:

Implementation of the principle of least privilege (PoLP):
- Create dedicated roles with precisely defined permissions
- Regular reviews and revisions of entitlements
- Automatic detection of redundant privileges (e.g., AWS IAM Access Analyzer)
Identity Federation:
- Integration with corporate identity management systems (AD, Okta, Ping)
- Single Sign-On (SSO) for unified authentication
- Centralized management of access policies
Temporary access:
- Just-In-Time (JIT) access to production environments
- Automatic rotation of credentials
- Time-limited sessions
Entitlement segmentation:
- Separation of environments (dev, test, prod) through separate accounts/subscriptions
- Isolation of critical resources in dedicated segments
- Boundary protection with controlled access points

Cloud-based network security

Cloud network architecture differs significantly from traditional infrastructure and requires a specific approach:

Defense in depth:
- Multi-layer security (perimeter, network, endpoint, data)
- Controls at every level of infrastructure
Network segmentation:
- Microsegmentation based on application communication needs
- Zero-trust network architecture (ZTNA)
- Detailed network access policies (NACL, Security Groups, Firewall Rules)
Connection security:
- Private endpoints/links for cloud services
- VPN or Direct Connect for secure access to the cloud
- Encryption of all communications (TLS 1.2+)
Protection against attacks:
- WAF for web application protection
- DDoS protection for key endpoints
- Threat intelligence and proactive blocking of malicious traffic

Challenges and trade-offs in securing the cloud

Implementing end-to-end security in a cloud environment inherently involves a number of challenges and trade-offs:

Security vs. speed of deployment :
- Strict security controls may slow development cycle
- Automation of security testing is essential to maintain momentum
- “Shift-left security” helps find balance
Cost vs. level of security :
- Advanced security features (WAF, CSPM, CASB) generate additional costs
- Redundant environments for security testing increase expenses
- Prioritize controls based on risk and business value
Centralization vs. autonomy of teams :
- Central security management ensures consistency
- Autonomy of teams speeds up implementation
- Hybrid approach with central standards and decentralized implementation
Vendor lock-in vs. comprehensive security features :
- Cloud provider’s native security tools are well integrated
- Third-party solutions offer broader capabilities and independence
- Strategically balancing integration and independence

Successfully securing cloud environments requires a holistic approach that includes technology, processes and people. By implementing infrastructure as code, automated monitoring and testing, and ongoing team education, you can minimize the risk of configuration errors and increase the overall level of security.

After securing cloud environments, another important component of a security strategy is proper management of backups, which are the last line of defense in the event of security incidents.

What practices to follow when creating and verifying backups?

Backups are a critical component of any security strategy, providing the last line of defense against a variety of threats – from ransomware attacks to accidental data deletion to hardware failures. In a DevOps environment, where infrastructure is often dynamic and ephemeral, traditional backup approaches can fall short.

Fundamental principles of backup

Regardless of the environment and technology, a few fundamental principles should underpin any backup strategy:

3-2-1 rule

The classic 3-2-1 rule remains the gold standard, although it needs modification in modern environments:

3 copies of the data – the original plus at least two backups
2 different media – store copies on different types of media or systems
1 off-site copy – at least one copy stored off-site at the primary location

In the DevOps context, “different media” can mean different cloud services or a combination of cloud and local storage. “Off-site” can mean a different cloud region or a different provider.

Some organizations are expanding this rule to 3-2-1-1-0:

Additional 1 means immutable (unchangeable) backup
0 means zero errors on playback (regularly tested)

Types of backups

In a DevOps environment, different approaches to backup should be considered, depending on the type of data and requirements:

Backup type	Description	Advantages	Disadvantages	Suitable for
Full	Complete copy of all data	The simplest playback	Large volume of data, time-consuming	Critical systems with low RPO
Incremental	Copy of data changed since last backup	Efficiency of space and time	Complex recovery, dependence on previous backups	Systems with large amounts of data, frequent backups
Differential	Copy of data changed since last full backup	Faster restoration than incremental	Greater use of space than incremental	Balance between performance and playback speed
CDP (Continuous Data Protection).	Continuous tracking and recording of changes	Minimal RPO, point-in-time recovery	High resource requirements	Critical systems with very low RPO
Snapshot	Image of the system status at a specific moment	Fast creation, low system load	Often platform-dependent	Quick restore points, virtual systems

The specifics of backups in a DevOps environment

The DevOps environment introduces specific challenges and requirements for backup strategies:

Backup of infrastructure components

In DevOps architecture, backup goes beyond traditional production data and should include:

Infrastructure configuration:
- IaC code (Terraform, CloudFormation)
- Configuration of containers and orchestration (Docker, Kubernetes)
- CI/CD pipeline definitions
System artifacts:
- Code repositories (Git) and their metadata
- Container images and registry configurations
- Application packages and libraries
Operational data:
- CI/CD and management systems databases
- System and audit logs
- Metrics and monitoring data

Backup of container environments

Containerization introduces new challenges for backup:

Stateful vs. Stateless :
- Stateless containers can be restored by definition, without traditional backup
- State data (persistent volumes) requires dedicated backup strategies
Container backup levels:
- Image backup – preserving container images in the registry
- Backup of container data – volume copies and persistent storage
- Backup definitions – Kubernetes configurations (deployments, services, etc.)
- Backup the entire cluster – tools like Velero for backup K8s
Container backup challenges:
- Ephemerality – containers are dynamically created and destroyed
- Dispersion – data can be distributed between multiple containers
- Consistency – ensuring consistent state of multi-container applications

Backup automation

In a DevOps environment, where automation is the foundation, manually managing backups is impractical. Key practices include:

Backup as code:
- Defining backup policies as code
- Versioning and testing of backup configurations
- Integration with CI/CD pipelines
Dedicated tools:
- CloudBackup (AWS Backup, Azure Backup, GCP Backup).
- Cross-cloud solutions (Veeam, Commvault, Rubrik)
- Specialized tools (Velero for K8s, Percona XtraBackup for databases)
Schedulers and orchestration:
- Automatic backup schedules
- Copy retention and rotation management
- Automatic scaling of backup resources

Testing and verification of backups

A backup is only valuable if data can be successfully restored from it. Regular testing is absolutely key:

Backup testing levels

Comprehensive testing should include various levels of verification:

Integrity verification:
- Checksum control
- Verification of the correctness of the file structure
- Checking the completeness of the backup
Functional testing:
- Reproduction in a test environment
- Verification of application performance
- Data integrity tests
DR (Disaster Recovery) tests:
- Simulation of total environmental loss
- Restoration in an alternative location
- Measurement of actual RTO/RPO times

Backup test automation

Playback tests should be automated and performed regularly:

Scheduled tests:
- Regular, scheduled playback tests (weekly/monthly)
- Rotation of components under test
- Detailed documentation of the results
Chaos engineering:
- Random, unannounced playback tests
- Simulation of various failure scenarios
- Verification of procedures and automation
Continuous restore verification:
- Continuous playback testing of new backups
- Automatic playback tests after each backup
- Integration of results with monitoring systems

Backup security

The backups contain the organization’s complete data, making them an attractive target for attackers:

Encryption of backups:
- End-to-end encryption (during transmission and storage)
- Encryption key management (HSM, KMS).
- Separation of keys from backups
Protection against ransomware:
- Immutable backups (WORM – Write Once, Read Many)
- Air-gapped copies (physical isolation)
- Versioning and overwrite protection
Access control:
- Strictly limit access to backup and management systems
- Multi-level authentication for playback operations
- Detailed logging and access monitoring

Metrics and KPIs for backups

Effective backup management requires measurement of key indicators:

Performance Indicators:
- Recovery Point Objective (RPO) – maximum acceptable period of data loss
- Recovery Time Objective (RTO) – maximum recovery time
- Backup completion rate – percentage of successful backups
- Restore success rate – percentage of successful restores
Operational Indicators:
- Backup window – the time it takes to perform a backup
- Storage efficiency – space efficiency
- Time to detect backup failures – time to detect backup failures
- Mean time to repair (MTTR) – average time to repair problems

Backup strategy – key challenges

Data consistency in distributed systems: Difficulty in ensuring synchronization and consistency of backups between distributed components
Balancing costs vs. RPO/RTO : Lower RPO/RTO usually means higher costs.
State data in containerized environments: The complexity of backing up state data in microservices architecture
DR testing without impacting production: The challenge in reliably testing catastrophic scenarios without risk to the production environment
Scaling your backup strategy: Maintain performance as data volume and infrastructure complexity grows

A successful backup strategy in a DevOps environment requires a holistic approach that integrates technological, process and organizational aspects. The key is to automate both the backup itself and its testing, while ensuring the security and verifiability of the entire process.

Having a robust backup strategy is the last line of defense, but it is equally important to proactively reduce the attack surface by effectively managing updates and security patches.

How do you implement an effective update and patch management strategy?

Managing security updates and patches is a fundamental part of protecting DevOps environments from known vulnerabilities. The challenge is balancing the need to deploy security updates quickly with the need to ensure the stability of production systems.

Challenges of patching in a DevOps environment

Traditional approaches to update management often fail in dynamic DevOps environments because of several key challenges:

Scale and complexity – DevOps environments can include hundreds of servers, thousands of containers, and dozens of different technologies, significantly increasing the number of components requiring upgrades
Dynamic environments – short-lived instances and containers that are constantly being created and destroyed make traditional approaches to updating difficult
Continuous availability – requiring uninterrupted service availability makes it difficult to plan service windows
Dependencies – complex relationships between components increase the risk that updating one component will disrupt others
Diversity of technologies – the mix of different operating systems, frameworks and programming languages requires different approaches to patching

Strategic approaches to patching

In a DevOps environment, there are several main approaches to update management, each with its own advantages and trade-offs:

Approach	Description	Advantages	Disadvantages	Best use
Traditional patching	Update existing systems on site	Familiar, low initial cost	Risk of downtime, difficult automation	Legacy systems, small environments
Immutable infrastructure	Instead of upgrades, deploy new instances with already upgraded components	Predictability, easier rollbacks	Higher infrastructure requirements	Containers, cloud-native environments
Canary deployments	Gradual deployment of upgrades to a subset of systems	Early detection of problems, minimization of risks	Complexity, longer time for full implementation	Critical manufacturing systems
Blue/green deployments	Maintaining two identical environments and switching between them	Zero downtime, instant rollback	Higher infrastructure costs	Systems requiring high availability

The most successful organizations often combine these approaches depending on the context – using, for example, immutable infrastructure for containers, blue/green for key applications and traditional patching for immutable infrastructure elements.

Elements of an effective patching strategy

A comprehensive update management strategy in a DevOps environment should include the following elements:

1. monitoring and prioritization of vulnerabilities

The starting point for effective patching is knowing which components need to be updated and what are the risks associated with each vulnerability:

Automatic vulnerability scanning – regularly checks all components for known vulnerabilities
Integration with CVE databases – automatic download of information about new vulnerabilities
Risk-based approach – prioritizing updates based on real risk, not just the overall CVSS rating
Dependency tracking – tracking dependencies between components for better impact assessment

Modern solutions in this category include:

Open-source tools: Trivy, OWASP Dependency Check, OpenVAS
Commercial solutions: Tenable Nessus, Qualys VM, Snyk, Rapid7 InsightVM

2. automation of the update process

At DevOps scale, manually managing updates is virtually impossible. Automation of the entire process is key:

Infrastructure as Code (IaC) – defining infrastructure including component versions
Configuration management – tools like Ansible, Chef or Puppet for configuration management
CI/CD for patching – integration of updates into CI/CD pipelines
Orchestration – coordination of updates in distributed systems

An example of an automated patching process might look like this:

Automatic detection of a new vulnerability
Risk assessment and prioritization
Automatic creation of a branch with an update
Running tests for the updated version
Deployment to a test environment
Automated testing and validation
Plan and execute production deployment

3. strategies to minimize deployment risk

The implementation of the upgrade itself must be designed to minimize the risk of negative impact:

Progressive deployment – incremental deployment of updates with monitoring of impact
Feature flags – ability to quickly disable problematic functionality
Automated rollback – automatic rollback of changes when problems are detected
A/B testing – comparing the behavior of updated and non-updated components

4 Testing before implementation

A critical part of the process is comprehensive testing of the update before production deployment:

Automated testing – unit, integration and end-to-end testing
Vulnerability verification – verifying that the update actually removes the vulnerability
Compatibility testing – verification of compatibility with other components
Performance testing – checking the impact of updates on performance
Chaos testing – failure simulation to verify the resilience of updated systems

5. post-implementation monitoring

The process does not end with the implementation – it is necessary to monitor the systems after the upgrade:

Real-time monitoring – tracking key system metrics
Anomaly detection – automatic detection of unusual behavior
User feedback – collecting information from users
Post-mortem analysis – analysis of problems that arose after implementation

Specifics of different types of updates

Different components of the DevOps environment require specific approaches to upgrades:

Operating systems

Traditional servers – scheduled maintenance windows, live patching (e.g., Ksplice, KernelCare)
Cloud inst ances – immutable infrastructure, instance rotation
Containers – base image update, rebuild and redeployment

Applications and dependencies

Libraries and frameworks – updating by package managers, dependency scanning
Language-specific dependencies – npm, pip, gem, gradle with appropriate strategies
Custom applications – CI/CD, feature flags, canary deployments

Databases

Traditional RDBMS replicas, blue/green deployments
NoSQL – rolling upgrades, sharding
DBaaS – managed updates with minimal impact

Infrastructure

Networking – redundant components, rolling upgrades
Storage – RAID, distributed storage, rolling upgrades
Security devices – high availability pairs, staggered updates

Compromises and challenges

Implementing a comprehensive patch management strategy involves a number of trade-offs:

Security vs. stability – Faster deployment of updates improves security, but may compromise stability
Automation vs. control – More automation speeds up the process, but reduces control
Standardization vs. flexibility – A unified approach to patching simplifies management, but may not suit all systems
Costs vs. coverage – Complex update management requires significant resources

Best practices

In summary, an effective upgrade strategy in a DevOps environment should take into account:

Automation of the entire process – from vulnerability detection, to testing, to deployment
Risk-based approach – prioritization based on actual risk, not just CVSS assessment
Immutable infrastructure – where possible, preferring to deploy new instances instead of upgrading existing ones
Clear SLAs – defined response times for different categories of vulnerabilities
Complex testing process – comprehensive testing before production deployment
Integration with CI/CD – integration of update management into existing pipelines
Metrics and KPIs – measuring the effectiveness of the patching process

Implementing a robust update management strategy is a key component of a mature DevSecOps approach. Effective patch management not only reduces the risk of security breaches, but also increases the stability and reliability of the entire environment.

The implementation of technical safeguards and processes must be supported by appropriate documentation that complies with international standards, which will be the subject of the next section.

How to document security processes according to ISO/NIST standards?

Adequate documentation of security processes is not only a requirement of many regulations and standards, but also the foundation of effective security management in an organization. In a DevOps environment, where changes occur quickly and frequently, the traditional approach to documentation may not be sufficient.

The role of standards in security documentation

International standards like ISO 27001 and the NIST Cybersecurity Framework offer a proven framework for security documentation. Before discussing the practicalities, it is useful to understand what they are and how the major standards differ:

Standard	Characteristics	Application area	Approach
ISO 27001	International standard for information security management	Comprehensive security management system (ISMS)	Process-based, based on the PDCA cycle
NIST Cybersecurity Framework	U.S. standard for cyber security practices	General framework for cyber security risk management	Functional, flexible
SOC 2	Audit standard for service organizations	Security, availability, process integrity controls	Based on trust, controls and attestation
PCI DSS	Payment data security standard	Payment card data protection	Prescriptive, very detailed
GDPR/RODO	European data protection law	Data protection	Based on the rights of individuals, the responsibility of the organization

Each of these standards requires a slightly different approach to documentation, but they all share some basic principles.

Hierarchy of security documentation

Effective security documentation should create a logical hierarchy, from general policies to detailed instructions:

Policies (Policies) – high-level documents that define general principles and directions:
- Information security policy
- Data classification policy
- Access management policy
- DevOps security policy
Standards (Standards) – documents that define specific requirements:
- Secure software coding standards
- Infrastructure configuration standards
- Password management standards
- Encryption standards
Procedures (Procedures) – detailed processes of “how” to carry out tasks in accordance with policies:
- Incident response procedure
- Change management procedure
- Procedure for managing entitlements
- Procedure for implementing security updates
Instructions (Work Instructions) – detailed, step-by-step instructions for performing specific tasks:
- WAF Configuration Manual
- Server hardening instructions
- Vulnerability scanning instructions
- Instructions for conducting penetration tests
Records (Records) – evidence of activities, audit results, logs:
- Audit reports
- Access logs
- Penetration test reports
- Documentation of incident response

Challenges of documenting security in DevOps

The DevOps environment presents unique challenges for security documentation:

Rapid pace of change – traditional, static documentation quickly becomes outdated
Automation – processes are increasingly automated, changing the nature of documentation
Dispersion of responsibility – in the DevOps model, responsibility for security is dispersed
Scaling up – as infrastructure grows, it is difficult to maintain up-to-date documentation
Balance of detail – too general documentation is useless, too detailed difficult to maintain

A modern approach: Documentation as Code

In response to these challenges, modern DevOps organizations are implementing a “Documentation as Code” (DaC) approach that treats documentation like code:

Key principles of Documentation as Code

Storage in version control systems (Git):
- History of documentation changes
- Full auditability
- Access control
- Documentation review processes
Automatic generation of documentation:
- From the source code (e.g. via Javadoc, JSDoc, Sphinx)
- From the definition of infrastructure (e.g., Terraform documentation)
- From configuration (e.g., architecture diagrams generated from code)
- From testing (e.g., API documentation from integration testing)
Text-based format:
- Markdown, AsciiDoc, reStructuredText
- Ease of editing and comparing versions
- Possibility of cooperation through pull requests
- Conversion to various output formats (PDF, HTML)
Automated documentation testing:
- Syntax and consistency validation
- Checking the links
- News verification
Continuous Documentation:
- Documentation update as part of CI/CD
- Automatic publishing of new versions
- Informing about changes

Tools to support Documentation as Code

A number of tools have emerged in the DevOps ecosystem to support DaC:

Static documentation generators: Jekyll, Hugo, MkDocs, Sphinx
Diagramming tools as code: PlantUML, Mermaid, WebSequenceDiagrams
Compliance as Code: InSpec, Compliance Masonry, OpenControl
API Documentation Management: Swagger/OpenAPI, API Blueprint.
Wiki integration: GitBook, Wiki.js (with Git integration)

Mapping documentation to standards requirements

When implementing Documentation as Code, care must be taken to map documents to specific standards requirements:

ISO 27001

ISO 27001 requires documentation for 114 controls in Appendix A. Key areas of documentation include:

Context of organization and scope of ISMS
Information security policy
Risk assessment methodology and risk assessment report
Statement of Applicability
Risk management plan
Operating procedures for security management
Security incident records
Results of ISMS reviews and audits

NIST Cybersecurity Framework

The NIST CSF organizes documentation around five key functions:

Identify – documentation of asset inventory, risk assessment
Protect – documentation of access control, awareness, security procedures
Detect – documentation of monitoring, detection of anomalies
Respond – incident response plans, communications
Recover – recovery procedures, business continuity plans

Practical implementation in a DevOps environment

Combining the requirements of standards with the DevOps approach, the following documentation model can be implemented:

Inventory automation – automatic generation and updating of asset inventory:
- Automatic detection of resources in the cloud
- Network and asset scanning
- Monitoring of configuration changes
Policies as code – implementation of policies as verifiable rules:
- Open Policy Agent (OPA) for automatic compliance checking
- HashiCorp Sentinel for policy-as-code
- AWS Config Rules / Azure Policy for IaC compliance
Pipeline safety documentation:
- Automatic generation of documentation as part of CI/CD
- Compliance checks integrated into pipelines
- Automatic recording of changes and test results
Compliance management platforms:
- GRC (Governance, Risk, Compliance) with API for integration
- Continuous Compliance Monitoring
- Automation of technical audits

Common mistakes and how to avoid them

Many organizations make the same mistakes when documenting security:

Documentation for documentation’s sake:
- Problem: Create documents only to meet audit requirements
- Solution: Focus on practical value and utility
Overly general policies:
- Problem: High-level documents without specific guidelines
- Solution: Supplement with specific, verifiable standards
No updates:
- Problem: Documentation created once and forgotten
- Solution: Automation and Continuous Documentation
Detachment from reality:
- Problem: Documents describing ideal processes, not actual ones
- Solution: Documentation generated from the actual configuration
Excessive formalism:
- Problem: Overly complex and formal documents deterring users
- Solution: accessible form, practical examples, visualizations

Effective security documentation – key principles

Automation – generating documentation from code and configuration
Verifiability – the ability to automatically check compliance
Timeliness – continuously updated as part of CI/CD
Affordability – documentation that is practical and understandable to teams
Adaptability – flexibility to adapt to changing requirements
Tracking changes – a complete history of changes and why they were made

Properly documented security processes create a solid foundation for regulatory compliance, but it is equally important to ensure that the entire DevOps team has the right knowledge and skills. This brings us to another key element of DevSecOps’ strategy – ongoing cyber security training.

Why is continuous cyber security training essential for DevOps teams?

In the DevOps era, where development and operations teams work closely together and deployment cycles are getting shorter, the traditional model where security is the domain of a specialized team is becoming insufficient. Continuous cybersecurity training is becoming not just part of good practice, but a fundamental requirement for protecting organizations.

The evolution of security training in the context of DevOps

The approach to security training has evolved with the transformation of software development methodologies:

Era	Approach to training	Characteristics	Restrictions
Traditional	Silowe	Mandatory compliance training, uniform for all	Lack of role alignment, low knowledge retention
Agile	Design	Training related to specific projects, basics for developers	Fragmentation, lack of overall picture
DevOps	Integrated	Security as part of the teams’ daily work	Requires cultural change, difficult to implement
DevSecOps	Continued	Ongoing skill development, hands-on exercises, culture of “security champions”	Requires significant resources and commitment

This evolution reflects a shift from viewing security as a “necessary evil” to a fundamental element of software and infrastructure quality.

Why traditional training is not enough

The classic approach to cybersecurity training often fails in a DevOps environment for several reasons:

Threat dynamics – the threat landscape is changing so rapidly that one-year training courses are quickly becoming outdated
Diverse roles – DevOps teams perform a variety of functions that require specific security expertise
Practical application – traditional training often focuses on theory, without practical application
Lack of context – generic training does not take into account the specific technology and processes used in the organization
Training fatigue – long, one-off sessions lead to low knowledge retention

Effective training program for DevOps teams

An effective cybersecurity training program for a DevOps environment should take into account the specifics of this methodology and be based on several key principles:

1. continuity and iterability

Instead of one-time intensive training, a continuous approach is more effective:

Micro-trainings – short, 15-30 minute sessions focused on specific issues
Regular updates – weekly/monthly briefs on the latest threats
Progressive development path – gradual building of skills from basics to advanced

2. personalization for different roles

Different roles on the DevOps team require different security competencies:

The role of	Key training areas	Recommended formats
Developers	Secure coding, OWASP Top 10, secure design patterns	Interactive coding exercises, code reviews
Infrastructure engineers	Harden, ng systems, cloud security, container security	Hands-on labs, infrastructure as code
Operators	Security monitoring, incident detection, vulnerability management	Incident simulations, toolbox workshops
Product Owners	Risk modeling, risk management, compliance	Workshops, case studies
Scrum Masters	Security in agile processes, support for security champions	Coaching, best practices

3. practical and interactive approach

Effective training should focus on practical application instead of dry theory:

Capture The Flag (CTF) – competition in finding and fixing vulnerabilities
Security hackathons – team-based solutions to security problems
Workshops hands-on – workshops with actual tools and technologies
Secure coding challenges – programming challenges focusing on security
Red team exercises – simulated attacks on team infrastructure

4 Contextuality and relevance

Training should be tailored to the specific technology and processes used in the organization:

Stack-specific security – security of specific technologies used by the team
Custom vulnerable apps – training apps that reflect real projects
Post-incident learning – learning from actual incidents
Project-based security reviews – project-specific security reviews

5. safety culture

Training alone is not enough – it is necessary to build a culture of safety:

Security Champions – identifying and developing security leaders in each team
Positive reinforcement – rewarding good safety practices
Blameless postmortems – analysis of incidents without seeking blame
Executive sponsorship – visible management support for security initiatives

Innovative training methods

Modern approaches to cyber security training use a range of innovative methods:

Gamification

Using game elements to increase engagement:

Points and badges for completed training and tasks
Rankings and competition between teams
Scenario games that simulate real-world attacks
Rewards for detecting and reporting vulnerabilities

Sandbox environments

Safe environments for experimentation and learning:

Vulnerable by design – specially prepared vulnerable applications
Cyber Ranges – comprehensive environments for simulating attacks
Cloud lab environments – temporary cloud environments for practice
Containerized security labs – easy to deploy containerized labs

Just-in-time learning

Delivering knowledge exactly when it is needed:

Security linting – IDE tools that suggest safe practices when coding
Contextual security hints – security hints in the development process
Pull request security reviews – educational code reviews focusing on security
Security checklists – checklists for key activities

Measuring the effectiveness of training

To ensure that a training program is truly valuable, it is essential to measure its effectiveness:

Key metrics

Reduction in security issues – reducing the number of vulnerabilities introduced into the code
Mean time to remediate – reducing the time to remediate detected vulnerabilities
Security awareness scores
Security tool adoption – increasing the use of security tools
Secure design implementation – frequency of implementation of secure design patterns

Evaluation methods

Pre-post assessments – pre- and post-training tests
Practical evaluations – practical evaluation of skills
Simulated ph ishing – simulated phishing attacks
Bug bounty metrics – results from bug bounty programs
Peer reviews – peer reviews from a security perspective

Challenges and how to overcome them

Implementing an effective training program faces a number of challenges:

Challenge	Description	Strategies for overcoming
Lack of time	DevOps teams work under pressure of time and deadlines	Micro-training, integration with existing processes, automation
Different levels of knowledge	Team members have different levels of competence	Personalized learning paths, mentoring, materials at different levels
Rapidly changing technologies	Continuous evolution of the technology stack	E-learning platforms with updated content, in-house experts
Measuring ROI	Difficulty in quantifying the value of training	Clear KPIs, benchmarking, linking to actual incidents
Cultural resistance	Perception of security as an obstacle	Executive buy-in, success stories, inclusion in team goals

Effective training programs – key principles

Continuity instead of one-offs – regular, short sessions instead of infrequent, intensive training sessions
Practicality over theory – hands-on labs, CTF and real-life scenarios
Personalization for roles – customizing content to meet the specific needs of different team members
Up-to-date content – constantly updated with the latest threats and techniques
Supportive culture – security as a shared responsibility and value
Measurable results – clear KPIs and regular performance evaluation

Investing in ongoing cyber security training for DevOps teams not only reduces the risk of incidents, but also builds organizational resilience. As teams develop their competencies, security becomes a natural part of the development cycle rather than an additional burden.

Effective training is the foundation, but measuring the effectiveness of implemented safeguards is equally important, which will be the topic of the next section.

How to measure the effectiveness of security through key KRI/KPIs?

Measuring security effectiveness is one of the biggest challenges in the cybersecurity field. Without proper metrics, it is difficult to assess the return on security investment, identify areas for improvement or effectively communicate the state of security to stakeholders. In a DevOps environment, where change is dynamic, traditional approaches to measuring security often fail.

The difference between KRI and KPI in the context of security

Before we get into specific metrics, it’s worth understanding the difference between risk indicators (KRIs) and performance indicators (KPIs):

Aspect	Key Risk Indicators (KRI)	Key Performance Indicators (KPIs)
Target	Measuring the level of risk	Measuring the effectiveness of activities
Orientation	Advance (leading)	Delayed (lagging)
Examples	Number of unpatched vulnerabilities, percentage of systems without current updates	Average incident detection time, percentage of incidents resolved in SLA
Use of	Early warning, prioritization of actions	Assessing the effectiveness of the security program, benchmark

An effective measurement strategy should consider both KRIs and KPIs, creating a complete picture of the state of security.

Security metrics framework for DevOps

A comprehensive metrics framework for a DevOps environment should cover several key areas:

1 Threat Metrics.

Metrics that measure the level of external and internal threat:

Number of attack attempts – the number of security breaches detected
Threat intelligence coverage – percentage of monitored threat types
Trending threats – analysis of trends in attacks on the organization and the industry
Attack surface – size of the attack surface (public endpoints, APIs, etc.)

2. vulnerability metrics (Vulnerability Metrics).

Metrics focusing on potential system weaknesses:

Vulnerability density – number of vulnerabilities per unit of code/infrastructure
Mean time to patch – average time to patch identified vulnerabilities
Patch coverage – percentage of systems with up-to-date security patches
Critical vulnerability exposure – time of exposure to critical vulnerabilities
Backdoor commits – detected attempts to introduce malicious code

3. DevSecOps Process Metrics (DevSecOps Process Metrics)

Metrics to evaluate security integration into the DevOps cycle:

Security testing coverage – percentage of code/components covered by automated security tests
Security issues in pipeline – number of security issues detected in CI/CD pipeline
Mean time to remediate – average time to remediate security problems detected in the pipeline
Security debt – the number of known unpatched security problems
Security requirements coverage – percentage of security requirements implemented in the project

4 Incident Metrics

Metrics related to actual security breaches:

Mean time to detect (MTTD) – the average time from occurrence to detection of an incident
Mean time to respond (MTTR) – average time from detection to incident response
Mean time to contain (MTTC) – the average time from detection to containment of an incident
Mean time to recover (MTTR) – average time from detection to full recovery
Incident impact – the actual impact of incidents (financial, reputational, etc.)
Repeat incident rate – frequency of repeated similar incidents

5 Compliance Metrics

Metrics to assess compliance with regulations and standards:

Compliance rate – the percentage of compliance checks that meet the requirements
Compliance violations – number of violations of security policies
Audit findings – number and criticality of audit findings
Time to compliance – the time it takes to achieve compliance with new requirements
Compliance automation – percentage of compliance checks automated

Implementation of the measurement program

Simply defining metrics is not enough – an effective implementation of the measurement program is required:

1. identifying the target state

Before you start measuring, define:

Security program objectives
Acceptable level of risk
Security priorities
Regulatory and industry requirements

2. metrics selection

Select metrics that:

Are relevant to your organization
They can be objectively measured
They have clear alarm thresholds
Are understood by stakeholders
They can be compared over time

3. automation of data collection

In a DevOps environment, manual collection of metrics is impractical:

Integration with existing tools and platforms
Automatic aggregation of data from various sources
Central repository of metrics
Automatic data validation

4. visualization and reporting

Effective communication of metrics:

Real-time dashboards
Reports tailored to different audiences
Automatic alerts when thresholds are exceeded
Trend analysis and forecasting

5. continuous improvement

The metrics program should evolve:

Regular reviews of the effectiveness of metrics
Adapting to changing threats
Developing granularity and coverage
Benchmark with industry standards

Example dashboards of security metrics

Effective metrics dashboards should be tailored to different audiences:

1. executive dashboard

For senior management:

Overall level of cyber security risk
Trends in key areas
Comparison with industry benchmarks
Business impact (costs, compliance)
ROI on security investments

2 Security Team Dashboard

For the security team:

Detailed operational metrics
Alerts about exceeded thresholds
Tracking the progress of repairs
Root cause analysis
Trend analysis for different types of threats

3. DevOps Team Dashboard

For development and operations teams:

Security issues in their areas of responsibility
CI/CD pipeline metrics
Results of code and infrastructure scanning
Vulnerability trends in their projects
Security debt and recovery plan

Challenges in measuring security

Effectively measuring security in a DevOps environment presents numerous challenges:

1. security paradox

Success in security is… the absence of incidents. It is difficult to measure something that does not occur:

Focus on preventive (not just reactionary) metrics
Measuring the maturity of security processes
Use of simulation and testing (red team, penetration testing)

2. false confidence

Good metrics can give a false sense of security:

Balancing quantitative and qualitative metrics
Verification through external tests and audits
Awareness of “unknown unknowns” – risks we don’t know

3. changing threat landscape

A static set of metrics can quickly become outdated:

Cyclic review and update of metrics
Incorporating threat intelligence into the interpretation of metrics
Adaptive alarm thresholds

4. cultural differences

Dev and Ops teams’ different approaches to metrics:

Adjusting the language and context of metrics
Link to business objectives
Transparency of methodology

Practical examples of KPIs/KRIs

Specific metrics with sample goals and ways to measure them:

Metrics	Definition of	Target	Measurement method	Frequency	Responsibility
Security testing coverage	% of code passing automatic security tests	>95%	Jenkins/GitLab CI metrics	Daily	Dev Team
Mean time to patch critical	Average time to repair critical vulnerabilities	<48h	JIRA/Vulnerability management tool	Weekly	Ops Team
Security findings per sprint	Number of security problems found in a sprint	<5 high/critical	SAST/DAST reports	Per sprint	Security Team
Repeated vulnerabilities	% of vulnerabilities repeated in new code	<10%	Security scanning history	Monthly	Security Champions
Mean time to detect	Average time to detect an actual incident	<24h	SIEM/security monitoring	Quarterly	SOC Team

Maturity of the security metrics program

The security metrics program is evolving with the maturity of the organization:

Maturity level	Characteristics	Example metrics	Challenges
Initial	Ad-hoc measurements, reactive	Number of incidents, basic compliance	Lack of standardization, fragmentation
Managed	Regular measurement of basic metrics	MTTR, patch coverage, vulnerability count	Limited automation, long feedback cycle
Defined	Defined set of KPIs/KRIs, regular reporting	Risk scores, security process metrics, trend analysis	Integration with dev processes, excessive amount of data
Measured	Automation, correlation of metrics, predictive analytics	Predictive risk indicators, business impact metrics	Complexity, maintenance of automation
Optimized	Adaptive metrics, AI/ML for analytics, full integration with business	Adaptive risk thresholds, real-time business value metrics	Maintaining a balance between comprehensiveness and transparency

Effective measurement of collateral – key principles

KRI/KPI balance – measuring both risk (anticipatory) and performance (delayed)
Automation – automatic data collection and analysis for the current image
Business context – linking security metrics to business objectives
Adaptability – adapting metrics to the changing threat landscape
Transparency – clear methodology and interpretation for all stakeholders
Continuous improvement – regular reviews and updates of the metrics program

An effective security performance measurement program allows an organization to make informed data-driven decisions, optimize security investments and demonstrate the value of security initiatives. This is especially important in a DevOps environment, where change is rapid and traditional periodic security assessments are insufficient.

Even the best security and most accurate metrics do not completely eliminate the risk of incidents. That’s why another key component of a DevSecOps strategy is an effective incident response plan.

How to create an incident response plan with automation in mind?

In a DevOps environment, where change is frequent and rapid and infrastructure is extensive and complex, traditional approaches to incident response often prove inadequate. An effective Incident Response Plan (IRP) must be as agile and automated as the DevOps process itself.

The evolution of incident response in the DevOps era

The traditional approach to incident response has been significantly transformed in a DevOps environment:

Aspect	Traditional approach	DevSecOps approach
Responsibility	Dedicated security team	Joint responsibility of Dev, Sec and Ops teams
Speed of response	Hours/days	Minutes/seconds through automation
Documentation	Static playbooks	Dynamic, executable procedures
Scale	Manual analysis and response	Automatic response to common incidents
Perspective	Reactive, focus on repair	Proactive, continuous learning and adaptation
Infrastructure	Stable, rarely changed	Dynamic, ephemeral, defined as code

This evolution requires a new approach to planning and implementing incident response processes.

Components of a modern incident response plan

An effective incident response plan in a DevOps environment should include the following elements:

1. organizational structure and roles

Clearly defined roles and responsibilities are the foundation of an effective response:

Incident Commander – the person responsible for coordinating the entire process
Technical Lead – a technical expert who leads the analysis and repairs
Communications Lead – responsible for internal and external communications
Security Analyst – a security specialist who analyzes an incident
DevOps Engineer – an engineer who deals with operational aspects
Business Representative – a person who assesses business impact and priorities

In the DevSecOps model, it is crucial that these roles are distributed among different teams, rather than concentrated solely in the security team.

2. categories and prioritization of incidents

Not all incidents are equally critical. An effective plan should define:

Level	Characteristics	Examples	Response SLA
P1 – Critical	Direct impact on key production systems, customer data or security	Data breach, ransomware attacking production	Immediately (15-30 min)
P2 – High	Significant impact on important systems or data, but without direct exposure to customers	Suspicious activity in production environment, DDoS attack	1-2 hours
P3 – Medium	Limited impact on non-production systems or non-critical data	Breach of dev/staging system, phishing	4-8 hours
P4 – Low	Minimal risk, low potential impact	Minor policy violations, perimeter scanning	24-48 hours

Automation can support prioritization by automatically assessing the impact and criticality of an incident based on defined rules.

3. response playbooks

Detailed, workable procedures for different types of incidents:

Detection – how to recognize and confirm an incident
Analysis – how to determine the extent and impact of an incident
Containment – how to limit the spread of an incident
Eradication – how to remove the cause of the incident
Recovery – how to restore normal operation
Post-Incident – how to learn lessons and prevent similar incidents from happening again

In a DevOps environment, playbooks should be:

Executable – automated where possible
Testable – regularly verified
Versioned – stored in version control systems
Contextual – taking into account the specifics of the infrastructure

4. tools and integrations

An effective response requires the right tools:

Category	Features	Example tools	Integrations
SIEM/SOAR	Log aggregation, event correlation, response orchestration	Splunk Enterprise Security, IBM QRadar, Cortex XSOAR	Security APIs, monitoring systems
EDR/XDR	Detecting and responding to threats at endpoints	CrowdStrike Falcon, SentinelOne, Microsoft Defender for Endpoint	SIEM, threat intelligence
Threat Intelligence	Information on current threats	Recorded Future, Mandiant, AlienVault OTX	SIEM, firewalls, WAF
Forensics	Collection and analysis of evidence	Volatility, KAPE, GRR Rapid Response	Storage systems, backup platforms
Communication	Team coordination during an incident	Slack, Microsoft Teams, PagerDuty	Alerting systems, ticketing
Automation	Response automation	Tines, Shuffle, n8n	All other tools

The key is to ensure that these tools are properly integrated, allowing for a consistent flow of information and automation of activities.

Automation of incident response

Automation is a critical component of modern incident response, especially in a DevOps environment:

Levels of automation

Automation can be implemented gradually, at different levels:

Level 1: Automatic detection – automatic detection of potential incidents
- SIEM with correlation rules
- Anomaly detection based on ML
- User behavior analytics
Level 2: Automatic alerting – notifying the appropriate people
- Intelligente routing alerts
- Prioritization and de-duplication
- Contextual notifications
Level 3: Automatic enrichment – enriching alerts with context
- Automatic download of logs
- Correlation with data from other systems
- Threat intelligence lookup
Level 4: Automatic containment – automatic containment actions
- Isolation of infected systems
- Blocking suspicious IPs
- Revocation of credentials
Level 5: Automatic remediation – full automation of repair
- Automatic patching
- Redeployment of clean environments
- Automatic security reconfiguration

Security Orchestration, Automation and Response (SOAR).

SOAR platforms allow for end-to-end automation of incident response:

Workflow automation – creating automated sequences of activities
Case management – incident management from detection to closure
Integration hub – central integration of various tools and systems
Playbook builder – visual creation of automated playbooks
Metrics and reporting – tracking KPIs related to response

An example of an automated workflow for a phishing incident:

Automatic detection by SIEM/email security
Creating an incident in SOAR
Automatic enrichment (checking sender, URLs, attachments)
Categorization and prioritization
Automatic quarantine of similar emails
Notification of the security team
Automatic collection of data on potential victims
Generate a report for the team

Integration with CI/CD

In a DevOps environment, incident response should be integrated with CI/CD pipelines:

Automatic rollback when security problems are detected
Feature flagging to quickly disable problematic functionality
Chaos engineering to test incident resilience
Automated forensics in the pipeline

Infrastructure as code for incident response

The IaC approach can also be applied to incident response:

Incident response as code – defining playbooks as executable code
Disaster recovery as code – automated restoration of environments
On-demand forensic environments – automatic creation of environments for analysis
Immutable security monitoring – monitoring infrastructure defined as code

Practical implementation of the incident response plan

Successful implementation requires a systematic approach:

1. preparation

Risk assessment – identification of key assets and risks
Baseline creation – establishment of normal patterns of operation of systems
Tool selection – selecting the right tools for your environment
Playbook development – creating and documenting procedures
Training – training teams in response

2 Testing

Tabletop exercises – simulations of incidents without actual impact on systems
Red team exercises – simulated attacks on infrastructure
Purple team exercises – collaboration between red and blue teams for mutual learning
Chaos engineering – deliberate introduction of failures to test resilience
Post-exercise reviews – drawing conclusions from exercises

3. continuous improvement

Incident metrics – tracking KPIs related to response
Post-incident reviews – detailed analysis of actual incidents
Lessons learned database – knowledge base with lessons learned from incidents
Feedback loops – using lessons learned to improve processes
Regular updates – updating playbooks and tools

Challenges and mitigation strategies

Automating incident response poses a number of challenges:

Challenge	Description	Mitigation strategies
False alarms	Automation can generate too many false alerts	Tuning rules, ML to reduce false positives, progressive automation
Excessive automation	Automating the wrong processes can cause problems	Start small, focus on high-value/low-risk automations first
Complicated infrastructure	Complex environments make automation difficult	Standardization, infrastructure as code, service mapping
Lack of specialists	Limited number of experts combining security and DevOps	Cross-training, security champions program, external expertise
Overreactions	Overly aggressive automatic responses can hurt business	Risk-based approach, tiered automation, human-in-the-loop for critical actions

An effective incident response plan – key principles

Shared responsibility – involvement of Dev, Sec and Ops teams
Automation – using SOAR tools to speed up response
Playbooks as code – treating procedures like code, with versioning and testing
Continuous testing – regular exercises and simulations
Learning – learning from each incident and exercise
Integration with DevOps – using DevOps tools and processes in response

An effective, automated incident response plan is an essential component of a mature DevSecOps strategy. It allows you to quickly detect and contain threats, minimize their impact on the business, and continuously improve security.

A look into the future allows us to see new trends and technologies that will shape the security of DevOps environments in the coming years. Let’s take a look at these trends in the last section of this article.

Which trends in securing DevOps environments will shape the industry in 2025?

Cybersecurity and DevOps methodologies are constantly evolving, with innovative approaches emerging at their intersection that are redefining how modern development environments are secured. Understanding upcoming trends allows organizations to prepare for tomorrow’s challenges and gain a competitive advantage by implementing the most effective practices early.

Identity-First Security: A new security paradigm

The traditional approach to security, based on the concept of a network perimeter, is giving way to a model in which identity – of both users and systems – becomes the foundation of security.

Why is this a trend?

In the era of cloud, microservices and remote work, traditional network boundaries are blurring or disappearing altogether. Resources are dispersed between public clouds, private clouds and on-premises environments. In such an ecosystem, identity-based access control becomes a key security mechanism.

Key aspects of Identity-First Security:

Zero Trust Architecture – a security model that assumes that no person or system should be trusted by default, even if they are on an internal network
Contextual Authentication – authentication that takes into account the context of access (location, device, time, behavior)
Identity as Code – identity and privilege management as code, with versioning and automated testing
Embedded identity controls – identity security integrated directly into applications and infrastructure, instead of an externally applied layer

Implications for organizations:

In 2025, organizations will need to redefine their approach to access control with a focus on:

Implementation of advanced identity management systems
Implementation of continuous identity and authorization verification
Segmentation of access based on roles and context
Eliminate long-term credentials in favor of dynamically assigned rights

Organizations that effectively implement these practices will significantly reduce the risk of unauthorized access and potential security breaches.

Software Supply Chain Security: Responding to a Growing Threat

Attacks on the software supply chain have become one of the most serious cyber security threats. In 2025, protecting the entire software lifecycle – from source code to components to delivery – will be a priority for organizations.

Key developing practices:

SLSA (Supply-chain Levels for Software Artifacts) – a framework that defines security standards for the software supply chain, from level 1 (basic) to level 4 (advanced)
Software Bill of Materials (SBOM) – a formal, structured inventory of all components used in software
Artifact Signing – cryptographic signing of all artifacts in the manufacturing process
Immutable Build Systems – non-modifiable build environments, increasing repeatability and security

Changes in the software supply chain:

Supply chain security will evolve in the coming years:

Automatically verify the origin and integrity of all components
Central registries of trusted components and suppliers
Industry standards for secure software building and distribution practices
Regulations imposing security requirements on software vendors

Organizations will need to implement comprehensive supply chain risk management strategies that include supplier assessment, component verification and continuous monitoring.

Serverless Security: New security paradigms

Serverless architecture (functions as a service – FaaS) is gaining popularity due to its scalability, cost effectiveness and operational simplicity. However, it introduces new security challenges that require a specific approach.

Unique security aspects of serverless architecture:

Ephemerality – functions exist only for the duration of execution, making traditional monitoring difficult
Dispersion – serverless applications consist of dozens or hundreds of functions, increasing the attack surface
Shared responsibility – the line between supplier and customer responsibility is often ambiguous
Dependencies – serverless functions often depend on numerous external libraries

Upcoming changes in securing serverless environments:

In 2025, securing serverless architectures will focus on:

Dedicated function security analysis tools
Runtime security for monitoring function behavior during execution
Automatically analyze and secure dependencies
Smallest possible permissions for each function

Organizations deploying serverless will need to integrate security into the early stages of the development process, focusing on secure design and automated code verification.

AI-Powered Security: Transforming Defense and Attack

Artificial intelligence and machine learning are revolutionizing cyber security, changing both defense and attack methods. In 2025, AI will become an even more important part of security strategy.

Innovative applications of AI in cyber security:

Predictive Security – predicting potential threats before they occur
Autonomous Response – automatic response to threats in real time
Behavioral Analysis – identifying anomalies in user and system behavior
Intelligent Vulnerability Management – prioritization of vulnerabilities based on actual risk
Natural Language Processing for Threat Intelligence – automatic analysis of reports and alerts

AI challenges in security:

The growing use of AI comes with new challenges:

Adversarial AI – attackers using AI techniques to bypass security features
Model poisoning – manipulation of training data of AI systems
Ethical concerns – privacy and surveillance issues related to advanced behavior analysis
Skills gap – shortage of specialists combining security and AI expertise

Organizations that effectively integrate AI into DevSecOps practices will gain an advantage through faster threat detection and response, reduced false positives and more effective risk management.

Policy as Code: Automating compliance and governance

As organizations adopt an “everything as code” (infrastructure as code, pipeline as code) approach, security policy management is also evolving into “policy as code” – defining, enforcing and auditing policies as code.

Key Aspects of Policy as Code:

Declarative policies – defining an expected security state rather than specific steps
Automatic validation – testing for policy compliance in the CI/CD process
Rule libraries – modular, reusable policy components
Version control – managing changes to policies as code

Tools and Standards in Policy as Code:

In 2025, we expect the tool ecosystem to grow:

Open Policy Agent (OPA) – will become the de facto standard for policy as code
Cloud Native security frameworks – specific to container environments/Kubernetes
Compliance as Code platforms – tools that combine regulatory requirements with automated testing
Policy visualization tools – tools for visualizing policy relationships

Organizations implementing Policy as Code will gain greater security consistency, faster compliance cycles and easier adaptation to changing regulatory requirements.

Security Mesh Architecture: a decentralized approach to security

Security Mesh Architecture is an approach in which security is distributed and independent of the physical location of resources. It addresses the increasingly distributed nature of today’s IT environments.

Key features of Security Mesh Architecture:

Decentralization – security defined around identity and resources, not location
Composability – modular safety components that can be combined in various configurations
Integration by design – standard interfaces between security components
Consolidated policy management – central management of policies with distributed enforcement

Benefits of Security Mesh Architecture:

Improved security scalability in distributed environments
Greater flexibility to adapt to changing business requirements
Reducing the complexity of security management
Security consistency in heterogeneous environments

In 2025, organizations will move away from monolithic security solutions to flexible, component-based architectures better suited to dynamic DevOps environments.

Quantum-Safe Security: Preparing for the era of quantum computing

The development of quantum computing poses a potential threat to many current cryptographic algorithms. While a quantum computer capable of cracking current ciphers may still be a long way off, organizations should already be preparing for this eventuality.

Key aspects of Quantum-Safe Security:

Post-Quantum Cryptography (PQC) – algorithms resistant to quantum attacks
Crypto agility – the ability to quickly change cryptographic algorithms
Inventory of cryptographic assets – inventory of all cryptography applications in the organization
Migration planning – migration planning for post-quantum cryptography

Standardization status:

NIST finalizes standards for post-quantum cryptography
First PQC implementations appear in major cryptographic libraries
Organizations begin testing PQC in non-production environments

For DevOps environments, it will be crucial to implement crypto agility – designing systems so that they can easily adapt to new cryptographic algorithms without significant architectural changes.

Implications for the organization: Preparing for the future

To effectively prepare for the 2025 trends, organizations should take proactive steps:

Education and competency building:
- Train teams on new technologies and security approaches
- Building interdisciplinary competencies combining DevOps, security and AI
- Cooperation with universities and industry organizations
Strategic Investments:
- Development of a multi-year security strategy that takes into account new trends
- Budgeting for tools and technologies to support new paradigms
- Balancing current needs and preparing for the future
Experimentation and pilots:
- Testing new approaches in controlled environments
- Pilot programs for the most promising technologies
- Gathering metrics and experiences from pilots
Ecosystem Collaboration:
- Active participation in open source communities
- Work with suppliers and partners on new solutions
- Sharing knowledge and best practices

Preparing for future trends – key recommendations

Evolution instead of revolution – gradual implementation of new paradigms, starting with basic elements
Balanced investments – a balance between solving current problems and preparing for future challenges
Continuous learning – building a culture of continuous learning and experimentation in the area of security
Flexible architecture – designing systems to adapt to changing security paradigms
Long-term perspective – integrating security into an organization’s long-term technology strategy

Summary

Security in DevOps environments is a complex, multifaceted discipline that requires a holistic approach. In this article, we discuss a comprehensive strategy for securing DevOps environments, from fundamental principles to practical tools to future trends.

Key elements of a successful DevSecOps strategy include:

Implement a DevSecOps model that integrates security into the entire manufacturing cycle, shifting responsibility for security “to the left” – to earlier stages of the process.
Security automation at every stage, from code scanning to penetration testing to infrastructure monitoring and incident response.
Secure infrastructure as code, treating infrastructure definitions like application code, with a full cycle of testing, review and version control.
Comprehensive access and identity management, based on the principle of least privilege, with dynamic privilege assignment and continuous verification.
Securing containers and microservices, from baseline images to vulnerability scanning to network segmentation and mutual TLS.
Effective secret management, centralization of credential storage and automatic rotation.
Regular penetration testing and audits, integrated into DevOps processes and automated where possible.
Document security processes as code, with versioning and automatic compliance verification.
Continuous training and building a culture of safety, involving all members of DevOps teams.
Measure security effectiveness through defined KRIs and KPIs, giving a complete picture of security status.
An automated incident response plan supported by SOAR tools and integrated with DevOps processes.
Monitor trends and adapt to the changing threat and technology landscape.

In an era of digital transformation where speed is key, security cannot be a brake on innovation. DevSecOps allows you to simultaneously improve security and accelerate the development cycle, creating synergy instead of compromise. Organizations that successfully implement the practices discussed in this article will not only reduce the risk of security breaches, but also gain a competitive advantage through their ability to deliver value quickly and securely.

Keep in mind that DevSecOps is not just a set of tools or practices, but more importantly a cultural change – a shift from silos to collaboration, from security as a “blocker” to security as an “enabler,” from a reactive to a proactive approach. This transformation requires commitment at all levels of the organization, from developers to operations teams to top management.

In a rapidly changing technology world, the only constant is change. Building resilient, adaptive security practices that evolve with your organization and the threat landscape is key to long-term success.