In March 2025, Ridge Security announced the release of RidgeBot 5.2 with the groundbreaking RidgeGen module – a new layer of generative artificial intelligence that fundamentally changes how the platform identifies, analyzes, and reports security threats. This isn’t marketing – it’s a real evolution in automated pentesting capabilities.
Dual AI Engine: TensorFlow + GenAI
RidgeBot has always utilized artificial intelligence in the form of the RidgeBrain engine, based on TensorFlow. This proven engine is responsible for intelligent decision-making during tests: selecting attack techniques, prioritizing targets, and adapting strategies based on results.
RidgeGen introduces a second layer – generative AI language models (GenAI), specifically trained for security-related tasks. This combination gives RidgeBot 5.2 unique capabilities:
- TensorFlow Engine – responsible for attack logic, decision-making, exploitation
- GenAI Engine (RidgeGen) – responsible for context analysis, result interpretation, report generation
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Key Feature: Completely Offline Operation
In an era when many AI solutions require cloud connectivity, RidgeGen stands out with a fundamental feature: it operates completely onboard, without access to external servers.
For organizations, this is a crucial difference:
- Data security – information about infrastructure, vulnerabilities, and test results never leaves the customer environment
- Regulatory compliance – no data transfer to the cloud makes it easier to meet GDPR, NIS2, and sector-specific requirements
- Operation in isolated environments – RidgeBot can work in air-gapped networks, OT environments, and critical infrastructure
- Cost predictability – no fees for API calls to external AI services
Three New Security Validation Scenarios
RidgeBot 5.2 with RidgeGen introduces three entirely new test scenarios that previously required manual testing:
1. Endpoint Security Validation
RidgeGen enables assessment of endpoint protection solutions (EDR/XDR/AV) effectiveness. The platform:
- Simulates various malware delivery techniques
- Tests detection by protection systems
- Verifies whether alerts are generated and properly escalated
- Reports “blind spots” in endpoint security configuration
2. Data Exfiltration Testing
The new scenario enables validation of data leak prevention controls:
- Simulation of various exfiltration channels (DNS tunneling, HTTP(S), encoded transfers)
- Testing DLP solutions
- Verification of SIEM/SOC alerts for exfiltration attempts
- Assessment of network segmentation effectiveness
3. Active Directory Reconnaissance
RidgeGen introduces advanced Active Directory environment reconnaissance capabilities:
- Automatic AD structure mapping
- Identification of privileged accounts
- Detection of dangerous configurations (Kerberoasting, AS-REP Roasting)
- Privilege escalation path analysis
GenAI in Practice: What Does RidgeGen Change?
Intelligent Context Analysis
Traditional scanners report vulnerabilities in isolation. RidgeGen analyzes context:
- Is the vulnerability reachable from the network?
- What data is accessible after exploitation?
- Are there compensating controls?
- What is the real business impact?
Multilingual and Multi-format Support
The RidgeGen model supports multiple languages and data formats. This means:
- Reports in local languages (including Polish)
- Log and configuration analysis in various formats
- Interpretation of error messages from different systems
Reduction of False Negatives
One of the biggest challenges in automated pentesting is false negatives – vulnerabilities that exist but weren’t detected. RidgeGen significantly reduces this problem through:
- Better understanding of application context
- Adaptive fuzzing techniques
- Intelligent generation of payloads tailored to the target
Results: DEFCON Benchmark Bakeoff 2025
During the DEFCON 2025 Benchmark Bakeoff, RidgeBot with RidgeGen achieved impressive results:
- Highest completion rate among tested tools
- 0 false positives – zero false alarms
- Significant reduction of false negatives – detection of vulnerabilities missed by competitors
For comparison, other leading web security testing tools achieved lower scores.
Practical Applications of RidgeGen
Scenario 1: SOC Validation
An organization wants to verify whether their Security Operations Center actually detects threats:
- RidgeBot with RidgeGen conducts a series of controlled attacks
- Each attack is designed to generate specific alerts
- RidgeGen correlates actions with SIEM logs
- The report shows which attacks were detected and which went unnoticed
Scenario 2: Pre-deployment Assessment
Before deploying a new application:
- RidgeBot analyzes the application’s APIs and interfaces
- RidgeGen identifies potential attack paths
- The platform validates through exploitation
- The report contains precise guidance for developers
The Future: From Automation to Autonomy
RidgeGen is a step toward autonomous security testing. In future versions, Ridge Security announces:
- Multi-agent AI systems collaborating during tests
- Automatic attack scenario generation based on threat intelligence
- Continuous adaptation to new attack techniques
Summary
RidgeGen in RidgeBot 5.2 is a breakthrough in penetration testing automation. The dual AI engine, operating completely offline, provides:
- More precise threat identification
- Zero false positives
- New test scenarios (Endpoint, Exfiltration, AD)
- Compliance with regulatory requirements
For organizations looking for a way to continuously validate security without compromising data privacy, RidgeBot 5.2 with RidgeGen is the natural choice.
Want to see RidgeGen capabilities in your environment? Contact us and schedule a demonstration.
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