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Knowledge base Updated: February 5, 2026

What is GitHub Copilot and how to use it?

Artificial intelligence is entering the world of software development with a force that is forever changing the way developers work. One of the most groundbreaking tools that has come to symbolize this revolution is GitHub Copilot....

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Artificial intelligence is entering the world of software development with a force that is forever changing the way developers work. One of the most groundbreaking tools that has come to symbolize this revolution is GitHub Copilot. It is much more than a simple code autocomplete - it is an advanced AI-based assistant that can write entire functions, suggest algorithms and translate commands from natural language into code. The promise of drastically speeding up work and increasing productivity is huge, but with it come serious questions.

What are the real implications of using AI-generated code? Who owns it? What are the security and intellectual property leakage risks of integrating Copilot into corporate projects? Safe and informed implementation of this powerful tool requires not only enthusiasm, but more importantly, strategy, security policies and an understanding of its limitations. In this guide, we’ll take a look at what GitHub Copilot is, how it works, its advantages and disadvantages, and how to introduce it to a development team in a controlled and secure manner so that it becomes a real enhancement rather than a source of chaos and risk.

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What is GitHub Copilot and how is artificial intelligence changing the work of programmers?

GitHub Copilot is an advanced programming assistant created by GitHub in collaboration with OpenAI. It works as an extension to popular code editors (such as Visual Studio Code) and analyzes in real time the context of a programmer’s work - code already written, open files and even comments - to suggest whole lines or blocks of code that fit the current task. It can be described as an extremely advanced version of autocomplete, which, instead of suggesting individual keywords, can generate a complete function, unit test or code snippet that implements complex logic described in a natural language comment.

The artificial intelligence on which Copilot is based fundamentally changes the nature of programming work. It shifts the burden from simply “writing” code to “reviewing” and “integrating” it. Instead of spending hours painstakingly writing repetitive, standard code (the so-called “boilerplate”) or searching for solutions to common problems on online forums, a programmer can generate the skeleton of a solution in seconds and focus on revising, customizing and integrating it with the rest of the system. AI becomes a partner in the creative process, which speeds up the implementation phase.

This change has huge implications. On the one hand, it dramatically increases productivity, allowing software to be delivered faster. On the other, it lowers the barrier to entry, helping novice developers learn and solve problems. At the same time, however, it requires developers to develop new competencies - above all, critical thinking, the ability to formulate queries with precision (prompt engineering) and, most importantly, the ability to thoroughly review and test machine-generated code that is not always perfect or secure.

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What data has Copilot been trained on and what are the implications?

GitHub Copilot, like other large language models, owes its effectiveness to the process of training on a huge, diverse dataset. Its “brain” is the GPT (Generative Pre-trained Transformer) family model from OpenAI, which has been trained on billions of lines of source code from public repositories on the GitHub platform, as well as other publicly available text and documentation. This means that the model has “learned” patterns, structures, idioms and solutions to programming problems by analyzing code written by millions of developers around the world in dozens of programming languages.

This gigantic collection of training data is a source of both strength and weakness for Copilot. On the one hand, it allows it to generate code that is often idiomatic and consistent with common practices. On the other hand, it carries with it several important implications that every company must consider.

First, code in public repositories sometimes varies in quality. This means that Copilot may have learned not only good, but also bad, inefficient or outdated programming practices. The code he suggests is not always optimal. Second, public code often contains security vulnerabilities. There is a real risk that Copilot, learning from vulnerable code, will itself suggest a solution that contains a similar vulnerability (such as a bug leading to SQL Injection). Third, software licenses are an extremely problematic issue. Code in public repositories is made available under various licenses (e.g. MIT, GPL, Apache). There is a risk that Copilot will generate a piece of code that is unique enough to be considered a derivative work under a restrictive license (e.g. viral GPL), which could create serious legal problems for a company using that code in its commercial product.

What specific programming tasks can Copilot speed up or automate?

GitHub Copilot is a versatile tool that can significantly speed up and simplify a wide range of daily programming tasks, from simple tasks to more complex problems. Its use allows developers to save time and mental energy, which they can devote to solving key business problems.

One of the most common applications is writing repetitive and standard code (boilerplate). In every application there is a need to write similar fragments, such as class definitions, HTTP request handling, database connection configuration or simple utility functions. Copilot can generate such code in seconds based on context or simple comments alone, saving the programmer from tedious manual writing. It is equally effective at translating code between different programming languages and working with new libraries and frameworks. Instead of spending hours reading documentation, a programmer can ask Copilot to generate an example of how to use a particular function from an unfamiliar library.

Copilot is also extremely helpful in creating unit tests. Writing tests is crucial for ensuring software quality, but is often seen as a time-consuming task. Copilot, by analyzing a function’s code, can suggest a set of apt unit tests that check its performance in various scenarios. Other practical applications include code refactoring (suggesting improvements and simplifications to existing code), working with regular expressions (regex), which are powerful but difficult to write, and rapid prototyping and exploration of new ideas, where the speed of code generation is more important than its final quality.

What are the biggest concerns about the quality and security of AI-generated code?

Despite its enormous potential for productivity gains, the use of code generated by AI tools such as GitHub Copilot raises serious concerns about its quality and, above all, its security. Uncritically accepting Copilot’s suggestions without thorough verification is one of the most dangerous practices a development team can adopt.

The biggest concern is the risk of introducing security vulnerabilities. As mentioned earlier, Copilot has learned from a huge base of public code that often contains vulnerabilities. There is a documented risk that the tool could suggest a piece of code containing classic bugs, such as vulnerability to SQL Injection, Cross-Site Scripting (XSS) attacks, use of weak cryptographic algorithms or improper error handling, which could lead to the leakage of sensitive information. A programmer, acting in haste, may fail to notice such a vulnerability and deploy code into production, which becomes an open door for attackers.

Another issue is the quality and efficiency of the generated code. Copilot’s suggestions are not always optimal. The code may be inefficient, consume excessive amounts of memory or CPU power, as well as be difficult to maintain and incompatible with the company’s accepted coding standards. The tool may also suggest the use of outdated libraries or functions that are no longer considered good practice. Relying solely on AI can lead to technology debt and reduce the overall quality of the software being developed.

Finally, there are risks associated with “hallucinations” of the AI model. Sometimes Copilot can generate code that looks correct at first glance, but in fact doesn’t work, calls non-existent functions, or contains subtle logic errors that are very difficult to detect during a cursory review. That’s why it’s absolutely crucial that every piece of code generated by AI is treated as if it came from a new, inexperienced programmer - it must undergo a thorough review (code review) and be covered by comprehensive testing.

The question of copyright in code generated by artificial intelligence is one of the most complex and still not fully regulated aspects of using tools such as GitHub Copilot. The answer to this question is not simple and depends on the jurisdiction and interpretation of existing laws, which were not created with machine-generated works in mind.

According to GitHub’s official stance, the user owns the code he writes using Copilot. Suggestions generated by the tool are treated similarly to suggestions from a compiler or other development tool. By accepting, modifying and integrating this code into their project, the user takes full responsibility for it, both in terms of functionality and security, as well as copyright. However, this simple declaration does not close all legal issues.

The main problem lies in the training data. Because Copilot learned on public code, there is a small but real risk that it could generate a code snippet that is a verbatim or very close copy of existing copyrighted code and a restrictive license. If the generated snippet is long enough and unique enough to be considered a protected work, its use in a commercial product without respecting the original license (such as the viral GPL, which requires that all source code be made available) could lead to serious legal claims. GitHub Copilot has built-in filters to prevent such situations, but they don’t provide a 100% guarantee.

In most jurisdictions, including the U.S., copyright can only be enjoyed by a human creator. A work created fully autonomously by a machine is not covered by copyright protection. However, in the case of Copilot, there is a “collaborative” model, where a human approves, edits and directs the creative process. This gray area makes it necessary for companies to approach this issue with great caution, implementing policies that minimize risk and consciously managing licensing issues.

  • GitHub’s official position: you own the code and are responsible for it.

  • Main risk: The possibility of generating code that is a copy of existing code covered by a restrictive license (such as the GPL).

  • Potential consequences: Legal claims, the need to release proprietary source code.

  • How do you protect yourself? GitHub filter enabled by default to block suggestions that match public code; thorough review (code review) and team licensing awareness.

  • Legal status: the issue is still not fully settled; it requires a careful and informed approach.

What are the best practices for verifying and testing code suggested by AI?

Treating the code generated by GitHub Copilot as a ready-made and error-free solution is a simple path to disaster. In order to use this tool safely and effectively, one should adopt the principle of “trust but verify” and implement a rigorous process for verifying and testing every suggestion. Code from AI should be treated with the same, if not more, criticism as code written by a new, junior programmer.

The first and most important practice is a thorough code review (code review). Every piece of code accepted from Copilot’s suggestions must be reviewed by another experienced team member. The reviewer should pay special attention not only to logical correctness and compliance with the company’s standards, but especially to potential security vulnerabilities. Look for classic errors such as lack of input validation, vulnerabilities to injection attacks or use of unsafe functions.

The second pillar is comprehensive testing. It is not enough to verify that the code “works” in the base case scenario. You should create a set of unit and integration tests that verify that the code works in a variety of cases, including edge cases. The tests should check how the code handles invalid input, how it handles errors and whether it causes unexpected side effects in other parts of the system. Automating these tests as part of the continuous integration (CI) process is key.

In addition, use automated code security analysis tools (SAST - Static Application Security Testing). Such tools, integrated into the development process, can automatically scan code (including that generated by AI) for known vulnerability patterns and security flaws, providing an additional safety net. Developers should also develop their skills in asking precise questions and describing context in comments, which significantly improves the quality and relevance of suggestions generated by Copilot.

How is Copilot for individual developers different from the business version (Copilot Business)?

GitHub Copilot is offered in two main variants, which, although based on the same AI technology, are designed for different audiences and differ in key management, security and privacy features. Choosing the right version is crucial for any organization that wants to deploy the tool in a controlled and secure manner.

Copilot Individual is designed for individual developers, freelancers and hobbyists. Users pay for the subscription individually and manage their account independently. This version offers the full capabilities of the AI assistant, but lacks any tools for central management and control from within the organization. Most importantly, in the individual version, snippets of a user’s code (so-called “snippets”) can be used by GitHub to further train and improve the model, which poses the risk of intellectual property leakage if an employee uses a private account to work on company code.

Copilot Business is a version designed specifically for companies and development teams. Its key feature is a central administration console that allows a company to manage licenses and assign them to individual employee accounts. The most important difference from a security and privacy perspective is that in the Business version, snippets of user code are never used to train public AI models. Telemetry data (such as how often suggestions are accepted) is collected, but the source code itself remains confidential.

In addition, the Business version offers policy management, which allows administrators to configure certain behaviors of the tool, such as enabling or disabling a filter that blocks suggestions that match public code in GitHub repositories. Integration with the company’s systems and the ability for central invoicing are other advantages. For any company that is serious about implementing Copilot, the Business version is the only reasonable and safe choice.

How do you implement Copilot in a team to actually increase productivity rather than chaos?

Implementing such a powerful tool as GitHub Copilot requires a well-thought-out strategy to ensure that its potential is fully realized while avoiding quality, security and code consistency issues. The key is to treat this as a project in the area of change management, not just as an installation of new software.

The first step is to establish clear rules and good practices. Before the team starts using Copilot, a “code of conduct” should be jointly developed and documented. It should make it clear that every AI suggestion must be treated as a proposal, not a ready-made solution, and must be subject to rigorous code review. It should be defined in which situations Copilot is most helpful (e.g., test writing, boilerplate), and in which situations it should be approached with extreme caution (e.g., implementation of business logic, handling sensitive data).

Next, you need to provide training to the entire team. It is not enough to show how to install the plugin. The training should focus on effective use of the tool (how to write good comments and context to get better suggestions), but most importantly on risk awareness. You need to discuss security, code quality and licensing issues, sensitizing developers to potential problems and teaching them how to verify the generated code.

It is also a good idea to start with a pilot project in a smaller, experienced group. This will allow you to gather initial experience, refine internal rules and identify potential challenges in a controlled environment. Fostering a culture of open discussion and knowledge sharing is also key. The team should regularly discuss their experiences with Copilot, share examples of good and bad suggestions, and jointly work out the best ways to integrate the tool into existing workflows.

Could Copilot pose a risk of leaking the company’s confidential source code?

The question about the risk of intellectual property leakage is one of the first and most important concerns that arise in the context of implementing AI tools in the development process. The answer to this question depends crucially on what version of the product the company is using.

For Copilot Individual, the version for individual users, the risk is real and significant. According to the privacy policy for this product, GitHub reserves the right to use code snippets (snippets) and other telemetry data to further train and improve its AI models. This means that if an employee, using his or her private Copilot Individual account, works on confidential company code, snippets of that code may be sent to GitHub’s servers and included in the training collection, constituting a serious and unacceptable leak of intellectual property.

It is in response to these concerns that the Copilot Business version was developed. It was designed with businesses in mind and its fundamental principle is to protect the privacy and confidentiality of customer code. GitHub explicitly guarantees that if you subscribe to Copilot Business, no snippets of source code (snippets) are stored or used to train public AI models. The company only collects telemetry data on the use of the service itself (e.g., latency information, errors, suggestion acceptance statistics), but not the content of the code the developer is working on.

Therefore, for any company that cares about its intellectual property, the only safe and acceptable solution is to use only Copilot Business licenses. Allowing business use of individual versions is asking for trouble and poses a huge, uncontrollable risk of leaking the company’s most valuable assets.

What security policies should a company have in place before allowing the use of Copilot?

Before making GitHub Copilot available to developers, a company must implement a set of clear and enforceable security policies that minimize risk and ensure that the tool is used in a responsible and controlled manner. These policies should become part of the overall software development security policies.

The first and most important policy must be to mandate the use of only corporate Copilot Business licenses. There should be an explicit prohibition on using private Copilot Individual accounts to work on any code related to the company’s business. This rule is crucial to protect intellectual property and prevent source code leaks.

The second element is a mandatory verification and testing policy for AI-generated code. A rule should be formally established that no code suggested by Copilot can enter the production branch of the repository (e.g., main or master) without a thorough code review by another team member and without being covered by appropriate automated tests. The policy should emphasize that the ultimate responsibility for code quality and security always rests with the developer, not the tool.

Third, create a policy for handling sensitive data and secrets. Employees must be instructed never to include any sensitive data such as passwords, API keys, access tokens or personal information in code or comments (which are sent to Copilot as context). All such “secrets” must be managed with dedicated, secure tools (e.g. password safes, secret management systems), and there should only be references to them in the code. It’s also a good idea to set up filters in Copilot to prevent suggestions that match publicly available code, to minimize licensing risks.

How do we measure Copilot’s impact on the speed and quality of the development team?

Measuring the impact of a tool like GitHub Copilot is crucial to justify the investment and understand its real value to the organization. This measurement should be multidimensional and include both quantitative (speed, productivity) and qualitative (code quality, developer satisfaction) metrics.

Metrics from version control (e.g., Git) and project management systems (e.g., Jira) can be used to measure speed and productivity. It is worth analyzing metrics such as cycle time, which is the time from the start of work on a task to its deployment to production, and deployment frequency. A reduction in cycle time and an increase in deployment frequency after the introduction of Copilot can indicate an increase in productivity. GitHub itself, as part of its Enterprise version, offers metrics showing what percentage of code was written using Copilot, which also gives some insight into its impact.

Measuring code quality is more difficult, but also possible. Metrics such as the number of bugs (bugs) reported after deployment and the change fail rate should be monitored. A decrease in these metrics may suggest that Copilot, for example by making it easier to write tests, is helping to improve quality. It is also worth analyzing the results of automated code analysis tools for technology debt and standards compliance.

An extremely important, albeit “soft” aspect, is developer satisfaction and experience (Developer Experience, DevEx). It can be measured by regular, anonymous surveys that ask the team how Copilot affects their daily work, whether it reduces frustration with repetitive tasks and whether it allows them to focus on more interesting problems. A satisfied and engaged team is more productive and innovative, which is one of the biggest, albeit indirect, benefits of implementing the tool.

How can nFlo’s AI and cybersecurity consulting help your company safely deploy tools like Copilot and maximize their potential?

The deployment of disruptive AI-based tools such as GitHub Copilot opens up huge opportunities for companies, but at the same time creates new and complex challenges in the areas of security, compliance and governance. At nFlo, we have a unique combination of expertise in cutting-edge AI applications and deep, practical knowledge of cyber security. This allows us to support our clients holistically, ensuring that they can maximize the benefits of Copilot while minimizing the associated risks.

Our consulting services begin with support in developing strategies and policies for secure deployment. We help create clear and practical policies for using Copilot in your organization, covering code security, intellectual property protection and license compliance. We provide dedicated training for development teams that focuses not only on effective use of the tool, but more importantly on building awareness of risks and learning best practices for verifying and testing AI-generated code.

As part of our application security services, we help integrate Copilot into the Secure Software Development Lifecycle (Secure SDLC). We advise on how to configure automated code analysis tools (SAST) to effectively scan suggestions from AI as well, and how to implement code review processes that take into account the specifics of working with an AI assistant. In turn, our security audits and penetration tests can verify that the applications Copilot has been involved in developing are resistant to attacks.

When you work with nFlo, you get a partner who understands both sides of the coin - the enormous productivity potential offered by AI and the fundamental need to protect corporate assets. We help you find the golden mean so that your company can reap the full benefits of innovation without exposing itself to unnecessary risk.

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