Artificial intelligence (AI) and machine learning (ML) in software development aren't just about helping DevOps teams write and ship code faster and more efficiently. AI and ML have can help organizations ship better, more secure code and minimize security risk to their organization and customers.
Here are a few ways AI can help bolster your organization’s security:
1. Mitigate security vulnerabilities faster
When a security vulnerability is detected, the first step in fixing it is understanding it — and this is a place where AI stands out. Developers and security professionals can use AI to summarize detected vulnerabilities and help them understand the potential security threat, how attackers could exploit it, and how to fix it. More advanced AI-powered tools can even provide a suggested mitigation with sample code for each vulnerability.
2. Make code reviews more efficient and effective
When a developer's code is ready for review, there are a few ways AI can help speed things up and help catch any quality or security issues.
AI can help the author choose the best reviewer — one who's familiar with the code base and more likely to catch important issues, and less likely to ignore the code review request, say that someone else should review it, or provide insufficient feedback. While choosing the most appropriate code reviewers can be a complex task for a human, a machine learning algorithm can analyze the changes and the project’s contribution graph to help identify reviewers.
AI also can generate a summary of the merge request to help reviewers quickly understand what they're being asked to review and to ease the code review handoff process.
3. Generate tests to ensure proper test coverage
Thoroughly testing code changes is one of the most important ways to ensure code works as expected and doesn’t introduce security issues — but writing tests can be time-consuming and difficult, so code is often pushed without appropriate test coverage.
AI can look at code changes and suggest relevant tests along with test files, so developers can spend less time thinking about and writing tests and more time coding.
In fact, many DevOps teams are already using AI to generate tests. In our 2023 State of AI in Software Development report, 41% of respondents whose organizations were using AI said they were using it for automated test generation.
4. Protect your proprietary data when using AI
For many organizations, it’s important that the efficiency gains of using AI and ML don’t come at the cost of privacy, security, or compliance. An overwhelming majority of survey respondents (79%) said they’re concerned about AI tools having access to private information or intellectual property.
Before using an AI tool, make sure to understand how your proprietary data will or won’t be used to train its machine learning models. Allowing DevOps teams to use the wrong AI tool can lead to painful and costly leaks of top-secret data and source code.
Find out the questions to ask when building your AI strategy.
Improve security with AI-powered DevSecOps workflows
With GitLab Duo, the suite of AI capabilities powering workflows, DevOps teams can use AI to improve security throughout their software development lifecycle.
GitLab Duo capabilities include vulnerability summaries, suggested tests, suggested reviewers, and merge request summaries.
Note: GitLab Duo does not train ML models with customers’ proprietary data or source code and is designed with a privacy-first approach to help enterprises and regulated organizations adopt AI-powered workflows.
