September 30, 2024 at 08:00PM
AI code-generation assistants have increased coding speed but also lead to more defects and vulnerabilities, resulting in a rise in false positives for application vulnerabilities. Reachability analysis is being used to prioritize remediation requests, reducing the number of vulnerabilities needing patching. Overall, reducing non-reachable code helps cut remediation work by 60%.
After reviewing the meeting notes, the main takeaways are:
1. AI code-generation assistants have led to a surge in code pushed to GitHub, but they also generate code with defects and vulnerabilities, leading to false positives in application-vulnerability reports.
2. Application-security teams are using reachability analysis to prioritize remediation requests and reduce the number of reported vulnerabilities. This helps in filtering out non-reachable code and reducing the workload by significant percentages.
3. Static application security testing (SAST) tools have proven ROI, but false positives reduce their benefits and trust in the tools. Developers believe that the faster cadence of development with automation has increased the number of false positives.
4. There are two approaches to reachability analysis: static code analysis and code instrumentation. Both methods aim to determine whether specific code may be executed and label that code as reachable.
5. Combining reachability with other contextual information, such as exploitability and business impact, further reduces the workload and can significantly decrease the number of alerts per organization.
These takeaways outline the challenges and strategies discussed in the meeting notes regarding the impact of AI assistants, application-vulnerability reports, reachability analysis, SAST tools, and reducing false positives in the development process.