How to Audit AI-Assisted Software Development for Security Risks

Summary

The article recommends auditing AI-assisted development by tracking tool use and code outputs, benchmarking models for security weaknesses, assessing developer skills, and linking AI use to secure business outcomes.

Key points

• Track which AI coding tools developers use, how often they use them, and where their generated code enters the software development lifecycle.
• Benchmark AI models against known vulnerability patterns and standardize tools that produce secure code.
• Monitor model context protocol integrations so AI agents access only approved tools and data sources.
• Assess developer skills and provide training, since the article says top security-proficient developers outperform LLMs overall.
• Connect audit findings to productivity, code quality, and secure outcomes to guide tool selection and governance.