Artificial intelligence (AI) is transforming software development, enabling tools like GitHub Copilot and ChatGPT to generate production-ready code. However, this innovation introduces significant application-level security risks that many organizations are unprepared for. Ensuring AI-generated code meets or exceeds human-produced code security standards is crucial. Traditional security methods, such as Static and Dynamic Application Security Testing (SAST and DAST), often overlook AI-influenced vulnerabilities that stem from logical errors rather than syntax.
Organizations must adopt three priorities for effective application security: establish AI code governance by tagging and reviewing AI-generated code, expand testing methods to include behavior context, and train developers to verify AI outputs. Embracing a “Think-Wide” mindset that involves collaboration among developers, data scientists, and compliance teams is essential. If used responsibly, AI can enhance application security by automating testing and vulnerability assessments. Ultimately, managing the synergy between human developers and AI systems will define the future of software security.
Source link
