Security teams are scrambling to adapt as AI-driven development floods enterprises with machine-generated code that traditional static analysis tools can't adequately protect. The surge in AI-assisted coding has created a dangerous gap: while code output has exploded, security testing hasn't scaled to match, forcing teams to pivot toward runtime testing to catch vulnerabilities in live applications.
This isn't just about volume—it's about the fundamental mismatch between how AI generates code and how security teams have historically protected it. Static analysis works when humans write predictable patterns, but AI models produce code with subtle vulnerabilities that only surface during execution. We've been tracking this collision for months: first with AI tools spamming open source projects with bogus bug reports, then 28.65 million secrets leaked in AI-generated GitHub repos, and now a 4.5x spike in security incidents from over-privileged AI systems.
The shift to runtime testing isn't optional anymore—it's survival. Static analysis was built for a world where code changes were deliberate and reviewable. But when AI assistants can generate thousands of lines in minutes, security teams need tools that can watch applications behave in real-time, not just scan code at rest. The industry is essentially relearning application security from scratch, this time with AI as both the problem and potentially the solution.