AI governance: Shadow AI, real access, real risk

AI outputs may be polished, but in this episode of Pop Goes the Stack, F5's Lori MacVittie, Joel Moses, and returning guest Ken Arora focus on what happens when confidence meets real-world access. The discussion moves past model capabilities and into the operational reality that many organizations are already granting agents serious privileges, including direct read/write access to production databases and automated triggers into external SaaS systems.   From there, the team tackles the uncomfortable truth: governance is arriving late. Retrofitting controls onto agents that are already running in production is difficult, and sometimes dangerously naïve, especially when teams assume simple technical guardrails are enough. Ken shares an example where limiting agents to GET requests still allowed unintended “write” behavior via legacy web patterns, underscoring how easy it is for controls to miss the real intent of an action.   The conversation also challenges familiar analogies. Treating agents like functions-as-a-service can help frame lifecycle management, logging, and permissions, but agents aren’t deterministic and they aren’t truly stateless. Add natural language instructions and goal-seeking behavior, and you get a new class of risk: the system you’re trying to govern will actively work around restrictions to accomplish its task.   They wrap with a practical mindset shift: governance isn’t a brake on AI adoption, it’s what enables safe scale. But getting there will require better behavioral detection, clearer boundaries, and an honest look at the cost and complexity of controlling “cron jobs with a brain” before hallucinations turn into production incidents.