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AI governance is shifting from a side project into a core operational function, and the organizations still treating it as an add-on are falling behind. The CIO.com piece captures a real inflection point: as enterprises move from AI pilots into scaled deployment, governance overhead is compounding faster than most teams anticipated. Infosys EVP Anant Adya says risk management time has grown year-over-year, though his team is embedding it into operating models rather than bolting it on. The least-privilege principle, the security rule that agents get only the access they actually need, is emerging as the practical anchor.
What this means for your business
The telling detail in Adya’s comment isn’t the added workload, it’s the phrase “embedded into our operating model.” Organizations that have already built governance into how they ship AI are experiencing this moment as friction. Organizations that haven’t are experiencing it as a crisis. If your security team is still reviewing AI deployments as one-off exceptions rather than through a repeatable framework, the scaling math is brutal: every new agent or model integration triggers a fresh round of manual review, and agentic AI systems, where software takes autonomous actions on behalf of users, multiply that surface area fast.
The least-privilege instinct borrowed from traditional security is sound, but it undersells the problem. Classic least-privilege assumes static permissions for known systems. Agentic AI systems negotiate permissions dynamically, often in ways the original architects didn’t predict. That means the control surface isn’t just “what can this agent access” but “what can this agent be convinced to do with that access.” CISOs who are mapping AI governance onto existing identity and access management frameworks without accounting for that behavioral layer are building a fence around the wrong yard.
The teams that come out ahead aren’t the ones who slow AI deployment the most aggressively. They’re the ones who invest now in governance infrastructure that scales without proportional headcount growth, policy-as-code approaches, automated risk scoring, and model behavior monitoring. The falsification condition is simple: if your governance costs grow linearly with your AI footprint rather than flattening as tooling matures, the model isn’t working, and the board conversation about AI risk will eventually happen on someone else’s terms.
Based on reporting from AI governance is fast becoming an unmanageable chore, originally published 2026-09-25 06:04:00.
