What compliance frameworks apply to enterprises deploying AI agents (SOC 2, ISO 42001, NIST AI RMF)?

Enterprises deploying AI agents typically need to map their controls against several overlapping frameworks rather than relying on a single standard. ISO 42001 is the dedicated AI management system standard, providing a structured approach to how an organization governs AI throughout its lifecycle. The NIST AI Risk Management Framework offers a widely referenced set of practices for identifying, assessing, and managing AI specific risk, and is increasingly used as a common reference point even by organizations not directly required to follow it. SOC 2 has begun incorporating AI specific addendums in some audit scopes, and various sector specific regulations add further requirements depending on industry, particularly in areas like finance and healthcare.

A common thread across these frameworks is that they generally expect evidence of ongoing risk assessment and testing, rather than treating compliance as something achieved once through a certification process and then considered permanently satisfied. This reflects the same underlying reality that governance frameworks increasingly recognize, which is that AI system behavior can change as models get updated or new capabilities get added, meaning a compliance posture that was accurate six months ago may no longer reflect current risk.

Platforms that provide continuous red teaming and monitoring generate exactly this kind of ongoing evidence as a natural byproduct of how they operate. Akto's Argus, for example, produces continuous testing results and runtime monitoring logs that double as compliance evidence, which increasingly matters as auditors move away from accepting point in time assessments as sufficient proof of an ongoing AI compliance posture.