A practitioner details the real-world challenges of audit, compliance, and governance for autonomous AI agents, including identity, approvals, logging, and accountability, while asking the community for solutions.
Apparently my new full-time job is explaining to very serious adults that the autonomous workflow that can spend money, email customers, and open jira tickets is not just a macro with vibes. leadership wants agentic systems that take actions end to end. mention audit trails or approvals and you're blocking innovation. Right now our governance model is a google doc called ai guardrails final final v7 that nobody reads, a confluence page where someone copy pasted nist ai rmf and called it a framework, and a spreadsheet that's supposed to be our system of record for agents but is really a graveyard of half shipped pocs. meanwhile these systems are chaining tools like a caffeinated intern with root, calling internal apis we didn't know existed, and sending emails in a tone legal never approved. Security wants traceability and logs showing who or what took which action under which policy. legal wants explainability and decision provenance. finance wants cost attribution. product wants to ship yesterday. I just want to know which non-human approved a 40k spend last tuesday. We keep hearing "treat agents like users": identities, short-lived tokens, least privilege, activity logs, human in the loop for sensitive actions. matches current guidance on agent identity and on-behalf-of tokens. In reality i am is already overloaded with humans and service accounts, and auditors are about to see sox controls that read change approved by fulfillment_routing agent prod. I still don't have one view that says this agent ran, used these tools, touched this data, and this person is accountable if it goes wrong. The part nobody's answered for me yet is what actually enforces the approval step at runtime, not just logs it after the fact, and whether anyone's solved log integrity so an auditor can trust the trail wasn't editable after the action happened. For folks past the slideware stage: how are you handling audit trails, identity, approvals, and policy enforcement day to day in a way that keeps security, compliance, and engineering all functional? and if you've got something working, what's still the biggest pain, identity, logging, or getting auditors to accept it?
A practitioner discusses the challenge of implementing audit trails for AI agents in production, mentions a vendor solution, and seeks input from the community on real-world setups.
A practitioner shares concerns about an upcoming audit revealing undocumented AI agents in production, highlighting governance gaps and risks with customer PII access.
A discussion thread seeking input on how to handle authority and permissions for AI agents that take real actions, including audit trails and scope of permissions.
The author describes a challenge with AI auditability where an agent's decision lacked traceability to the active policy version, and asks for advice on building effective decision trails for AI agent decisions.
A discussion about designing AI agent systems in heavily regulated environments, focusing on the challenge of false positives and how to present model confidence to users without adding cognitive load.