Case Study: Forensic AI Agent Liability Audit
An enterprise adopting agentic AI workflows discovered the real risk was never in the code, it was in undefined agent authority.
Client profile (anonymized): An enterprise integrating autonomous AI agents into internal operational workflows.
The Problem
The organization had passed every standard code review on its new agentic AI deployment. No one had audited what the agent was actually authorized to do once deployed, versus what its designers assumed it would do.
The Approach
A forensic audit was run adversarially, treating the deployed agent the way a penetration test treats a network: probing for what it could be made to do, not confirming what it was documented to do. The review specifically targeted undefined permission boundaries, not code correctness.
The Outcome
The audit surfaced rogue agent liability exposure estimated at over 50 million dollars in potential enterprise risk, entirely separate from any code defect. Formal operating boundaries were defined and enforced before the deployment was allowed to scale further.
Frequently Asked
How was a $50M liability missed by standard code review?
The risk was not in the code itself, it was in authority the agent was granted but that was never formally defined or adversarially tested, which a correctness-focused code review does not check.