AI failures are no longer limited to inaccurate outputs. They can now result in harmful actions, sensitive data exposure, regulatory risk, and loss of operational control. One autonomous agent, for example, deleted an organization’s mail server and published a list of people it flagged as suspicious, unprompted. Most still treat safety as a default model property. They layer on policies and guardrails without first understanding how their systems behave. Controls built this way are not safeguards. They are assumptions.
This whitepaper sets out a different approach: diagnose before you enforce. It introduces a structured auditing methodology, adapted from Anthropic’s Petri and Bloom frameworks. The methodology tests systems against realistic and adversarial scenarios, then measures failure rates with statistical precision. It maps these findings onto a three-layer enforcement architecture: specification, supervision, and containment. Continuous monitoring closes the loop, feeding new findings back into the diagnostic process. We also describe an example from the banking industry, showing the model in practice.
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