compliance
The Evidence Plane for AI Systems
The missing layer between what your system must prove and how your organization proves it. A framework synthesis connecting obligations, controls, evaluations, evidence artifacts, and the response loop.
The Regulatory Mapping Table
An interactive reference that turns EU AI Act high-risk obligations into operating controls, verification methods, evidence artifacts, owners, and review cadence. Filter by role, article, cluster, or cadence to map obligations into your operating responsibilities.
What Your Agent Logged vs. What the Auditor Needed
The trace says what happened. The auditor asks why, under what authority, and what changed. Most agent deployments log enough to debug a success but not enough to investigate a failure.
From Obligation to Evidence in 90 Minutes
Pick one requirement. Map it to a control. Write the eval. Generate the artifact. Assign the owner. A hands-on walkthrough of the full compliance loop using EU AI Act Article 14.
The Incident Response Gap in AI Systems
You built the controls. You still cannot contain the failure. Most organizations have started building AI controls. Far fewer have built AI incident response.
Mapping the EU AI Act to Engineering Evidence
The regulation tells you what to prove. It does not tell you how to build the proof. This essay maps every major obligation from the EU AI Act to a specific control, eval, and evidence artifact.
Anatomy of an Evidence Pack
Your system passed the eval. Can you prove it? An evidence pack is a structured, continuously generated collection of artifacts — traces, eval results, approvals, config snapshots, and incident records — that proves your AI system did what you said it would do.
Controls Are Not Guardrails
A guardrail catches the output. A control proves the system works. The difference is the evidence layer — obligation, mechanism, eval, evidence, owner.
What Should an AI System Actually Prove?
You diagnosed the problem five different ways. Now build the answer. The proof loop: obligation, control, evaluation, evidence, response.
Who Owns the Agent's Mistake?
The legal answer is converging fast. Courts are rejecting the 'AI did it' defense. The question is whether your organization has the infrastructure to assign accountability when an agent fails.
Guardrails Are Not Safety
Boundary guardrails are the AI equivalent of locking the front door while leaving the windows open. Real safety requires observability, containment, least privilege, and structured human review.