Accountability
14requirements · AIUC-1
When an AI agent makes a decision that impacts the business, the board's first question isn't "what happened." It's "who is responsible."
AI Failure Plans
Documented plans for security breaches, harmful outputs and hallucinations with notification procedures.
Assign Accountability
Change approval policy with defined RACI and code signing for production deployments.
Log Model Activity
Capture inputs, outputs and metadata with tamper-evident storage and controlled retention.
AI Disclosure Mechanisms
Disclosure in text, voice, generated content and automation. Answer "Are you AI?" directly.
"Accountability is not knowing what AI did. It is being able to prove it to whoever asks."
When an AI agent makes a decision that impacts the business, the board's first question isn't "what happened." It's "who is responsible."
Accountability in AI is traceability. It's knowing who decided what, when, based on which data, and being able to prove it.
What the market believes
The market treats AI logs like system logs: timestamp and payload. But when a regulator asks why the agent made a certain decision, "the model decided" is not an acceptable answer.
Accountability requires a complete audit trail: input, context, reasoning (when available) and output. Without this, compliance becomes a statement of intent.
What AIUC-1 requires
Complete auditability. Decision logging with context. Input-to-output traceability. Documentation of who configured, trained and authorized the agent.
Keywords
Audit TrailLoggingTraceabilityIn practice
If the AI agent operates in production and there's no way to reconstruct the decision chain 30 days later, the organization operates without accountability. The log exists. The auditability probably doesn't.
Accountability is not knowing what AI did. It is being able to prove it to whoever asks.