Assurance, Monitoring & Response

How do we know controls work and remain effective in production?

Create an evidence chain from requirements and pre-release evaluation to operational monitoring, independent review, incident response, improvement and retirement.

Operating principles

What good
looks like.

Assure claims

Testing starts with explicit claims, risks and acceptance thresholds.

Monitor outcomes

Technical metrics connect to safety, service, equity and user outcomes.

Separate challenge

Independence increases with risk and consequence.

Learn and respond

Signals lead to investigation, containment, communication and improvement.

Put it into practice

Make it
operational.

Build an assurance case

Connect intended use, claims, hazards, controls, tests, results and residual risk.

Define acceptance criteria

Set thresholds, test populations, failure responses and authority before evaluation.

Operate surveillance

Monitor performance, drift, overrides, complaints, incidents, equity and control health.

Prepare response

Define triage, shutdown, fallback, notification, investigation and prevention.

Minimum evidence set

Make the decision
reviewable.

01Assurance case and evaluation report

02Production monitoring plan and dashboard

03Independent review or audit results

04AI incident and corrective-action records

Move from knowledge to practice

Design governance teams can operate.

Connect accountability, evidence and oversight to the way AI is actually delivered and used.

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