AI production assurance
Build controls in which every alert corresponds to a risk, a population, an action and a person who is genuinely empowered.
A dashboard can describe the past without protecting the service. A control names the risk sought, signal, population, window, baseline, threshold, authority, action and evidence of closure. If authority or action is missing, it is an observation.
The signal must arrive in time, be linked to the right version and be sensitive enough to the harm sought. We examine missing data, delays, selection bias, small volumes, noise and opportunities for circumvention.
An explicit threshold includes the metric or condition, population, window, baseline, uncertainty and associated decision. Pre-alert, restriction and stop levels need not all be numerical: an unknown version or inability to interrupt the system can be a critical condition.
Cadence depends on how quickly risk emerges and how long action takes. Availability, version integrity or security may require continuous controls; quality analysis may run in batches; a change or incident triggers an event-driven review; contextual effects may require human judgement.
The decision matrix distinguishes who receives the alert, qualifies it, decides, executes and verifies recovery. Authority must have the competence, support and access required. An on-call role without the power to stop is not an authority.
Keeping may include dated enhanced monitoring. Restriction identifies the affected function, population, channel, region, version or volume. Stopping leads to the defined safe state. Every decision records its rationale, duration, residual risk and exit test.
| Decision | Scope to write | Exit condition |
|---|---|---|
| Keep | Version and accepted reservations | Defined reassessment or signal |
| Restrict | Capability or population actually limited | Return test and authority |
| Stop | Safe state and suspended operations | Correction, regression test and authorised recovery |
We inject a synthetic signal or replay a known event to test detection, escalation, execution of restriction and preservation of evidence. The exercise also measures actual delays and human or technical single points of failure.
Primary sources
Last documentary review: 6 September 2026.
Continuous risk management, deployer duties, post-market monitoring and serious-incident reporting.
Monitoring categories and measurement challenges for AI systems in real-world operating conditions.
Govern, map, measure and manage the risks of an AI system.
Architecture, lifecycle, dependencies, pre-production tests and logging.
Before you write to us
No. An unknown version, missing log or inability to stop the system can be a critical condition.
No. It depends on the speed of risk, tolerable delay before harm, response time and applicable duties.
By injecting a signal or replaying a known case, then observing detection, escalation, decision, action and closure evidence.
Decision chain
Present the signal, risk and current route. We identify the link preventing an executable decision.
Exercise the control