Can AI assessment prevent AI incidents?

Place assessment inside the SAFER control map

Use the SAFER map to decide what assessment can and cannot do: Scope, Authority, Friction, Evidence, and Response. Each element must have an operational owner outside the assessment.

An assessment mainly contributes to Evidence. It may also test whether a participant understands Scope and Authority or responds correctly to Friction. It does not configure production access, block a risky action, monitor every live event, or execute incident response.

The NIST AI RMF Core treats risk management as continuous across govern, map, measure, and manage. It calls for documented roles, context-sensitive measurement, production monitoring, incident response, recovery, communication, and continual improvement. That structure supports a layered conclusion: assessment can inform risk decisions, while prevention and response depend on operating controls that continue after the test ends.

• Scope: define approved uses, prohibited data, relevant models, affected workflows, and foreseeable misuse.
• Authority: name who may approve, override, stop, and resume AI-assisted work.
• Friction: implement technical and workflow barriers such as access controls, confirmation steps, restricted actions, and independent review.
• Evidence: collect logs, work artifacts, tests, user reports, exceptions, and assessment observations with known coverage limits.
• Response: maintain triage, communication, containment, recovery, correction, and learning procedures.

Sources: [1]

Design scenarios around observable control behavior

Begin with an incident pathway, not a generic knowledge quiz. Write the chain from trigger to harm: a participant receives an ambiguous request, encounters sensitive data, asks an AI tool for help, receives a confident unsupported answer, and faces time pressure to send it. Identify the decision points at which a control should operate.

Then build a realistic scenario with evidence traps and a usable escape route. The participant should be able to inspect source material, notice a conflict, choose an approved tool or decline an unsafe action, verify a consequential claim, record uncertainty, and escalate to a named authority. Score only observable behavior and the submitted artifact. If the scenario never allows escalation, it cannot support a conclusion about escalation behavior.

The NIST Generative AI Profile identifies risks specific to generative AI and recommends context mapping, testing, documentation, human oversight, monitoring, and treatment aligned to organizational priorities. Use those categories to design scenario hazards and evidence requirements, not to claim that a single exercise proves safe operation.

Sources: [2]

Worked example: a public claims workflow

This synthetic scenario involves a communications analyst preparing a public briefing. The provided pack contains an approved results table, an outdated draft, a legal note limiting which claims may be made, and an inbox message asking for publication within 30 minutes. The AI assistant invents a causal explanation and selects the larger figure from the outdated draft.

The assessment observes five control behaviors. The participant identifies the authoritative table, compares the date and scope of the conflicting files, rejects the causal wording, recalculates the approved percentage, and asks the legal reviewer to resolve an ambiguous disclosure. The final briefing labels the unresolved point and withholds the disputed claim. These actions support a bounded finding that the participant demonstrated verification and escalation in this scenario.

Now change one condition: the production publishing tool automatically posts AI-generated copy when a timer expires. The assessment result does not stop that automation. Prevention still requires a technical hold, an approval gate, and an accountable release owner. Likewise, a participant who missed the outdated draft may reveal a training or workflow need, but the miss does not diagnose the entire incident system. The useful output is an evidence record tied to specific observed actions and limitations.

Connect findings to controls without turning scores into access keys

Translate each observed failure into a control question. A missed source conflict prompts review of document versioning and source labels. An unsupported claim prompts review of citation and approval requirements. Mishandled sensitive data prompts review of access, redaction, and tool restrictions. Failure to escalate prompts review of authority maps, time pressure, and available channels.

Do not automatically grant production privileges or remove supervision because someone passed a simulation. Use assessment evidence alongside role-specific training, supervised practice, technical controls, prior operating evidence, and the risk of the intended workflow. Reassess when tools, policies, tasks, or failure modes materially change.

The OECD AI Incidents and Hazards Monitor methodology describes an evidence base for tracking actual incidents and hazards and highlights the importance of consistent concepts and learning from reported events. Use incident records to refresh scenarios and control hypotheses. Do not infer incident rates or universal causes from a convenience collection, and do not treat absence of a recorded incident as proof that a system is safe.

Sources: [3]

Verify the prevention claim and preserve the boundary

For every proposed use of assessment, complete a claim table with four columns: observed behavior, supported inference, unsupported inference, and required operating control. “Verified two consequential claims in a timed procurement task” may support a scenario-specific verification signal. It does not support “will catch every hallucination,” “can operate unsupervised,” or “the workflow is safe.”

Track leading evidence such as completion of approval steps and timely escalations, and lagging evidence such as incidents, near misses, rework, and control exceptions. Review both for gaming, missingness, and changes in exposure. The assessment team should not own every resulting control; it should route findings to the people responsible for policy, systems, workflow, security, legal review, and incident management.

Two Prune helps customers run realistic AI-enabled work simulations and produce evidence-backed assessment reports about observed performance. It does not certify safety, replace production monitoring, control live systems, predict all future behavior, or make employment decisions. Its contribution is a clearer evidence signal for human review within a broader risk-management system.