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A record tied to the actual workflow
AI risk assessment
An AI risk assessment examines how a defined AI-enabled workflow could fail, who or what could be affected and which controls or decisions follow. This guide provides a practical risk-record structure, not a universal numerical score or a legal approval. Begin with the actual system, inputs, users and actions. Keep the uncertainty and evidence visible so the accountable owner can decide what must change before use.
Create a risk record that connects each credible failure to a control, test and accountable decision.
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
New to the topic? Read the background on artificial intelligence.
A record tied to the actual workflow
what system are we assessing?
Record the purpose, users, inputs, outputs and permitted actions before listing risks.
An illustrative support assistant that drafts a reply has a different boundary from one that can issue a refund. Name the source material, supplier and human review step. The voluntary NIST framework provides a reference for contextual risk management. An AI readiness checklist identifies preparation gaps, but readiness alone does not establish that a task’s consequences are acceptable or that the intended controls have been tested.
Board-level oversight of these risks is covered in AI transformation advisory.
Define the system boundary.
List permitted actions.
Identify affected people.
A record tied to the actual workflow
what should a useful risk row contain?
Describe a specific failure, its consequence and the conditions that could cause it.
For the support example, a row might describe an outdated policy producing an incorrect refund promise. Record the affected customer, source-version dependency, current review and person responsible for the control. Avoid a label such as hallucination risk without a task-specific consequence. AI governance framework links the record to decision rights. Where probability is unknown, say so rather than inventing a precise likelihood from a small demonstration.
A record tied to the actual workflow
how should controls be evaluated?
Check whether the control changes behaviour on relevant examples.
A source date displayed in the interface may help a reviewer, but it does not prove the answer used the correct source. Test an expired document, conflicting versions and a request the material cannot answer. LLM evaluation explains representative cases; AI evaluation consulting provides practical support. Keep the result and the test conditions next to the control so reviewers can distinguish demonstrated behaviour from an intended design.
| Check | What to establish |
|---|---|
| 1 | Test the control itself. |
| 2 | Include awkward cases. |
| 3 | Preserve the test conditions. |
A record tied to the actual workflow
who decides what remains acceptable?
Name the owner of each residual issue and the authority for proceeding or stopping.
A project team should not silently accept a consequential risk merely because the release date is near. AI governance consulting defines approval and escalation responsibilities. The NCSC development guidance provides lifecycle security context. Legal, information-security and professional questions belong with the relevant specialists; this working record does not replace their assessment or certify that every obligation has been satisfied.
A record tied to the actual workflow
when should the assessment be revisited?
Review it when the task, sources, users, tools or observed failures change materially.
A system may gain a new action, retrieve different information or be used by a group the original tests did not represent. AI implementation roadmap makes reassessment part of delivery, and AI operations consulting supports review after release. Record the trigger, changed evidence and resulting decision. An assessment is useful as a maintained reasoning record; a date on a spreadsheet is not evidence that the live workflow still matches its assumptions.
Define review triggers.
Record new failures.
Update the decision rationale.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to ai risk assessment and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing ai risk assessment.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
Continue with the right question
Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our ai consultancy service or read the ai implementation consulting overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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