Describe
Confirm purpose, system boundaries and actual workflow.
AI risk assessment for business useFIND THE RISK.
FIND THE RISK.
PRIORITISE THE WORK.
Blanche assesses AI use in its real business context so management can see which risks need control, deeper review or documented acceptance.
01Use-case based02People and data impact03Supplier dependencies04Prioritised actions
What should an AI risk assessment cover?
It should cover the system's purpose, data, users, affected people, supplier, limitations, decision influence, possible harm, human oversight, security, legal context and available controls.
01 / Context first
Assess the use, not just the tool.
The same AI product can present very different risks in different settings. A drafting assistant used for internal notes is not equivalent to a feature used to screen candidates, flag fraud or influence access to a service.
Blanche documents the intended purpose, users, data, affected groups and consequences before assessing likelihood, impact and control strength.
02 / Risk areas
Look across the full decision chain.
A practical assessment considers inaccurate output, bias, privacy, confidentiality, cybersecurity, intellectual property, supplier dependence, transparency and over-reliance on automation. It also checks whether a person can detect and correct an error before harm occurs.
- Personal or confidential data exposure
- Unfair or inaccurate outcomes affecting people
- Poor-quality output reaching customers
- Automation bias and ineffective human review
- Unclear supplier terms, retention or model changes
- Missing logs, escalation or incident evidence
03 / Output
A decision record management can use.
The output identifies risks, existing safeguards, recommended treatment, owners and review dates. Uncertain classification or legal questions are clearly marked rather than hidden inside an overall score.
Where a use may be high-risk, affect fundamental rights or require a DPIA, the assessment can show why a deeper specialist review is needed.
Clear sequence. Clear ownership.
Identify
Map harms, causes, affected groups and dependencies.
Evaluate
Assess likelihood, impact and existing control strength.
Treat
Assign actions, owners, evidence and a review point.
Common questions
Useful answers before you start.
01Is an AI risk assessment the same as a DPIA?+
No. A DPIA is a GDPR process focused on high-risk personal data processing. An AI risk assessment can cover a wider set of operational, human, supplier and regulatory risks. Both may be needed.
02Does a low-risk score mean the AI Act does not apply?+
No. A business risk score is not a legal classification. Applicability and high-risk status require a separate role and classification analysis.
03When should an assessment be reviewed?+
Review it when the intended purpose, model, supplier, data, affected people or decision influence changes, and at a proportionate regular interval.
04Can you assess third-party AI software?+
Yes. The review can use available contracts, product documentation, supplier answers, internal workflow evidence and testing, while clearly recording information gaps.
Ready when you are
Ready to put this in place?
Tell us briefly how your organisation uses AI. We will review the context and confirm the most useful next step before discussing scope.
No assessment or estimate is required before you enquire.