Data and privacy
Unlawful data use, weak provenance, poor data quality, excessive retention, personal data leakage, and inappropriate secondary use.
AI risk register and control framework services
By Nuvexia Advisory Team
AI Governance Consulting
29/08/2026
AI Risk Management
Nuvexia builds AI risk registers and control frameworks that connect specific risks, including bias, data leakage, model drift, security threats, supplier exposure, and regulatory breaches, to controls, accountable owners, review dates, and residual risk ratings. We align the work with ISO/IEC 42001, NIST AI RMF, and MAS guidance where relevant.
What an AI risk register contains
A useful register is tied to real systems and decisions. It should help risk owners see what could go wrong, which controls reduce the exposure, whether those controls operate, and who must act next.
| Register field | What it should record |
|---|---|
| AI system and use case | The model, product, vendor tool, or automated process that creates the risk |
| Risk statement | The cause, event, and potential effect on people, operations, compliance, or the business |
| Inherent risk | Likelihood and impact before controls are considered |
| Controls | Preventive, detective, and corrective measures linked to evidence |
| Owner | The person accountable for treating and reviewing the risk |
| Review schedule | The regular cadence and events that trigger an earlier review |
| Residual risk | Remaining exposure after controls, including acceptance or further action |
Risks the framework should cover
Performance and drift
Inaccurate outputs, changing performance, weak validation, automation bias, and failure for particular user groups.
Security and resilience
Prompt injection, adversarial inputs, insecure integrations, excessive permissions, unavailable services, and weak incident response.
Suppliers and regulation
Undisclosed subprocessors, model changes, unclear accountability, contractual gaps, and failure to meet applicable rules.
The register can also cover ethical, reputational, financial, operational, and business-continuity risks. Categories should match the organisation's existing enterprise risk language where possible.
Nuvexia's process
1
Build the AI inventory
Identify in-house systems, vendor products, AI embedded in business software, pilots, and employee use that creates material exposure.
2
Run risk workshops
Bring together business owners, engineering, data, security, privacy, legal, compliance, procurement, and internal audit as needed.
3
Map controls
Connect each risk to current and planned controls, evidence, testing methods, and relevant framework requirements.
4
Assign decisions
Name owners, calculate residual risk, agree treatment actions, and document who can accept exceptions.
5
Set the review cycle
Establish quarterly review as a baseline, with earlier updates for deployments, material changes, incidents, and control failures.
Framework alignment without duplicate registers
Nuvexia can map one operating register to several requirements. ISO/IEC 42001 provides an AI management system and risk process. NIST AI RMF organises work through Govern, Map, Measure, and Manage. MAS guidance adds sector expectations for regulated financial institutions.
The result should not be three separate lists that drift apart. A control mapping can show which entries support each framework while risk owners work from one current register.
Who this is for
- Companies operating several AI systems without a central view of risk
- Boards and risk committees that need traceable risk ownership and treatment
- Teams preparing for ISO/IEC 42001, regulatory review, procurement assurance, or internal audit
- Organisations adding AI risk to an established enterprise risk programme
Frequently asked questions
What is the difference between an AI risk register and an AI governance policy?
A governance policy sets organisation-wide rules, principles, roles, and decision rights. A risk register tracks specific exposure for actual systems, including controls, owners, review dates, and residual risk.
How often should an AI risk register be updated?
Review it at least quarterly and after any material trigger. Triggers include a new deployment, model or data change, supplier change, control failure, incident, drift, or relevant regulatory update. Higher-risk systems may need monthly or continuous monitoring alongside the formal register review.
Can Nuvexia integrate this with our enterprise risk register?
Yes. We map AI risks to the existing taxonomy, scoring scale, control library, ownership model, and reporting cycle. Nuvexia can provide a structured register for the client's chosen governance, risk, and compliance system, so the organisation does not need a competing risk method.