Definition

An AI risk register is a structured artefact that catalogues each AI system's identified risks, aligned to the taxonomy in ISO/IEC 23894:2023 (AI risk management guidance) and the NIST AI Risk Management Framework (AI RMF 1.0) GOVERN, MAP, MEASURE, MANAGE functions. It records risk type (bias, opacity, data poisoning, misuse, availability), inherent and residual scores, treatment options and review cadence. The register feeds directly into ISO 42001 Clause 6.1 risk treatment planning.

Why it matters

The pressure on AI Governance (ISO 42001) programmes is shifting in specific, observable ways:

• ISO 42001 Clause 6.1.2 requires a documented risk assessment process; an absent or undated register is the most common major nonconformity found in Stage-2 AIMS audits.
• NIST AI RMF MAP function (MAP 1.1–1.6) requires that risks be contextualised per deployment scenario — generic registers that ignore use-case context fail external evaluations.
• UAE PDPL Article 10 and KSA PDPL Article 29 require algorithmic decision risks affecting personal data to be documented and mitigated, making the register a dual-purpose regulatory artefact.
• Investor and enterprise-client due-diligence questionnaires (notably from ADGM-regulated funds) now explicitly request AI risk registers as part of vendor risk assessments.

Evidence sources to capture

What an auditor or reviewer will sample for — wire each source into your evidence repository before the next review cycle:

• Risk register spreadsheet or GRC tool (e.g. ServiceNow IRM, Diligent) — system ID, risk category (ISO 23894 taxonomy), inherent score, control reference, residual score, owner.
• Model cards — bias evaluation results (demographic parity difference, equalised odds), dataset provenance, known failure modes.
• NIST AI RMF MEASURE function outputs — red-team test logs, adversarial robustness scores, drift-detection alerts from Evidently AI or Arize.
• DPIA/AI Impact Assessment — data categories, legal basis, risk description, mitigating measures, DPO sign-off date.
• Incident log — AI-related incidents linked to register entries, root-cause category, treatment update triggered.
• Risk review meeting minutes — attendees, risks rescored, escalations, next review date.

Recommended next actions

A 90-day plan, sequenced so each step produces evidence the next step depends on:

• Day 0–30: AI Risk Officer inventories all production and pilot AI systems; maps each to ISO 23894 risk categories and NIST AI RMF tiers; assigns preliminary inherent scores.
• Day 31–60: Data Science Lead populates evaluation evidence (bias metrics, accuracy benchmarks, adversarial test results) for each registered system; Risk Officer validates residual scores.
• Day 61–90: Legal reviews systems processing personal data against UAE PDPL Article 10 / KSA PDPL Article 29; tags systems requiring DPIA and links to register entries.
• Day 90+: GRC Manager loads register into ServiceNow IRM or equivalent; configures quarterly review workflow with automated reminders to system owners.
• Ongoing: Risk Officer re-scores any system after major model update, significant incident, or regulatory change; escalates risks scoring ≥15 to AI Governance Board.

Example metrics

Instrument these and report them monthly to the executive sponsor; sustained adverse trends become board-level conversations:

• Register completeness: 100% of production AI systems with an entry, risk score and named owner within 45 days of launch.
• High-risk treatment rate: ≥90% of risks scored ≥12 have an accepted treatment plan within 60 days of identification.
• Review cadence: 100% of register entries reviewed at least quarterly; high-risk entries reviewed monthly.
• Incident-to-register linkage: ≥95% of AI incidents traceable to a register entry within 5 business days.
• Bias metric threshold: demographic parity difference ≤0.05 for all high-risk classification models before production deployment.

The working checklist

Use this list during your next AI Governance (ISO 42001) review cycle. The phrasing is intentionally observable — every item is something a reviewer can sample for, not an aspiration.

• Verify: shadow AI use cases that never reached the intake.
• Verify: model cards that document the model but not the deployed system.
• Verify: no human-oversight design for high-risk use cases.
• Verify: data lineage that breaks at the embedding store.
• Verify: AI policy.
• Verify: use-case intake and approval workflow.

Pitfalls we keep seeing

Across MAST Consulting Group's AI Governance (ISO 42001) portfolio, the same recurring failure modes show up cycle after cycle. None are exotic; all are expensive when they reach the audit report.

• Pattern: data lineage that breaks at the embedding store. What good looks like: the same control evidenced inside the workflow it governs, not separately for the audit.
• Pattern: shadow AI use cases that never reached the intake. What good looks like: the same control evidenced inside the workflow it governs, not separately for the audit.
• Pattern: model cards that document the model but not the deployed system. What good looks like: the same control evidenced inside the workflow it governs, not separately for the audit.
• Pattern: no human-oversight design for high-risk use cases. What good looks like: the same control evidenced inside the workflow it governs, not separately for the audit.

Tooling we actually reach for

MAST Consulting Group is deliberately tool-agnostic, but in practice the same shortlist keeps appearing on AI Governance (ISO 42001) engagements because the integrations are cheap and the evidence is defensible:

• model registries (MLflow, SageMaker Model Registry, Vertex) — used not because it is fashionable, but because the audit trail it generates is one the reviewer accepts on the first ask.
• evaluation harnesses (Ragas, DeepEval) — used not because it is fashionable, but because the audit trail it generates is one the reviewer accepts on the first ask.
• policy-as-code for model guardrails — used not because it is fashionable, but because the audit trail it generates is one the reviewer accepts on the first ask.

How MAST Consulting Group can help

MAST Consulting Group runs AI Governance (ISO 42001) programmes for banks, insurers, healthcare networks, payments providers, telcos and government entities across the UAE, KSA, India and the wider GCC. We bring Lead Practitioners, sector specialists, and a working library of policies, risk methodologies and evidence templates that have passed audit at firms recognisable to your board.

If anything in this checklist is relevant to a programme you are scoping or rescuing, the fastest next step is a 30-minute working session with the practice lead. We will look at your specific situation, share what we have seen work for AI Governance (ISO 42001) programmes at similar scale, and tell you honestly if the work is something you should bring to us or run in-house.