AI Governance Readiness (ISO 42001): Simple Guide for Leaders

AI Governance Readiness (ISO 42001)

A practical, business-first guide to AI governance. Learn what ISO 42001 is, why it matters, and how to stand up an AI Management System (AIMS) with tools you already own — model inventory, risk, human oversight and monitoring — plus audit-ready evidence patterns in Microsoft 365.

What is ISO 42001 AIMS building blocks Top AI risks Evidence patterns (M365) Quick-start checklist

What is ISO 42001?

ISO 42001 is the international standard for an AI Management System (AIMS). It helps organisations govern AI safely and responsibly by defining how AI is approved, used, monitored and improved. Think of it as ISO 27001 for AI — a management system that turns good intentions into repeatable practice.

Business value

Reduce risk, increase trust and accelerate responsible adoption.

What it covers

Policy, roles, model inventory, AI risk, oversight, monitoring and improvement.

Who cares

Boards, executives, risk/compliance, security, product and data/ML teams.

Why it matters (in plain English)

Trust & transparency

Show customers, partners and regulators that AI decisions are controlled and explainable.

Risk & compliance

Contain data leakage, bias and misuse; align with privacy rules and emerging AI regulation.

Speed without chaos

Enable innovation with a clear, repeatable approval process and live monitoring.

Key components of an AI Management System (AIMS)

1) Policy & roles

Define where AI is allowed, who approves it, and what must be recorded.

AI Policy & Acceptable Use
RACI across Product, Legal/Privacy, Security, Data/ML

2) Model inventory

One register for all AI systems with owner, purpose, data, risk tier and deployment surface.

Inputs/outputs, vendors & dependencies
Lifecycle status (pilot → prod → retire)

3) Risk & evaluations

Assess threats like leakage, bias, hallucination and abuse; run red-team tests and evals.

PIA/DPIA integration (ISO 27701 / APPs)
Adversarial & fairness evaluations

4) Human oversight

Define when a human must approve, review or can override the AI’s output.

HITL checkpoints by risk tier
Escalation and exception handling

5) Monitoring & incidents

Track performance, drift and complaints; route incidents for triage, learning and fixes.

KPIs: accuracy, bias, reject rate, errors
Playbooks for rollback and comms

6) Evidence & improvement

Keep decisions and logs traceable to speed audits and drive continual improvement.

Versioned artefacts with timestamps
Management review cadence

Top AI risks every organisation should manage

Beyond technical concerns, AI introduces business risks — from confidentiality breaches to reputational harm. ISO 42001 helps you identify, assess and treat these risks before they impact trust or compliance.

Data leakage

Staff pasting sensitive data or source code into public AI tools. Mitigate: Private AI, DLP, training.

Shadow AI

Unapproved tools and scripts in business units. Mitigate: Mandatory inventory & approvals.

Bias & fairness

Unintended discrimination in outputs. Mitigate: Fairness testing & diverse data.

Hallucinations

Confident but false answers. Mitigate: HITL for high-impact decisions; retrieval grounding.

Privacy breach

Personal data used without lawful basis. Mitigate: PIA/DPIA, ISO 27701 alignment, minimisation.

Drift & degradation

Performance declines as data changes. Mitigate: Monitoring, retraining, rollback plans.

IP & licensing

Use of copyrighted training data or outputs. Mitigate: Vendor due diligence; legal review.

Ethical harm

Opaque decisions damage trust. Mitigate: Explainability, clear user disclosures.

Third-party reliance

Model/vendor outages & lock-in. Mitigate: SLAs, portability, vendor SOC/pen-test evidence.

Evidence patterns using Microsoft 365 (no new platform)

Keep AI governance evidence where your team already works. These simple patterns produce audit-ready artefacts with timestamps and versioning.

SharePoint libraries

Per model: Inventory, Evals, Approvals, Incidents
Versioning & retention enabled

Power Automate runs

Monthly snapshots of configs/logs to dated folders
Graph exports for Entra/Defender/Intune/Purview

Saved queries

Sentinel/Defender KQL saved with owners & links
Attach screenshots of dashboards as evidence

Quick-start checklist (90-day plan)

Weeks 1–2

Publish AI Policy & AUP; set RACI
Stand up a SharePoint “AIMS” site
Spin up model inventory template

Weeks 3–6

Define risk tiers & HITL thresholds
Start evaluations (red-team, fairness)
Save first Sentinel/Defender queries

Weeks 7–12

Automate monthly snapshots
Run management review #1
Close gaps; prep audit pack

AI Governance FAQs

How is ISO 42001 different from ISO 27001?

ISO 27001 secures information broadly; ISO 42001 governs how AI is approved, used, monitored and improved. They complement each other: privacy (ISO 27701) often links in too.

Do we need new tools?

Not to start. You can build an effective AIMS in Microsoft 365 (SharePoint, Power Automate, Defender, Sentinel). Add ML-specific tooling later as needed.

What will auditors expect to see?

A live model inventory, risk/evaluation records, approvals (HITL), monitoring evidence, and a management review trail — all versioned and timestamped.