AI Readiness Checklist
AI Risk Management Checklist
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AI Readiness Checklist

Assess your organisation's AI risk management and readiness.

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AI Readiness Checklist

The readiness checklist: the foundations that let you adopt AI at speed without adding unmanaged risk.

Strategy and operating model

Define the AI outcomes and value expected

Align the AI and agent vision across teams

Create AI roles: security team, model owners, data custodians

Fund it as a programme, not a pilot

Data and technology foundation

Run a data readiness assessment and quality scorecard

Audit data lineage and provenance

Match tools to functional and risk requirements

Evaluate AI security posture management tooling

People and culture

Deliver role-based AI training and upskilling

Give the board and decision-makers AI literacy

Train engineers on model-specific threats

Build AI champions and a sanctioned route to AI

Third-party and responsible AI

Fold AI risk into third-party risk management

Tighten AI clauses in vendor contracts

Run bias, fairness and privacy impact assessments

Align to ISO 42001 and NIST AI RMF

Readiness score

Rate your AI readiness and risk posture — move from “block” to a risk-monitored “safe yes”.

AI Risk Management Checklist

A board-level checklist for CISOs, CIOs and CTOs: the controls you must own, evidence and be able to prove when AI is questioned.

Govern and own it

Name one accountable owner for AI risk

Stand up a cross-functional AI Governance Council

Approve an AI acceptable use and ethics policy

Set a standing board reporting date

Discover and classify

Keep a live inventory of sanctioned and shadow AI

Include vendor AI features enabled by default

Classify by decision impact (EU AI Act, NIST AI RMF)

Confirm your role: provider or deployer

Secure the AI stack

Treat internal AI platforms as tier-one assets

Enforce input validation, patching, privileged access

Test for prompt injection and data poisoning (OWASP LLM)

Require out-of-band callbacks and FIDO2 passkeys

Assure and respond

Build one control set to the strictest bar

Evidence it once, then map to each regime

Audit models for bias, drift and accuracy

Keep humans in the loop on consequential decisions

Extend incident response to AI misuse and deepfakes

Board test

Have you assigned accountability, can you produce the inventory, show the risk classification, and evidence human oversight — today, without notice?

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