Regulated teams do not need a checklist that says use AI responsibly. They need a checklist that turns responsibility into reviewable facts. The checklist should help a team decide whether an AI system can launch, what conditions apply, and what evidence must be retained.
A useful checklist starts before procurement or deployment and continues after launch. AI risk changes when the model changes, prompts change, retrieval sources change, users change, or an agent receives new tools.
System identity
Record the system name, owner, business purpose, user group, deployment environment, vendor or model provider, and launch status. Add whether the system is internal, customer-facing, employee-facing, or embedded in a regulated workflow.
Ownership matters. A system without a named owner should not be treated as production-ready, because no one is accountable for review, incidents, change requests, or evidence.
Data and autonomy
Identify data categories, personal data, confidential information, regulated records, retrieval sources, and retention expectations. Then document autonomy: does the system only suggest, does it draft for human review, or can it take action?
For AI agents, include the tool list. Tool access is often where risk lives. An agent that can read documents is different from one that can send email, change records, approve transactions, or trigger workflows.
Risk and obligation mapping
Classify the system using factors such as user impact, sector, geography, data sensitivity, autonomy, and external exposure. Map applicable obligations from internal policy, customer commitments, privacy rules, security controls, and AI-specific frameworks.
The checklist should capture the rationale, not only the final tier. Reviewers need to know why the system was considered low, medium, or high risk.
Approvals and controls
List required reviewers, decisions, conditions, and expiry dates. Conditions should be testable. Human review before external send is testable. Exercise caution is not.
Then connect controls to enforcement points: access limits, tool restrictions, human approval, logging, monitoring, prompt injection checks, output validation, incident response, and change review.
Evidence and review
Before launch, confirm where evidence will be stored. After launch, keep runtime logs, approval records, incidents, exceptions, and changes tied to the system. Schedule periodic review for higher-risk systems.
The checklist is complete when it can answer three questions without a meeting: what is this AI system, why is it allowed to operate, and what proof shows it is controlled?