The AI workflow risk register
AI governance does not need to start as a legal document. For a team, it starts with a clear list of workflow risks, owners, mitigations, and review triggers.
Recent AI safety and governance updates are aimed at frontier systems, but the operating lesson applies to everyday AI work: define risks, assign owners, monitor changes, and update the framework as capabilities change. OpenAI’s frontier safety blueprint emphasizes durable governance and resilience. OpenAI’s Frontier Governance Framework covers risk assessment, mitigation, security management, incident response, expert input, and updates. Anthropic’s Responsible Scaling Policy similarly treats AI safety as a framework that must evolve with model capability.
For a knowledge-work team, the practical version is an AI workflow risk register.
The skill
An AI workflow risk register is a short table for one real workflow. It names what could go wrong, how likely it is, who owns review, what mitigation is in place, and when the workflow must stop or be revised.
AI workflow risk register
Workflow:
{specific repeated AI-assisted workflow}
Risk:
{what could go wrong}
Impact:
{low / medium / high}
Likelihood:
{low / medium / high}
Mitigation:
{checklist, approval rule, source rule, access limit, test, rollback}
Owner:
{person or role responsible}
Trigger:
{event that requires review, pause, or escalation}Risks to track
Start with practical workflow risks, not abstract categories:
- Wrong source: AI relies on stale email, unofficial notes, or outdated files.
- Silent action: AI updates, sends, creates, or deletes before review.
- Bad confidence: AI presents assumptions as confirmed facts.
- Data exposure: Sensitive customer, employee, financial, or legal data enters the wrong tool.
- Review gap: Nobody owns the final check.
- Capability drift: A model, connector, or tool gains new actions the workflow was not designed for.
A worked example
Imagine a team uses AI to draft customer renewal briefs from CRM, support tickets, call transcripts, and email.
Workflow:
Customer renewal briefing.
Risk:
AI treats old email as current account status.
Impact:
High.
Likelihood:
Medium.
Mitigation:
CRM is the official source for account status.
Email is context only.
AI must flag source conflicts.
Owner:
Account owner.
Trigger:
If CRM and email disagree, the brief is marked "needs review" and cannot be sent.The prompt
Use this with an AI assistant before scaling a workflow:
Help me create an AI workflow risk register.
Workflow:
{describe the AI-assisted workflow}
Inputs:
{files, apps, emails, tickets, databases, transcripts, websites}
Outputs or actions:
{summaries, drafts, updates, messages, tickets, records, recommendations}
Current review process:
{who checks what before use}
Create a risk register with:
1. The top 8 workflow risks
2. Impact and likelihood
3. Mitigation for each risk
4. Review owner
5. Stop or escalation trigger
6. One test we should run before using this workflow broadly
Prioritize practical risks that could create rework, data exposure, customer harm, wrong decisions, or silent system changes.Review triggers
Every register needs triggers. These are conditions where the team pauses and revises the workflow:
- The AI gains a new connector or tool permission.
- The workflow starts writing to a shared system instead of drafting.
- The source-of-truth system changes.
- People report repeated corrections or hidden rework.
- A model update changes output style, reliability, or capability.
- A sensitive data class enters the workflow.
The rule
A risk register should be short enough to use and specific enough to change behavior. If it does not name an owner, mitigation, and trigger, it is not operational yet.