Where is AI already in use, and which workflows are high risk?
AI Risk Guide
AI governance should help teams move faster with fewer blind spots.
This page is the practical bridge between your AI questions and the AI Risk Audit offer. It focuses on inventory, risk tiering, review rules, and buyer-ready explanation.
What the AI Risk Audit is designed to answer
What data classes are involved, and where do review requirements need to be stronger?
How do we explain AI guardrails to buyers, leadership, and internal operators in one consistent way?
AI RMF mapped as an operating flow
Govern
- Define who approves AI usage and who owns higher-risk reviews.
- Establish approved-use guidance and escalation rules.
- Create a baseline for employee and leadership expectations.
Map
- Build an AI inventory across departments and workflows.
- Map data classes and vendor dependencies to each use case.
- Identify where shadow AI is creating governance ambiguity.
Measure
- Review prompt data handling, output review needs, and misuse risk.
- Define which use cases need stronger testing or human oversight.
- Separate low-friction use from higher-impact decisions.
Manage
- Assign AI risk tiers and mitigation priorities.
- Document vendor expectations and buyer-facing trust language.
- Keep the AI operating model usable instead of overly academic.
Typical outputs
AI Inventory + Risk Tiering
Use-case visibility by data class, business impact, and review requirements.
Guardrail Roadmap
Actionable steps for approved use, restricted use, and higher-risk review paths.
Buyer-Ready AI Language
A cleaner way to explain AI governance in trust materials and diligence conversations.