AI governance foundations
Build a risk map from the use cases you actually run rather than from a generic framework.
— Operations & Process
AI risk map
Classifies and scores the risks carried by your actual AI use cases and maps each to practical controls and owners.
- Operations & Process
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~40 minutes
For a full population, not a sample
No coding required
- Input
- Your AI use cases and supporting documents
- Output
- Use-case-level risk classification and scores
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use AI risk map for
Use-case approval
Score a proposed AI use case and attach the controls needed before it goes live.
Regulatory readiness
Show, per use case, which risks are identified and which controls address them.
What it works from
- Your AI use cases and supporting documents
- Org map and ownership context
- Your risk taxonomy and scoring scales
What you get back
- Use-case-level risk classification and scores
- Practical controls mapped to each risk
- A risk table with owners and priority
— HOW IT BEHAVES
How AI risk map produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Consistent classification of risks
Risks are sorted into your categories using the same rules each time, which is what makes a large volume of risk registers, controls and policy documents readable.
Scored against your criteria
Each risk is assessed against criteria you control and weight, so the same standard applies to every risk in the risk universe in scope.
Risks banded, not argued
Every risk lands in a defined band, so thresholds you set decide the outcome instead of whoever is interpreting it that day.
Risk registers, controls and policy documents pulled into one schema
Every risk is captured in the same field structure, so records drawn from different documents and sources stay comparable.
Grounded in your own context
Your strategy, standards and prior work are loaded first, so conclusions about the risk universe in scope are anchored to your situation.
Anchored to your actual structure
Analysis runs against your real reporting lines and risks rather than an assumed org shape.
Why this is expensive by hand
AI governance stalls when the risk register is written in the abstract. AI governance foundations is the typical trigger — build a risk map from the use cases you actually run rather than from a generic framework. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 2–3 weeks of experienced attention.
How this Skill produces it
As a Skill, the work is already sequenced. You bring the evidence, and the run produces use-case-level risk classification and scores plus practical controls mapped to each risk. What sits between input and output is the codified method: thresholds, sequencing and the points where a human confirms a call — all of it visible and editable in the Skill. Net effect: 2–3 weeks down to ~40 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Who it's for
- Operations and continuous improvement leads
- Transformation and automation teams
- Service delivery and shared services managers
- Consultants running process diagnostics
Run it in Skillsize — or export it anywhere
AI risk map exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
ChatGPTClaudeCopilotAI Products (MCP)
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