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
Items are sorted into your categories using the same rules each time, which makes volume readable.
Scored against your criteria
Assessment happens against criteria you control and weight, so the same standard applies on every run.
Banded results
Results are placed into defined bands, so thresholds decide the outcome instead of individual interpretation.
Extraction to a fixed schema
Content is pulled into the same field structure every time, so records from different sources stay comparable.
Anchored to your org map
Analysis runs against your actual structure and reporting lines rather than an assumed org shape.
Live external research
Current external sources are researched during the run rather than recalled from training data, and the sources travel with the output.
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
- Independent consultants codifying their own methodology
- Strategy and transformation teams standardising delivery
- Internal advisory functions under pressure to produce faster
- Operators who need defensible output, not a one-off chat answer
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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