AI Governance Evidence Mapper.

Required input fields.

• Use case (required)
• Model type (required): Rules or classical model, Machine learning model, GenAI or LLM, Agentic or tool-using AI
• Data type (required): Public or synthetic, Internal business, Personal or sensitive, Regulated or high-impact
• Autonomy level (required): Assistive, Workflow automation, Tool-using or agentic
• Deployment context (required): Sandbox or pilot, Internal production, Customer-facing, Regulated or safety-relevant
• Named owner (required)
• Review cadence (required): Ad hoc, Quarterly, Continuous monitoring

Data handling: this interface uses the L2ET protected same-origin instrument engine. Do not enter confidential, regulated, privileged, incident, medical or sensitive operational data.

Evidence Matrix logic.

Weights use case impact, data sensitivity, autonomy and deployment context, then maps the resulting pressure band to evidence artifacts and approval gates.

Source families

Assumptions

• Risk class is preliminary.
• Evidence needs depend on jurisdiction, policy and actual deployment.
• The tool assumes the user describes the intended use honestly.

AI governance evidence dossier.

The generated artifact includes findings, assumptions, limitations, recommended next actions and exportable structured output.

Export options

Example input and output.

Example input

GenAI internal copilot using internal data with workflow automation and quarterly owner review.

Example output

Produces intended-use statement, data assumptions, required evidence, monitoring needs, review gate and residual-risk memo.