aia-generation

To generate a structured AI impact assessment, provide the specific AI use case, such as HR résumé screening. The system then builds an audit-ready document with executive summaries, data inputs, oversight, and risk mitigations.

Regulatory risk classification evaluates an AI system against configured regulatory regimes to determine its compliance status. It maps prohibited practices and transparency obligations while tagging relevant citations for governance documentation.

Checking AI policy alignment requires diffing the proposed use case against house AI policy commitments. This process generates decision-ready outputs, highlighting necessary conditions and flagging where separate deliverables are required.

You need to flag a privacy handoff when the AI impact assessment identifies data inputs or affected parties requiring separate privacy analysis. This explicit handoff ensures specialized privacy reviews are triggered for the deployment workflow.

Yes, evaluating AI deployment across multiple regulatory regimes is supported through regime-by-regime classification. It provides citation attribution and explicit handoff flags for vendor or follow-on analysis within the regulatory footprint.

Automated policy diffing for AI governance documentation is limited to comparing use cases against configured house policies. It cannot replace legal counsel but structures the evaluation and flags where separate deliverables like FRIA are required.