AI Risk & Compliance
Connect AI risk to controls, evidence, and frameworks.
AI risk programs fail when risk registers, policies, and control evidence live in different tools with no shared vocabulary. Regulators and auditors increasingly expect organizations to show how AI-specific obligations map to operating controls.
The challenge
Questions leaders need to answer
- 01 Which AI uses carry material risk, and why?
- 02 Which controls address that risk, and are they operating?
- 03 How do NIST AI RMF, ISO/IEC 42001, and the EU AI Act map to what we already do?
- 04 Can we produce evidence on demand for an audit or a board?
QAISG links observed AI activity to the controls and frameworks that govern it, so risk assessments rest on evidence rather than surveys.
What QAISG provides
Outcomes
- Risk context
Observed AI activity assessed against policy, vendor posture, and sensitivity indicators from integrated controls.
- Control mapping
Controls defined once and mapped across AI and security frameworks.
- Audit-ready evidence
Evidence collected and reviewed with freshness and ownership tracked.