AI Governance Ratings · Artificial Intelligence Governance Ratings
A structured ratings language for enterprise AI governance.
AIGR™ provides Artificial Intelligence Governance Ratings—AI Governance Ratings built from defined scope, governed evidence, critical-gate logic and controlled reviewer judgment. The methodology turns a complex evidence record into a comparable governance signal without collapsing material failures into a single unexplained number.
Eight domains produce a balanced governance view.
Governance & oversight
Accountability, decision rights, policy and board visibility.
Organizational readiness
Skills, operating model, training and management capability.
Risk management
Classification, impact analysis, control design and remediation.
Responsible AI practices
Intended use, transparency, fairness, human oversight and recourse.
Enterprise architecture
System design, integration, data flows and technical control points.
Cybersecurity governance
Access, model/data security, third parties and incident readiness.
Regulatory alignment
Mapping to applicable obligations and jurisdictional requirements.
Operational governance
Monitoring, change management, lifecycle control and evidence continuity.
Standardize
Apply a repeatable governance assessment architecture across systems.
Measure
Evaluate demonstrated capability rather than self-declared maturity.
Surface risk
Make material gaps and blockers explicit.
Support oversight
Give boards and executives a structured governance signal.
Compare
Build toward defensible comparisons as assessment volume grows.
Monitor
Keep the opinion tied to current evidence and system state.
A rating earns comparability through consistent use.
Benchmarking should follow standardized method, repeatable review, sufficient assessment volume, controlled calibration and consented cohort construction—not a marketing claim.