Compass© AI Governance
AI governance that delivers control, accountability, and speed. Built on the NIST AI Risk Management Framework to provide a structured, defensible approach across the full AI lifecycle.
Why AI governance matters
AI moves faster than legacy oversight. Without effective governance, modernization efforts expose the enterprise to bias, drift, security gaps, and compliance failure. Strong AI governance builds trust into every model, keeps systems explainable and auditable, and lets organizations scale AI with confidence instead of risk.
Compass© AI Governance
Risk Intake → Fairness Testing → Bias Detection → Continuous Compliance
What Compass Delivers
- An operational AI governance system aligned to the NIST AI RMF
- Built-in controls across the AI lifecycle, from design through deployment
- Standardized risk, compliance, and accountability for AI use cases
- Measurable, auditable governance that supports enterprise-scale AI
- Continuous visibility into AI performance, risk, and compliance
Compass Playbook
- AI use case discovery, classification, and risk tiering
- TEVV implementation (Testing, Evaluation, Validation, Verification)
- Control definitions for fairness, explainability, security, and reliability
- Monitoring, validation, and exception handling procedures
- Clear roles, decision points, and accountability across the AI lifecycle
Compass Outcome
- Controlled AI deployment without slowing innovation
- Reduced regulatory, ethical, and operational risk
- Consistent, repeatable AI governance across the enterprise
- Audit-ready evidence of compliance and control
- AI systems that perform reliably in real-world operations
Enterprise Oversight & TEVV
- Institutionalize enterprise AI oversight and accountability
- Identify AI adoption opportunities and quantify ROI
- Classify and risk-rank AI systems
- Implement TEVV protocol with independent verification for complex systems
Govern AI with confidence, without slowing innovation.
Compass© operationalizes the NIST AI RMF across the full AI lifecycle: classifying use cases, applying TEVV, defining controls, and monitoring AI systems from intake through deployment.
Source: McKinsey & Oxford, Delivering large-scale IT projects
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