AI Governance
Governance for Responsible and Verifiable AI
AI Governance defines who oversees AI trust, how assurance is enforced, and which policies apply across the AI lifecycle. This framework ensures that AI systems operate with clear accountability, measurable assurance, and continuous oversight in regulated and high-assurance environments.
Built on TSCP’s federated governance model, AI Governance balances innovation with control, enabling trusted AI adoption without sacrificing transparency or compliance.
Governance Model Overview
Federated Oversight for AI Trust
AI Governance operates through clearly defined policy authorities that coordinate assurance, lifecycle control, and cross-domain interoperability.
Federated Policy Management Authority (FPMA)
The FPMA provides overarching governance across all TSCP trust domains. It ensures consistency in policy interpretation, assurance mapping, and interoperability between Federal, Aviation, and AI trust ecosystems.
AI Policy Management Authority (AI PMA)
The AI PMA is responsible for AI-specific governance. It defines policy requirements for AI identity, lifecycle management, evidence handling, and runtime controls. The AI PMA also approves AI assurance mappings and oversees compliance across AI participants.
Interaction with Federal and Aviation PMAs
The AI PMA coordinates with Federal and Aviation Policy Management Authorities to maintain alignment across domains. This ensures AI systems can interoperate securely with Federal PKI and Aviation trust frameworks while preserving domain-specific requirements.
Policy Structures
The Foundation of AI Assurance
AI Governance relies on standardized policy structures that define how trust, risk, and assurance are measured and enforced.
Common Core Policy Baseline (CCPB)
The CCPB establishes shared policy requirements across trust domains. It defines minimum controls for identity, cryptography, auditability, and lifecycle governance that AI systems must meet.
Assurance Equivalence Matrix (AEM)
The AEM provides a structured method for mapping AI assurance levels across frameworks and jurisdictions. It enables consistent evaluation of trust, even when policies differ across organizations or regions.
AI-Specific Lifecycle Rules
AI Governance includes dedicated lifecycle policies covering registration, deployment, operation, update, suspension, and retirement of AI systems. These rules ensure AI behavior remains aligned with approved use cases and assurance levels.
Risk Management
Managing AI Risk with Precision
AI Governance embeds risk management directly into how AI systems are approved and operated.
Model Onboarding
Each AI system undergoes structured onboarding that validates identity, ownership, assurance level, and intended function before trust is granted.
Use-Case Approval
AI systems are approved for defined use cases, data classes, and operational environments. This prevents misuse and limits exposure beyond authorized boundaries.
Safety and Ethics Review
Governance processes include safety, ethics, and impact considerations to ensure AI systems align with responsible use expectations and regulatory principles.
Monitoring and Compliance
Continuous Oversight, Not One-Time Approval
AI Governance emphasizes ongoing monitoring rather than static certification.
Continuous Audit
AI systems are subject to continuous audit and evidence collection to verify compliance throughout their operational life.
Key Event Reporting
Significant events such as model updates, policy violations, or security incidents are logged and reported for governance review.
Revocation Triggers
Defined triggers allow rapid suspension or revocation of trust when assurance is compromised, policies are violated, or risk thresholds are exceeded.
Why Governance Matters
From Safety to Trust to Compliance
Strong AI Governance connects safety, trust, and regulatory compliance into a single operational framework. By defining clear oversight, enforceable policies, and continuous accountability, governance enables AI systems to be deployed with confidence in environments where failure is not an option.
Governance transforms AI from an experimental capability into a trusted operational asset.