Certification, Audit, and Conformance for Trusted AI Operations
The TSCP AI Governance and Certification framework defines how AI components achieve, maintain, and demonstrate compliance within the TSCP Federated Trust ecosystem. It establishes clear requirements for certification, audit, and ongoing conformance to ensure AI systems operate safely, transparently, and in alignment with global regulatory expectations.
This framework enables trusted deployment of AI models, agents, APIs, and workflows in regulated and high-assurance environments.
Certification Framework Overview
How AI Components Become Trusted Under TSCP
AI certification under TSCP follows a structured, policy-driven process designed to validate identity, provenance, and operational controls.
AI Identity Registry Enrollment
Each AI component is registered with a unique, verifiable identity. Registration establishes ownership, scope, and accountability before certification begins.
ASCCS Control Categories
The AI Service Component Controls Specification defines required controls across governance, security, lifecycle management, and operational behavior. Controls scale based on assurance level.
Issuance and Attestation Steps
Once controls are validated, certificates and attestations are issued through the AI Bridge PKI. These artifacts serve as cryptographic proof of compliance and trust status.
Conformance Levels
Scalable Assurance Based on Risk and Use Case
TSCP supports a tiered conformance model to align assurance with operational risk.
Basic
Designed for low-risk AI use cases. Focuses on identity verification, basic provenance, and limited operational controls.
Intermediate
Applies to moderate-risk deployments. Includes expanded lifecycle controls, monitoring requirements, and documented governance practices.
Advanced
Reserved for high-risk and mission-critical AI systems. Requires full provenance, continuous monitoring, safety instrumentation, and rigorous audit readiness.
This tiered approach ensures flexibility without compromising trust or regulatory alignment.
Assessment and Audit Requirements
Evidence-Driven Compliance
Certification requires clear, verifiable evidence that AI systems meet applicable controls.
Required Evidence
Documentation includes identity records, training data lineage, model governance artifacts, security controls, and operational policies.
Continuous Monitoring
Certified AI components are subject to ongoing monitoring to detect changes in behavior, configuration, or risk posture.
Annual Audits
Formal audits validate continued compliance with ASCCS controls and assigned conformance levels. Findings are recorded and addressed through defined remediation processes.
Alignment with Global Standards
Built for Regulatory Readiness
The TSCP AI Governance and Certification framework aligns directly with leading global standards and regulatory models.
ISO/IEC 42001 – AI management system governance and accountability
EU AI Act – Risk-tier classification and trustworthiness principles
NIST AI Risk Management Framework – Risk identification, mitigation, and monitoring
This alignment enables organizations to meet multiple compliance obligations through a single, unified certification approach.
Certification Lifecycle
End-to-End Trust Management
AI certification is managed as a continuous lifecycle rather than a one-time event.
Registration
AI components are enrolled in the AI Identity Registry.
Assessment
Controls and evidence are evaluated against ASCCS requirements.
Issuance
Certificates and attestations are issued through the AI Bridge PKI.
Monitoring
Operational behavior and risk indicators are continuously observed.
Renewal
Certification is renewed based on reassessment and updated evidence.
Revocation
Certificates are revoked if controls fail, risks change materially, or policy violations occur.
This lifecycle approach ensures trust remains current, measurable, and enforceable.