AI Governance and Regulation
Strategic Advisory
TIV advises institutions on AI governance operating models that define accountability structures, risk-tiering approaches, lifecycle controls, and post-deployment oversight mechanisms. This includes designing governance processes for model approval and change management, documentation and transparency practices, oversight review workflows, and mechanisms for monitoring bias and performance risk over time. Our role is to help institutions integrate AI into mission delivery while maintaining operational control over safety, reliability, fairness, and regulatory compliance obligations.
TIV closely tracks and operationalizes major AI governance frameworks used across federal, multilateral, and national contexts, including the UNESCO Recommendations on the Ethics of Artificial Intelligence, the OECD AI Principles, the NIST AI Risk Management Framework (AI RMF), and the EU AI Act’s risk-based regulatory approach. These are treated not as political signals but as converging governance reference points, whose shared principles, i.e., risk management, accountability, transparency, human oversight, and lifecycle governance, are translated into institutionally implementable processes, governance templates, and oversight mechanisms..
AI Training and Education
AI governance fails when treated either as a technical specialty or as abstract ethics. TIV delivers training and institutional enablement that equips leadership, program staff, policy teams, and technical practitioners to operationalize governance expectations throughout the AI lifecycle. This includes risk-tiering methodologies, documentation practices, governance review processes that maintain delivery momentum, and procurement approaches that embed responsible AI expectations into vendor relationships..
Assessment and Audit
TIV conducts AI governance assessments focused on deployment readiness and the defensibility of oversight. These include AI impact assessments, governance maturity diagnostics, and targeted reviews of model risk, documentation completeness, and oversight effectiveness. Assessments are designed to help institutions move from informal experimentation toward structured, auditable, and responsibly governed AI deployment while maintaining proportional governance requirements aligned with institutional capacity.
