AI Governance
AI governance consulting
for regulated industries
Solari advises organizations on AI governance obligations under the EU AI Act, ABA Formal Opinion 512, the NIST AI RMF, and equivalent state and sectoral rules. One domain within a broader advisory practice.
What It Is
What an AI governance framework is, and why it fails
An AI governance framework is the set of policies, roles, processes, and controls that governs how an organization develops, deploys, and monitors AI systems. It specifies who owns AI decisions, how risk is assessed, and what happens when a system produces an adverse outcome.
The EU AI Act imposes conformity assessment and risk classification obligations, ABA Formal Opinion 512 sets competence and supervision duties for law firms using generative AI, and the NIST AI RMF and ISO/IEC 42001 supply the baseline for governance maturity. Frameworks usually fail because they do not map to how the organization operates. For a plain map of the field, AI governance and law explains the statutes, conduct rules, and frameworks and how they meet in practice.
The Framework
What AI governance covers
Accountability
Named ownership of each system: who authorizes deployment, who monitors performance, and who answers for adverse outcomes.
Transparency
Explainable, auditable systems, with documentation and audit trails that demonstrate compliance to regulators and counterparties.
Compliance
Alignment with the EU AI Act, NIST AI RMF, ISO/IEC 42001, and ABA Formal Opinion 512, built to adapt as those requirements evolve.
The Build
How to build an AI governance program
Map the systems already deployed, trace who uses them and which decisions they inform, and identify the requirements that apply given sector, geography, and risk classification. That inventory drives the accountability structure, documentation, and compliance obligations. The sequence holds for AI governance for mid-market companies and for global enterprises.
The Advisory Work
What AI governance advisory addresses
Platform tools handle documentation workflows; Solari designs the governance structure they plug into. Solari works directly with the leader who owns the decision.
Engagements cover governance architecture, accountability mapping, regulatory alignment across the EU AI Act, ABA Formal Opinion 512, and the NIST AI RMF, policy design, board-level reporting, and audit readiness.
Common Questions
AI governance, explained
What is AI governance in simple terms?
AI governance is the organizational infrastructure that determines who is responsible for AI systems, what rules they must follow, and how compliance is verified. In practice that means documented policies for AI use, deployment authorization, documentation requirements, and a defined incident response protocol.
What are the three pillars of AI governance?
The three pillars are accountability, transparency, and compliance: named ownership of each system, explainable and auditable behavior, and adherence to applicable laws and standards. Most failures trace to accountability, because the other two pillars have no home when no one owns a system's outputs.
What are the key elements of an AI governance framework?
The key elements are explicit ownership, risk classification, model documentation covering data provenance and intended use, bias assessment, incident response, vendor oversight, audit trails, and board-level accountability. Integration matters more than the list. A compliance checklist will not hold under regulatory examination.
What is AI governance compliance?
It means demonstrating that AI systems satisfy applicable laws and standards, including the EU AI Act's conformity assessment and risk classification requirements, ABA Formal Opinion 512 for law firms, and the NIST AI RMF as the US baseline. Programs built on accountability and transparency infrastructure adapt as requirements evolve.
What is the difference between AI governance and AI ethics?
AI ethics defines what AI systems should do and what harms to avoid. AI governance enforces those commitments inside the organization through ownership, process, and verification. Governance gives ethical commitments institutional force.
How does an organization build an AI governance framework?
Start by mapping the AI systems in use, who uses them, and which decisions they inform, then identify the requirements that apply given sector, geography, and risk classification. A template applied before that mapping rarely functions as governance.