Solari advises organizations navigating AI governance obligations under the EU AI Act, ABA Formal Opinion 512, NIST AI RMF, and equivalent state and sectoral rules. This is one domain within a broader advisory practice.
An AI governance framework is the structured set of policies, roles, processes, and controls that governs how an organization develops, deploys, and monitors AI systems. It specifies who owns accountability for AI decisions, how risk is assessed before deployment, what documentation is required, and how the organization responds when a system produces an adverse outcome.
The legal environment has shifted the question from whether organizations need such a framework to which regulatory requirements apply and on what timeline. The EU AI Act imposes conformity assessment obligations and risk classification requirements for AI systems in regulated applications. ABA Formal Opinion 512 establishes competence and supervision obligations for law firms deploying generative AI. NIST's AI Risk Management Framework has become the institutional baseline against which AI governance maturity is assessed. ISO/IEC 42001 adds a certifiable management system standard. Organizations deploying AI without a governance framework carry regulatory and reputational exposure that is increasingly difficult to disclaim.
The more common failure, however, is not the absence of a framework but the design of one that does not map to how the organization actually operates. A governance document that sits outside the deployment process provides neither legal cover nor operational safety. Solari begins with the actual AI use in the organization, traces the accountability gaps and regulatory obligations that use creates, and builds governance infrastructure from that foundation.
Clear ownership of AI decisions and outcomes. Someone named responsible for each system's deployment authorization, ongoing performance, and the organizational response when an adverse result occurs.
Explainability and auditability of AI systems and their outputs. Documentation, audit trails, and the organizational capacity to demonstrate compliance to regulators, counterparties, and affected stakeholders.
Alignment with applicable laws and standards. The EU AI Act, NIST AI RMF, ISO 42001, and ABA Formal Opinion 512 are the AI governance standards that define the regulatory perimeter governance programs must satisfy and be designed to adapt to.
The AI governance market is dominated by platforms and monitoring tools that automate model tracking and generate compliance documentation. Credo AI, OneTrust, and Clarifai serve a real function in that market. They do not address the analytical work of determining which risks actually matter for a specific organization, how accountability should be structured when leadership does not understand the technology, or how to identify and close the governance gaps between written policy and operational practice.
Solari works with the economic buyer to design governance structures calibrated to the organization's actual operating environment, regulatory exposure, and institutional risk profile. AI governance consulting engagements cover governance architecture, accountability mapping, regulatory alignment across the EU AI Act, ABA Formal Opinion 512, and NIST AI RMF, AI governance policy design, board-level reporting infrastructure, and audit readiness.
The AI governance best practices that survive regulatory examination are built on accountability architecture and deployment mapping rather than on documentation production. The analytical standard applied to this work comes from policy advisory engagement at the intersection of AI deployment and institutional risk, and from operational experience designing AI-enabled systems that hold under regulatory scrutiny at scale.
AI governance is the organizational infrastructure that determines who is responsible for AI systems, what rules those systems must follow, and how the organization verifies compliance. In operational terms: documented policies for which AI tools are used and by whom, authorization processes for AI deployment, documentation requirements, and a defined incident response protocol. The governance function matters most at the point where AI outputs affect decisions with legal, financial, or reputational consequence.
Accountability, transparency, and compliance. Accountability specifies who owns each AI system and its outcomes. Transparency requires that system behavior be explainable and auditable. Compliance demands adherence to applicable laws and standards. Most governance failures trace to the accountability pillar: when no one is clearly responsible for an AI system's outputs, the transparency and compliance functions have no organizational home to operate from.
A governance structure with explicit ownership; a risk classification process for AI systems; model documentation requirements covering data provenance, intended use, and known limitations; bias and fairness assessment protocols; incident response procedures; third-party AI vendor oversight; audit trail capabilities; and board-level accountability mechanisms. The elements matter less than their integration. Governance frameworks designed as compliance checklists rather than operational infrastructure will not hold under regulatory examination.
Demonstrating that an organization's AI systems satisfy the requirements of applicable laws and standards. The EU AI Act imposes conformity assessment and risk classification requirements for AI systems in regulated applications. ABA Formal Opinion 512 establishes competence and supervision obligations for law firms. NIST AI RMF is the US institutional baseline. Governance programs need to be designed for regulatory evolution, which means building compliance on accountability and transparency infrastructure rather than on document production that satisfies current requirements while providing no adaptability for the next iteration.
AI ethics is the normative discipline: what AI systems should do, what values they should reflect, what harms they should avoid. AI governance is the operational discipline: how those commitments are enforced inside the organization. Ethics without governance produces principles documents with no operational effect. Governance without ethics produces compliance programs that satisfy regulatory requirements while generating the kinds of outcomes those requirements were designed to prevent. The governance structure is what gives ethical commitments institutional force.
By mapping the AI systems currently deployed, tracing who uses them and what decisions they inform, and identifying which regulatory requirements apply given the organization's sector, geography, and the risk classification of each system. That foundation drives the accountability structure, documentation requirements, and compliance obligations. The design failure most common in practice is starting with a framework template rather than with the actual deployment landscape. A governance program that does not map to what the organization is actually deploying will not function as governance in any meaningful sense.
Every engagement begins with a structured session in which Solari assesses the governance problem at hand, identifies the applicable regulatory obligations, and maps the gap between current state and what the situation requires. A written analysis follows within three days. Where the scope warrants a sustained engagement, a retainer proposal accompanies the diagnostic.
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