Explainer

AI governance consulting and frameworks

AI governance is the system of decision rights, policies, controls, and records through which an organization decides which AI systems it will use, for what purposes, under whose supervision, and with what evidence that its obligations are met. AI governance consulting designs that system against the law that actually applies to the organization, and builds it so that it can be shown to a board, a client, or a regulator.

Last updated 21 October 2026

AI governance advisory

The framework

What an AI governance framework is

An AI governance framework is the documented architecture of the system: who decides, by what criteria, with what controls, and how the decision is recorded. It is distinct from an AI policy, which states rules, and from a standard, which states what good practice looks like. A framework connects the two to the organization's actual decisions.

Frameworks most often fail in the space between policy and practice. The policy is approved, but no one owns approvals, the inventory of systems is incomplete, and the records that would demonstrate compliance were never kept.

Components

A complete framework contains

Decision rights, from the board to the individual deployment
An inventory of AI systems in use, including those embedded in vendor software
Risk classification of each use case against the applicable law
An AI governance policy and the procedures that make it operable
Approval and escalation pathways, including the authority to refuse a deployment
Human oversight requirements proportionate to risk
Vendor and model diligence
Documentation sufficient to withstand external review
Monitoring, incident response, and a fixed review cycle

Reference instruments

AI governance standards, regulations, and principles

Four instruments do most of the work in program design. They are complementary rather than alternatives: one is binding law, one is a certifiable management standard, one is a voluntary risk framework, and one states principles.

EU AI Act: Binding regulation that classifies AI systems by risk and imposes obligations on providers and deployers. Following the Digital Omnibus on AI, obligations for stand-alone high-risk systems apply from 2 December 2027. It reaches organizations outside the EU whose systems or outputs are used within it.
ISO/IEC 42001: An international standard for AI management systems, against which an organization can be independently certified.
NIST AI RMF: A voluntary framework organized around four functions, Govern, Map, Measure, and Manage, widely used in the United States as the reference for program design.
OECD AI Principles: Intergovernmental principles for trustworthy AI, adopted in 2019 and updated in 2024, that underlie many national strategies.

The principles of AI governance

Across these instruments the same principles recur: accountability, transparency, fairness, safety and robustness, privacy, and human oversight. They describe what a program must achieve, not how. An organization that has adopted the principles but not assigned owners, controls, and records for each has a statement of values rather than a program.

Primary sources: Regulation (EU) 2024/1689 · ISO/IEC 42001 · NIST AI RMF · OECD AI Principles

Best practices

How to build an AI governance program

The sequence matters more than any single control. Programs built in this order produce evidence as they go; programs built in reverse produce documents that describe a system no one operates.

01Assign accountabilityName the executive who answers for AI risk, and give the board a defined oversight role and a reporting line.
02Inventory and classifyRecord every AI system in use, including those embedded in vendor products, and classify each against the law that applies to it.
03Set policy and approvalAdopt an AI governance policy, constitute an AI governance committee where the organization's size warrants one, and define how new uses are approved or refused.
04Install controlsApply human oversight, testing, and vendor diligence in proportion to risk.
05Document and trainKeep the records that evidence each decision, and train the people who make them.
06Monitor and reviewTrack incidents and changes in the law, and review the program on a fixed cycle.

AI governance maturity

Maturity is best measured by what an organization can prove rather than what it has written. At the first stage, AI use is ad hoc and unrecorded. At the second, policy exists but is applied inconsistently. At the third, approvals, controls, and owners operate as designed. At the fourth, the organization can produce, on request, evidence that each system in use was approved, supervised, and reviewed.

UAE and the Gulf

Implementing AI governance frameworks in UAE businesses

The UAE has no standalone federal AI statute. An organization operating there builds its framework against a combination of federal data-protection law, the national AI charter and ethics guidance, and, in the financial free zones, dedicated regulation: Regulation 10 of the DIFC Data Protection Regulations governs personal data processed through autonomous and semi-autonomous systems, and the ADGM applies its own data-protection regime. Sector regulators and, for law firms, the Ministry of Justice's guiding principles add expectations of their own. In June 2026 the UAE also established a Federal Authority for Artificial Intelligence and Data, whose mandate includes proposing legislation, so the framework should be expected to harden.

Because the framework rests more on guidance than on statute, the evidentiary record carries more weight in the UAE than in jurisdictions with a single law to point to. Organizations with operations in the EU must also account for the extraterritorial reach of the AI Act.

Advisory

What AI governance advisory addresses

AI governance is an executive accountability question before it is a technology question. Solari Advisory works with leadership teams on the decisions a platform cannot make for them: who is accountable, which uses are acceptable, what the organization will refuse, and how each decision is evidenced. The practice is vendor-neutral and works across the United States and the UAE, with dedicated work for law firms.

Engagements begin with a Diagnostic, a structured session on the organization's current AI use and obligations, followed within three days by a written diagnostic.

AI governance advisory

Common questions

AI governance, explained

What is AI governance?

What is an AI governance framework?

The documented architecture of that system: decision rights, an inventory of systems, risk classification, policy, approval pathways, oversight, vendor diligence, documentation, and review. It connects policy and external standards to the organization's actual decisions.

What are the key pillars of AI governance?

Accountability, transparency, fairness, safety and robustness, privacy, and human oversight. Each becomes operative only when it has an owner, a control, and a record.

Who should own AI governance in an organization?

A named executive with authority to approve and refuse deployments, overseen by the board and supported by an AI governance committee drawing on legal, risk, security, and the business. Placing it solely within IT leaves the accountability questions without an owner.

What is the difference between AI governance and data governance?

Data governance controls the quality, lineage, access, and protection of data. AI governance controls the decisions made with AI systems, including their purpose, risk, oversight, and accountability. AI governance depends on data governance but asks questions data governance does not.

How do you implement an AI governance framework in a UAE business?

By mapping the obligations that apply, which may include federal data-protection law, DIFC Regulation 10 or the ADGM regime, sector guidance, and the EU AI Act where there is EU exposure, then building decision rights, controls, and records against that map. In the UAE the record carries particular weight because much of the framework rests on guidance.

What does an AI governance consultant do?

An AI governance consultant maps the obligations that apply to an organization, designs the framework and policy to meet them, assigns accountability, and builds the records that evidence compliance. An independent advisor does this without a stake in which platform or model the organization buys.

How long does it take to implement AI governance?

A defensible first stage, comprising accountable owners, an inventory, a policy, and an approval pathway, can typically be in place within weeks. A program that operates as designed and produces evidence on request generally takes several months, depending on the number of systems and jurisdictions involved.

Solari Institute · State Bar of California MCLE provider 21051 · Dubai Legal Affairs Department accredited provider TC-2026-825