04 · GOVERNANCE

AI Governance Consulting, Risk & Compliance

Turn executive direction into accountability, policies, controls and evidence that can stand up to review.

When it helps

AI activity is moving faster than policy, ownership and oversight—or leadership needs a governance structure that works in practice and supports regulatory, audit and administrative accountability.

Focus

Governance model and decision rights
Policies, controls and approval pathways
Monitoring, escalation and documentation

Outcome

Defined accountability
Operational guardrails
Review-ready governance evidence

The situation

A policy nobody can apply is not governance. It is a document.

In most organizations AI use arrived before AI governance did. Tools were adopted inside departments, vendor terms were accepted without review, confidential material went into systems whose data handling nobody checked, and the policy — if one exists — was written once, circulated by email and never connected to how approvals actually happen.

AI governance work turns executive direction into something operational: who decides, who approves, what is prohibited, what requires human review, what gets documented, and what happens when something goes wrong. The test applied throughout is simple — could this organization show a regulator, a court, an auditor or a client what it decided, who authorized it and on what basis?

This is the GOVERN stage of the practice, and it is written by someone who practices law. Governance here is built to be used by non-lawyers in daily work and to hold up when it is examined later.

How it works

How governance gets built.

Governance is designed around the organization's real approval paths, not imported from a template.

01 · Current state

What is in use and who approved it

An inventory of AI systems in use, the data flowing through them, the vendor terms accepted, the decisions being influenced and the approvals that did or did not happen.

Deliverable

AI use and data inventory with risk classification and the authority gap for each item.

02 · Decision rights

Authority, in writing

Definition of who may approve what: adoption decisions, high-risk uses, data categories, vendor terms, client-facing or regulated applications, and where authority stops and escalation begins.

Deliverable

Governance model with decision rights, escalation paths and accountable owners by role.

03 · Policy and controls

Rules people can follow

Drafting of the AI use policy, acceptable and prohibited uses, confidentiality and data handling rules, human review requirements, vendor assessment criteria and disclosure expectations — written in the language of the work, not of compliance.

Deliverable

AI use policy, control set, approval checklists and vendor review criteria.

04 · Evidence and monitoring

Proof that the system runs

Documentation standards, logging of material decisions, periodic review cadence, incident and escalation procedure, and the reporting leadership needs to see that governance is operating rather than filed.

Deliverable

Documentation and monitoring framework, review calendar, incident procedure and board reporting format.

What you receive

AI use and data inventory with risk classification
Governance model with decision rights and escalation paths
AI use policy and control set written for daily use
Vendor and contract review criteria for AI procurement
Documentation, monitoring and board reporting framework

What I need from you

Disclosure of AI tools in use, including those adopted without approval
Existing policies, vendor agreements and confidentiality obligations
Access to the functions where AI touches client, personnel or regulated data
A named owner who will hold governance after the engagement ends

Scope & investment

Scope and investment.

Governance engagements are scoped on the number of entities, the regulatory environment and whether policy is being created from nothing or corrected. Work is quoted in writing as a fixed-fee engagement after the fit call, and can be delivered as a full governance build or as a focused review of policy, vendor terms or a single high-risk use.

Where an organization also needs an objective baseline, the AI Readiness & Governance Assessment covers governance as one of its seven domains and is often the better first step. Governance work after an assessment is faster, because the evidence already exists.

Book a 20–30 minute fit call

Questions leaders ask

What is AI governance?

AI governance is the system of authority, policies, controls, documentation and monitoring used to direct how an organization selects, approves, uses and oversees artificial intelligence.

How do you implement AI governance?

Start by identifying current AI uses and risks, assigning decision rights, defining approval and escalation paths, creating usable policies and controls, and building documentation and monitoring into everyday workflows.

What risks arise without clear AI governance?

Organizations lose visibility over data, vendors and decisions; apply inconsistent standards across departments; create legal, workforce or reputational exposure; and lack credible evidence when activity is later reviewed.

Is an AI policy enough?

No. A policy with no decision rights, no approval path, no documentation and no review cadence cannot be applied and cannot be evidenced. The policy is one artifact of governance, not the whole of it.

Does governance work require a lawyer?

Governance that has to survive regulatory, contractual or professional review benefits from legal judgment in its drafting. SAGAZ is led by a practicing attorney; the engagement is advisory and does not by itself create an attorney-client relationship.

Let’s clarify your next step.

A focused conversation about your organization, the decisions ahead and the support that fits.

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