DataCounsel — Privacy • Governance • TrustU.S. Privacy, Data Governance & Responsible Technology Advisory

DataCounsel / SolutionsSolution 06

AI Governance

Make AI review practical enough to keep pace with how teams build and deploy.

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DECISION CONSOLELIVE VIEW

Primary signalAI use caseReview required

Operating view06AI Risk, Review & Oversight

Solution Overview

Connect AI use-case intake, risk review, approvals, evidence and monitoring into a workable governance path.

AI governance works when review happens close enough to delivery to influence decisions. We help connect privacy, security, legal, product and business stakeholders around clear gates and ownership.

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Built for the operating modelDesigned around people, process, data and technology—not the tool alone.

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Connected decisionsClear ownership, decision points and evidence make the capability easier to run.

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Ready to improveUse the operating signals to refine the capability as the organization changes.

Core Capabilities

What the solution needs to make easier.

AI governance works when review happens close enough to delivery to influence decisions. We help connect privacy, security, legal, product and business stakeholders around clear gates and ownership.

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Use-case intake

Capture context, purpose, data use and ownership at the start.

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Risk assessment

Apply proportionate privacy, security and responsible-use review.

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Approval & evidence

Define decision rights, conditions and records that can be maintained.

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Monitoring & oversight

Track changes, exceptions, performance signals and review cadence.

Solution Workflow

Move an AI use case through the right review without creating a second development process.

Each stage connects a business need to a clear operating action and a measurable outcome.

Intake

Capture the use case, business purpose, data and owner.

Outcome: shared context

Assess

Identify material privacy, security and responsible-use concerns.

Outcome: clear decision

Decide

Set approvals, conditions, controls and evidence requirements.

Outcome: usable execution

Monitor

Revisit the use case as models, data and impact change.

Outcome: continuous improvement

CONTEXTProduct & engineering

DECISIONLegal & privacy

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EXECUTIONSecurity

OUTCOMELeadership

A Deeper Perspective

Clarity for AI decisions before they become production dependencies.

AI governance is most useful when teams can understand what needs review, who decides and what conditions must be met before moving forward.

The aim is proportionate control: enough structure to surface material risk early without turning every experiment into a slow, heavyweight approval exercise.

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What Good Looks Like

Designed to improve the work, not add another layer.

Useful solutions create visible progress, accountable ownership and evidence teams can maintain.

OUTCOME 01

Faster decisions

Teams know which review path applies.

OUTCOME 02

Visible accountability

Decision rights and conditions are explicit.

OUTCOME 03

Continuous oversight

Governance keeps pace as AI use evolves.

Where It Lands

Built to connect the people closest to the work.

Different teams use the same capability from different angles. The operating model keeps those views connected.

01Product & engineeringBuild review into the delivery lifecycle.

01Legal & privacySet proportionate risk and decision criteria.

01SecurityCoordinate controls for data, access and deployment.

01LeadershipSee AI exposure, exceptions and material decisions.

Extend the capability when the operating model calls for more.

Explore adjacent capabilities that often connect to this solution.

Frequently Asked Questions

Questions teams ask before putting this capability to work.

Answers stay specific to the solution so the page remains useful when someone is evaluating fit, scope and implementation.

How much review should an AI use case receive?

Use a proportionate model based on purpose, data, impact, deployment context and material risk rather than applying the same burden to every use case.

Who should own the final decision?

Decision rights should be explicit and aligned to the risk involved, with the right mix of business, privacy, legal, security and technical ownership.

When should governance happen?

Early enough to influence design, with additional review at meaningful changes in data, model, use, impact or deployment context.

Set AI decisions before deployment.

Discuss the review, approval and oversight model your teams need.

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