47 AI
programs governedenterprise-ready
AI Governance ConsultingMake your AI deployments auditable, compliant, and trusted
Boards and regulators are asking hard questions about AI. Your governance framework should answer them before they do.
Redefine helps CTOs and CIOs build AI governance structures that work in production, not just on paper. Policy, controls, and monitoring, scoped to your stack.
The business challenge
Most AI programs outrun their controls
AI moves faster than policy. The gap between deployment and governance is where risk lives.
Without governance
- Models in production with no audit trail and the board cannot verify decisions.
- EU AI Act, SOC 2, and GDPR requirements accumulate with no clear ownership.
- Vendor AI policy reviewed only at contract renewal, not before deployment.
- Incident response for AI failures is improvised, not structured.
With Redefine
governance
- Every model decision logged, versioned, and traceable to a policy owner.
- Regulatory mapping built into the framework before deployment begins.
- Third-party AI vendor review integrated into procurement and onboarding.
- AI incident runbooks defined and tested before an incident occurs.
Pain context · fragmented AI risk review process
AI governance lifecycle
Six phases from AI inventory to continuous oversight
Select any phase to see how we scope and deliver that work. Every phase ships a real document or control, not a slide deck.
1
AI Inventory and
Classification
Map every AI system in
production and in planning
2
Risk Tiering and Impact
Assessment
Score each system by decision
impact and data sensitivity
3
Policy and Controls Framework
Authoring governance policies
tied to your regulatory context
4
Deployment Review Gate
Pre-production checks before
any model goes live
5
Monitoring and Drift
Detection
Continuous model performance
and behavior monitoring
6
Board Reporting and Audit
Readiness
Documented trail for regulators
and the board
AI System
Registry14 systems mapped
Large language
model-based email triage · Tier 2 (Medium)
Owner: CTO · Vendor: Internal
· Last reviewed: Mar 2025
Credit scoring model
· Tier 1 (High)
Owner: CFO · Vendor: External
· Last reviewed: Jan 2025
Demand forecast model
· Tier 3 (Low)
Owner: VP Ops · Vendor:
Internal · Last reviewed: May 2025
Deliverable
shipped with this phase
AI System Inventory
Register (spreadsheet and policy reference) · 14 line items, ownership assigned, tier scores
documented
Risk Impact Assessment
Tier 1: High Risk
3
systems requiring immediate
governance action
Tier 2: Medium Risk
6
systems in 30-day remediation
window
Scoring
dimensions applied
Decision autonomy
Data sensitivity
Regulatory
exposure
Policy Documents
Published
AI Acceptable Use
Policy
v1.2 · Approved by Legal ·
Board-ready
AI Vendor Procurement
Standard
v1.0 · In review by
Procurement
Model Lifecycle and
Retirement Policy
v1.1 · Approved by CTO ·
Published
Aligned to: EU AI Act ·
ISO/IEC 42001 · NIST AI RMF · GDPR
Pre-Production Review:
Customer Scoring Model v2.1
Bias and fairness
evaluationPass
Data lineage
documentedPass
Explainability
threshold metFail, blocked
Incident runbook
assignedPass
Deployment blocked: 1 control failure.
Return to model team with remediation brief.
Live Model Health
Monitoring
active
99.2
Uptime %
2.1%
Drift score
0
Open alerts
Last 7 days,
prediction distribution
Q2 AI Governance
Summary: Board Pack
Systems reviewed
14
of 14 in registry
Controls in
place
38
across 6 policy areas
Regulatory
alignment status
EU AI Act (risk
classification)Aligned
GDPR (data processing
records)Aligned
ISO/IEC 42001 (AI
management)In progress
What governance work covers
Every deliverable ships as a document, a control, or a process
No PowerPoint-only engagements. Every phase produces something your team can act on or present to your board.
AI Policy Authoring
Acceptable use, model lifecycle, vendor assessment, and incident response policies written and versioned. Aligned to EU AI Act, ISO/IEC 42001, NIST AI RMF, and your industry regulator.
Acceptable Use
PolicyVendor
Assessment StandardModel Lifecycle
PolicyIncident
Runbook
AI Risk Register
A tiered registry of every AI system in production and in development. Risk scored, owner assigned, review cycle defined.
Regulatory Mapping
Requirements from EU AI Act, GDPR, and sector-specific regulators mapped to your current controls. Gaps identified and prioritized.
Monitoring and Observability
Model drift thresholds, performance alerts, and logging architecture defined. Integrates with your existing observability stack or builds a new one.
Deployment Review Gate
Pre-production checklist for every AI system before it ships. Bias tests, data lineage, explainability thresholds, and incident runbooks verified before go-live.
Board and Executive Reporting
Quarterly AI governance summary packs built for your board and audit committee. What is live, what is at risk, what has been remediated, and what the team is working on next.
Quarterly board
packAudit trail
documentationRegulator-ready
format
Team Enablement
Governance training for engineers, product leads, and executives. Practical workshops, not compliance theater.
Value · AI governance analyst mapping compliance framework
Client result
35% improvement in compliance targeting accuracy
Proof · enterprise analytics team reviewing compliance dashboards
Client
Enterprise Apparel and Retail Organization
EnterpriseAnalytics
Multi-division retail and apparel enterprise requiring AI-driven analytics governance for revenue operations.
The problem
Analytics models were producing revenue recovery recommendations with no governance layer. Decisions from predictive systems were applied without audit trail, ownership, or regulatory review. Reporting was fragmented across ERP systems and consumed over 40% of team capacity in manual compilation.
The result
0
% improvement
in compliance targeting effectiveness
0
% reduction in manual
reporting time
1
centralized governance
model replacing 4 fragmented systems
Predictive analytics now operate with a defined governance layer. Recovery and targeting efforts traceable to policy controls.
Governance architecture
How the control layer fits your stack
Governance does not require ripping out your infrastructure. It sits across your existing AI toolchain as a policy and monitoring layer.
Data and Model Layer
Training data
registry
Model versioning (MLflow,
Weights)
Feature store access
controls
Governance layer
Policy and Controls
Pre-deployment review
gate
Policy version
control
Role-based access control
on model access
Drift and anomaly
alerts
Reporting and Audit Layer
Immutable decision
logs
Quarterly board pack
automation
Regulator-ready audit
trail
EU AI
ActISO/IEC
42001NIST AI
RMFGDPRSOC 2
Type IIHIPAA
(healthcare clients)FCA
(financial services clients)
Why this team
Most AI governance consulting is generic and proof-light
The biggest providers in this space produce broad frameworks and leave implementation to your team. Redefine scopes to your stack and stays through execution.
Scoped before work starts
You receive a line-by-line scope document before any engagement begins. No ambiguous retainers. No scope that expands without a written change.
Deliverable-based, not advisory
Every phase ends with a document, control, or process your team owns. Not a presentation summarizing what the team should do.
Matched to your context, not general
Redefine works with CTOs and CIOs at mid-market and enterprise companies deploying AI in production, not with organizations still evaluating whether to use AI.
Proof you can verify
Every engagement references real prior work. Not aggregate statistics. Not anonymized summaries. Specific deliverables from specific types of programs.
Regulatory currency
The EU AI Act enforcement timeline, ISO/IEC 42001 certification pathway, and NIST AI RMF adoption are tracked and factored into every engagement, not mentioned once in a kickoff slide.
The right team size
You work with a senior consultant throughout, not a delivery team you will never see again after the statement of work is signed. The person who scoped the engagement leads it.
Common objections
Questions before you engage
Internal risk teams understand your business context well. What they often lack is AI-specific governance expertise and regulatory fluency around EU AI Act, ISO/IEC 42001, and NIST AI RMF. We work alongside your risk team, not in place of them, and hand off a framework they can operate independently.
An initial AI inventory and risk tiering sprint takes 2 to 3 weeks. A full policy framework and governance operating model runs 6 to 10 weeks depending on the number of systems in scope. We scope before work starts, so you know the timeline before committing.
Most organizations using any SaaS tool purchased in the last two years are running AI whether they realize it or not. Predictive scoring in customer relationship management systems, automated routing in support platforms, and large language model features in productivity tools all count as AI under most regulatory frameworks. Governance is relevant from the first system, not from the tenth.
Every document and decision log produced in a Redefine engagement is formatted for audit readiness. Version controlled, owner attributed, and dated. We do not produce slide-based outputs that become stale within a quarter. If you are in a regulated industry (financial services, healthcare, insurance), let us know in the brief and we will tailor the documentation format to your specific regulator.
Pricing is scoped to the number of AI systems in scope, your regulatory context, and the phases selected. A focused 2-week AI inventory sprint starts from $8,500. A full governance framework for 10 to 20 systems runs $22,000 to $45,000 depending on complexity. Ongoing monitoring retainers are available post-framework. See full AI consulting pricing.
Is this the right service
Who this engagement is for
Good fit
CTO or CIO at a
mid-market or enterprise organization with 3 or more AI systems in production.
Board or regulator
has asked questions about AI risk that you cannot yet answer with documentation.
Operating in
financial services, healthcare, insurance, or another regulated industry with specific AI
obligations.
AI is in
production or imminent, not still in the "should we use AI" stage.
Not the right fit
Organizations
still in the discovery phase with no AI in production or on a near-term roadmap.
Teams looking
for a one-day workshop as a governance shortcut without ongoing framework work.
Organizations
where the CTO has no mandate to implement governance recommendations post-engagement.
Startups
needing a lightweight AI ethics statement rather than an enterprise governance operating model.
Not sure where you fall? Tell us your situation and we will be straight with you. No pitch if there is no fit.
Start the conversation
Get a scoped AI governance proposal
No commitment. No pitch. Submit your brief and receive a line-by-line scoped proposal within 3 business days.
form
Brief received
Brief received, we will review your workflow and send a scoped proposal within 3 business days.
Call within 48 hours
Proposal in 3 days
47 programs governed
You own all outputs
Next step
Book an AI strategy call
30 minutes. Your AI inventory, your regulatory exposure, and whether there is a fit. No commitment.
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