Enterprise AI Governance
Board-ready governance that moves at the speed of your AI ambitions. Decision rights, embedded controls, and an evidence trail your auditors will thank you for.
Governance Deliverables
Governance Charter
Council composition, decision authority, meeting cadence, and escalation paths—ready for executive sign-off.
Tiering Rubric
Risk-based classification (Tier 1–3) with clear criteria for data sensitivity, autonomy level, and regulatory exposure.
Intake Workflow
Standardized use-case submission, automatic tier assignment, and routing to the right approvers.
Evidence Pack Checklist
Model cards, data sheets, test plans, approval records, and monitoring plans—scoped by tier.
Board Dashboard Sample
Quarterly reporting template covering portfolio health, risk posture, value realization, and incidents.
Incident & Kill-Switch Playbook
Detection, escalation, remediation, and communication protocols for AI-related incidents.
Operating Model
A clear operating model separates strategic oversight (board / executives) from day-to-day controls (council / teams). Adapt titles to match your organization.
| Role | Responsibilities |
|---|---|
| Board / Audit Committee | Oversight, risk appetite, accountability; receives quarterly AI risk and value reporting. |
| Executive Sponsor | Sets priorities, resolves conflicts, ensures funding and cross-functional alignment. |
| AI Governance Council | Approves high-impact use cases, policies, and tiering rules; tracks the portfolio. |
| Risk / Compliance / Legal | Defines controls, reviews high-risk uses, ensures regulatory and contractual compliance. |
| Product / Engineering | Builds and operates AI systems; maintains documentation, monitoring, and incident response. |
| Data Governance | Data quality, lineage, and access controls; ensures proper data use and retention. |
Decision Rights by Tier
Regulatory Alignment Matrix
Our governance framework provides directional alignment with leading AI regulations and standards. This crosswalk shows how each governance component addresses specific regulatory expectations.
| Governance Control | NIST AI RMF | EU AI Act | SEC Guidance | ISO/IEC 42001 |
|---|---|---|---|---|
| Risk Tiering & Classification | MAP 1.1, MAP 1.5 | Art. 6 (Risk categories) | Risk factor disclosure | 6.1.2 (Risk assessment) |
| Governance Council & Decision Rights | GOVERN 1.1, 1.2 | Art. 9 (Risk management) | Board oversight requirements | 5.1 (Leadership) |
| Evidence Artifacts & Documentation | MAP 3.4, MEASURE 2.6 | Art. 11 (Technical documentation) | Internal controls | 7.5 (Documented information) |
| Monitoring & Drift Detection | MANAGE 3.1, 4.1 | Art. 72 (Post-market monitoring) | Continuous disclosure | 9.1 (Performance evaluation) |
| Incident Response & Kill Switch | MANAGE 4.2, 4.3 | Art. 62 (Serious incident reporting) | Material event disclosure | 10.2 (Nonconformity) |
| Human Oversight Requirements | GOVERN 6.1 | Art. 14 (Human oversight) | Management accountability | 8.1 (Operational planning) |
| Audit Trail & Accountability | GOVERN 1.7 | Art. 12 (Record-keeping) | Books & records | 9.2 (Internal audit) |
Note: This mapping provides directional alignment. Specific compliance obligations depend on your jurisdiction, industry, and the nature of your AI systems. Regulatory landscape is evolving—we update mappings quarterly.
Assistive vs Operative
Assistive
A human reviews, edits, and approves every AI output before it reaches a customer, system, or decision.
- ✓ Draft generation, summarization, search
- ✓ Internal analysis and recommendations
- ✓ Human always in the loop
Operative
AI takes actions, makes decisions, or communicates with external parties without per-action human approval.
- ⚠ Automated customer responses
- ⚠ Autonomous workflows and triggers
- ⚠ Requires Tier 2–3 controls minimum
Why this matters: The assistive/operative boundary determines your minimum control requirements. Misclassifying an operative system as assistive is one of the most common governance failures. For operative workflows, we recommend default-deny approval with explicit human override capability.
Lifecycle Control Gates
Governance is effective when embedded into delivery. These gates define where controls apply, what evidence is produced, and who approves.
Tap or click each gate to see evidence requirements, approvers, and typical SLA.
Evidence Artifacts (by Tier)
For Tier 2–3 systems, the following artifacts are recommended as part of the evidence pack. Tier 1 systems require a subset based on risk.
- Model Card: Purpose, training data summary, limitations, intended users, and known risks.
- Data Sheet: Sources, lineage, quality checks, retention rules, and access controls.
- Test Plan: Accuracy, bias, robustness, security, and red-team results.
- Approval Record: Sign-offs, tier classification, and required mitigations.
- Monitoring Plan: Metrics, alert thresholds, drift detection, and incident runbooks.
Incident & Kill-Switch Protocol
When an AI system misbehaves, response time is measured in minutes, not weeks. This protocol defines detection triggers, escalation tiers, and communication templates so your team responds decisively.
Detection Triggers
Drift Score Breach
Model output deviates beyond acceptable thresholds on accuracy, bias, or quality metrics. Auto-triggers L1 review.
Usage Anomaly
Unexpected volume spikes, novel input patterns, or access from unauthorized contexts. Flagged for immediate triage.
Compliance Alert
Regulatory change, audit finding, or customer complaint triggers policy review and potential system pause.
Escalation Matrix
L1 — Auto-Pause
- Response: System automatically paused or throttled. Fallback mode activated.
- Owner: On-call engineering lead
- SLA: Triage within 30 minutes. Resolve or escalate within 4 hours.
L2 — Council Review
- Response: System remains paused. Governance council convenes. Root-cause analysis initiated.
- Owner: AI Governance Council chair + risk/compliance
- SLA: Council meeting within 24 hours. Written assessment within 48 hours.
L3 — Executive Kill-Switch
- Response: Full system shutdown. External communications prepared. Regulatory notification if required.
- Owner: Executive sponsor + general counsel
- SLA: Kill-switch decision within 2 hours of L2 escalation. Board notification within 24 hours.
Communication Templates
Internal
Stakeholder notification with incident summary, current status, impacted systems, and expected resolution timeline.
Customer
Transparent disclosure of service impact, data exposure assessment, remediation steps, and point of contact for questions.
Regulator
Formal notification per jurisdictional requirements, including incident classification, timeline of events, and corrective actions.
Post-Incident Review: Within 5 business days of resolution, a written review is produced covering root cause, timeline, control gaps, and policy updates. Reviewed by council quarterly and summarized in board reporting.
Minimum Viable Policy Pack
Policies should be short, enforceable, and aligned to your operating model. Start with this minimum set and expand as your portfolio grows.
Acceptable Use
Approved, prohibited, and restricted AI tools/models across the organization.
Data Use & Privacy
Permissible data categories, retention rules, and sensitive data handling requirements.
Model Risk Management
Validation requirements, bias testing protocols, and documentation expectations by tier.
Vendor & Third-Party
Procurement requirements, security reviews, and contractual protections for AI vendors.
Human Oversight
Where humans must remain in the loop, override capabilities, and escalation paths.
Incident Response
Detection, reporting, remediation, and post-incident communication for AI events.
Board Dashboard Preview
Your board and audit committee receive a quarterly AI governance report. Here’s what they’ll actually see—not a slide deck, but a live dashboard built from real governance data.
Enterprise AI Portfolio Health
Risk Distribution
58% Tier 1, 29% Tier 2, 13% Tier 3. All Tier 3 systems have active monitoring and incident runbooks.
Key Actions This Quarter
- Completed Tier 3 red-team assessment for customer-facing chatbot
- Updated vendor due diligence for 3 new AI tool procurements
- In progress: EU AI Act readiness gap analysis
Sample dashboard with representative data. Your dashboard is populated from your governance platform with real metrics.
30 / 60 / 90 Day Roadmap
Start small, enforce consistently, iterate. This roadmap prioritizes leverage and speed—not perfection.
Days 1–30: Foundation
- Name an executive sponsor and form a small governance council
- Define tiering criteria and minimum evidence artifacts
- Publish acceptable-use and vendor guardrails policies
- Create a single intake form and approval workflow
Days 31–60: Operationalize
- Implement monitoring for priority systems (drift, incidents, value KPIs)
- Establish incident response playbook and escalation paths
- Integrate control gates into delivery pipelines
- Begin quarterly reporting to executives and board
Days 61–90: Harden
- Expand the policy pack and training program for teams
- Harden vendor due diligence and contractual controls
- Introduce periodic audits for high-impact systems
- Iterate governance based on metrics and incident learnings
Success Metrics & KPIs
Governance without measurement is governance theater. These KPIs track whether your framework is actually working—not just whether it exists on paper. We recommend reviewing monthly with council and quarterly with the board.
| KPI | Target | Measurement Method | Review Cadence |
|---|---|---|---|
| AI use cases classified by tier | 100% | Intake system coverage / total known AI use cases | Monthly |
| Mean time to governance approval | < 10 business days | Intake submission to council/team-lead sign-off | Monthly |
| Incident detection → response | < 30 min (L1) | Alert timestamp to first responder acknowledgment | Per incident |
| Policy coverage ratio | ≥ 6 core policies | Minimum viable policy pack completeness | Quarterly |
| Evidence artifact completeness | ≥ 90% (Tier 2-3) | Required artifacts present / required artifacts total | Per release gate |
| Board reporting compliance | 100% on schedule | Quarterly reports delivered on time with required metrics | Quarterly |
| Post-incident review completion | ≤ 5 business days | Incident resolution to written review delivery | Per incident |
| Training completion rate | ≥ 95% | Employees completing AI governance training / required total | Quarterly |
Tip: Start by tracking 3–4 KPIs in Month 1. Add the rest as your governance program matures. The goal is actionable visibility, not dashboard overload.
FAQ
What data do you need access to?
Can we start under NDA before procurement?
Do you retain client materials? For how long?
Do you subcontract governance work?
What does the first two weeks look like?
How does this differ from the free governance framework?
How does this map to NIST AI RMF?
What does the board dashboard look like?
Ready to Govern AI with Confidence?
Start with a free assessment—we’ll identify your gaps and recommend the lightest path to board-defensible governance.