The Enterprise AI Governance Handbook

Governance cannot survive as a grassroots initiative. It requires a mandate from the highest levels of the organization to enforce standards that may temporarily slow down reckless innovation in exchange for long-term velocity.

1.1 The "Why" Pitch to the C-Suite

Do not pitch governance as "compliance." Pitch it as "market acceleration."

Risk Quantification:

• Financial Risk: The cost of IP leakage (e.g., engineers pasting code into public LLMs).
• Reputational Risk: The cost of a customer-facing bot hallucinating racial bias or incorrect pricing.
• Regulatory Risk: Preparing for the EU AI Act, NIST AI RMF, and emerging local laws.

1.2 Defining the Budget Structure

An underfunded governance program is merely "security theater." You need a dedicated budget line item that is separate from general IT.

CAPEX (Capital Expenditure):

• Platform Procurement: Sourcing an AI Guardrails/Governance platform (e.g., Prime).
• Consulting: Initial legal counsel to interpret new AI laws.

OPEX (Operating Expenditure):

• Headcount: An AI Governance Lead (dedicated role) and partial FTEs for legal/compliance liaisons.
• Compute Costs: The cost of running "evals" and guardrail models (which consume tokens) alongside production models.
• Training: Continuous education programs for employees on AI ethics.

1.3 The "Charter" Deliverable

If Governance is the "Department of No," business units will hide their AI usage. You must position your program as a service provider that solves their problems (security reviews, procurement delays, legal approvals).

2.1 The "Listening Tour"

Before writing a single policy, interview the heads of Marketing, HR, Engineering, Sales, and Product.

• Ask Engineering: "Are you using Copilot? How are you ensuring private keys aren't leaked?"
• Ask Marketing: "Are you generating copy? How do you ensure you aren't infringing on copyright?"
• Ask HR: "Are you using AI to screen resumes? How are you testing for bias?"

2.2 The "Carrot" Strategy: Fast-Track Lanes

Create a "Governance Service Level Agreement (SLA)."

2.3 Identifying "Shadow AI"

Conduct a gentle audit of current usage. Look at firewall logs for traffic to OpenAI, Anthropic, Midjourney, etc.

This body creates the standards and arbitrates the "grey areas" where business value conflicts with potential risk.

3.1 Committee Composition & Roles

Chair (AI Governance Lead): Sets the agenda, drives execution, and holds the tie-breaking vote (or escalates to the CIO/CEO).

Technical Sponsors (CIO & CISO):

• Role: Ensure the AI is technically feasible and secure.
• Veto Power: Yes, on security grounds (e.g., "This model is not SOC2 compliant").

Legal & Privacy Counsel:

• Role: Interpret GDPR, CCPA, and IP laws.
• Veto Power: Yes, on regulatory grounds.

Business Unit Representatives (Rotation):

• Role: Provide the "voice of the user" to ensure policies aren't impractical.
• Veto Power: No, but they have strong influence on "usability."

3.2 Operating Cadence

• Phase 1 (Setup - First 3 Months): Meet Weekly. The focus is on drafting the initial policies and approving the first "lighthouse" use cases.
• Phase 2 (Steady State): Meet Bi-Weekly or Monthly. The focus shifts to reviewing "High Risk" exceptions and reviewing quarterly metrics.

3.3 The Escalation Matrix

Define clearly what the committee doesn't need to see.

• Low Risk: Approved automatically by the platform/process.
• Medium Risk: Approved by the AI Governance Lead asynchronously.
• High Risk: Must be presented to the full Committee.

You need a consistent yardstick to measure risk. "I think this is safe" is not a strategy.

4.1 The Intake Form (The "Front Door")

Create a mandatory digital form for any AI project. Key questions must include:

• Data Classification: Does this touch PII (Personally Identifiable Information), PHI (Health Info), or MNPI (Material Non-Public Info)?
• User Volume: Is this for 5 internal analysts or 5 million external customers?
• Agency: Does the AI take action (e.g., refund a charge) or just advise (e.g., draft an email)?
• Model Source: Is it a public API (OpenAI) or a private hosted model (Llama 3 on Azure)?

4.2 The Scoring Logic (Risk Tiering)

Develop an automated scoring system based on the intake answers.

4.3 Documentation Requirements

For High Risk projects, mandate an "AI System Card" that documents:

• Model limitations (what it can't do).
• Training data provenance (if custom trained).
• Bias testing results.

You cannot rely on prompting strategies ("Please be nice") to secure LLMs. You need a deterministic technical layer—a firewall for intelligence.

5.1 The Architecture of a Guardrail

The platform must sit as a proxy between your applications and the LLMs.

Input Rail (Pre-Processing): Scans the user's prompt before it hits the LLM.

• Checks for: Prompt Injection attacks ("Ignore previous instructions"), PII (Social Security numbers), and Toxic Language.

Output Rail (Post-Processing): Scans the LLM's response before it hits the user.

• Checks for: Hallucinations, Bias, Competitor mentions, and Regulatory violations.

5.2 Key Capabilities Required

When selecting a platform, ensure it supports:

• Custom Policy Engine: The ability to write rules specific to your business (e.g., "Never promise a refund over $50").
• Semantic Matching: Rules should not be keyword-based (which are brittle). They should use AI to understand intent (e.g., recognizing that "I want to end it all" is a self-harm risk, even if the word "suicide" isn't used).
• Model Agnosticism: The platform must work with Azure OpenAI, AWS Bedrock, Anthropic, and local models. You do not want to be vendor-locked to one model provider.
• Latency & Scalability: The guardrails must run in milliseconds so they don't degrade the user experience.

5.3 Configuration vs. Coding

The platform should allow non-technical Governance Leads to configure policies (e.g., "Turn on GDPR mode") without needing to ask developers to rewrite code. This separates policy from application logic.

The Governance loop is never "done." It requires constant monitoring and tuning.

6.1 Operational Dashboards (For the CISO/Lead)

Your platform should provide real-time views into:

• Attack Surface: How many prompt injections were blocked today?
• Data Leakage: How many credit card numbers were redacted?
• Top Violators: Which users or departments are consistently triggering safety flags? (Target them for training).
• Latency Impact: How much time are guardrails adding to requests?

6.2 Executive Reporting (For the Board)

Translate technical metrics into business value.

6.3 The "Feedback Loop"

Use the data to update your policies.

Scenario: If you see a spike in users trying to use the AI for "medical advice" (which is prohibited), do not just block it. Update the system prompt to politely redirect users to human HR resources, and update your employee handbook.