AI Governance Quickstart

Simple, sane guardrails to start tomorrow.

Why Governance?

Governance isn’t bureaucracy—it’s how teams ship useful assistants safely and keep them useful as models, prompts, and content change. Start lightweight, make it visible, and iterate.

Principles (Day-1 Defaults)

Purpose-bound: Every assistant has a clear goal and audience.
Minimum data: Collect only what’s needed; retain only as long as needed.
Transparent: Users know when they’re interacting with an AI system.
Human override: Obvious escalation to a human for edge cases.
Measurable: Quality/econ metrics are tracked and reviewed.

Roles & RACI

Owner

Accountable for outcomes, approves changes, watches KPIs.

Builder

Prompts, tools, RAG, evals, release notes.

Data Steward

Source quality, privacy classification, retention, access.

Reviewer

Policy/safety checks, red-teaming, incident response.

Policies That Fit on One Page

Acceptable Use: Allowed/blocked tasks, disclosure, user consent.
Privacy & Data: PII handling, masking, retention periods, export rights.
Content Rules: Cite sources, no invented links, disclaimers where needed.
Security: Authentication, API tokens, secrets management, audit logs.
Review cadence: Monthly review; emergency freeze/rollback process.

Data Handling (Quick Model)

classification:
  PUBLIC, INTERNAL, CONFIDENTIAL, RESTRICTED

retention:
  CONFIDENTIAL: 30d logs max, redact PII
  RESTRICTED: no persistent logs; aggregate metrics only

access:
  role_based: owner, builder, analyst, viewer
  approvals: data steward + owner

storage:
  tenant_owned: yes
  encryption: at_rest + in_transit

Safety Controls

Input Filters

PII/PHI detection, profanity, harmful intent, jailbreak patterns.

Policy Guards

Allow/deny lists; refusal templates; escalation messaging.

Grounding & Citations

Require sources for claims; “unknown” path over guessing.

Human-in-the-Loop

Approval steps for publishing, emails, or risky actions.

Metrics & Evals

Quality: groundedness, faithfulness, completeness, helpfulness.
Ops: p95 latency, cost/session, tool success %, handoff rate.
Feedback: thumbs, reasons, “couldn’t answer” categories.

scorecard:
  groundedness: >=0.85
  faithfulness: >=0.9
  completeness: >=0.8
  p95_latency_ms: <=1300
  cost_delta: <=+10%
gate:
  require_all: true

Change Management

Version prompts, tools, and KB snapshots. Keep a changelog.
PR-style review for prompt diffs and policy updates.
Canary release to 5–10% users; monitor scorecard before full roll-out.
Rollback plan with previous artifacts stored.

Incident Response (Lean)

triage:
  severity: S1..S4
  examples: data leak, harmful output, policy breach, outage
actions:
  contain -> notify owner/stakeholders -> root cause -> fix -> postmortem
SLA:
  S1 acknowledge: 15m, mitigate: 2h

Risk Register (Starter)

id, risk, impact, likelihood, owner, mitigation, status
R-001, Prompt injection, High, Med, Reviewer, Input/policy guards + evals, Active
R-002, Stale knowledge, Med, High, Owner, Freshness rules + citations, Active
R-003, PII exposure, High, Low, Data Steward, Redaction + retention, Active

Rollout Checklist (1-Week Pilot)

Define purpose, audience, success metrics, and constraints.
Draft one-page policy + create roles (Owner/Builder/Steward/Reviewer).
Ingest minimal data; tag sensitivity; enable citations.
Implement input filters and refusal patterns.
Create golden set (50 tasks) + scorecard gates.
Canary release; collect feedback, cost, latency.
Review + iterate; document decisions.

Starter Templates

Prompt playbook (roles, rubrics, formats)
RAG checklist (ingestion, freshness, latency)

Need a quick governance starter?

I can stand up a one-page policy, scorecards, and review flow in a week—so you can ship with confidence.

Train your team → Design a custom GPT →