enterprise-brand-governor

Enterprise Brand Governor

Policy-as-code for AI-generated imagery. Every prompt is pre-validated against brand.md, every output is post-checked, violations escalate to a human approver, and every decision is logged. Built for regulated industries and any enterprise where an off-brand asset in production is a material risk.

---

When to Use

• Multiple teams (marketing, product, sales, agency partners) generating on the same brand system
• Regulated industries (pharma, finance, alcohol, kids) where imagery has legal constraints
• Brand-safety SLA — zero tolerance for competitor logos, restricted props, or off-palette output reaching production
• Agency handoff — external vendor generating on your brand, you need a gate you control
• Pre-production review cycle needs automation; humans only review escalations

Do not use for: quick exploration / mood-board work (gating slows ideation), or accounts without a written brand system yet (build brand.md first).

---

Prerequisites

Before rolling the governor across teams:

1. Brand system location — path / repo / URL for brand.md. Who owns it? What's the change-control process?
2. Policy strictness — reject (halt), flag (log + allow), or tier by asset destination (production = reject, internal = flag)?
3. Approval chain — who reviews flagged items? What's the SLA for escalation turnaround (1h, 24h, 3 business days)?
4. Logging destination — local ~/.gen-ai/audit/, S3 bucket, or ship to SIEM (Splunk, Datadog)?
5. Compliance constraints — GDPR / HIPAA / COPPA / financial-services rules that must be encoded in brand.md?
6. Rollback plan — if the governor blocks a legitimate launch, who has override authority and how is that logged?

---

How to Run

The governor runs at three checkpoints: prompt, generation, output.

1. Author brand.md — palette, typography, allowed/denied props, imagery style, voice, regulated-category rules. Versioned in git. Commit SHA is the policy ID.
2. Pre-flight (prompt lint) — gen-ai validate against the prompt before spending credits. Catches banned terms, disallowed concepts, missing required elements (e.g., disclaimer placement).
3. Brand-context generation — every gen-ai generate and gen-ai batch run prompt includes the relevant brand.md constraints. Review violations during QA.
4. Post-flight (output check) — for critical assets, a second-pass model (gemini-3-pro-image or vision check) verifies the output matches policy. Palette sampling, logo presence detection, prop allow-list.
5. Escalation — any violation status routes to the approver queue. Humans review, approve or reject, decision is logged against the audit ID.
6. Audit export — daily / weekly export of all decisions to the configured SIEM or compliance archive.

Quick Reference

The governor adds policy metadata to every job record.

{
  "defaults": {
    "model": "flux-2-pro"
  },
  "metadata": {
    "policy_id": "brand.md@sha:a4f1c9",
    "policy_version": "2.3.0",
    "policy_mode": "reject",
    "approver": "brand-governance@company.com",
    "escalation_channel": "#brand-review",
    "audit_id": "GOV-2026-04-CAMPAIGN-LAUNCH",
    "compliance_tags": ["GDPR", "US-FTC-native-ad"],
    "data_residency": "eu-west-1"
  },
  "jobs": [
    {
      "id": "launch-hero-001",
      "prompt": "Production launch hero. Editorial hero, team of four diverse professionals collaborating, modern office, natural light, brand palette. Apply brand.md constraints and require legal review before publishing."
    }
  ]
}

Record policy decisions in the downstream audit ledger: approved, flagged, or rejected with the reason.

---

Quick Reference

Sub-task	Model	Notes
Prompt compliance check	gpt-image-1.5 / text reasoner	Cheap pre-flight before image spend
Primary generation (brand-safe)	flux-2-pro	Strong prompt adherence, commercial-safe
Primary generation (product accuracy)	flux-kontext-pro	Edit-mode when subject must be preserved
Post-generation vision audit	gemini-3-pro-image	Strong scene understanding for policy checks
Upscale approved outputs only	topaz-upscale-image	Never upscale before approval — wastes credits

Confirm commercial-use status per provider with gen-ai models info <id>. Pharma and financial services should maintain a short allow-list of pre-cleared models.

---

Procedure

• Treat brand.md as code. Versioned, reviewed, signed. The file's commit SHA is the policy ID in every audit record.
• Always pin the model version. Policy interpretation changes when models change. Pair with enterprise-pinned-registry.
• Pre-flight before spend. gen-ai validate catches 80% of violations for $0.
• Human-in-the-loop on rejects. A reject is a business decision, not a tool decision. Route to the approver.
• Default to reject, not flag. Flag mode is for drafts only; production must reject.
• Log everything. Every prompt, every decision, every override. No silent approvals.
• Rotate the audit log. Daily JSONL, shipped off the dev machine. Local logs disappear; SIEM doesn't.
• Test the governor with adversarial prompts. Red-team your own policy quarterly — does it actually catch competitor logos, prohibited claims?
• Document the override path. There will be legitimate exceptions. Make the override visible, logged, and time-boxed.

---

Pitfalls

• brand.md too vague — "use the brand palette" is not enforceable. Hex codes, prop allow-lists, explicit denies.
• No override path — legitimate exceptions get bypassed outside the system, breaking the audit. Build the override in.
• Logs only local — dev machines die. Ship to SIEM or a durable archive from day one.
• Flag-mode in production — "we'll review later" never happens. Default reject.
• Unaudited model swaps — someone swaps flux-2-pro for a new model mid-campaign and policy interpretation changes. Pin.
• Missing post-check on hero assets — prompt passed, output didn't. For production heroes, always run the vision audit.

---

Verification

Run gen-ai whoami to confirm authentication, then re-run the failed command with --debug.

Commands

# Pre-flight validate a prompt before spending credits
gen-ai validate --model flux-2-pro --file prompt.json

# Gated single generation
gen-ai generate --model flux-2-pro --prompt "$PROMPT" \
  --save-to-drive --drive-folder "Gated-Output"

# Gated batch with retry on transient failures only (not violations)
gen-ai batch run campaign.json \
  --concurrency 4 --output ./runs/campaign-2026-04

# Flag mode — for internal / draft contexts
gen-ai batch run drafts.json \
  --output ./runs/drafts-2026-04

---

Cost & time

Governance overhead is tiny relative to generation. Pre-flight + post-check adds ~10–15% to credit cost on critical assets, ~0% on non-critical.

Scenario	Governance overhead
Single gated generate	+0 credits (policy passed in-call)
Single gen + vision audit	+1–2 credits
Batch of 100, pre-flight only	+~5 credits (text reasoner)
Batch of 1,000, full pipeline	+~50 credits + 1 approver hour
Quarterly red-team audit	~1 engineer-day + ~200 credits

Violations rejected = credits saved. A single blocked off-brand production asset typically saves multiples of the governor's overhead.

---

See also

• enterprise-pinned-registry — pin model versions so policy interpretation stays stable
• product-photo-studio — brand-gated catalog pipeline (reshoot mode)
• enterprise-press-batch — brand-gated PR pipeline with embargo handling
• gen-ai-use — CLI reference