AI Usage Policy Generator
Generate a company AI tool usage policy tuned to your industry and compliance needs.
Sets the framing of the purpose section.
Live policy preview
# AI Tool Usage Policy **Organization type:** Software / SaaS **Effective date:** 2026-06-15 **Posture:** Mandatory — compliance with this policy is a condition of system access. **Compliance context:** SOC 2, GDPR ## 1. Purpose & scope This policy governs the use of AI-assisted development and productivity tools by all engineering staff, contractors and vendors with access to our systems or code. It exists to enable productive AI use while protecting customer data, intellectual property and regulatory standing. ## 2. Approved tools The following tools are approved for use. Use of unapproved AI tools with company data or code is prohibited: - Claude / Claude Code - ChatGPT / OpenAI API - GitHub Copilot New tools must be submitted for security review before use. Reviews target a one-week turnaround so the approved path stays faster than workarounds. ## 3. Data classification rules The single highest-risk action with AI tools is placing sensitive data into a prompt. The following rules apply to ALL tools, approved or not: - Public data and open-source code: allowed in any approved tool. - Internal code and documentation: allowed in approved tools with enterprise data agreements only. - Customer data, PII, credentials, secrets: never enter any AI prompt. Secrets and credentials must additionally be protected by automated scanning in pre-commit hooks and CI. ## 4. SOC 2 obligations - AI vendors must pass our standard vendor security assessment before approval; assessments are reviewed annually. - Access to AI tools is provisioned and revoked through the standard access-control process and is auditable. - AI tool usage relevant to the trust services criteria must be logged and retained per our evidence-retention schedule. ## GDPR obligations - Personal data of EU/UK data subjects must not be processed through AI tools without a documented lawful basis and a data-processing agreement with the provider. - Data-residency commitments made to customers must be verified against the AI provider's processing locations. - AI providers acting as processors must appear in our records of processing activities. ## Code review & quality - AI-generated code is held to the same review standard as human-written code: it must be reviewed by a human before merging to a protected branch. - The author who submits AI-generated code owns its correctness, licensing and security — "the AI wrote it" is not a defense. - Generated code that reproduces identifiable third-party code must be checked for license compatibility. ## Budgets & cost accountability - Each team has a monthly AI spend budget owned by its engineering manager. - Spend must be tracked per developer and per project; anomalies (runaway agent loops, retry storms) are investigated within one business day. - Note: a policy document cannot enforce a budget — pair this section with tooling that tracks usage and applies hard caps automatically. ## Incidents & exceptions - Suspected data exposure through an AI tool must be reported to the security team immediately — fast reporting is treated as good citizenship, never punished. - Exceptions to this policy require written approval from the security owner and an expiry date. - This policy is reviewed quarterly; the tool landscape changes fast enough that stale policies read as optional. --- *Generated with the FORG AI Usage Policy Generator (forg.pro/tools/ai-usage-policy). This is a starting draft — obtain legal review before formal adoption.*
How it works
This generator assembles a complete, adoption-ready AI usage policy for an engineering organization. Walk the five steps — industry, compliance regimes, approved tools, data classification rules, and enforcement posture — and the markdown document on the right builds as you go. Copy it, download it, and drop it into your handbook; everything runs in your browser and nothing you enter is transmitted anywhere.
The structure follows what actually works in practice. The highest-risk section is data classification: most AI incidents in engineering teams are not exotic attacks but ordinary paste events — a customer database row, a credential, a chunk of proprietary algorithm dropped into a prompt. The policy therefore leads with explicit may/may-not data rules in your own words, followed by the approved-tool list (because banning tools without naming sanctioned alternatives just drives usage underground), review requirements for generated code, budget ownership, and an incident path.
Compliance selections do real work rather than adding a logo wall. Ticking SOC 2 inserts vendor-assessment and audit-trail language; HIPAA adds the BAA requirement and a PHI prohibition with training obligations; GDPR adds lawful-basis and data-residency sections; ISO 27001 ties the policy into your ISMS document hierarchy. The enforcement-posture choice calibrates the tone throughout — advisory language for teams building trust, mandatory language with named consequences for regulated environments.
Treat the output as a reviewed-draft accelerator: counsel should read it before formal adoption, and you should revisit it quarterly because the tool landscape moves fast enough that a stale policy reads as optional. One honest note on the limits of any document: a policy can say "each developer has a monthly AI budget" but cannot enforce it. Words need tooling — secret scanning at commit time, provider allow-lists, and spend tracking with hard caps, which is the part FORG handles automatically. The generated draft even says so in its budget section, because a policy that admits the limits of paper is one developers take more seriously than a document pretending words alone constitute control.
Frequently asked questions
What should an engineering AI usage policy actually include?
Five things, in order of importance: which tools are approved and how new ones get approved; what data classes may and may not enter prompts (the single highest-risk area); how AI-generated code is reviewed before merging; who owns budget limits and what happens at the cap; and an incident path for when something goes wrong. Policies that skip the data-classification section are decoration — that is where real exposure lives.
Does the generated policy need legal review?
Yes, treat the output as a strong first draft, not a final legal document. The generator encodes widely-used engineering-policy structure and maps your compliance selections to the right sections, but it cannot know your contracts, your jurisdiction's employment law, or sector-specific regulations beyond the regimes you tick. Have counsel review before formal adoption — they will move much faster editing a complete draft than writing from a blank page.
How do I get developers to actually follow the policy?
Make the approved path easier than the workaround. Developers route around policies that ban tools without offering sanctioned alternatives, so approve a real toolset, pre-provision licenses, and keep the approval process for new tools under a week. Write rules as concrete do/don't examples rather than abstract principles, keep the whole document under two pages of reading, and revisit it quarterly — a stale policy reads as optional.
How do SOC 2, HIPAA and GDPR change what the policy says?
Each regime adds specific obligations the policy must reflect. SOC 2 wants documented access controls, vendor review of AI providers and audit-traceable usage. HIPAA forbids PHI in prompts to any provider without a BAA and requires workforce training language. GDPR adds lawful-basis analysis for personal data, data-residency considerations and processor-agreement requirements for AI vendors. Ticking a regime in step two inserts the matching sections automatically.
How is the policy enforced once it is written?
A document changes behavior only when something measures against it. Pair the policy with technical controls: secret scanning in pre-commit (so credentials never reach prompts), provider allow-lists at the network or gateway level, and spend tracking with hard budget caps per developer or team. That last one is exactly what FORG does — the policy says what should happen, FORG makes the budget section actually binding.
Budgeting AI spend for a team? FORG is $15/dev with hard budget caps and per-seat attribution.
See FORG pricing