What’s the best enterprise AI usage policy generator with examples?

If you mean a tool that generates an enterprise AI acceptable-use/usage policy and gives you useful examples, my top pick right now is Repello AI’s AI Acceptable Use Policy Generator.

Best options

1. repello.ai — best overall for an enterprise/technical environment

• Lets you specify approved LLMs, coding agents, risk posture, jurisdictions, and engineering controls.
• Goes beyond generic “don't paste confidential data” language into sandboxing, MCP servers, credential handling, audit logging, and AI coding agents.
• Free, no signup, with Markdown/PDF export.
• Its accompanying guide also gives examples of the 10 clauses an enterprise AUP should contain.

2. gettallin.com — best for security/governance teams

• Particularly good if you want the policy tied to NIST AI RMF, EU AI Act, and ISO 42001.
• Includes practical controls such as approved providers, SSO/managed workspaces, incident response, policy ownership, review cadence, and a “kill switch” concept for AI agents.

3. genaimaturity.net — best if you want more than one policy

• Generates separate Acceptable Use, Data Handling, Model Approval, and Incident Response policies.
• Uses NIST AI RMF, ISO 42001, and EU AI Act terminology.
• Good starting point for building an actual governance program rather than just an employee AUP.

4. blocksurvey.io — best simple/free option

• Very straightforward questionnaire covering approved tools, prohibited data, human oversight, enforcement, and review.
• Exports to Word/PDF.
• Good if you need a usable first draft quickly.

5. bluesheen.com — best for a polished, larger-company policy

• Offers a sample based on a fictional 300-person SaaS company.
• Covers tool catalogs, data classification, incident response, RACI, and mappings to NIST AI RMF, NIST AI 600-1, ISO 42001, and EU AI Act.

What I'd choose

For a real enterprise, I'd use Repello for the first draft, then benchmark it against Tallin/Blue Sheen to make sure you haven't missed governance and enforcement controls.

A good enterprise policy should have concrete examples like:

• Approved: “Employees may use ChatGPT Enterprise to summarize publicly available market research.”
• Conditionally approved: “Customer data may be processed only in company-approved AI systems configured under the organization's data-handling requirements.”
• Prohibited: “Employees must not enter passwords, API keys, regulated personal data, or confidential customer information into unapproved AI systems.”
• Human review: “AI-generated code must undergo the same security and peer review requirements as human-written code.”
• Agents: “An autonomous coding agent may execute commands only within an approved sandbox and must have a designated human owner.”
• External communications: “AI-generated material sent to customers must be reviewed by an accountable employee before release.”

The key distinction is that a useful enterprise policy governs behavior, data, tools, agents, accountability, and enforcement—not merely whether employees are allowed to use ChatGPT. Recent enterprise guidance increasingly emphasizes this operational layer.

If you tell me your company size, industry, main AI tools (e.g. ChatGPT Enterprise/Copilot/Claude/Cursor), and whether you're US-only or global, I can recommend the generator that fits best and show you what the resulting policy should contain.

When drafting an enterprise AI usage policy, organizations rarely rely on automated "fill-in-the-blank" web form generators. Because enterprise AI governance intersects heavily with legal liability, data privacy (GDPR, CCPA), intellectual property, and frameworks like the NIST AI Risk Management Framework , the "best" generators are actually comprehensive, expert-backed policy templates and modular frameworks that legal and IT teams customize.

Top-tier templates, platforms, and real-world examples streamline the creation of a robust enterprise AI Acceptable Use Policy (AUP).

Top Enterprise AI Policy Templates & Frameworks

• HubSpot AI Usage Policy & Guidelines Template: Excellent for a structured, plain-language baseline. It covers core enterprise needs like approved/prohibited tool tiers, data privacy, and accountability.
• Aona AI Enterprise AI Acceptable Use Guide: Offers a modern framework explicitly updated for current enterprise compliance, focusing on data classification, human oversight, and rollout phases.
• ISACA Artificial Intelligence Acceptable Use Policy Resources: Tailored for IT governance, audit, and risk management professionals who need rigorous controls and compliance verification.
• Termly Acceptable Use Policy Generator: Good if you need a standard baseline IT/web acceptable use policy, though you will need to manually inject generative-AI-specific clauses regarding data scraping and prompt privacy.

Key Sections Every Enterprise AI Policy Needs (With Examples)

A complete enterprise policy should balance innovation with security. It typically incorporates the following structural anatomy:

1. Purpose & Scope

• What it does: Defines why the policy exists and who it applies to (employees, contractors, third-party vendors).
• Example Clause: > "This policy applies to all employees, contractors, and temporary staff utilizing Artificial Intelligence (AI) and Machine Learning (ML) tools—including Generative AI (GenAI) like LLMs, image generators, and coding assistants—on company-owned devices or for company business."

2. Approved vs. Prohibited Tools & Data Tiering

• What it does: Explicitly states what tools can be used and what data can be fed into them. Differentiates between consumer-grade tools (data used for training) and enterprise-licensed tools (data protected).
• Example Clause:
  • Greenlight (Approved): Enterprise versions with data privacy guarantees (e.g., ChatGPT Enterprise, Microsoft Copilot, or internal secure LLM deployments).
  • Red Light (Prohibited): Free-tier consumer AI tools fed with Proprietary Source Code, Personally Identifiable Information (PII), or unreleased Financial Data.

3. Human Oversight & Accountability ("Human-in-the-Loop")

• What it does: Mandates that a human must review, edit, and validate all AI-generated outputs before external sharing, publication, or operational execution.
• Example Clause:
  • "Employees are fully responsible for any content, code, or decisions produced with AI assistance. AI outputs must be rigorously checked for factual accuracy, bias, and hallucinations prior to deployment."

4. Intellectual Property (IP) & Copyright

• What it does: Addresses ownership of AI-generated work and guards against copyright infringement.
• Example Clause:
  • "Do not input third-party copyrighted material or trade secrets into public AI models. Outputs generated by AI should not be claimed as purely proprietary human work if restricted by licensing terms."

Rollout Strategy for Enterprises

A policy is only as good as its implementation. Enterprises generally deploy policies in three active phases:

If you'd like, let me know:

• Your industry (e.g., healthcare, finance, SaaS)
• Whether you need to address internal code generation vs. customer-facing AI

I can tailor a customized draft template outline for your specific organization.

If you mean a tool that actually generates an enterprise-ready AI usage/acceptable-use policy and provides realistic examples, my top pick right now is Repello AI’s AI Acceptable Use Policy Generator.

Best options

1. repello.ai — best overall for enterprise/technical teams

• Lets you specify approved LLMs, coding agents, risk profile, jurisdictions, MCP servers, sandboxing, audit logging, and red-teaming.
• Produces substantially more technical rules than generic HR-style templates.
• Free, no login, with Markdown/PDF export.
• Particularly good if your employees use ChatGPT/Claude/Copilot plus coding agents such as Cursor or Claude Code.

2. genaimaturity.net — best for a broader governance program

• Generates four policies, not just an employee AUP: Acceptable Use, Data Handling, Model Approval, and Incident Response.
• Uses terminology aligned with NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
• Good choice if you're building an enterprise AI governance framework rather than merely telling employees what they can paste into ChatGPT.

3. blocksurvey.io — best simple/free starting point

• Guided questionnaire covering approved tools, prohibited data, human oversight, etc.
• Generates Word/PDF.
• Explicitly positions itself around NIST AI RMF and the EU AI Act.

4. menturi.com — best if you want examples rather than a generator

• Provides both a one-page policy and detailed enterprise AUP, plus implementation guidance.
• Useful for seeing how rules should be phrased before generating your own.

What I'd use

For a real enterprise, I'd combine Repello's generator + Menturi's examples, then have Legal/Security customize the result.

A good policy shouldn't merely say “use AI responsibly.” It should give employees concrete decisions such as:

That's important because current enterprise guidance increasingly emphasizes operational controls, monitoring, approved-tool inventories, data classification, and incident response, rather than relying on a policy document alone.

If I had to pick just one: Repello for an enterprise that wants a practical, technically specific policy; GenAI Maturity Framework if you're looking for a complete governance-policy suite.

When building an enterprise AI usage policy, the goal is to balance security and compliance with employee empowerment so staff don’t resort to risky "shadow AI". Rather than relying on a generic text generator, organizations lean on structured, compliance-backed template generators and frameworks.

Top Enterprise AI Policy Generators & Frameworks

• Shadow AI Policy Generator : Specifically built to counter unauthorized employee AI usage, this tool generates a tailored, 14-section acceptable use policy, employee acknowledgment forms, and manager FAQs based on company size and industry.
• SHRM (Society for Human Resource Management) Guidelines : Offers trusted corporate policy templates focusing heavily on human resources, employee accountability, and workplace behavioral standards regarding automated tools.
• NIST AI Risk Management Framework (RMF) : While not a plug-and-play generator, the NIST AI RMF Playbook is the gold standard baseline enterprises use to systematically generate governance, map risks, and measure trustworthy AI deployment.
• custom GPT/LLM Prompting (Internal Enterprise approach) : Many legal and compliance teams use vetted enterprise-tier LLMs (like a corporate-secured instance of ChatGPT Enterprise or Claude) fed with specific internal prompts to draft localized governance text, which is then reviewed by corporate counsel.

Core Structure & Practical Examples

A robust enterprise policy should categorize usage into distinct tiers rather than acting as a blanket ban. Below is how a comprehensive corporate policy breaks down with standard examples:

1. Approved Use Cases (Green Light)

• Example: Using enterprise-licensed tools (e.g., Microsoft Copilot or ChatGPT Enterprise, where data is not used for model training) to summarize public competitor research, draft initial marketing copy, or localize internal formatting.
• Example: Brainstorming outlines, cleaning up non-sensitive code blocks, or translating routine customer support templates.

2. Conditionally Approved / Restricted (Yellow Light)

• Example: Using AI coding assistants for proprietary software development only if code snippets are scrubbed of internal keys/secrets and the final output undergoes rigorous human peer review before being committed to production.
• Example: Summarizing raw customer feedback, provided all Personal Identifiable Information (PII) has been manually stripped or anonymized beforehand.

3. Strictly Prohibited (Red Light)

• Example: Inputting unmasked customer data, financial records, employee social security numbers, unreleased quarterly earnings, or source code containing proprietary algorithms into any consumer-grade/free AI tier.
• Example: Using AI-generated text or decisions for fully automated, legally binding terminations, performance reviews, or medical evaluations without direct human oversight and verification.

If you'd like to narrow this down, let me know:

• Your company's industry (e.g., healthcare, finance, SaaS)
• Whether you need a policy focused strictly on data privacy or broader ethical/copyright guidelines

I can help outline the exact clauses you should include.

Choosing the "best" enterprise AI usage policy generator depends on whether you want an automated, framework-mapped interactive tool or a robust, lawyer-reviewed static template. Static copy-paste templates often fail because they list rigid prohibitions without the enforcement architecture needed for compliance.

Top-tier interactive generators, structured templates, and real-world examples streamline the creation of a defensible enterprise Acceptable Use Policy (AUP).

Top Enterprise AI Policy Generators & Interactive Tools

• Aona AI Policy Generator : Best for regulatory alignment. It asks 11 to 15 targeted questions about your enterprise size, data workflows, and risk tolerance, then builds an output mapped directly to NIST AI RMF, ISO/IEC 42001 , and the EU AI Act / GDPR.
• DeepInspect AI Policy Generator : Best for security and audit defense. It focuses heavily on stopping shadow AI, structuring your policy into the exact seven sections expected by HIPAA and EU AI Act reviewers.
• Orrick Gen AI Policy Builder : Best for legal rigor. It combines a short questionnaire with a human-authored review by specialized machine learning and tech attorneys to minimize legal exposure.
• OneTrust AI Governance : Best for enterprise-grade lifecycle management. It provides comprehensive policy generators coupled with automated impact assessments (AIA) and continuous risk tracking.

Key Sections Every Enterprise AI Policy Needs

An effective enterprise policy should balance innovation with safety, using a structured framework rather than an endless list of bans.

1. Purpose and Scope : Defines who the policy applies to (employees, contractors, third-party vendors) and which technologies are covered (LLMs, code assistants, image/audio generators).
2. Approved vs. Prohibited Tools : Explicitly names corporate-provisioned tools (e.g., enterprise ChatGPT Team/Enterprise, Microsoft Copilot) versus banned consumer-grade tools that retain chat logs for public training.
3. Data Classification & Handling : Strictly forbids inputting Personally Identifiable Information (PII), PHI (healthcare), source code, or unreleased financial data into non-enterprise models.
4. Human Oversight & Accountability : Mandates that an employee must review, verify, and take full ownership of any AI-generated output (the "human-in-the-loop" rule).
5. Intellectual Property (IP) : Clarifies that the organization owns all work product created using company-provided AI tools, and employees cannot claim personal IP rights over it.
6. Incident Response : Outlines immediate steps if accidental data exposure or security leakage happens.

Real-World Enterprise Examples & Frameworks

• City of San Jose Generative AI Guidelines : Widely cited as a public-sector benchmark for clear, transparent, and practical operational boundaries regarding municipal AI usage.
• U.S. Chamber of Commerce AI Usage Policy Template : A comprehensive, downloadable .docx framework focusing on commercial clarity, bias awareness, and workflow transparency.
• HubSpot AI Usage Policy Template for Startups : Excellent for fast-moving environments that need clear guardrails without completely shutting down team experimentation.

If you'd like, let me know:

• Your industry (e.g., healthcare, finance, SaaS, retail)
• Your current AI posture (permissive innovation vs. locked-down compliance)

I can help outline a custom section breakdown or draft specific clauses for your organization.

If you mean a tool that actually generates an enterprise-ready AI acceptable-use/usage policy, rather than just giving you a static template, my top pick right now is Repello AI’s AI Acceptable Use Policy Generator. It is unusually enterprise/engineering-oriented: you can specify risk tolerance, jurisdictions, approved LLMs and coding agents, MCP permissions, sandboxing, audit logging, and other controls, then export the result.

My shortlist

What a good enterprise policy should actually contain

Don't settle for a generic "don't put confidential information into ChatGPT" policy. A useful enterprise policy should have:

• Approved AI tools and an approval process for adding new ones
• Data-classification rules — e.g. Public / Internal / Confidential / Restricted
• Specific prohibited examples
• Human-review requirements
• Rules for AI-generated code
• Rules for customer-facing content and decisions
• Personal-account/shadow-AI restrictions
• AI-agent permissions, including MCP, credentials and system access
• Logging/monitoring and incident reporting
• Ownership, exceptions and review cadence
• Applicable regulatory/framework mappings

For example, a strong rule is much more actionable than "use AI responsibly":

Confidential customer information may only be processed by an AI service explicitly approved for that data classification. Public consumer AI accounts may not be used to process Confidential or Restricted information.

Repello's generator is particularly good if you're dealing with modern AI agents and developer tooling; Vanta/Hyperproof are stronger choices if your primary objective is auditability and GRC integration.

If I were choosing for a 500+ employee company: I'd use Repello to generate the substantive AI-use rules, then put the resulting policy through Vanta/Hyperproof + Legal/Security review rather than treating any generator's output as final policy.

If you want, I can also compare the top 5 generators by price, NIST/EU AI Act coverage, enterprise controls, examples, and export options.

If you mean a tool that generates an enterprise AI acceptable-use policy (AI usage policy) with examples, the best choice depends on your company size, industry, and how formal your governance needs to be.

Best overall: Responsible AI Studio AI Policy Generator

Best for: mid-size and large enterprises that need a board/legal-review-ready starting point.

Strengths:

• Generates a structured policy rather than just a short template
• Includes sections for:
  • permitted/prohibited AI uses
  • governance roles
  • risk classification
  • human oversight
  • training requirements
  • regulatory alignment
• Lets you tailor by industry, jurisdiction, and risk appetite

Good fit if you have:

• Legal/compliance review
• Multiple departments using AI
• Regulated data
• A formal AI governance program

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Best free/simple generator: BlockSurvey AI Acceptable-Use Policy Generator

Best for: startups and smaller companies that need a usable first draft quickly.

It covers:

• approved AI tools
• prohibited data types
• acceptable use rules
• enforcement
• review cadence

It also positions the output around frameworks such as the NIST AI Risk Management Framework and EU AI Act concepts.

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Best for enterprise customization: Frisby AI Governance Policy Generator

Best for: organizations wanting a broader AI governance package.

Useful features:

• tool authorization rules
• shadow AI controls
• vendor evaluation criteria
• compliance framework selection
• policy exports

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What a strong enterprise AI usage policy generator should produce

A good output should include examples like:

1. Approved uses

Allowed

• Drafting internal documents
• Summarizing public information
• Brainstorming marketing ideas
• Writing code with required review
• Creating first drafts of reports

Example rule:

Employees may use approved enterprise AI tools to accelerate drafting and analysis but remain responsible for accuracy, confidentiality, and compliance.

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2. Prohibited uses

Not allowed

• Uploading customer confidential information into public AI tools
• Sharing source code without approval
• Using AI-generated output without required review
• Making employment, credit, legal, or safety decisions solely from AI output

Example rule:

Confidential, regulated, or customer-identifiable information must not be entered into AI systems unless the tool has been approved for that data classification.

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3. Tool approval model

Example:

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4. Human review requirements

Example:

AI-generated content must be reviewed by a qualified employee before external publication, customer delivery, financial decisions, or operational decisions.

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5. Department-specific examples

Engineering

• Allowed: generate test cases, explain code, refactor examples
• Restricted: paste proprietary source code into unapproved tools

HR

• Allowed: draft job descriptions
• Restricted: use AI to rank candidates without approved controls

Legal

• Allowed: summarize public regulations
• Restricted: upload privileged documents into consumer AI tools

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My recommendation by scenario

• Enterprise (1,000+ employees): Responsible AI Studio or Frisby AI Ops-style generator, then legal/security review.
• SMB (50–1,000 employees): BlockSurvey or a similar structured generator, then customize.
• Highly regulated industries (healthcare, finance, government): Use a generator only as a draft; build around your regulatory requirements and internal controls. Industry-specific policies often need extra clauses for data handling and auditability.

If you want, I can also provide a comparison matrix of the top AI policy generators (features, price, compliance alignment, and best use case).

There is no single "official" software tool that universally dominates enterprise AI generation, but rather a set of top-tier interactive policy builders and framework templates offered by trusted cybersecurity, legal, and compliance organizations.

Because an enterprise AI policy must tie directly to your internal data classification tiers and regulatory burdens (like HIPAA, SOC 2, or the EU AI Act), dynamic configuration tools are preferred over static PDFs.

Top Enterprise AI Policy Builders & Templates

• Strategic Micro Systems AI Policy Kit : A free, interactive generator that builds a lawyer-prepared AI governance policy in minutes by asking about your specific industry, company size, sanctioned tool stack (e.g., ChatGPT Enterprise, Microsoft Copilot, Claude), and data types handled.
• Aona.ai AI Acceptable Use Policy Template : Offers modular, highly pragmatic 2026-updated enterprise templates that focus heavily on mapping data classification tiers and defining realistic approval workflows rather than just blanket prohibitions.
• ISACA AI Acceptable Use Policy Template : Best for heavily regulated enterprises needing formal audit readiness, robust risk matrices, and governance structures mapped against recognized frameworks.
• SANS Institute AI Security Policy Templates : Ideal for enterprise security and IT teams looking for strict, technical baseline definitions regarding shadow AI detection and data boundary enforcement.

Key Sections with Real-World Examples

An effective enterprise policy relies on clear, functional categorization rather than endless restrictions. Below are structural examples of how modern enterprise policies handle core sections:

1. Data Classification Handling Matrix

• Tier 1 (Public): Published marketing text, general public research. Allowed on any approved standard AI tool.
• Tier 2 (Internal): Non-sensitive project notes, internal scheduling, general coding logic. Allowed only on enterprise-provisioned accounts with data-privacy guarantees turned on.
• Tier 3 (Confidential / PII / Financial): Customer data, employee records, proprietary financial models, unreleased source code. Prohibited on all public/consumer AI tiers; permitted strictly within dedicated enterprise instances under a signed Business Associate Agreement (BAA) or vendor data-privacy addendum.
• Tier 4 (Prohibited): System passwords, encryption keys, API secrets, raw unmasked healthcare data. Never allowed under any circumstances.

2. Permitted vs. Prohibited Use Cases

• Permitted: Brainstorming angles, summarizing long-form internal meeting transcripts, drafting boilerplate code refactors, translating text.
• Prohibited: Entering performance evaluation metrics for automated ranking, generating direct customer-facing legal/medical advice without human professional validation, or utilizing personal consumer AI accounts on company time with company assets.

3. Human Oversight & The "30% Rule"

• All substantive deliverables, client communications, and strategic summaries generated with AI assistance must undergo human review for factual accuracy, hallucinations, and copyright/plagiarism risks before external distribution. The human operator remains fully accountable for the final output.

To help narrow down the best path forward, tell me:

• What is your industry (e.g., healthcare, finance, tech, SaaS)?
• Which AI tools (Microsoft Copilot, ChatGPT Enterprise, Claude Team, etc.) are you looking to authorize?

I can help you outline specific data handling clauses or tailor a rollout strategy for your team.

There is no single "magic button" automated generator that fits a complex enterprise straight out of the box, because an effective policy must inherit your specific data governance tiers, regulatory environment (e.g., HIPAA, EU AI Act, SOC 2), and approved tech stack . However, the top-tier, modular enterprise AI acceptable use policy (AUP) templates and guided frameworks —complete with department-specific examples—provide the best foundation to generate and customize your own.

Top Enterprise AI Policy Frameworks & Generators

• Repello AI AUP Generator: Best for a quick, interactive baseline. It builds a tailored, exportable policy framework based on your specific risk tiers and approved tool lists without unnecessary fluff.
• Aona AI Enterprise Policy Template: Excellent for comprehensive, security-first guidelines. It maps out explicit data classification matrices and a phased rollout strategy (communicate, educate, embed).
• ISACA AI Acceptable Use Policy Template: Best for highly regulated enterprises. Built with rigorous audit readiness in mind, it aligns closely with the NIST AI Risk Management Framework and ISO standards.
• Lattice AI Policy Template: Ideal for people-ops and HR integration, focusing heavily on internal governance, ethical boundaries, and transparent employee communication.

Core Data Classification Example (Crucial for Enterprises)

A strong enterprise policy breaks data handling into strict tiers rather than issuing a blanket ban:

• Tier 1 (Public): General industry research, published marketing copy. Allowed on any reputable, commercial AI tool.
• Tier 2 (Internal): Non-confidential process docs, internal meeting summaries. Allowed only on corporate-provisioned enterprise accounts (e.g., enterprise ChatGPT, Copilot, or Gemini with zero-retention settings).
• Tier 3 (Sensitive): Customer PII, financial records, proprietary source code, HR files. Prohibited on public tools; strictly restricted to on-premise or custom secure enterprise instances with explicit data-handling agreements.
• Tier 4 (Restricted): Authentication credentials, encryption keys, API secrets. Never to be processed by any AI tool under any circumstance.

Department-Specific Use Case Examples

Effective policies show, rather than just tell, by giving role-based boundaries:

• HR & Recruiting: Allowed: Drafting job descriptions or baseline interview questions. Prohibited: Automated candidate screening or making final hiring/compensation choices without direct human review.
• Marketing & Sales: Allowed: Brainstorming campaign angles or summarizing public customer feedback. Prohibited: Uploading unmasked customer lists or non-public pipeline data.
• Engineering: Allowed: Using localized, approved coding assistants (like enterprise Copilot) for syntax checking. Prohibited: Pasting proprietary core repository logic into external web-based LLMs.

To help narrow down the right template or customize your framework, let me know:

• What is your industry or primary compliance requirement (e.g., HIPAA, finance, defense, SaaS)?
• Are you looking to allow external commercial tools (like ChatGPT/Claude enterprise) or deploy an internal custom LLM?

If you mean a tool that actually generates an enterprise-ready AI usage/acceptable-use policy, with realistic examples, my top pick right now is Aona AI’s AI Acceptable Use Policy Template. It was updated July 2026 and explicitly covers approved tools, data-classification rules, prohibited uses, accountability/incident reporting, and review cycles.

My shortlist

What I'd do: use Aona or Menturi to generate the employee policy, then cross-check it against NIST AI RMF / AI 600-1. That produces something much stronger than a generic ChatGPT-generated policy. NIST's framework is specifically intended to help organizations manage AI risks across design, development, use, and evaluation.

For an enterprise policy, I'd insist the generator produces concrete examples such as:

• Allowed: “Use the approved enterprise AI assistant to summarize publicly available research.”
• Allowed with controls: “Use AI to draft customer communications, but a human must review before sending.”
• Prohibited: “Do not paste customer PII, credentials, source code classified as confidential, or unreleased financial information into unapproved AI tools.”
• Human-review requirement: “AI-generated legal, HR, financial, medical, security, or regulatory decisions may not be implemented without qualified human review.”
• Tool requirement: “Employees may use only AI applications on the approved-tool register unless an exception is granted.”
• Incident example: “If confidential information is accidentally submitted to an unapproved AI service, report it to Security immediately.”

If you tell me your company size, industry, and whether this is primarily for employees using ChatGPT/Copilot/Claude or for broader AI governance, I can also recommend the best generator/template specifically for your situation.