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An AI Policy is an internal governance document that defines the rules, responsibilities, and boundaries governing how employees and contractors use artificial intelligence tools in the course of their work. It covers which AI systems are approved for use, what categories of data may be entered into them, when human review of AI-generated outputs is required, and what consequences apply to violations. As generative AI tools become embedded in everyday business workflows β writing, coding, analysis, customer communications β an AI policy gives organizations the consistent behavioral framework needed to capture the productivity benefits while managing data privacy, accuracy, and accountability risks.
Without a written AI policy, employees make individual judgment calls about AI use β and those calls routinely result in confidential customer data entering public AI platforms, AI-generated content reaching clients without any review for accuracy, and new tools being adopted without security assessment. The risks are concrete: a single employee pasting financial records into a public AI chatbot can trigger a data breach notification obligation; AI-generated legal or medical content published without review can expose the business to professional liability; and undocumented AI use in hiring or credit decisions can create regulatory exposure under fair lending or employment discrimination rules. This template gives you a structured, immediately usable policy that closes these gaps in a few hours β without requiring a legal team or a compliance department to build it from scratch.
| If your situation is⦠| Use this template |
|---|---|
| General employee guidance on AI tools across the whole organization | AI Policy |
| Governing the use of generative AI specifically for content creation | Generative AI Acceptable Use Policy |
| Setting rules for AI-assisted decision-making in HR or hiring | AI in Hiring Policy |
| Defining data handling requirements when using AI vendor APIs | Data Classification Policy |
| Managing risk from third-party AI tools and vendors | Third-Party Vendor Risk Assessment |
| Communicating AI ethics principles to customers and the public | AI Ethics Statement |
| Governing AI model development and deployment by an internal tech team | AI Model Governance Policy |
Why it matters: New AI tools launch every week. A policy listing ChatGPT, Copilot, and Gemini by name becomes incomplete the moment an employee installs a new browser extension with AI features.
Fix: Define scope by capability β 'any software that uses machine learning, generative models, or automated decision-making' β so the policy applies to tools that don't exist yet.
Why it matters: Without explicit rules on what data may be entered into AI tools, employees will default to convenience β which often means pasting confidential customer or financial data into public AI platforms that use prompts for model training.
Fix: Map each data classification tier to a clear AI permission: public data is freely usable, internal data requires an approved tool, confidential and restricted data may not be entered into any external AI system.
Why it matters: Saying 'employees are responsible for all AI outputs' without defining what responsible review looks like means accountability is unenforceable when an error reaches a client.
Fix: Specify the review actions required by output type β fact-checking for research summaries, legal review for contract language, manager sign-off for external communications β so employees know exactly what is expected.
Why it matters: Employees who were never trained on the policy cannot fairly be disciplined for violating it, and your compliance posture is undermined in any audit or litigation.
Fix: Attach a training completion deadline to the policy launch and require a signed acknowledgment. Integrate AI policy training into new-hire onboarding within the first 30 days.
Why it matters: AI regulation, vendor data practices, and tool capabilities are changing on a monthly basis. A policy reviewed annually will be materially out of date within six months in most organizations.
Fix: Commit to a six-month review cycle and trigger an immediate review whenever a significant AI incident occurs, a major new tool is adopted, or new regulatory guidance is published.
Why it matters: Without a defined approval workflow, employees self-approve AI tools based on convenience and cost β not on data security, sub-processor agreements, or regulatory compliance.
Fix: Create a simple one-page Tool Assessment Request form covering data residency, training opt-out, and security certification. Set a maximum 10-business-day SLA for IT review so the process is fast enough that employees use it.
Decide whether the policy covers all AI tools or only specific categories (generative AI, automated decision-making, etc.). Assign a named owner β typically the Head of IT, CISO, or Chief Operating Officer β who is accountable for maintaining and enforcing it.
π‘ Scope by capability, not by tool name. 'Any software using machine learning or large language models' ages far better than a list of named products.
Survey your teams before drafting the acceptable-use section. A shadow IT audit often reveals 5β10 AI tools in active use that IT was unaware of β these inform both the approved list and the prohibited-use rules.
π‘ Check browser extensions and app integrations, not just standalone tools β AI is increasingly embedded in productivity software employees already use daily.
Map your existing data classification tiers (public, internal, confidential, restricted) to specific AI permissions. For each tier, state explicitly whether data in that category may be entered into external AI tools and under what conditions.
π‘ If you do not have a formal data classification policy, create a simple two-tier version (shareable vs. non-shareable) as an appendix to the AI policy to unblock this step.
Write at least four concrete approved use cases and at least four explicit prohibitions. Use specific examples β 'drafting a first-pass sales email for human review' and 'entering a customer's financial records into a public AI tool' β rather than abstract principles.
π‘ Organize acceptable uses by department (marketing, engineering, finance) so employees can quickly find the rules relevant to their own work.
List every AI tool currently approved for use and the data tiers each may process. Include the vendor's data retention period and whether they use prompt data to retrain models. This list lives in Appendix A and is updated as tools are approved or deprecated.
π‘ Check each vendor's DPA (Data Processing Agreement) and sub-processor list before adding them. Many consumer AI tools have no DPA available β that alone is grounds for restriction to public-data-only use.
State which output types require human review before use (client deliverables, external communications, hiring decisions) and what 'review' means β not just reading, but verifying facts, checking for bias, and confirming the content meets quality standards.
π‘ For high-stakes outputs like legal documents or financial analysis, require sign-off by someone with domain expertise in the subject matter, not just the employee who generated the output.
Specify the training format (live session, e-learning module, or written acknowledgment), the completion deadline for existing employees, and the consequences for violations. Tie the policy acknowledgment to your existing onboarding workflow.
π‘ A signed acknowledgment form β even a simple email confirmation β creates a record that the employee received and read the policy, which matters in any enforcement action.
Set a calendar reminder for the first policy review β six months from publication β before you distribute the document. Include the review date in the policy header so employees know it is current.
π‘ Assign the review to the policy owner in your HR or project management system at the time of publishing, not as an afterthought six months later.
An AI policy is an internal governance document that defines the rules employees must follow when using artificial intelligence tools at work. It covers which tools are approved, what data may be entered into them, when human review is required, and what happens if the rules are violated. It serves as the organization's primary control for managing the risks that come with widespread AI adoption.
Without a policy, employees make individual judgments about AI use β often entering confidential or customer data into public tools that use prompts for model training, generating client-facing content without review, or relying on AI outputs that contain factual errors. A policy creates consistent behavior across the organization, reduces data exposure risk, and documents your governance posture for clients, auditors, and regulators who increasingly ask about AI controls.
At minimum: purpose and scope, definitions of covered tools, acceptable and prohibited use cases, data classification rules governing what information may enter AI systems, human oversight requirements for AI-generated outputs, an approved vendor list with an assessment process for new tools, a training and enforcement section, and a scheduled review cycle. Policies that omit any of these sections leave material gaps in employee guidance and organizational accountability.
Ownership typically sits with the CISO, Head of IT, Chief Operating Officer, or General Counsel depending on the organization's size and risk profile. The owner is accountable for keeping the policy current, managing the approved tool list, handling assessment requests, and coordinating enforcement. In small businesses, the owner is often the founder or operations lead until the company grows a dedicated function.
Every six months at minimum, given the pace of change in AI tool capabilities, vendor data practices, and regulatory guidance. An immediate out-of-cycle review is warranted when a significant AI-related incident occurs, when a major new tool is adopted organization-wide, or when new legislation or regulatory guidance applies to the company's industry or jurisdiction. Annual reviews are not sufficient for most organizations in the current environment.
Yes β generative AI tools like large language model chatbots and AI-assisted coding platforms introduce specific risks (hallucination, data ingestion into training sets, IP uncertainty in outputs) that are distinct from earlier rule-based automation. Your policy should address generative AI outputs by name, set specific review requirements for AI-generated text and code, and clarify whether employees may use generated content in external communications or client deliverables.
The core structure is the same β scope, acceptable use, data rules, accountability, and review β but the depth of each section should match your actual risk exposure and operational complexity. A 10-person consultancy needs clear rules about client data and external tools but does not need a full AI governance committee or formal model risk management framework. This template is calibrated for small to mid-sized businesses and can be expanded as the organization grows.
No single law in most jurisdictions currently mandates a standalone AI policy for all businesses, but several converging regulations create practical requirements that a policy helps satisfy. The EU AI Act imposes obligations on organizations using high-risk AI systems. GDPR and CCPA impose data protection requirements that apply whenever personal data enters an AI tool. Many enterprise contracts now include vendor AI governance clauses. Having a documented policy is evidence of due diligence across all of these frameworks.
An AI policy is an internal operational document that sets enforceable rules for employee behavior β it governs day-to-day AI use and has compliance consequences. An AI ethics statement is an external-facing declaration of the organization's principles and values around AI β it communicates intent to customers, partners, and the public but is not directly enforceable. Most organizations that need one also need the other, but they serve different audiences and purposes.
An acceptable use policy governs how employees may use company IT systems and networks broadly β covering email, internet access, device use, and software. An AI policy is a focused overlay that addresses the specific risks of AI tools: data ingestion, output accuracy, vendor training practices, and human oversight. Many organizations need both, with the AI policy referencing the broader acceptable use framework.
A data privacy policy defines how the organization collects, stores, and processes personal data β primarily an external-facing document for customers and regulators. An AI policy is an internal operational document governing employee behavior. The two are complementary: the AI policy should reference the data privacy policy's classification rules and restrict AI use to what the privacy policy permits.
An information security policy sets the organization's overall framework for protecting data and systems β covering access controls, incident response, and vendor management. An AI policy is narrower, addressing the specific behaviors and risks that arise when employees use AI tools. The AI policy should sit within the information security framework and reference its data classification and vendor assessment standards.
An AI ethics statement is an external-facing declaration of the organization's principles β fairness, transparency, accountability β intended for customers, partners, and the public. An AI policy is an internal operational document with enforceable rules and consequences. Both can coexist and should be consistent with each other, but they serve different audiences and carry different levels of enforceability.
Client confidentiality rules must explicitly prohibit entering client data into external AI tools; AI-drafted deliverables require partner or manager sign-off before distribution.
HIPAA-covered entities must restrict AI tool use to systems with a signed Business Associate Agreement and prohibit any PHI from entering general-purpose AI platforms.
AI use in credit, underwriting, or fraud decisions triggers fair lending and model risk management obligations; human review and audit trail requirements are especially critical.
Engineering teams need specific rules on AI-generated code β including IP ownership uncertainty, security review requirements, and restrictions on entering proprietary source code into external tools.
AI-generated product descriptions and customer communications require brand and accuracy review; customer data used in AI-driven personalization must align with privacy notice disclosures.
Policies must address student data privacy (FERPA in the US), AI-generated content in assessments, and transparency obligations to students and parents about AI use in educational tools.
| Path | Best for | Cost | Time |
|---|---|---|---|
| Use the template | Small to mid-sized businesses establishing AI governance for the first time with a straightforward tool stack | Free | 2β4 hours to customize and distribute |
| Template + professional review | Companies in regulated industries, those processing significant customer data, or those with enterprise clients requiring AI governance evidence | $300β$800 for a legal or compliance advisor review | 3β5 business days |
| Custom drafted | Organizations deploying AI in high-risk decision-making (hiring, credit, healthcare) or subject to the EU AI Act's high-risk system requirements | $2,000β$8,000 for specialist legal counsel | 2β4 weeks |
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