AI Acceptable Use Policy: 9 Elements to Include + Template

What Is an AI Acceptable Use Policy? 

An Artificial Intelligence Acceptable Use Policy (AI AUP) is a formal set of rules defining safe, ethical, and legal use of AI tools like generative text and coding assistants within an organization. It protects company data, prevents intellectual property leaks, and manages output accuracy. You can review a comprehensive overview via Tenable.

Core policy elements include:

• Scope and audience: Applies to all employees, contractors, and third parties using AI on company networks or devices.
• Approved vs. prohibited tools: Lists sanitized enterprise tools that safeguard data versus unauthorized public apps.
• Data privacy and confidentiality: Restricts uploading sensitive customer data, source code, or credentials to unvetted models.
• Human review and accountability: Requires workers to review and verify all AI-generated content before publishing or acting on it.
• AI-generated code: Requires human review, security testing, and licensing checks before AI-generated code is used in production.
• Transparency and disclosure: Defines when employees must disclose AI-generated or AI-assisted content and decisions.
• Approval process for new AI tools: Requires security, privacy, legal, and compliance review before adopting new AI tools.
• AI incident reporting: Establishes procedures for promptly reporting data exposure, harmful outputs, security issues, or other AI-related incidents.
• Enforcement and policy violations: Outlines consequences for policy violations and data breaches.

This is part of a series of articles about workspace security

Why Organizations Need an AI Acceptable Use Policy

Protect Confidential and Sensitive Information

AI tools often require users to input data, which can include confidential company information, customer records, or proprietary content. If employees are not guided by a clear policy, they may inadvertently share sensitive data with external AI platforms, risking data breaches or regulatory violations. An AI AUP sets strict boundaries for what information can be used with AI tools and clarifies the consequences of mishandling sensitive data.

Organizations that lack such policies are vulnerable to data leaks and non-compliance with privacy laws like GDPR or HIPAA. By explicitly stating what constitutes confidential information and prohibiting its exposure to unapproved AI tools, a well-crafted policy strengthens data protection efforts. This reduces both the likelihood and impact of accidental or malicious disclosures.

Reduce Shadow AI Risks

Shadow AI refers to the unsanctioned use of AI tools by employees outside the purview of IT or compliance teams. This creates blind spots for security, as these tools may not be vetted for data handling practices or compliance with company standards. An AI AUP addresses this by defining approved tools and requiring employees to seek authorization before adopting new AI solutions.

Without a policy, organizations face increased risks from shadow AI, such as unauthorized data processing or unintentional exposure of intellectual property. The policy acts as a deterrent and a reference point for enforcement, ensuring all AI usage is visible, auditable, and aligned with organizational risk management protocols.

Improve the Reliability of AI-Generated Work

AI-generated content can contain errors, biases, or inaccuracies that compromise the quality and reliability of business outputs. A robust AI AUP mandates human review of critical AI-generated work, clarifies acceptable use cases, and sets standards for accuracy and accountability. This helps prevent the unchecked dissemination of flawed information internally and externally.

By requiring human oversight and regular validation of AI outputs, organizations can reduce the risk of poor decisions or reputational harm resulting from faulty AI-generated work. The policy also encourages employees to treat AI as an assistive tool rather than a substitute for critical thinking, preserving the integrity and reliability of business operations.

What Should an AI Acceptable Use Policy Include?

1. Scope and Audience

The policy must clearly define who it applies to, such as full-time employees, contractors, consultants, or third-party vendors. This prevents ambiguity about who is responsible for following the policy and ensures comprehensive coverage across all parties interacting with organizational AI systems. Clearly stating the scope also helps in onboarding and training processes, making sure everyone is aware of their obligations.

Additionally, specifying the types of AI tools and scenarios covered by the policy is critical. This includes generative AI, machine learning platforms, AI coding assistants, and customer-facing AI chatbots. By laying out the breadth of the policy, organizations avoid gaps that might otherwise be exploited and ensure a unified approach to AI governance.

Key capabilities:

• Defines covered users, devices, systems, and AI technologies.
• Covers employees, contractors, consultants, and relevant third parties.
• Identifies business activities and environments subject to the policy.
• Establishes consistent requirements across departments and locations.

2. Approved vs. Prohibited Tools

An effective AI AUP must distinguish between approved AI tools that have been vetted for security, privacy, and compliance, and those that are prohibited due to unacceptable risk levels. This list should be regularly updated to keep pace with new AI offerings and evolving threat landscapes. By maintaining clear lists, organizations prevent the use of untrusted tools and direct users toward secure, compliant options.

The policy should also outline the process for evaluating and approving new AI tools. This helps employees understand the criteria for acceptance and discourages the use of unauthorized applications. Documenting the rationale for prohibiting certain tools can further reinforce the importance of compliance and reduce pushback.

Key capabilities:

• Maintains a current list of approved and prohibited AI tools.
• Defines permitted business uses for approved tools.
• Blocks or restricts unauthorized and high-risk AI services.
• Provides a process for requesting additional tools.

3. Data Privacy and Confidentiality

A strong AI AUP emphasizes strict controls on the type of data that can be shared with AI systems, especially those operated by third-party vendors. Employees must be prohibited from inputting personal, confidential, or sensitive business information into AI tools unless explicitly authorized. The policy should reference applicable data protection laws and standards to reinforce its importance.

The policy should also include guidelines on data retention, data minimization, and secure disposal of information processed by AI systems. This ensures that data privacy risks are managed throughout the AI lifecycle and that the organization remains compliant with legal and contractual obligations.

Key capabilities:

• Defines data that cannot be submitted to AI systems.
• Applies controls for personal, confidential, proprietary, and regulated data.
• Establishes data minimization, retention, and deletion requirements.
• Restricts sensitive data processing to appropriately approved tools.

Related content: Read our guide to data security

4. Human Review and Accountability

Mandating human oversight is essential for maintaining quality and trust in AI-generated outputs. The policy should specify which types of AI-assisted work require human review before publication or use in decision-making. This is particularly important in areas like finance, legal, healthcare, or customer communications, where errors can have significant consequences.

Additionally, the policy should define accountability structures for AI use, clarifying who is responsible for oversight, review, and remediation of issues arising from AI-generated work. Clear accountability discourages complacency and ensures that errors or biases are addressed promptly and transparently.

Key capabilities:

• Requires human verification of relevant AI-generated outputs.
• Assigns responsibility for AI-assisted work and decisions.
• Defines additional review requirements for high-risk use cases.
• Requires errors, bias, and inappropriate outputs to be corrected.

5. AI-Generated Code

If employees use AI tools to generate code, the policy should set standards for code review, security assessment, and documentation. AI-generated code can introduce vulnerabilities, copyright issues, or fail to meet organizational coding standards. Requiring manual review and testing of such code helps mitigate these risks.

The policy should also address licensing and intellectual property concerns, ensuring that any AI-generated code used in production does not infringe on third-party rights. This protects the organization from legal disputes and maintains the integrity of its software development lifecycle.

Key capabilities:

• Requires code review and testing before deployment.
• Checks AI-generated code for vulnerabilities and insecure dependencies.
• Addresses licensing, copyright, and intellectual property risks.
• Requires compliance with internal development and documentation standards.

6. Transparency and Disclosure

Transparency is critical when deploying AI tools, especially those that interact with customers or the public. The policy should require clear disclosure when content, decisions, or communications are generated or influenced by AI. This builds trust and ensures users are aware of when they are engaging with automated systems.

Internally, the policy should mandate documentation of AI use cases, tool selection, and rationale for deployment. This supports auditability, continuous improvement, and the ability to respond effectively to incidents or regulatory inquiries.

Key capabilities:

• Defines when AI use must be disclosed to customers or other users.
• Requires documentation of significant internal AI use cases.
• Supports traceability of AI-assisted content and decisions.
• Establishes disclosure requirements based on legal and contractual obligations.

7. Approval Process for New AI Tools

The policy should outline a standardized process for evaluating and approving new AI tools before they are adopted. This process should include security, privacy, legal, and operational reviews to ensure the tool aligns with organizational requirements. By centralizing approval, organizations can avoid fragmented tool adoption and maintain consistent standards.

Clear documentation of the approval process, criteria, and responsible parties ensures transparency and accountability. It also simplifies the onboarding of new tools and provides employees with a clear path to request and justify the adoption of new AI technologies.

Key capabilities:

• Establishes a formal process for requesting new AI tools.
• Requires security, privacy, legal, and operational assessments.
• Reviews vendor data handling, permissions, and model-training practices.
• Records approval decisions, conditions, and responsible parties.

8. AI Incident Reporting

An AI AUP must include procedures for reporting incidents related to AI use, such as data breaches, performance failures, or unintended outputs. Employees should know how and where to report incidents, and the policy should define what constitutes an AI-related incident. Prompt reporting enables quick response and mitigation of potential harm.

The policy should also specify the steps for investigating, documenting, and addressing reported incidents. This ensures lessons are learned, systemic issues are corrected, and stakeholders are informed as appropriate. Regular reviews of incident reports can drive ongoing improvements to both the policy and AI governance practices.

Key capabilities:

• Defines which AI-related events employees must report.
• Provides clear reporting channels and escalation procedures.
• Requires prompt investigation and documentation of incidents.
• Supports remediation and policy improvements based on findings.

9. Enforcement and Policy Violations

The policy must spell out the consequences for violating its provisions, ranging from retraining to disciplinary action, up to termination in cases of severe breaches. Clear enforcement mechanisms deter non-compliance and demonstrate the organization’s commitment to responsible AI use. Consistent enforcement also builds trust in the policy among employees.

Additionally, the policy should outline procedures for investigating suspected violations, ensuring fairness and due process. By specifying both preventive and corrective measures, organizations can address issues quickly and maintain the integrity of their AI governance program.

Key capabilities:

• Defines consequences based on the severity of violations.
• Establishes procedures for investigating suspected non-compliance.
• Supports corrective actions such as retraining or access restrictions.
• Documents violations and remediation for consistent enforcement.

AI Acceptable Use Policy Examples 

Marketing Team Using Generative AI

A marketing team may use generative AI to draft blog posts, social media content, or ad copy. The AI AUP should specify that all AI-generated materials must be reviewed by a human before publication to ensure accuracy and adherence to brand standards. It should also prohibit sharing confidential marketing strategies or customer data with external AI platforms unless approved by legal and compliance teams.

Additionally, the policy should require marketers to disclose when content is AI-generated if transparency is necessary for compliance or customer trust. By following these guidelines, organizations can leverage AI for efficiency while minimizing risks related to brand reputation, misinformation, or regulatory non-compliance.

Example scenario:

A marketing specialist uses an approved generative AI tool to draft a product announcement using non-sensitive product information. Before publication, the specialist verifies all claims, removes an incorrect feature description, and submits the final copy for normal editorial review. No customer data or confidential launch information is entered into the tool.

Developers Using AI Coding Assistants

Developers might use AI tools to generate code snippets, automate testing, or assist with debugging. The AI AUP should require that all AI-generated code be thoroughly reviewed and tested for security vulnerabilities, compliance with coding standards, and intellectual property concerns before being merged into production systems. This prevents accidental introduction of faulty or insecure code.

The policy should also clarify which AI coding tools are approved and set expectations for documentation and attribution of AI-generated code. This ensures traceability and helps avoid licensing or ownership disputes, while also maintaining high standards for code quality and security.

Example scenario:

A developer uses an approved coding assistant to generate a function for an internal application. Before merging the code, the developer reviews its logic, runs security and unit tests, and checks dependencies and licensing requirements. A vulnerable package suggested by the assistant is identified and replaced before deployment.

Employees Using AI Meeting Assistants

Employees may use AI meeting assistants to record, transcribe, summarize, or extract action items from calls. The AI AUP should require employees to use only approved tools and obtain any consent required before recording or processing a meeting. Sensitive discussions, customer data, credentials, or regulated information should not be processed unless the tool is approved for that data.

The policy should also define how meeting recordings, transcripts, and summaries are stored, shared, and deleted. Employees should review AI-generated notes for errors before relying on them or distributing them to others. Access controls and retention rules can further reduce the risk of confidential meeting information being exposed or retained longer than necessary.

Example scenario:

A project manager wants an AI assistant to transcribe a customer meeting. The manager confirms that the tool is approved for the meeting’s data, obtains required consent, and reviews the generated summary before sharing it. The recording and transcript are then retained according to the organization’s approved retention schedule.

AI Acceptable Use Policy Template 

Organizations can adapt the following AI Acceptable Use Policy template to their industry, risk profile, approved technologies, and applicable legal requirements.

• Purpose and scope: Explain why the policy exists and identify who must follow it. For example, the policy may apply to employees, contractors, consultants, and other individuals using AI on the organization’s behalf. It should also clarify which technologies are covered, including generative AI tools, coding assistants, meeting assistants, image generators, and other AI-enabled applications. Real-world AI policy templates commonly begin by defining both purpose and scope so users understand where the rules apply.
• Approved tools and permitted uses: State which AI tools employees may use and for which business purposes. Organizations should maintain a list of approved tools and require employees to obtain authorization before introducing a new AI application. The policy can also distinguish low-risk activities, such as brainstorming or summarizing non-sensitive information, from restricted uses that require additional review.
• Data privacy and confidentiality: Specify what information users may and may not submit to AI systems. Confidential business information, personal data, credentials, proprietary source code, regulated information, and information covered by confidentiality agreements should not be entered into public or unapproved AI tools. The policy should also address data minimization, access controls, retention, and compliance with applicable privacy requirements.
• Human review and accountability: Make clear that employees remain responsible for work produced with AI assistance. AI-generated information should be checked for factual accuracy, completeness, bias, and appropriateness before it is relied upon or distributed. For consequential decisions, organizations should establish additional human oversight and clear lines of accountability rather than treating AI output as an independent final decision.
• Responsible and ethical use: Prohibit uses of AI that are unlawful, deceptive, discriminatory, harmful, or inconsistent with organizational values. Employees should not use AI to impersonate people, knowingly spread misinformation, bypass security controls, or create content that infringes intellectual property rights. The policy should also direct users to consider potential bias and the impact of AI-generated material on other people.
• AI-generated code: Require developers to review and test AI-generated code before it enters production. Code should be assessed for security vulnerabilities, errors, inappropriate dependencies, licensing concerns, and compliance with internal development standards. Proprietary source code or credentials should only be provided to AI coding tools that have been approved to handle that information.
• Transparency and disclosure: Define when employees must disclose that AI was used to create or substantially influence content, communications, or decisions. Disclosure may be necessary because of organizational policy, contractual requirements, professional obligations, or the nature of a customer-facing AI system. Internally, organizations may also require teams to document significant AI use cases for governance and auditing purposes.
• Approval of new AI tools: Establish a formal review process for new AI products and vendors. Reviews can consider how the provider stores and uses submitted data, whether prompts are used for model training, security controls, privacy terms, intellectual property provisions, integration permissions, reliability, and relevant legal or contractual obligations.
• Incident reporting: Tell employees how to report AI-related incidents, including accidental disclosure of confidential information, unauthorized AI use, security vulnerabilities, harmful outputs, discriminatory results, or materially inaccurate information. The organization should identify the department or contact responsible for receiving these reports and investigating them.
• Employee training: Require appropriate AI training for people who use AI as part of their work. Training can cover approved tools, data handling, AI limitations, hallucinations, bias, security risks, responsible prompting, and human review requirements. Existing AI policy templates also emphasize continuing education because both AI capabilities and associated risks evolve quickly.
• Policy violations: Explain what happens when someone fails to comply with the policy. Depending on the circumstances, responses may include removal of AI access, additional training, corrective action, or disciplinary measures under existing organizational policies. Employees should also be given a clear channel for reporting suspected violations or concerns.
• Policy review and updates: Treat the AI AUP as a living document rather than a one-time exercise. Review it regularly and update it when the organization adopts new tools, identifies new risks, or faces changes in legal or regulatory requirements. data.org’s template recommends annual review, while its broader guidance stresses updating AI policies as technologies and organizational practices evolve.

Source template: data.org – Generative AI Use Policy (PDF) 

AI Acceptable Use Policy Best Practices 

Organizations should consider the following best practices when devising acceptable use policies for AI.

1. Apply the Policy to Every Device Employees Use for Work

An AI AUP should apply regardless of whether employees access AI tools from company laptops, personal computers, tablets, or mobile devices. Otherwise, employees may bypass organizational controls simply by switching devices. The policy should make clear that work-related AI activity remains subject to the same security, privacy, and data handling requirements on every endpoint.

Organizations should support this requirement with technical controls where possible, such as identity-based access, mobile device management, browser security controls, and data loss prevention. These measures help enforce consistent rules even when employees work remotely or use multiple devices.

Key actions:

• Apply AI requirements to corporate and personal devices used for work.
• Enforce identity and access controls across supported endpoints.
• Use data loss prevention and device management where appropriate.
• Monitor compliance consistently across remote and office environments.

2. Govern AI Across Both Browser and Desktop Applications

AI tools are available through websites, desktop applications, browser extensions, plugins, and integrations with productivity software. An effective policy should cover all of these access methods rather than focusing only on browser-based AI services. Desktop and embedded applications may process the same sensitive information while operating outside standard web controls.

Organizations should maintain an inventory of approved AI applications and monitor how they connect to corporate data and systems. Security teams should also evaluate extensions, plugins, and integrations because they may gain access to files, emails, source code, or internal communications without users fully understanding the permissions involved.

Key actions:

• Inventory AI websites, desktop apps, extensions, plugins, and integrations.
• Apply approval requirements regardless of how an AI service is accessed.
• Review application permissions and corporate data connections.
• Restrict unauthorized tools and high-risk integrations.

Related content: Read our article about the top AI lifecycle governance platforms for large organizations

3. Follow the Principle of Least Privilege

AI tools should receive only the permissions and data access required for their intended purpose. Granting broad access to file repositories, email accounts, source code, or customer databases increases the impact of errors, compromised accounts, or insecure integrations. The AI AUP should require teams to limit permissions by default and expand them only when there is a documented business need.

Access should also be reviewed regularly and revoked when it is no longer necessary. Role-based permissions, scoped API access, and restricted data connectors can help enforce least privilege and reduce the amount of sensitive information available to an AI system.

Key actions:

• Grant AI tools only the minimum permissions required.
• Scope access to specific data, systems, and business functions.
• Review permissions regularly and remove unnecessary access.
• Restrict API keys, connectors, and privileged integrations.

4. Preserve User Privacy on BYOD Devices

When employees use personal devices for work, organizations must balance security requirements with employee privacy. An AI AUP should define what work-related AI activity may be monitored without giving the organization unnecessary access to personal files, applications, messages, or browsing activity. Monitoring should be limited to data and services associated with corporate accounts or managed work environments.

Technical controls such as managed application containers, separate work profiles, and identity-based policies can help isolate business activity from personal use. The policy should also explain what information the organization collects from BYOD devices, why it is collected, and how long it is retained.

Key actions:

• Separate managed work activity from personal device activity.
• Limit monitoring to corporate accounts, data, and managed environments.
• Clearly document what device information is collected and retained.
• Use work profiles or managed application containers where possible.

Related content: Read our article about how to build a BYOD policy

5. Build Controls into Employee Onboarding and Offboarding

AI governance should be part of the employee lifecycle rather than a one-time policy acknowledgment. During onboarding, employees should receive training on approved AI tools, prohibited data, review requirements, and incident reporting procedures. Access to AI systems should be granted according to the employee’s role and documented business needs.

When an employee changes roles or leaves the organization, AI-related permissions should be reviewed or revoked along with other system access. This includes accounts, API keys, plugins, data connectors, and access to shared AI workspaces. Integrating these checks into standard onboarding and offboarding processes reduces the risk of excessive or lingering access.

Key actions:

• Train new users on approved tools, data restrictions, and reporting procedures.
• Provision AI access according to roles and documented business needs.
• Review permissions when employees change roles.
• Revoke AI accounts, API keys, plugins, and connectors during offboarding.

Enforcing Your AI Acceptable Use Policy on Any Device with Venn

A written AI acceptable use policy sets the rules, but without a way to enforce those rules at the endpoint it remains a document rather than a control. Blue Border™ closes that gap by creating an isolated, IT-controlled secure enclave that runs locally on any personal PC or Mac. Company data never leaves the enclave, AI tools used inside it are always governed, and everything outside stays personal, with no invasive endpoint security and no compromise on user privacy. This matters because AI now runs across personal devices and native applications: tools like Copilot, ChatGPT, Claude Desktop, and local LLMs operate outside the browser, beyond the reach of traditional browser and network controls, and contractors, freelancers, and offshore teams often work on devices IT cannot enroll, monitor, or control.

Key capabilities of Blue Border™:

• Corporate AI governance: Restricts usage to approved LLMs and corporate credentials, neutralizing the threat of shadow AI and personal account leakage.
• Absolute data isolation: Completely blocks unsanctioned AI access to high-stakes data by confining sensitive applications within a protected perimeter.
• Zero-training assurance: Prevents proprietary information from being ingested into public AI training sets, so intellectual property stays yours.
• Full visibility without managing the entire device: IT decides which AI tools are allowed inside Blue Border™, and data cannot be pasted or uploaded into unapproved AI tools running outside it.
• AI compliance you can demonstrate: Meets SOC 2, HIPAA, PCI, FINRA, and emerging AI governance requirements with enforceable controls on AI tool access across the entire BYOD workforce, including contractors and offshore teams.
• Protection beyond the browser: Governs AI at the application and OS level, covering natively installed AI applications and local LLMs that enterprise browsers cannot see.
• Simple to deploy: Contractors and remote workers install Blue Border™ on their own hardware in minutes, no virtual desktops, no device management, and full IT control over AI access from day one.
• Preserved user privacy: Personal AI tools, files, and email on the same device remain untouched and free of IT monitoring.

Learn more about AI security for your remote workforce with Venn

Securing contractors and remote employees doesn’t have to be a pain. For years, IT teams were stuck choosing between virtual desktops that are slow, complex, and expensive. Or buying, locking down, and shipping laptops across the globe. Thankfully, there’s a better way. Introducing Venn, a breakthrough in remote work security. Venn creates a secure enclave on any unmanaged PC or Mac used by contractors and remote employees. No VDI, no need to fully manage the device, and no compromise on security and compliance. Work applications run locally within the enclave, visually indicated by Venn’s blue border, protecting and isolating work from personal activity on the same computer. Both browser and installed apps run locally, natively, and securely. No hosting and no virtualization whatsoever. This approach preserves full app performance and user experience, while ensuring your organization’s DLP policies are always enforced. No file transfers, copy paste screenshots, or any other actions that could lead to data loss or compromise. Ready to see the future of remote work? Well, on behalf of all of us at Venn, we invite you to step inside the blue border. Find out more at Venn dot com.