How to create an AI acceptable use policy, plus template
With great power comes -- in the case of generative AI -- great security and compliance risks. Learn how an AI acceptable use policy can help ensure safe use of the technology.
AI has proliferated across industries, becoming a critical component of digital operations and organizational infrastructure. But this widespread adoption poses significant risks, particularly from a cybersecurity perspective.
A foundational element of managing and mitigating these risks and ensuring the security of an organization's sensitive data is an AI acceptable use policy. In short, such a policy spells out how the organization manages and mitigates AI risks, as well as sets up guidelines and expectations for the users of AI systems.
Why an AI acceptable use policy is important
AI systems, especially generative AI systems and large language models, are powerful tools that can process and analyze data at a scale and speed beyond human capability. This power, however, comes with inherent risks.
The same features that make AI systems efficient and effective can be exploited for malicious purposes, such as generating phishing content, writing malware, creating deepfakes or automating cyberattacks.
An AI acceptable use policy is vital for several reasons, including the following:
The same features that make AI systems efficient and effective can be exploited for malicious purposes, such as generating phishing content, writing malware, creating deepfakes or automating cyberattacks.
Security policies. It reinforces enterprise security policies, ensuring AI is not used to compromise sensitive information.
User accountability. It delineates clear boundaries for users, promoting accountability and reducing the likelihood of misuse.
Regulatory compliance. It helps in maintaining compliance with relevant regulations and standards, preventing legal and ethical violations.
Data integrity. It safeguards data integrity by restricting AI from generating false or misleading information.
Reputation management. It serves as a proactive measure to protect an organization's reputation from the potential fallout of AI misuse.
How to craft an AI acceptable use policy
Any AI acceptable use policy should be tailored to the specific needs and context of an organization. Generally, however, the following steps apply:
Assess the scope of AI use. Understand the breadth of AI deployment within your organization. What types of AI are in use? Who uses them and for what purposes? Make sure not to limit these questions to IT. Most AI use today is inside business units, often using unaccounted shadow AI tools.
Identify potential risks. Analyze the potential risks associated with AI usage. Consider the types of data AI has access to and the potential for misuse. Remember, risks are not just financial. Reputational risk is equally significant.
Engage stakeholders. Involve key stakeholders, including legal, IT, cybersecurity and compliance teams, to contribute their expertise. Ultimately, the company needs to balance the advantages any given AI tool provides against the risks associated with its use. Recognize these risks may change over time.
Draft clear guidelines. Clearly articulate what is allowed and what is not. Specify the types of behaviors and use cases that are prohibited, such as using AI to manipulate information or infringe on data privacy.
Include enforceable measures. Include enforceable measures and repercussions for policy violations to ensure users take the guidelines seriously. Companies can use technologies to help with this enforcement, such as data loss prevention tools that monitor the flow of confidential information, such as personal data and intellectual property, between systems.
Make regular updates. AI technology evolves rapidly, so it's critical to regularly revisit and update the policy to reflect new developments and threats. These assessments should occur at least annually. Given the rapid rate of change and deployment of AI, however, quarterly reviews are advisable.
AI acceptable use policy template
Click here to access our editable AI acceptable use template. Use it as a starting point for content and structure.
Essential elements to include in an AI acceptable use policy
An effective AI acceptable use policy should incorporate the following elements:
Purpose and scope. Define the purpose of the policy and its applicability within the organization.
User responsibilities. Outline the responsibilities of users, including adherence to security policies and ethical use standards.
Prohibited uses. Enumerate the specific uses of AI that are prohibited, such as unauthorized access to personal data or creation of fraudulent content.
Data governance. Establish rules for data protection and governance, including data access, sharing and processing guidelines.
Security requirements. Detail the security measures that AI systems and their users must employ, such as encryption and access controls.
Compliance and legal obligations. Address compliance requirements, as determined by applicable laws and regulations.
Reporting and consequences. Provide a mechanism for reporting misuse, and outline the consequences for usage policy violations.
Review and update process. Implement a process for the regular review and updating of the policy to adapt to new risks and technological advancements as they emerge.
An AI acceptable use policy is not merely a document, but a living framework that guides the safe and responsible use of AI within an organization. By creating and enforcing such a policy, organizations can harness the power of AI, while mitigating the risks it poses to cybersecurity and data integrity. This dual focus on innovation and risk management is paramount as AI continues to evolve and inevitably becomes even more integrated into our digital ecosystems.
Jerald Murphy is senior vice president of research and consulting with Nemertes Research. With more than three decades of technology experience, Murphy has worked on a range of technology topics, including neural networking research, integrated circuit design, computer programming and global data center design. He was also the CEO of a managed services company.
