Why should you care about AI? Well because AI is either already influencing your day-to-day operations or will be very soon. Whether you are a business owner, operations manager, or someone responsible for ground-floor tasks, AI is poised to impact every level of your business.
For security leaders, AI introduces both significant opportunities and notable challenges. Though it may seem like another tool, it’s far from that; it's a powerful technology that requires careful management, strategic implementation, and conscientious oversight. Therefore, developing a comprehensive AI security policy is essential to harnessing the benefits of this technology to safeguard against risks that come with it.
In this blog, we will explore the key components needed when creating a robust AI security policy.
As AI technology becomes increasingly integrated into business practices, it introduces new legal considerations such as data privacy, intellectual property, and consumer rights—areas that legislators worldwide are rushing to regulate. We at Rivial have already seen this impact our clients as auditors from both banks and credit unions begin to request AI policies as part of their IT security examinations.
Why should you have one in place? A clear AI policy not only guides employees in their interactions with AI, from decision-making to task automation but also helps them understand their evolving roles in an AI-integrated environment, ensuring the safe and effective use of AI technologies.
Additionally, establishing clear guidelines for AI use is a hallmark of good business practice. It allows businesses to manage risks related to ethical considerations, biases in AI algorithms, and data misuse. An AI policy isn't just about avoiding penalties or navigating complex legalities—it's about fostering a culture of compliance and responsibility.
Before developing your AI policy, it is crucial to remember that it should be distinct from your other security policies because AI introduces unique challenges and considerations that general security frameworks may not fully address. Additionally, it should integrate essential security components within its framework, which we’ll go over below.
If you need a quick refresher on popular AI security standards and frameworks, check out our recent blog “Preparing for AI Requirements”
Clearly articulate why the AI policy is being developed. This might include protecting sensitive data, ensuring ethical use of AI, or maintaining compliance with regulations. The purpose should address the specific needs and concerns of your organization. When it comes to scope, outline which AI technologies, tools, and systems the policy applies to. This includes specifying any AI-related projects, applications, or platforms in use. Define the boundaries of the policy, including any exclusions or special cases.
Establish a framework for oversight and evaluation of AI systems. This could involve creating an AI governance committee or appointing specific individuals responsible for policy enforcement and monitoring. Once established, schedule periodic evaluations of AI systems to ensure compliance with the policy. These reviews should assess both the technical and ethical aspects of AI. Finally, you should implement procedures for auditing AI systems to verify adherence to the policy. Ensure that there are mechanisms for reporting and addressing non-compliance.
Detail how sensitive data, including personal and proprietary information, should be protected when using AI. This includes encryption, anonymization, and secure storage.
Ensure your data protection practices comply with relevant regulations. While there are no specific federal laws for AI yet, state regulators have been applying existing privacy laws to AI activities. For instance, as of last year, “at least 12 states, including California and Texas, have regulations on how automated systems can profile consumers using personal data” (BrennanCenter). Though AI isn’t explicitly mentioned, these laws cover algorithms like AI and machine learning.
Lastly, have a response plan ready for data breaches or security incidents involving sensitive data. This plan should include procedures for notifying affected parties, strategies for containing the breach, and steps for remediation.
Define a framework for categorizing data based on sensitivity, such as public, internal, confidential, or restricted. Provide criteria for each classification level. Next, Establish procedures for handling each data classification. For example, data classified as “confidential” may require additional encryption and access controls compared to “internal” data. Lastly, outline how data should be managed throughout its lifecycle, from creation and storage to deletion or archiving. This ensures that data is handled appropriately at all stages.
Detail the processes for verifying the identity of users accessing AI systems and ensuring they have appropriate permissions. This might include multi-factor authentication and role-based access controls. Implement logging and monitoring systems to track access to AI systems and data and regularly review and update access permissions to ensure they remain appropriate as roles and responsibilities change.
Articulate clear guidelines on how data can be used within AI systems such as for training models or generating insights. Ensure that these uses align with organizational goals and legal requirements. Next, clearly state any restrictions on data usage, such as prohibiting the use of data for unauthorized purposes or for preventing the creation of discriminatory algorithms. Make sure to provide rules for sharing data with third parties (if you choose to do so) including requirements for data protection and compliance with contractual obligations.
Ensure that AI systems are included in your organization's risk assessment processes to identify potential risks associated with AI, including technical risks (e.g., algorithmic bias, data inaccuracies), operational risks (e.g., system failures), and ethical risks (e.g., privacy concerns).
A systematic process for assessing these risks might include impact analyses, vulnerability assessments, and scenario planning. Be sure to regularly update risk assessments to reflect new developments and vulnerabilities.
Establish policies regarding the acceptable use of AI technologies by employees. Define what actions are permitted and what is prohibited in the context of AI applications. This helps prevent misuse of AI tools and ensures that employees use these technologies responsibly and ethically.
By integrating these components into your AI policy, you create a robust framework that addresses the unique challenges posed by AI while safeguarding your organization's data and systems. Tailoring your policy to these specific needs helps ensure that AI deployments are secure, compliant, and aligned with organizational goals.
We understand that creating an AI policy from scratch can be a daunting and time-consuming task. To simplify the process, we’re offering a free AI policy template to help you get started. Our template is designed to be flexible and adaptable to fit your organization’s size and maturity level, making it easier to customize to your specific needs. Whether you're a small startup or a large enterprise, our template provides a solid foundation that you can tailor to suit your unique requirements.
*Keep in mind that while our template is a great starting point, it’s important to regularly review and update your AI policy to ensure it stays compliant with evolving regulations and aligns with your business needs.
*Keep in mind that while our template is a great starting point, it’s important to regularly review and update your AI policy to ensure it stays compliant with evolving regulations and aligns with your business needs.