It has only taken a couple of years for AI to become an indelible part of business operations. Many organizations are still struggling to understand the implications of this transformative technology — including the security threats it brings. AI comes with a unique set of security challenges, and the threat landscape is constantly changing.
Addressing these risks can seem daunting whether an organization is using gen AI tools, AI embedded in other software or customized machine learning models. The non-deterministic nature of AI requires a different approach to security than traditional software. AI models also learn and adapt, potentially opening up new vulnerabilities long after the model is deployed.
Because of these unique characteristics, it may seem as though AI requires an entirely new approach to security. However, many familiar concepts still apply. By adapting many of the tools, techniques and best practices used to secure the traditional IT environment, organizations will have a solid foundation for addressing AI-specific threats.
Understanding Risk, Defining Policies
As with any security strategy, the first step is to understand the threats associated with AI. A key threat is the exposure of confidential or sensitive information that’s used to train an AI model or to query a gen AI application. Malicious actors may also use various techniques to trick AI models into revealing sensitive information or taking unintended actions. Data poisoning can cause a model to make inaccurate predictions.
Armed with an understanding of the threats, stakeholders can establish the organization’s risk tolerance. The acceptable level of risk will likely vary depending on the specific application, dataset and other factors. Stakeholders should then articulate these thresholds through AI security policies, which in turn drive the selection and management of AI models, applications and tools.
AI policies also aid in the development of security training. Users are often the weakest link in security and may put sensitive information at risk without clear guidelines. Security training should cover policies for acceptable AI use and emphasize every user’s responsibility for protecting data and meeting regulatory requirements.
Getting the Framework in Place
Security frameworks provide a structured approach for mitigating vulnerabilities and reducing risk. One of the most popular is the National Institute of Standards and Technology Cybersecurity Framework, which has been adopted by almost half of organizations in the U.S. The NIST’s AI Risk Management Framework was developed with the input of more than 240 organizations in various sectors to facilitate the design, development and use of trustworthy AI systems.
The core focus of the AI RMF is centralized AI governance. Organizations should develop a governance structure based on their specific goals and tolerance for risk. The AI RMF also provides a flexible framework for identifying and mitigating potential risks in AI systems. It works with the CSF and other NIST frameworks to provide a comprehensive approach to risk management that’s consistent throughout the IT environment.
Of course, many fundamental cybersecurity concepts, such as access controls and data loss prevention, still apply to AI. Organizations should also use encryption, data masking and other techniques to protect sensitive information.
Addressing the Most Likely Threats
When implementing an AI security strategy, organizations should prioritize threats based on their likelihood and potential impact. It’s also critical to mitigate risk throughout the AI lifecycle, from data acquisition through model development, training and deployment. In addition, organizations should develop processes for evaluating and monitoring third-party models, applications and data.
Technologent’s AI and security teams are here to help you develop an effective strategy for minimizing AI risk. Through our extensive experience and proven methodologies, we can help you leverage the power of AI safely, securely and ethically.
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