As artificial intelligence (AI) becomes increasingly prevalent in business processes, organizations must prioritize the security of data accessed or generated by AI-powered tools. The rapid emergence of AI technology has left IT and security teams struggling to fully understand its inner workings and dependencies, making it an attractive target for threat actors looking to compromise data security. To ensure the security of AI technology, the CIA triad model – confidentiality, integrity, and availability – can be applied.
When it comes to data confidentiality, organizations must be aware of the risks associated with using third-party AI tools and educate users on best practices for maintaining compliance and security. Internal AI systems like Microsoft 365 Copilot also require attention to prevent data leaks and maintain data confidentiality through a least-privilege approach to data access rights and automated data discovery and classification.
Data integrity is another critical aspect of securing AI technology, as the decision-making process of AI models is often a black box, making it difficult to detect manipulations that could benefit threat actors. Strategies for ensuring trust in the integrity of AI systems include human auditors examining AI outputs, as well as implementing a dual-layered approach in which a secondary AI scrutinizes the decisions of the primary AI for irregularities and biases.
In terms of data availability, organizations must consider the potential impact of system overload, unauthorized requests, or maliciously crafted requests on AI models and the systems they enable. Security controls such as comprehensive access management, high-availability deployments, and filtering input are essential for ensuring the availability of AI models and processes, as well as protecting customers and the business.
While the AI industry is still in its early stages of evolution, organizations can leverage the CIA triad, existing security expertise, and security controls to build a solid foundation for securing AI-powered systems and processes. By understanding the risks associated with AI technology and implementing effective mitigation strategies, organizations can protect sensitive data and prevent compromise by threat actors.
