The attention on security risks associated with generative AI (GenAI) and large language models (LLMs) has been growing, as these technologies present real-world risks such as “hallucinations” and exposure of private and proprietary data. However, it is important to note that these risks are just a part of the broader attack surface that comes with the territory of AI and machine learning (ML).

The rise of AI has brought about significant changes in companies, industries, and sectors, leading to new business risks related to intrusions, breaches, and loss of proprietary data and trade secrets. While AI is not a new concept, the recent mass adoption of AI systems, including GenAI, has raised the stakes. Today, open software supply chains play a crucial role in innovation and business growth, but they also pose security risks. As more business-critical systems and workloads rely on AI, attackers are increasingly targeting these technologies.

One of the challenges is the lack of transparency in AI systems, making it difficult for businesses and government agencies to identify dispersed and often invisible risks. Without the necessary visibility and tools to enforce security policies, organizations may be vulnerable to AI-related security incidents similar to the SolarWinds or MOVEit breaches.

AI models typically involve a complex ecosystem of tools, technologies, open source components, and data sources, providing opportunities for malicious actors to inject vulnerabilities and malicious code into the AI development supply chain. With numerous elements in play, transparency and visibility become critical, yet most organizations struggle to achieve this level of insight.

To address these challenges, organizations should consider adopting a comprehensive AI security framework like MLSecOps, which provides visibility, traceability, and accountability across AI/ML ecosystems. This approach promotes secure-by-design principles without impeding regular business operations and performance.

Implementing an AI security program involves introducing risk management strategies to address security, bias, and fairness across the AI development stack. Advanced security scanning tools can help identify vulnerabilities in the AI supply chain, while creating an AI bill of materials enables organizations to track all components used in building AI systems. Utilizing open source security tools designed for AI and ML can also enhance security by detecting and protecting against potential vulnerabilities.

Encouraging collaboration and transparency through AI bug bounty programs can provide early insights into vulnerabilities and strengthen the overall security posture of the AI ecosystem. Ultimately, with the right processes and tools in place, organizations can effectively manage the risks associated with AI and ensure a secure and resilient environment for their operations.

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