Securing LLMs for Privacy Protection: Does the Traditional Compliance Model Work?
During the HLTH conference, I had the opportunity to meet security experts in health compliance, including Eric Rozier MBA, CCSFP of HITRUST. We delved into the future of compliance, especially in the context of emerging Generative AI technology.
Twenty years ago, when web-based servers emerged, the multi-tier design became the dominant paradigm for software architecture. It entailed separating infrastructure from data and application, visualizing them as interconnected layers.
This architectural approach streamlined the organization of the tech stack and the team structures. The back-end team oversaw everything behind the scenes, from database to application business logic. The client team, be it web, mobile, or voice, accessed APIs to deliver the user experience. Meanwhile, the DevOps or IT team managed the servers and infrastructure running the entire system. Broadly, this led to the categorization of various tech solutions as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS).
This distinction also influenced the structuring and definition of security measures. For example, KPMG introduced a widely-adopted Data Cycle Model outlining how to conceptualize and implement data compliance: from collection to transmission, usage, storage, and finally, retention policies.
The model efficiently delineated security and privacy measures: securing data storage through encryption, ensuring data safety during transit, or even when transferred between systems. Anonymization techniques provided a robust method to safeguard user privacy.
However, with the AI revolution, the landscape began to shift. Use cases like product recommendations necessitated a deeper understanding of users beyond their basic profiles. E-commerce platforms, like Amazon, don't just collect feedback; they also anticipate which products might interest a user based on their purchase history. Such predictive features are rooted in analyzing vast amounts of user data. While proper anonymization techniques can maintain user privacy, the distinction between the data and application layer remains discernible: "You asked for X; let's consult the data to suggest Y."
Enter the era of LLMs, exemplified by applications like ChatGPT. As Geoffrey Hinton, regarded as the Godfather of this technology, explained, these new systems continuously learn from every interaction. Each query or request for clarification adds to their knowledge, tying directly back to the individual users. As user queries transform into data, the boundaries between data, application, and user information blur. For LLMs to provide tailored responses, they must understand the user, embedding this knowledge as an integral part of their data structure, rather than just an analytical layer on anonymous, aggregated data.
This evolution poses a pivotal question for the security industry, especially in sensitive sectors like health and finance: Are traditional models like SOC and standard ISO controls adequate to guarantee user privacy and security?
Our friends at Pear.vc recently shared a thought-provoking blog post on the potential and innovation in the realm of Generative AI. They highlighted the need for innovative tools tailored for LLMs, especially in the security domain.
How do you perceive this paradigm shift affecting the security landscape? How can we safeguard data security and user privacy in this new age? Perhaps this is the next startup idea you've been seeking!
