Sean Falconer, Head of Marketing & Developer Relations @ Skyflow, talks about LLM security and privacy, preventing PII leaks. They delve into the challenges, fears of customer PII exposure, and leaking company IP. Discussions include the importance of data masking, governance, and compliance in ML lifecycle management. They also touch on data tokenization, API security, and de-identifying data for protection.
Balancing security and privacy in LLMs involves making data usable without compromising privacy.
Implementing de-identification techniques like tokenization and depersonalization is crucial for safeguarding customer data.
Deep dives
LLM Security and Privacy Challenges
LLM security and privacy pose significant challenges due to the complexity of handling personally identifiable information (PII) in the context of AI. The balance between security and privacy becomes crucial as securing data involves not just blocking access but also making data usable without compromising privacy. The shift from traditional databases to AI models like deep learning and neural networks creates difficulties in managing and protecting PII, lacking practical deletion methods for data within AI models.
De-Identification and Privacy Gateway Solutions
De-identifying data early in the lifecycle is essential to prevent mishandling sensitive information. Implementing de-identification at the storage level and retrieval level reduces risks associated with potential data exposures through APIs or logging errors. Concepts like data tokenization and depersonalization play a critical role in safeguarding customer data, ensuring that even if exposed, only de-identified values are accessed, reducing privacy breaches.
Compliance and Future Trends in Data Privacy
The growing emphasis on data residency requirements across regions highlights the need for businesses to manage customer data within specific jurisdictions, adhering to varying data transfer regulations. With upcoming regulations like the EU's AI Act and heightened scrutiny on AI practices, organizations face evolving compliance challenges that necessitate proactive measures to align with changing privacy standards. Technologies such as the data privacy vault aim to streamline compliance efforts by offering a centralized solution for secure PII management and governance.
Sean Falconer (@seanfalconer, Head of Dev Relations @SkyflowAPI, Host @software_daily) talks about security and privacy of LLMs and how to prevent PII (personally identifiable information) from leaking out
Topic 1 - Our topic for today is the security and privacy LLMs. What’s Sean’s origin story?
Topic 2 - Let’s dig into LLM security and privacy. We see this concern a lot on the podcast and we’ve touched on it with various past shows, but we haven’t dug in deep. First, let’s frame the problem. What are we talking about when we talk about LLM security and privacy?
Topic 3 - First, there is a fear that customer PII information might leak out. Second, company IP or confidential into might leak out related to products or offerings. We’ve seen examples of both to date. This could be exposed in the form of integration into a model (query it for the answer) or in the fine-tuning or RAG stage. Either one could lead to compliance issues, lost rev etc. But, that same data at risk is the potential differentiation of the models. How do you both mask the data but take advantage of the data?
Topic 4 - One thing I’ve noticed is many orgs only think about privacy in relation to the fine-tuning stage where they are taking a broad model and making it company specific. It is about much more than that though. Just like standard software development, we have different stages. How is the data collected and stored, how is it used for training and fine-tuning, how is it used after deployment and during interaction stage, etc. How should security and privacy be handled across all phases?
Topic 5 - Let’s talk beyond LLMs for a bit. What about Data Lakes and Data Warehousing? I see this as a problem across all big data, correct?
Topic 6 - How does API security fit into this? Much of what we are talking about is at the storage and retrieval level. But, increasingly we see API issues exposing data. How does that fit in here?
Topic 7 - Let’s talk podcasts, we had Jeff, the previous host of Software Engineering Daily on a few times. How are things over at Software Engineering Daily? Tell everyone a bit about the show.