Tracking DataFlows to LLMs

Product and engineering teams at large enterprises are embedding large language models like OpenAI and Llama 2 natively into their offerings, aiming to elevate both employee productivity and the end-user experience. They leverage their enterprise’s internal data to refine and apply techniques like fine-tuning, retrieval augmented generation (RAG). They do this to ensure their solutions are tailor-made for specific needs, use the most up-to-date information, and reduce instances of the model making unsupported claims.

But with rapid integration comes responsibility.

Integrating LLMs presents enterprises with 3 critical considerations:

1. Proprietary Data Exposure
   Using third-party LLMs might risk exposing a business's private information. This could potentially reveal vital company secrets and diminish their competitive advantage.
   
2. End-user Data Privacy
   Users' personal details can be at risk when they interact with LLMs. Ensuring this information remains confidential and protected is essential.
   
3. AI Governance
   With LLMs integrated across various products, platforms, and tools, tracking how data is used becomes challenging. Organisations need to ensure data shared with these models adheres to AI governance policies and frameworks. Important considerations include: What data is used for training? Why is specific data shared? Are strong security measures in place? Proper governance ensures that AI models are transparent, trustworthy, and behave in anticipated manners.

The earlier we address these issues in the development process, the less chance there is for governance issues and data leaks. This is where tools that transparently track these dataflows during software development life cycle become indispensable, ensuring LLMs are harnessed both powerfully and responsibly.

Tracking sensitive dataflows to LLMs

For engineering teams, the journey of integrating Large Language Models is multi-layered. It starts by gathering data from databases, user forms, or API endpoints, etc - these are the entry points. They then process this data to make it understandable for the LLMs. Once ready, they initiate the API or function call. After receiving the result, they process it for various uses, either to respond to users or save it in a database for future tasks.

Privado's proprietary Privacy Code Scanning AI engine reviews application code to identify "sources." These are variables and objects that process personal data from ingress points, such as databases, user forms, and API endpoints. It also detects "sinks," which are destinations where this data flows to, and maps the data flows between sources and sinks for clarity.

Privado is adept at identifying sinks, which in the context of AI, refers to points where data is sent to an LLMs through API calls and function invocations, stored in a vector store as an embedding, or bulk passed to a ML training platform (like Amazon SageMaker or HuggingFace Trainer) to be used for LLM training or fine tuning.

From Challenges to Confidence

Enterprise AI with Privado

Using Privado, enterprises can track all of the sensitive data that is being shared with LLMs. As Privado is integrated into the software development life cycle, enterprises get visibility during the development cycle before it goes to production, giving enterprises much needed control to enforce data protection policies and AI governance.

Here's how Privado simplifies and accelerates AI integration for enterprises:

1. Visibility: Privado lets you see sensitive dataflows to LLMs across all spots, helping you understand exactly where and how sensitive information is used.
2. Data Protection: Feel confident knowing your business data is shielded.
3. User Privacy: Privado ensures personal user details are kept safe and confidential.
4. AI Governance: Easily set rules on how sensitive data should be used in GenAI applications, ensuring they work the way you want them to.
5. Issue Detection: Stay ahead by spotting and fixing problems related to using sensitive data in LLMs swiftly.
6. Effective Oversight: With Privado, running a solid AI governance program becomes smoother and more efficient.

With Privado on their side, enterprises can dive deep into the AI waters without the usual worries, making the integration of LLMs both safe and effective.