Earlier this year, I was deep into researching past mergers and acquisitions in the data security space when I ran into a snag. I couldn’t remember the name of a startup that had been acquired ages ago. After exhausting my online search options, I decided to give ChatGPT a shot. I fed it the snippets of info I had, and—voilà!—it nailed the company name almost instantly. What would have taken me hours of manual digging was accomplished in minutes, all thanks to generative AI. This moment was a powerful reminder of how this technology is reshaping our world.
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Given this capability, it’s no wonder businesses are eager to tap into GenAI to unlock new value from their vast repositories of proprietary and confidential data. Yet concerns are mounting within information security teams about the risks tied to handling private and regulated data. Many companies have even pushed the brakes on using public GenAI services like ChatGPT because of security and compliance worries. Enter retrieval-augmented generation (RAG)—a technique that combines the power of large language models (LLMs) with a clever approach to data management, promising to address these concerns.
RAG brings together generative models with information retrieval systems to deliver highly context-aware and accurate content. Additionally, it doesn’t require companies to surrender large quantities of their sensitive data to a 3rd party for fine-tuning of an LLM because it can make use of existing data without additional data sanitization and labeling. It also has relatively modest computational and storage requirements that allow companies to run the system within existing IT or cloud infrastructure using open-source LLMs. At first glance, being able to own the infrastructure appears to address any security or compliance concerns.
But here’s the catch: maintaining control of infrastructure doesn’t necessarily mean you’re in control of the data or the results it generates. Unlike traditional systems with clearly defined access controls, GenAI systems can inadvertently leak sensitive information through cleverly crafted prompts. Imagine asking a database admin for a report on data access permissions. They’ll deliver a precise breakdown of who has access to what. Now, try the same request with a GenAI admin, and you might get a blank stare. This lack of transparency and control is a significant hurdle for information security teams, who are bound by rigorous compliance standards.
Ensuring the safe usage of sensitive data can enable enterprises to truly unlock the potential of GenAI for their applications without taking on undue risks of data exposure or breaches. Let’s dive into how to secure data used for RAG and regain control over data access to ensure compliance.
What RAG Actually Is
Before diving into data security for RAG, let’s look into what RAG actually is and how it stands out from other approaches. When using LLMs like ChatGPT, you’re essentially giving a prompt to an LLM, which then generates a response based on its training on public data. The results are bound by the limitations of this training set.
RAG, however, takes this process a step further. It captures your prompt and enriches it with relevant context from your own data before passing it to the LLM. This approach allows the LLM to gain a deeper, more nuanced understanding by incorporating private or sensitive information not part of its initial training. In essence, RAG enables you to harness the LLM’s language capabilities to delve into your own data and extract precise answers. It employs a “retriever” to navigate an indexed version of your dataset, selecting the most pertinent information to enhance the context of your prompt.
Additionally, access control on the responses generated by RAG systems is often lacking. Users with different roles need to interact with the RAG application, but while access controls could manage data access in the original data stores, enforcing these controls becomes difficult once data is mixed and indexed.
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The challenge is compounded by how GenAI systems produce responses—processing information token by token—which means access control decisions must be made for each segment of data included in the response. Furthermore, the risk of GenAI producing erroneous or misleading information, known as hallucinations, adds another layer of concern about the potential leakage of sensitive data.
How to Implement Data Security with RAGs
While the compliance problems for GenAI seem daunting, there is a very practical approach to securing the sensitive data values, and the approach draws its roots from security data for analytics. If we view the RAG pipeline through the lens of traditional data pipelines, it starts to make sense. Essentially, RAG can be seen as an evolution of data pipelines where the data warehouse or lakehouse is replaced with a document index, and the analytics application is swapped out for a retriever and LLM.
Similar to RAG and GenAI, data analytics faces challenges with privileged user access and maintaining control over data when it’s aggregated from various sources into one system. The key to addressing these issues in analytics lies in encrypting data at the field level—such as through an inline encryption proxy—before it even reaches the aggregated data repository. Encrypting at the source allows for a more informed decision about which data requires protection, ensuring that sensitive information remains secure no matter where it travels, whether to other applications or analytics projects. This approach, known as data-centric protection, provides greater assurance that the appropriate data values are safeguarded from the very beginning of the data pipeline.
Once encrypted, the data can move through the system without compromising security. When accessed from an analytics app or RAG system, a decryption proxy can handle the decryption process, granting access based on role-based access control (RBAC). This proxy offers a fine-tuned access control mechanism, providing users with either direct access or a masked version of the data depending on their roles and permissions.
One caveat of early field-level encryption is that it might impact data utility. However, compliance regulations primarily focus on PII and other sensitive data, which should be protected regardless of the effect on data results. Even if some responses from the RAG system are affected, safeguarding regulated data ensures compliance while still enabling the use of private data for most RAG scenarios. It’s about finding that sweet spot between security and utility.
RAG is poised to revolutionize content creation and personalization by integrating private datasets with public information. However, maximizing its potential requires a strong focus on data security and compliance. By adopting data-centric protection strategies, similar to those used in enterprise data pipelines, businesses can navigate compliance challenges while fast-tracking their GenAI and data analytics efforts. Baffle provides a seamless, no-code solution with encryption proxies that offer field-level encryption and RBAC, without the need for modifications to your existing applications. To learn more about how Baffle can support your GenAI initiatives, visit our website and schedule a consultation with one of our experts.
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