(US & Canada) Sid Dutta, CEO and Founder of Privaclave, speaks with Robert Lutton, VP, Sales and Marketing at Sandhill Consultants, in a video interview about the risks around rapid generative AI (GenAI) adoption, and RAG architecture and the concerns around it.

Dutta highlights that the rapid adoption of large language models (LLMs) by development teams often outpaces the implementation of robust security measures, exposing enterprises to significant privacy and security risks. Organizations frequently underestimate the vulnerabilities inherent in these technologies, including prompt injection, model denial-of-service, model theft, and supply chain issues.

From a privacy standpoint, Dutta points to three critical vulnerabilities — training data poisoning, insecure handling of outputs, and unintended disclosure of sensitive information. For instance, using sensitive or personal information during model training can lead to overfitting, memorization, and unintended data leaks, particularly when data scrubbing practices are insufficient.

Another key concern involves the accidental exposure of sensitive data by users through prompts, which can be exploited to bypass input filters or access restricted information. This creates scenarios where sensitive inputs provided by one user could unintentionally appear in outputs accessible to others.

To mitigate these risks, Dutta underscores the importance of robust data sanitization practices and vigilant handling of both inputs and outputs. Since interactions with LLMs establish a two-way trust boundary, enterprises must prioritize securing this interface to protect user data and maintain privacy integrity.

According to Dutta, organizations are increasingly adopting Retrieval-Augmented Generation (RAG) architectures to enhance the quality of outputs generated by large language models (LLMs). This technique aims to improve the accuracy, reliability, and relevance of LLM responses, particularly when dealing with information the models were not trained on, including sensitive or private enterprise data. By retrieving relevant documents and incorporating them as contextual input, RAG enables LLMs to deliver more informed and precise results.

However, Dutta highlights significant concerns surrounding this architecture. A key issue lies in how private information is integrated into these workflows. While LLMs typically operate on publicly available or previously trained data, enterprises may enhance prompts by incorporating private data stored within their systems. This augmented input allows the LLM to generate outputs that are more specific and contextual but raises the risk of exposing sensitive information.

A critical component of RAG is the use of vector databases, which store embeddings — numerical representations of data derived from private information. Dutta, warns that these embeddings, while not direct copies of the original data, can sometimes be reverse-engineered into near-accurate reconstructions through inversion attacks. Given the relatively nascent state of vector database security, these repositories could become prime targets for malicious actors.

Furthermore, Dutta notes that RAG workflows inadvertently expose overshared or sensitive data from various repositories, such as SharePoint, Google Drive, CRM systems, ERP platforms, and HR databases. This unintentional leakage underscores the pressing need for robust security measures to safeguard enterprise data within RAG implementations.

Dutta highlights that vector databases, while instrumental in managing diverse domain-specific information through embeddings, lack mechanisms to enforce granular data access controls. This absence creates challenges, as sensitive data could be unintentionally accessible to unintended users, leading to potential oversharing. He emphasizes that while AI systems are powerful in delivering relevant insights to the right people, they equally expose this data to malicious actors, making vector databases a novel attack surface.

The risks are amplified in Retrieval-Augmented Generation (RAG) workflows that integrate private data with third-party large language models (LLMs). Even when LLMs are operated on systems directly managed by an organization, the expanded attack surface and potential vulnerabilities present significant concerns.

CDO Magazine appreciates Sid Dutta for sharing his insights with our global community.