Sentra Empowers Enterprises to Curb Data Risks at Scale with Addition of Large Language Models to Classification Engine

  Enterprises can now more accurately classify and understand sensitive unstructured data in complex data risk landscapes

Sentra, the cloud data security leader, announced that large language models (LLMs) are now included in its data classification engine, enabling enterprises to accurately identify and understand sensitive unstructured data such as employee contracts, source code and user generated content. With LLMs now built directly into Sentra’s data security platform and classification engine, enterprises have the technology required to proactively reduce the data attack surface.

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By clearly understanding the business context of unstructured customer data, Sentra’s new approach to classification also enables enterprises to better align with compliance benchmarks, including GDPR, CCPA and HIPAA. This reinforces Sentra’s core mission of providing end-to-end data security no matter where data sits in multi-cloud environments.

Today’s complex, multi-cloud data landscape makes it challenging for enterprises to accurately classify and build a comprehensive catalog of sensitive unstructured data with a meaningful business context. Additionally, enterprises who rush to harness the power of AI need to safeguard against unauthorized users or applications manipulating LLMs while ensuring that they can detect and respond to security risks related to training AI models.

“Sentra is committed to innovating and paving the way for strong cloud data security in order to diminish data risks,” said Yoav Regev, co-founder and CEO at Sentra. “By taking a laser-focused approach to cloud data security, Sentra gives enterprises confidence when classifying large volumes of sensitive enterprise data at scale. With the addition of LLM technology, security teams can more accurately detect sensitive information, enabling them  to root out data risks wherever they exist.”

With the increasing number of regulation and privacy frameworks, leveraging LLMs allows Sentra to automatically understand proprietary customer data with additional context like data sovereignty and region, how the data will be used, and how it should be protected. For example, a company can create data security policies that ensure employee agreements are only accessed by HR or that legal contracts are stored within a legal department’s SharePoint site. Ensuring the highest level of security, Sentra only scans data with LLM-based classifiers within the cloud premises of the enterprise.

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Once a comprehensive data catalog is in place, Sentra’s ability to provide prioritized risk scoring takes multiple data layers into account, including data access permissions, activity, sensitivity, movement and misconfigurations. This gives enterprises greater visibility and control over their data risk management processes.

“As the world continues to explore the applications of generative AI and large language models, it is important for the cybersecurity industry to not only innovate new applications of LLMs, but integrate LLMs to enhance existing security technologies where appropriate,” said Ken Buckler, research analyst at Enterprise Management Associates. “Generative AI holds great potential to improve security risk detection and response, especially when it comes to human behavior. While it is important that we proceed with caution in this brave new AI frontier, we must do so with urgency, as cyber adversaries are also exploring generative AI’s potential applications.”

Key developments of Sentra’s classification engine include LLM-powered scanning of data asset content and analysis of metadata, like file names, schemas, and tags. Sentra empowers enterprises to train their LLMs and plug them into Sentra’s classification engine to classify proprietary data better.

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