SAN JOSE, Calif.--(BUSINESS WIRE)--Concentric AI, a leading vendor of intelligent AI-based solutions for autonomous data security posture management (DSPM), today announced it has been awarded a groundbreaking patent for its pioneering approach to data security. The patent, titled “Method and Electronic Device for Generating Semantic Representation of Document using Large Language Models to Determine Data Security Risk,” showcases Concentric AI’s leadership in the DSPM space and its advances harnessing cutting-edge AI technologies to better address the data security needs of organizations.
This patent describes Concentric AI’s unique approach to “understand” the context and content of every data record to inform and strengthen the security posture. It details the generation of a document-level semantic representation utilizing Large Language Models (LLMs), specifically deep-learning models. These representations, called embeddings, are analyzed to determine whether a document’s security posture is aligned with other similar documents, and how it should be secured.
According to Gartner®, “Foundation models (which are mostly large language models) are designed to replace task-specific models”…“Foundation models represent a huge step change in the field of artificial intelligence, due to their massive pretraining and significant improvements in accuracy across a variety of tasks.” 1
Existing tools largely ignore content and rely on metadata information such as the folder, document’s author, and document name to infer if a document might contain sensitive or important information. At best, the tools provide functionality to look for regex matches to predefined patterns. On the other hand, Concentric AI’s Semantic Intelligence™ is a comprehensive data security solution that utilizes advanced deep learning technology to protect both structured and unstructured data, as well as email, messaging text and attachments. Developed with extensive domain expertise, its deep learning model captures and builds upon the collective wisdom of Concentric AI’s massive dataset and a growing community of data owners, enabling it to identify and categorize data based on its true meaning and sensitivity.
“The popularity of models such as GPT-3 has attracted the attention of CIOs, CTOs, CISOs and data and analytics leaders, who are looking to exploit their potential for business use cases,” said Karthik Krishnan, Founder and CEO, Concentric AI. “Being the only company to leverage Large Language Models gives Concentric AI the advantage in enabling organizations to protect their data with unmatched contextual understanding, all in an accurate and efficient manner needed to address today’s complex and ever-growing data environments and their associated security risks. These models allow enterprises to move past regex or pattern-based discovery of sensitive data to understand semantic meaning at unmatched efficiency while maintaining high accuracy and minimal false positives and false negatives.”
This patent is the first in a series of patent disclosures that underlies Concentric AI’s DSPM solution available in the market today. LLMs have garnered wide attention in the past several months because of GPT and ChatGPT. Concentric AI’s Semantic Intelligence was the industry’s first product to leverage the power of LLMs to drive real value for clients by improving their data security posture, enabling it to analyze the entire content and capture the semantic meaning of each data record.
“Our newly patented method furthers Concentric AI’s market lead by enabling our solution to understand the context and content of customers’ data better than competing solutions,” added Krishnan. “Being granted this patent validates our unique method of discovering and categorizing sensitive data and shows that our technology backs up our messaging. We have an innovative product with a great engineering team that has now been validated, recognizing our pioneering efforts as we continue to innovate and lead the market with our category-defining product.”
Concentric AI’s DSPM solution scans organizations’ data, detects sensitive or business critical content, identifies the most appropriate classification category, and automatically tags the data. Concentric AI uses artificial intelligence (AI) to improve discovery and classification accuracy and efficiency to avoid endless regex rules and inaccurate end user labeling. In addition, Concentric AI can monitor and autonomously identify risk to financial and other data from inappropriate permissioning, wrong entitlements, risky sharing, and unauthorized access. It can automatically remediate permissions and sharing issues or leverage other security solutions and cloud APIs to quickly and continuously protect exposed data.
Note 1 – Gartner, Inc. “Innovation Insight for Artificial Intelligence Foundation Models,” by Arun Chandrasekaran, Magnus Revang, Arnold Gao. Oct. 27, 2022. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
About Concentric AI
With Concentric AI, organizations can finally address their unmet data security needs by discovering and protecting business-critical content. Concentric AI protects intellectual property, financial data, PII/PCI content, customer data, business confidential content and more, across on-premises and cloud-based data stores, as well as messaging and communication applications. The Concentric AI Semantic Intelligence™ Data Security Posture Management solution uses deep learning and Risk Distance™ analysis to accurately categorize data, assess risk, and remediate security issues – without relying on upfront rules or complex configuration. Concentric AI is venture-backed by leading Silicon Valley VCs and is headquartered in San Jose, Calif. For more information, see https://www.concentric.ai.
Concentric AI, Semantic Intelligence™, and Risk Distance™ are or may be registered trademarks of Concentric AI, Inc. All other marks and names mentioned herein may be trademarks of their respective companies.
