Organizations store sensitive data in countless places: cloud drives, emails, shared folders, collaboration tools, and more. Knowing where that data lives, how it’s being used, and whether it’s being mishandled is a daily challenge, especially when you're relying on manual classification, static rules, or incomplete visibility.
Concentric is a Data Security Governance Platform that uses Semantic Intelligence to understand the meaning of your data rather than relying on keywords or file names. It continuously analyzes your environment as data changes, identifying sensitive content in context without the need for regex, manual tagging, or static policies.
Autonomously discover sensitive files like PII, financials, contracts, and source code
Classify data contextually—even when no one labeled it
Monitor for risk across overexposure, misconfigured permissions, or access anomalies
Remediate quickly, with tools to revoke access, move files, tag data, and more
Concentric integrates with the platforms your team already relies on—across cloud storage, collaboration tools, databases, and security systems.
Flags sensitive files accessed, downloaded, or shared by departing employees—across cloud drives, email, Slack, and Salesforce.
Continuously monitors for overshared or public-sensitive files and flags mis-permissioned access before it spreads.
Maps and monitors PII, PHI, and financial data across your environment to help with audit readiness.
Uses Semantic Intelligence™ to find and classify sensitive content—without regex or labeling.
Identifies duplicate and outdated sensitive files to reduce risk and storage bloat.
Flags suspicious access and permissions changes automatically—so you can fix it before it becomes a problem.
Monitors Microsoft Copilot, ChatGPT, and other GenAI tools to prevent sensitive data from leaking through prompts.
Most security teams still can’t answer a fundamental question: where is our sensitive data, and who has access to it? In this episode of Tech 2 Exec, Karthik Krishnan, CEO of Concentric AI, joins Mike and Josh to explain why discovery is still broken—and how their context-aware language models are helping organizations operationalize data security in a way that actually works. We talk about why legacy approaches to data security fall short, how to evaluate whether a solution is truly AI-driven, and what steps organizations can take to reduce risk, streamline remediation, and lower costs.
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