Concentric AI

Concentric AI automatically discovers, classifies, and protects sensitive data across enterprise systems to prevent breaches and ensure compliance.

Description

Concentric AI offers a data security platform that automatically identifies and protects sensitive information—such as personal data and intellectual property—across both cloud and on-premises systems. Using advanced AI, it detects risks without relying on predefined rules and integrates seamlessly with tools like Microsoft 365, Snowflake, and Salesforce. The platform provides behavioral insights, access controls, and automated data loss prevention, helping security teams in industries like healthcare, finance, and enterprise IT improve compliance and safeguard critical data.

Customers

• Baron Capital
• Oceaneering
• Cadence
• Nutanix
• Ellement
• Dartmouth
• Petco

What Problem Does Concentric AI Solve?

Organizations struggle to track and secure sensitive data scattered across dozens of cloud apps and on-premises systems, leaving security teams blind to where customer records, financial data, and IP actually live. This creates massive compliance violations and data breach risks that can cost millions in fines and lawsuits. Concentric AI automatically discovers and classifies all sensitive data across an organization's entire infrastructure, then flags risky access patterns and remediates threats without manual intervention.

Pros

• Autonomous Data Classification: Concentric uses AI to autonomously discover, classify, and assess risk across structured and unstructured data without manual tagging.
• Business Context Awareness: Understands the context and sensitivity of data—such as financials, customer records, or IP—to inform risk scoring and policy enforcement.
• Non-Intrusive Deployment: Operates without agents or endpoint interference, making it scalable and easy to deploy across cloud and on-prem environments.

Cons

• Limited Behavioral Controls: The platform focuses on data classification and visibility but offers fewer tools for automated remediation or enforcement.
• Context Interpretation Challenges: In edge cases, AI-driven classification may mislabel nuanced or ambiguous documents, requiring human review.
• Integration Complexity: Connecting with legacy systems or highly customized data architectures may involve added setup and configuration effort.

Investors

• Top Tier Capital Partners
• HarbourVest Partners

Last updated: April 16, 2026