Prevalent AI provides a sovereign security data fabric that cleans, connects, and contextualizes fragmented enterprise security data into a knowledge graph. This enables security teams to understand not just what happened but why, while allowing AI agents to operate on trusted, in-house infrastructure. The platform supports exposure management and AI solutions built on this unified operational context.
Funding
Funding not disclosed
Founders
Product
Problem
Most enterprises operate on fragmented, contradictory, and incomplete data, forcing security teams to waste time reconciling tools instead of reducing risk. As AI adoption accelerates, this fragmented operational data becomes a cybersecurity risk because AI agents make poor decisions without the context needed to act safely.
Solution
Prevalent AI provides a sovereign security data fabric that cleans, connects, and contextualizes enterprise security data into a unified knowledge graph. This knowledge graph captures entities, relationships, dependencies, vulnerabilities, identities, decisions, and operational context across the environment, enabling decision-making that reflects real exposure and risk insights. The platform gives enterprises clarity and control by operating on infrastructure they own, keeping data within their environment and independent of shared infrastructure or model-provider lock-in. This foundation supports multiple enterprise applications, including exposure management and AI solutions deployed on trusted enterprise context rather than fragmented data.
Target Audience
Primary customers are enterprise security teams and organizations accelerating AI adoption that need to reduce risk from fragmented operational data while maintaining control over their security infrastructure.
Features
- Security data fabric that serves as a sovereign knowledge layer for cleaning, connecting, and contextualizing enterprise security data
- Knowledge graph technology that links entities, relationships, dependencies, vulnerabilities, identities, decisions, and operational context
- Exposure management capabilities for continuously identifying, prioritizing, and remediating the risks that matter most
- AI solutions deployed on trusted enterprise context, with data remaining within the customer's environment and under their control
- Infrastructure independence from shared infrastructure or model-provider lock-in