Data Security Intelligence (DSI).
DSI tools run point-in-time snapshot scans via read-only cloud APIs. Excellent for cataloging data at rest and establishing compliance baselines, but decoupled from live execution. When an employee interacts with an unvetted public LLM, snapshot-based systems remain blind until the next scheduled scan.

DAM and Network DLP.
Database Activity Monitoring (DAM) isolates visibility to transactions inside the database engine and is built around human patterns. When an autonomous AI agent queries production databases, traditional DAM struggles to parse the context, creating a blind spot around non-human intent. Once data is written to a local CSV file, visibility ends. Network DLP similarly relies on brittle regex strings that fail to parse local endpoint and application-to-application transfers.

Device-Level Instrumentation for Human and AI Intent.
Data movement occurs at the system execution layer via operating system calls. Matters instruments the endpoints, cloud compute layers, and SaaS touchpoints where actions occur. By monitoring at the system call level, we stitch user behavior, file hashes, parent-child process relationships, and network destinations into a unified lineage graph. Accessing the device layer is the only way to establish true human or AI intent.

Generative AI introduces complex data transformation paths. Enterprise IP is rarely just downloaded; it is summarized by local models, processed through autonomous scripts, and embedded into production vector databases via RAG workflows.

Traditional platforms view these transformed assets as entirely new, unrelated files, losing track of the sensitive data once its shape or encoding changes.

Because Matters tracks data movement via continuous file lineage and behavioral tracking at the device layer, it preserves context through every transformation. Whether data is ingested by an internal RAG pipeline, manipulated by a third-party MCP tool, or packaged into an LLM prompt, the platform identifies the true origin, evaluates both user and AI agent intent, and enforces enterprise policy before execution.

Data security has evolved past static inventory exercises. Knowing where your data resides is a useful starting point, but complete security requires real-time control over how that data behaves, how it flows through your ecosystem, and how your AI infrastructure utilizes it.

We are building the next fundamental enterprise security category from the ground up.