Database Activity Monitoring (DAM)
Identity Fragmentation
LLM applications translate prompts into database actions under generic service accounts, blinding legacy DAM to the true actor and human intent.
Static Log Blind Spots
Rogue agents bypass SQL injection alarms by using low-and-slow semantic or vector searches to systematically drain data undetected.
Data in Motion Vulnerabilities
DSI only scans data at rest. Without visibility into active data pipelines, security teams remain blind to live machine abuse.
Identity-Aware DAM
Unmasks the true actor by binding human identities to the specific autonomous AI agents executing backend database queries.
Semantic Classification
Analyzes the underlying intent of natural language prompts to detect abnormal AI-agent behavior before data is extracted.
Real-Time Lineage
Tracks sensitive data in motion as it streams directly into LLM context windows and connected AI pipelines.
Agentic Remediation
Deploys automated, machine-speed defenses to instantly isolate over-permissioned AI agents and redact unauthorized database payloads inline.
The Matters Standard
Neutralizes prompt-driven data exfiltration and machine abuse by shifting from static logging to active agent governance.


