NHIDR™
Real-Time Detection & Response for Non-Human Identities
Detect compromised NHIs, behavioral anomalies, and identity misuse before they lead to exposure, lateral movement, or production risk.
Detects threats. Guides the fix.
Activity Baseline
Learns normal behavior for each NHI and agent, including who uses it, where it runs, how often it is used, and what it typically accesses.
Anomaly Detection
Flags risky deviations such as new devices, unknown consumers, abnormal usage patterns, or privilege changes, then correlates and prioritizes them by severity.
Explainable Investigation
Connects signals into a clear explanation of what happened, what the identity can reach, why it matters, and the safest next step to remediate it.
See what static posture cannot
Real-world NHI compromise happens in motion. A token is used from a new machine. A service account performs a sensitive action for the first time. An agent touches systems outside its expected scope.
NHIDR™ helps security teams detect and triage those moments in real time by combining behavioral monitoring with deep identity context.
Understand identity behavior minus the noise
NHIDR™ creates a baseline for every NHI and AI agent, detects behavior that breaks from that baseline, and applies LLM-driven analysis with identity context like ownership, purpose, related credentials, and recent activity to keep false positives to a minimum.
Close the remediation loop. Fast.
Detection is only step one. Entro turns threat signals into actionable remediation through automated playbooks, native alerting, and SOAR integrations.
Notify owners, enrich incident investigation, disable risky access, revoke tokens or trigger follow-up actions without taking steps that risk breaking production.
What NHIDR™ helps you detect
Compromised identities
Detect signs that a secret, agent or NHI is being abused after exposure, theft, or misuse.
Suspicious privilege use
Catch access attempts or sensitive actions that do not match an identity’s normal role, scope, or expected purpose.
New or unknown consumers
See when an NHI is suddenly used by a different machine, workload, human user, or AI agent.
Agentic drift
Monitor how AI agents use identities and access resources, and detect when they begin acting outside expected patterns and policies.
Abnormal usage patterns
Identify meaningful deviations in timing, frequency, source, or behavior that break from an established baseline.
Risky change over time
Spot gradual behavioral drift, stale access, and identities that become more dangerous as permissions, usage, or ownership changes.
