Behavioral Anomaly Detection

For the AI era

Continuous
Observability

Anomaly
Detection

Automated
Response

Reveal builds behavioral baselines for every identity — AI, machine, and human — and surfaces behavior signaling risk, so you can contain the threat before damage is done.

See A Demo

The signals of an insider threat are distributed across your environment in ways siloed tools can’t see

Agents don’t behave the same way twice

Non-deterministic by design. The signature changes every run meaning rule-based detection falls short.

The threat is in the behavior sequence

Event by event detection misses the behavioral chain and the risk signal within them.

Agents move at machine speed

Four systems in four seconds. Human-in-the-loop can’t act at AI speed. Response must be automated.

Reveal Detects The Anomalies That Matter Across the Human-to-Agent Behavioral Journey

There’s not ‘one’ normal behavioral baseline, but many

Know Identity Behavioral Patterns

Reveal’s algorithm translates logs into behavioral clusters for each identity. Each cluster represents an identity’s many behavioral patterns and norms so you get a comphrensive and dynamic baseline that evolves over time.

Reveal-Behavioral-Anomaly-Detection-Image-1

Reveal-Behavioral-Anomaly-Detection-Image-2

Not all anomalies are a threat. Detect what’s risky.

Decipher Rare, From Risky

Reveal considers action sensitivity, deviation from historical patterns, and peer comparison to reason whether anomalous behavior is actually a threat. Fewer false positives means analysts spend their time on signals that matter.

Anomaly detection and action — in one place

Contain The Risky Behavior

Every identity has a Trust Budget. As anomalous behavior compounds, the Trust Budget depletes. When the budget runs out, automated containment stops threats in real time. Reveal’s behavioral reasoning ensures high-confidence response actions.

Reveal-Behavioral-Anomaly-Detection-Image-3

How We’re Different

vs. ueba

Multiple Behavioral
Clusters per Identity

One static baseline per entity

vs. Siem rules

Behavior Reasoning, Not Signature Matching

Match events to known bad patterns

vs. soar

Dectection and Response
in One Platform

Orchestrate handoffs between
detection and response

With And Without Reveal

THE STORY:

A Salesforce Agentforce agent has handled customer support tickets without incident for weeks. After a prompt-injection embedded in an incoming customer email, its behavior subtly shifts — but every action stays in policy. The agent authenticates legitimately, queries Salesforce, pulls additonal context from the data warehouse, and posts the enriched summary to an unfamiliar external endpoint. Every event passes every rule. Every step is authroized. The chain is the threat.