Embed Agentic Security Platform

Inserts AI-generated analysis, triage decisions, and enrichment into existing SIEM and SOAR case management workflows rather than requiring analysts to use a separate interface.

Assembles chronological attack timelines from raw events across multiple data sources automatically, reducing the time to build an initial incident narrative.

Applies ML classification to incoming alerts to filter false positives, group related events, and route high-confidence detections to analysts, reducing L1 analyst workload.

Accepts natural language queries over security telemetry and translates them to structured queries, enabling investigation without requiring analyst proficiency in SPL, KQL, or SQL.

Suggests the next investigative or containment steps for an alert or incident, with the supporting reasoning, so analysts can confirm and act rather than deciding from raw telemetry alone.

Generates investigation summaries and incident reports for analysts and leadership from completed investigation activity.

Recommends the next response actions to take based on investigation findings.

Performs initial triage of incoming alerts automatically, classifying and prioritizing them to cut tier-1 workload before a human touches the queue.

Identifies and dismisses false-positive alerts with documented rationale, reducing noise reaching human analysts.

Investigates alerts end-to-end from trigger to verdict and closes them out autonomously, so the full volume of raw alerts gets analyzed without resource-constraint concessions.

Lets analysts drive investigations and threat hunts through natural-language questions instead of query languages.

Automatically gathers and attaches context (threat intelligence, asset and identity data) to alerts during triage and investigation.

Reconstructs attack timelines and maps alert activity onto attack paths so scope and impact of an incident are clear.