Dropzone

Autonomously investigates incoming security alerts and classifies them as genuine threats or false positives without human intervention. Uses AI to analyze alert context and determine severity and actionability.

Gathers and analyzes contextual information about security alerts by querying integrated security tools and data sources. Provides enriched investigation context to help analysts understand the full scope of potential threats.

Identifies and filters out known false positive alert patterns based on historical data and learned patterns. Reduces alert noise by automatically dismissing low-confidence or known benign alerts.

Ranks and prioritizes security alerts based on risk level, threat severity, and business impact. Surfaces the most critical threats to analysts first while deprioritizing lower-risk alerts.

Connects with existing SIEM, EDR, firewall, and other security tools without requiring replacement or major infrastructure changes. Acts as a middleware layer that enriches and triages alerts across the security stack.

Captures analyst feedback on alert accuracy and investigation outcomes to continuously improve AI decision-making. Uses human expertise to refine triage and investigation models over time.

Generates reports and metrics showing the reduction in alert volume, false positives dismissed, and analyst time saved. Provides visibility into the impact of automation on SOC efficiency.