Threat Hunting

AI-driven proactive threat hunting to detect and respond to hidden cyber threats faster

Business impact

• Mean time to detect (MTTD) — AI accelerates identification of threats, shortening detection intervals
• Mean time to respond (MTTR) — Automated response reduces time to contain and remediate incidents
• Operational efficiency — Automation lowers manual effort and improves security team productivity
• Threat detection rate — Improved analytics increase the accuracy and coverage of threat identification
• Incident response time — Faster analysis and action reduce overall incident handling duration
• False positive rate — AI models reduce noise by filtering irrelevant alerts effectively
• Security posture — Proactive hunting strengthens defenses and reduces risk exposure
• Cost reduction — Lower operational costs through automation and fewer security incidents

Data requirements

• Network logs (Text) — Used to detect anomalous traffic patterns indicating threats
• Endpoint telemetry (Structured) — Provides device-level activity data for behavioral analysis
• Cloud environment logs (Text) — Monitors cloud assets for suspicious activities and misconfigurations
• Threat intelligence feeds (Text) — Supplies known indicators of compromise and attacker tactics
• Security Information and Event Management (SIEM) data (Structured) — Aggregates and correlates security events for analysis
• User activity logs (Structured) — Helps identify insider threats and unusual user behavior
• Natural language sources (e.g., chat, forums) (Text) — Analyzed for emerging threats and attacker chatter

AI methods and techniques

• Predictive AI — Forecasts potential threats based on historical and behavioral patterns
• Generative AI — Generates hypotheses and investigative leads for threat hunting workflows
• Agentic AI — Automates triage, investigation, and response with minimal human input
• Symbolic AI — Applies rule-based reasoning aligned with frameworks like MITRE ATT&CK

AI models and model families

GPT-4o, Gemini, Claude, LLM workflows, Graph Neural Networks

Industries

Real-world evidence

6 documented case studies on record.

Companies using this: CMC Energy, Coca Cola, Ernst & Young LLP, George Washington University, Google, Wiz.