Incident response

Agentic AI triage accelerates incident response by automatically consolidating alerts into an entity-centric investigation. Expert-authored playbooks collect evidence, explain findings, and prioritize risk, enabling analysts to respond faster with confidence, consistency, and defensible decisions during active incidents. 

Corelight provides authoritative network evidence to map incident impact. By indexing file metadata from network traffic, teams can trace payload propagation, identify every affected host, uncover lateral movement across SMB and RDP, and deliver an accurate, evidence-backed scope assessment.

Integrate with SOAR, EDR, and firewalls to enable rapid containment. Analysts can automate actions such as isolating compromised hosts and blocking malicious traffic, using Corelight evidence to trigger these responses, prevent lateral spread, and limit impact.

Support post-incident network forensics using protocol logs, file metadata, and Smart PCAP. Analysts can reconstruct sessions, validate attacker behavior, and trace data movement to produce defensible evidence for incident reports and compliance needs.

Corelight automatically synthesizes complex, multi-stage attack activity into a single, coherent investigation timeline. By correlating network evidence across protocols, hosts, and time, responders gain clear visibility into attacker actions, accelerate understanding, and drive faster, more confident containment and remediation decisions.

Integrates with SIEMs, SOARs, XDRs, and cloud security tools to enrich alerts with network context. Consistent, Zeek-based telemetry across on-premises and cloud environments ensures that network intelligence is embedded directly into SOC workflows.

A critical step in the alert triage process is aggregating and prioritizing alerts by risk to effectively filter out noise. The process should consolidate hundreds of isolated alerts into a single, entity-centric investigation. AI-assisted triage and guided workflows can automate this by collecting evidence, explaining findings in plain language, and prioritizing risks. This allows analysts to validate findings and act on defensible evidence instead of repeating manual review steps.

To accelerate incident response, security teams should leverage autonomous AI investigations and instant forensic pivots that don't require switching tools. By linking alerts directly to relevant network logs and packet capture (PCAP) data via unique identifiers, analysts can instantly move from a high-level alert to deep forensic data. Response times can be significantly reduced by automating data collection and utilizing integrated, one-click responses, such as isolating compromised hosts through EDR platforms. 

Organizations can significantly reduce their MTTR (mean time to respond) and MTTD (mean time to detect) times by implementing an Open NDR Platform with AI-powered workflows. Relying on comprehensive network detection and response evidence rather than fragmented alerts helps eliminate investigative dead ends. Furthermore, having real-time access to synthesized log summaries, automated alert scoring, and actionable next steps enables analysts to move from high-level alerts to raw packets in seconds, dramatically speeding up the response lifecycle.

False positives can be minimized by applying a multi-layered detection strategy that combines AI, machine learning, behavioral analytics, and curated signature-based detection to prioritize aggregated alerts based on actual risk. By integrating alerts, network telemetry, and PCAP data via unique identifiers, the triage process is streamlined, naturally filtering out noise so security analysts can focus exclusively on the alerts that truly matter. 

Finding evasive threats, such as "living off the land" techniques that bypass traditional endpoints, requires deep internal network visibility to uncover lateral movement. Defenders can expose these evasive threats by fingerprinting encrypted connections using techniques like JA3/JA3S hashing and behavioral analytics. This allows teams to track sophisticated threats that blend in with normal HTTPS, SSH, or RDP traffic, providing a clear path for investigations even when decryption is not feasible.