Maximizing SOC Efficiency with Evidence-Driven Investigations using Corelight’s Agentic Triage

Security Operations Centers (SOCs) are under pressure to respond quickly and effectively to an ever-increasing volume of alerts. To address this challenge, Corelight has introduced a new set of agentic AI capabilities designed to streamline SOC workflows, boost analyst efficiency, and build trust through greater transparency.

Agentic Triage: A Category-First Automated Investigation Capability

At the heart of this update is Agentic Triage, a category-first automated investigation capability that helps security teams move from high-volume alert noise to evidence-backed containment. This feature is powered by a modern GenAI agent architecture and driven by expert-written investigative playbooks. Agentic Triage automatically investigates the highest-risk entities in a customer’s environment on a daily basis, consolidating signals into entity-centric investigations and applying structured investigative logic to deliver a single, evidence-backed triage verdict.

Enhanced Identity and Network Insights

Corelight’s Lux agent ingests real-time identity data to enrich and complement network evidence, correlating insights about problematic entities connected to the network. This enables analysts to connect the “who” to the “what” that is happening on the network and take response actions directly on compromised identities. Integrations with Microsoft Azure AD/Entra and CrowdStrike allow for one-click actions such as universal logout and password resets without pivoting to a separate tool.

Seamless Collaboration with CrowdStrike

In addition, Corelight has released a new integration with CrowdStrike’s Charlotte AI and Agentic Response Collaboration, which enables seamless collaboration with other AI agents across the security stack to maximize the value of network data. This integration creates a CrowdStrike Fusion workflow that allows Charlotte AI to automatically pull Corelight ground truth data to help an analyst resolve an alert by validating host behavior against network reality.

The Future of AI in the SOC

The adoption of AI in the SOC is no longer a question of if, but how quickly and comprehensively. As adversaries increasingly leverage generative AI to automate reconnaissance and accelerate attacks, defenders need AI that can accelerate response and provide transparency. Corelight’s Agentic Triage is designed to meet this need, providing a trusted and explainable AI solution for the modern SOC.

Advanced Machine Learning and Behavioral Detections

To support the advancement of AI in the SOC, Corelight has also introduced an expansion of its advanced machine learning and behavioral detections. A new suite of statistical models is designed to detect evasive, post-exploitation techniques, including tunneling anomalies and VPN anomalies, without requiring decryption. By analyzing the statistical shape and behavioral metadata of traffic, Corelight is able to transform encrypted blind spots into high-fidelity evidence.

This allows security teams to better identify covert command and control (C2) channels and lateral movement, even in environments where traditional inspection is impossible. Corelight’s new ML models detect evasive threats that traditional signatures miss by analyzing behavioral patterns across the network, flagging unauthorized VPNs, identifying uncommon tunneling activity at the subnet level, and catching credential theft techniques like DCSync and NTDS.dit dumps before attackers can pivot.

The platform has also expanded its brute force detection surface, correlating both low-and-slow and high-volume credential attacks across critical vectors including Kerberos, RDP, SMB, and SSH. Together, these models give security teams high-fidelity visibility into post-exploitation activity without requiring decryption.

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