Corelight has introduced a new set of agentic AI capabilities designed to transform how modern security operations centers (SOCs) manage detection, investigation, and response. With cyber threats becoming more advanced and security teams increasingly overwhelmed by alert volumes, the company is focusing on automation, speed, and transparency to improve operational efficiency. As part of this launch, Corelight rolled out Agentic Triage, advanced machine learning models, and deeper integrations across the AI-enabled SOC ecosystem to enable faster and more effective threat containment.

At the core of this innovation is Corelight’s Agentic Triage capability, which aims to eliminate the inefficiencies associated with manual alert analysis. Traditionally, SOC analysts spend significant time reviewing hundreds of alerts, often leading to delays and inconsistencies in threat response. However, Corelight’s new approach leverages AI-driven investigation workflows to reduce this burden. As a result, security teams can move from high alert noise to actionable, evidence-backed decisions much faster reportedly improving triage speed by up to 10 times.

“By pairing the industry’s highest-fidelity network telemetry from Corelight with an expert-governed AI agent, we are giving security teams the evidence they need to trust, verify, and act on AI-generated insights,” said Vijit Nair, Corelight vice president of product. “Only Corelight delivers true agentic AI triage in NDR, uniquely transforming overwhelming alert queues into verified, defensible investigations by applying expert playbooks to industry-leading network evidence with AI reasoning, drastically reducing time-to-triage and equipping analysts with definitive answers.”

The Agentic Triage system operates using a GenAI-driven architecture combined with expert-defined investigative playbooks. Instead of analyzing alerts individually, the platform aggregates signals into entity-based investigations. It then applies structured logic to produce a single, comprehensive triage verdict supported by transparent reasoning. This “show-your-work” methodology ensures that every decision is auditable, making it particularly valuable for regulated industries where accountability and explainability are critical.

In addition to improving investigation workflows, Corelight has expanded its integration capabilities to support faster response actions. By ingesting real-time identity data and correlating it with network activity, the platform allows analysts to connect user identities with suspicious behaviors. Consequently, teams can initiate immediate response actions such as universal logouts or password resets through integrations with platforms like Microsoft Azure AD/Entra and CrowdStrike, without switching tools. This seamless workflow reduces response time and enhances operational efficiency.

Corelight has also strengthened its collaboration within the broader AI security ecosystem. Its integration with CrowdStrike’s Charlotte AI enables automated workflows where security teams can validate alerts using real-time network evidence. This cross-platform intelligence sharing ensures that investigations are grounded in accurate, contextual data, improving both detection accuracy and response confidence.

“The question facing every CISO today is not whether to adopt AI in the SOC but rather how quickly and how comprehensively,” said Andrew Braunberg, principal analyst at Omdia. “Adding to the urgency is the weaponization of generative models by adversaries to automate reconnaissance, accelerate attacks, and evade detection. Defenders need AI that can accelerate response, and critically, that shows its work. To build trust in these solutions, explainability isn’t a nice-to-have; it’s a requirement, particularly in regulated environments.”

Beyond automation and integrations, Corelight is also addressing one of the most challenging areas in cybersecurity: detecting threats within encrypted traffic. The company has introduced a new suite of machine learning models designed to analyze behavioral patterns and metadata rather than relying on traditional decryption methods. This allows security teams to identify hidden threats such as tunneling anomalies, unauthorized VPN usage, and covert command-and-control (C2) channels.

Moreover, these models enhance detection of post-exploitation activities, including credential theft techniques like DCSync and NTDS.dit extraction. They also expand brute-force attack detection across protocols such as Kerberos, RDP, SMB, and SSH. By focusing on behavioral indicators, Corelight enables organizations to uncover sophisticated threats that traditional signature-based tools often miss.

Overall, Corelight’s latest innovations reflect a broader industry shift toward AI-driven, transparent, and integrated security operations. As cyber threats continue to evolve and attackers leverage AI to scale their operations, organizations must adopt equally advanced defenses. Through agentic AI, explainable workflows, and advanced threat detection capabilities, Corelight is helping SOC teams transition from reactive alert management to proactive, intelligence-driven security operations.

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