Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Corelight Agent Builder Library: AI Investigation Demo

What happens when you ask a generic SIEM AI assistant and a Corelight-powered threat hunter agent the exact same question about a suspicious IP? The difference is not the model. It is the investigation expertise. In this demo, we walk through a side-by-side comparison using Elastic's agent builder. A default AI assistant returns a surface-level summary. An agent built with the Corelight Agent Builder Library identifies lateral movement, flags potential ransomware and data exfiltration, surfaces IDS alerts, maps involved hosts, and recommends next steps.

AI Agent Security: Detecting Risky Behavior (Live Demo)

Watch five AI agents tackle a CTF and start coordinating with one another. Learn how to detect risky agent behavior using @goteleport graphs, risk scoring, and custom classifiers. Ben Arent explores lessons from the Hugging Face incident, then demonstrates how agent identity, scoped access, and activity monitoring help secure AI agents running against infrastructure.

The AI Agent Whose Builder Already Left

Somebody in operations builds an automation inside a sanctioned platform to solve a problem in their own workflow. It works, other people come to depend on its output, and eighteen months later that person leaves. ‍ The platform still lists the automation. Nobody inherits it, because it was never anybody's asset to begin with, and the offboarding checklist has no line for a thing that was never recorded as belonging to the person departing. ‍

Agentic AI for ITOps: Is your organization ready for the security challenges?

AI is set to provide increasing value in IT by enabling more ways to simplify complex IT operations, offering actionable insights, automating routine tasks with context-aware automation, and detecting abnormal events with advanced machine learning algorithms. With agentic AI, systems can understand what is happening in an IT environment, reason about the most probable root causes, determine the right course of action, and implement these changes without requiring additional human intervention.

What OpenAI's Misalignment Reports Tell Security Teams About AI Agents

AI Agents are no longer the new insider threat. They’ve become firmly planted as the primary challenge for security leaders when it comes to insider threat activity. They have credentials, they act autonomously, and increasingly, they can take real actions on real systems. How would you know when one of your digital workers starts doing something it’s never done before?

We Researched Four AI Evidence Analysis Tools for TPRM. Here's What We Found.

Analyzing vendor evidence is a massive undertaking, which is why more third-party risk management (TPRM) tools now offer AI capabilities that let teams upload evidence and get a faster read on a security assessment. When these capabilities come up in a vendor evaluation, the conversation almost always narrows to one question: how accurate are the AI results? A tool can answer every individual question correctly and still leave you exposed.

How Content Scarcity Creates Bugs in LLM-Generated Code

Large language models are now a core part of the software development lifecycle. The 2025 Stack Overflow Developer Survey found that 82% of developers used OpenAI’s GPT models in their work last year, and Google has reported that AI now writes over 25% of new code committed at the company. All of that rests on one assumption. The model understands what you asked, and its answer is accurate.

From shadow AI visibility to AI threat detection

AI adoption is outpacing enterprise control. The 2026 Verizon DBIR found that 45% of employees regularly use AI on corporate devices, and 67% of those users access AI through non-corporate accounts. Cyberhaven Labs reports that 39.7% of data sent to AI tools is sensitive, while endpoint AI app adoption grew 509% year over year.

5 Ways to Address Claude Mythos Cybersecurity Risks

To address Claude Mythos cybersecurity risks, security teams need to adapt for a world where AI can accelerate the path from vulnerability discovery to exploitation. That means moving toward continuous exposure management, shortening the time from discovery to verified remediation, prioritizing based on real exploitability rather than severity alone, reassessing older software as new risks emerge, and making AI usage part of the organization’s broader exposure picture.