Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every security vendor says their data is better. Corelight decided to test that claim directly. Using real nation-state attack scenarios, including Salt Typhoon-related activity, the same AI model was evaluated against multiple security data sources to measure investigation accuracy, threat visibility, and incident response coverage. The only variable was the data.

At Black Hat last year, we sat down with Kevin Mandia to talk about what's coming. His take: offense is going to accelerate with AI. Not slow down. Not plateau. Accelerate. When you've run more red teams than practically anyone on the planet, the pattern is clear. Getting into a victim network is already a race. AI compresses those time frames further. The attack surface isn't changing. Misconfigurations, things that slipped, controls that were on and got turned off. The entry point stays the same. AI just makes the race to exploit it faster.

See how Veriato IRM delivers behavioral visibility to help organizations detect and prevent insider threats before they escalate. This demo showcases Veriato’s Insider Risk Management platform in action, including AI-driven risk scoring, behavioral analytics, sentiment analysis, anomaly detection, and real-time alerting. Learn how security, compliance, and leadership teams can move beyond reactive monitoring to proactively identify risky behavior, investigate incidents faster, and strengthen organizational resilience.

Behavioral visibility is no longer just a security requirement. It is essential business intelligence. This demo showcases how Veriato delivers organization-wide visibility across security, HR, compliance, legal, finance, and operations teams. By capturing and analyzing behavioral data in real time, Veriato helps organizations uncover risk, improve operational clarity, and make smarter decisions. Inside this demo.

As enterprises shift from conversational to agentic AI, the real risk moves from model outputs to the action layer; the MCP servers and APIs through which agents execute real-world tasks. The Agentic Security Graph frames this risk across three interconnected layers (LLM, MCP servers, APIs), showing how compromises at any layer can propagate and why existing LLM-focused controls leave the most consequential surface unmonitored.