Large language models are fundamentally reshaping the threat landscape, acting simultaneously as an attacker's tool, a high-value target, and a new vector for insider risk. According to SANs, 81% of security professionals express deep concern over AI-powered threats, and traditional detection stacks were never built to catch them. Fortunately, adversaries leave behind an immutable footprint in the global DNS.
Join us for an exclusive session where we reveal how a comprehensive, multi-signal approach that combines global DNS intelligence enriched with web content, X.509 certificates, and IP telemetry, empowers security teams to spot and neutralize LLM-era threats before they strike. Moving beyond reactive incident cleanup, you'll learn how to track attacker infrastructure during the staging phase and shift your strategy to true prevention.
What You'll Learn:
- LLM as a Tool: Spot generative tooling fingerprints, from templated site content to batch-issued certificates.
- LLM as a Target: Defend exposed AI models and APIs against model distillation, intellectual property theft, and cognitive FQDN spoofing targeting autonomous AI agents.
- LLM as an Insider Risk: Detect grey-market token reseller usage before it fuels adversary-in-the-middle attacks and targeted phishing.
Walk away with actionable detection patterns mapped to MITRE ATT&CK®, powerful pivot workflows, and a practical blueprint for integrating multi-source infrastructure intelligence into your SOC, fraud, and AI-governance programs, all measured in outcomes your board cares about: threats blocked and analyst hours saved.
*Sponsored by Domain Tools
