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The Invisible Line: Securing Endpoints in a World Without Boundaries

Endpoints are no longer just devices like laptops and smartphones; the lines keep moving and endpoints are suddenly everywhere and nowhere at once. AI is redefining endpoint security by predicting threats and adapting in real-time. But as defenses strengthen, so do attackers. This session explores how AI redraws the security landscape, with real-world examples and strategies for staying ahead.

We once saw endpoints as predictable: laptops, desktops, smartphones—each a contained node we could secure. But those days are over. The lines we drew around devices have dissolved into something invisible—if they ever existed at all. Security now feels like trying to grasp air.

Enter Large Language Models (LLMs), which are rewriting endpoint security. LLMs are doing more than spotting anomalies—they’re predicting them. By processing vast amounts of global threat intelligence (CVE, CWE, NVD) and local organizational knowledge, these models adapt to specific conditions, ensuring defenses aren’t just reactive but contextualized and proactive.

Imagine a new malware variant appears. Instead of waiting for patches or relying on static defenses, LLMs pull from global and local knowledge to generate real-time, customized responses. These models learn and anticipate threats, enabling defenses to evolve dynamically. AI-driven security moves beyond traditional detection methods, offering near-autonomous protection that adapts in real-time.

Yet, as AI strengthens defenses, it also strengthens attackers. AI-generated malware morphs at speeds traditional patching can’t match, blending into legitimate behavior so seamlessly even the best models can struggle to differentiate. This new reality requires security that thinks and acts in tandem.

Through reasoning and action frameworks, AI-driven models break down threats step by step, recalibrating defenses dynamically. This ensures that as attackers adapt, defenses can respond in real-time, reducing vulnerabilities before they’re exploited.

This talk isn’t about quick fixes. It’s about understanding that endpoints—whether IoT devices, cloud APIs, or containerized apps—are no longer static. They’re shape-shifters, and securing them requires defenses that can shift just as fast. We’ll explore real-world examples of AI-driven security and confront the hard questions: How do we stay ahead when the lines between legitimate use and attack blur? How do we evolve security models fast enough to counter AI-driven threats?

By the end, you’ll see the endpoint as a moving target. You’ll leave with a deeper understanding of how LLMs can help secure it—not with static solutions but with AIdriven adaptability in a world where the endpoint is everywhere, and nowhere, at once.