Imagine a cybersecurity system so advanced it can understand the story of an attack as it unfolds, like a digital detective piecing together clues. That's the promise of a new approach using Large Language Models (LLMs), the same technology behind AI chatbots, to detect "hands-on-keyboard" cyberattacks. These attacks, where hackers directly manipulate systems, are notoriously difficult to catch. Traditional methods often rely on recognizing known patterns, but sophisticated hackers constantly evolve their tactics. This new research transforms raw endpoint data—logs of every process, file operation, and registry edit—into narrative "endpoint stories." These stories provide context and sequence, painting a picture of activity on the endpoint. LLMs then analyze these narratives, learning to distinguish between normal computer operations and the telltale signs of malicious intent. The results are impressive, demonstrating that LLMs can significantly outperform traditional machine learning methods in early attack detection, especially when precision is key. This approach not only enhances accuracy but represents a paradigm shift in cybersecurity. Instead of relying on static rules, the system learns and adapts to ever-changing attack strategies. While challenges remain, like balancing accuracy and responsiveness, this LLM-powered approach represents a huge step forward in protecting systems from human-driven threats. As AI continues to evolve, so too will our ability to stay ahead of even the stealthiest cyberattacks, ensuring a safer digital world.