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In a world drowning in data, bigger isn't always better—especially when it comes to Cyber Threat Intelligence (CTI). As security teams struggle to keep up with bloated threat feeds and ever-growing lists of malicious IPs, a smarter approach is emerging: using Machine Learning to not only accelerate detection but to trim the fat without losing the threat. This talk dives into the cutting-edge fusion of CTI and ML, revealing how AI-driven models can intelligently reduce massive IP datasets while preserving—and even enhancing—their ability to detect real threats. We'll explore practical strategies, success stories, and pitfalls to avoid when implementing these techniques in live environments. Expect a fast-paced, thought-provoking session with real-world takeaways for anyone serious about improving detection speed, lowering resource demands, and staying ahead of the threat curve. If you're a cybersecurity leader, threat analyst, or technologist looking to level up your defenses with smart, lean data—this is the session you can’t afford to miss.