Insider Threat Detection and Response System Utilizing Offline Behavioral Analytics and Intent Classification

This study introduced OZONE DLP—an Insider Threat Detection and Response System built as a modern endpoint security framework for the insider-risk mess that companies and schools keep running into. The problem is getting louder, not quieter. As organizations lean harder on cloud services, hybrid work setups, AI-driven tools, and nonstop digital communication, the old-school security playbook just doesn’t cut it anymore when sensitive data can be exposed by an insider—on purpose or by sheer carelessness.   This framework goes for a different kind of endpoint security: decentralized, able to run offline, and still stubbornly protective when the network gets flaky. It locks things down at the Windows API layer, watches behavior in real time, syncs with the cloud only when it makes sense, and leans on machine-learning threat analysis to spot trouble fast. Then it reacts—immediately. No waiting around.Traditional EDR tools? They’re often glued to constant cloud access. Pull the plug on the internet, jam the connection, or let the network wobble, and their “always-on” promise starts to look shaky. OZONE DLP doesn’t play that game. It keeps monitoring. It keeps enforcing. Even if someone tries a deliberate internet-blocking attack.Where could this head next? A few upgrades are on the table. Federated Learning, so endpoints can learn without shipping raw data back and forth. Reinforcement Learning to tune responses—because blunt, one-size-fits-all reactions get old fast. Lighter kernel-level monitoring too, so you gain visibility without dragging performance through the mud. Blockchain-backed forensic checks might help prove evidence hasn’t been tampered with. And an AI-driven policy engine that adapts on the fly—because static rules age like milk.

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