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Traditional cybersecurity paradigms, primarily dependent on static signature-based detection, are increasingly inadequate against the evolution of polymorphic malware, zero-day exploits, and sophisticated insider threats. This paper proposes a robust framework for AI-based Behavioral Analysis that transitions defense mechanisms from reactive to predictive. By leveraging Machine Learning (ML) and Deep Learning (DL) architectures—specifically long Short-Term Memory (LSTM) networks and Isolation Forests—the proposed system ingests multi-source telemetry from network traffic and user endpoints to establish dynamic "normalcy" baselines.