AI and machine learning are reshaping cybersecurity by enabling faster detection, prediction, and response to increasingly sophisticated threats. This white paper explains how traditional signature-based defenses struggle against zero-day attacks, fileless malware, and polymorphic threats. It outlines how ML models analyze massive datasets to detect behavioral anomalies, predict emerging attack patterns, and reduce false positives. Real-world applications include ransomware detection, phishing prevention, insider threat identification, and automated incident response. The paper also discusses challenges such as model bias, data quality, adversarial AI, and explainability. By combining AI-driven intelligence with human expertise, organizations can strengthen resilience, improve response tim

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