Adaptive AI Agents for Real-Time Decision-Making in Automated Cybersecurity Operations
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Abstract
Cybersecurity threats have become increasingly sophisticated, requiring faster and smarter defense mechanisms. This research introduces an adaptive AI agent framework for real-time decision-making in automated cybersecurity operations. The proposed system employs deep reinforcement learning and anomaly detection techniques to identify, analyze, and respond to cyber threats autonomously. Performance evaluation demonstrates high detection accuracy, reduced response time, and enhanced protection against zero-day attacks compared to traditional automated security tools.
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References
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