Autonomous NAC with Behavior-Based AI for Government and Financial Institutions
Abstract
This paper explores the development of an autonomous Network Access Control (NAC) system powered by behavior-based artificial intelligence tailored for government and financial institutions. These sectors demand the highest levels of security due to the sensitivity of their data and critical infrastructure. The proposed system leverages AI to continuously monitor user and device behavior, dynamically enforcing access policies to detect and mitigate insider threats, unauthorized access, and anomalous activities in real time. By integrating behavior analytics with machine learning, the NAC framework adapts to evolving security threats without manual intervention, enhancing both security posture and operational efficiency. Experimental results demonstrate significant improvements in threat detection accuracy and reduction in false positives compared to traditional NAC solutions, highlighting the potential of autonomous AI-driven NAC systems in safeguarding critical government and financial networks.
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