AI-Powered Threat Intelligence Platforms for National Cybersecurity Resilience
Keywords:
Artificial intelligence; Cyber threat intelligence; National cybersecurity resilience; Machine learning; Natural language processing; Knowledge graphs; Threat intelligence platforms; Critical infrastructure securityAbstract
Artificial intelligence driven cyber threat intelligence platforms have emerged as a critical capability for strengthening national cybersecurity resilience in an increasingly complex and hostile digital environment. Traditional threat intelligence approaches are often limited by manual analysis, fragmented data sources, and delayed response, which constrain their effectiveness at national scale. This study examines how AI-powered threat intelligence platforms enhance national cybersecurity resilience by enabling automated data ingestion, intelligent threat detection, contextual correlation, and proactive defense across critical infrastructure sectors. Drawing on recent advances in machine learning, natural language processing, knowledge graphs, and large language models, the paper analyzes the architectural components, operational functions, and intelligence sharing mechanisms that underpin modern AI-enabled threat intelligence systems. The study further evaluates the role of standardized frameworks and platforms such as STIX, MISP, and MITRE ATT&CK in supporting interoperability, coordinated response, and cross-organizational intelligence exchange. Performance benefits, including improved detection accuracy, reduced incident response time, and enhanced situational awareness, are discussed alongside governance, privacy, and implementation challenges at the national level. The findings highlight that AIpowered threat intelligence platforms are not merely technical tools but strategic assets that support proactive risk management, informed decision-making, and sustained national cybersecurity resilience.
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