Cognitive Cloud Defense AI Frameworks for Enterprise Threat Intelligence
Keywords:
Enterprise Cybersecurity, Artificial Intelligence in Security, Machine Learning for Threat Detection, Malware Detection, Advanced Persistent Threats (APT), Threat Intelligence Platforms, AI-Driven Threat Intelligence, Cyber Attack Detection, Security Vulnerability Management, Proactive Threat Mitigation, Attack Techniques Tactics and Procedures (TTPs), Cyber Threat Analytics, Security Operations Centers (SOC), Intelligent Security Frameworks, Real-Time Threat Detection, Cloud Security Threats, Cyber Defense Automation, Security Incident Response, Predictive Threat Modeling, Enterprise Security Architecture.Abstract
The rapid evolution of enterprise information technology has increased the complexities of information systems to levels that present augmented challenges for proactive identification and mitigation of security vulnerabilities by information security practitioners and organizations. The growing volume and increasing sophistication of attacks against enterprise infrastructure are exposing vulnerabilities in traditional protection measures. Consequently, enterprise security teams face a rising tide of cyber incidents that, despite major investments in security-prevention measures, continues to exceed their ability to control.
The present study introduces an artificial intelligence–machine-learning modelling framework designed to enable and assist enterprises in the detection and prevention of the majority of malware and APT attack vectors. The modelling framework employs, generates, and uses AI-driven threat intelligence that encompasses extensive visual and searchable data representations of the attack techniques, tactics, and procedures (TTPs) of cyber adversaries across the globe. The modelling framework was evaluated by comparing its detection efficacy and average time taken to detect both traditional malware-based attacks and advanced persistent threats against those of an organization that is globally recognized as a leader in providing security for enterprise infrastructure. The results revealed a notable reduction in average time taken to detect advanced persistent cloud threats over a more conventional and established security control approach.
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