Threat Intelligence and Predictive Analytics in Usa Cloud Security: Mitigating Ai-driven Cyber Threats
Year: 2024
Type: article
Abstract: The rapid adoption of cloud computing in the United States has revolutionized data storage, processing, andaccessibility for enterprises, governments, and individuals. However, this expansion has also led to an increase insophisticated cyber threats, particularly those leveraging artificial intelligence (AI) for adversarial attacks. Threatintelligence and predictive analytics have emerged as essential components in enhancing cloud security, enablingproactive defense mechanisms against evolving threats. By integrating real-time data analysis, machine learningmodels, and behavioral analytics, organizations can detect anomalies, predict cyberattacks, and mitigate risksbefore they escalate. Traditional cybersecurity measures rely on reactive responses, often failing to address zeroday vulnerabilities and AI-driven cyber threats. In contrast, predictive analytics leverages historical threat data,AI pattern recognition, and anomaly detection techniques to forecast potential attack vectors. The synergy betweenAI-driven threat intelligence and cloud security frameworks strengthens automated incident response, reducinghuman intervention and improving detection accuracy. However, challenges such as adversarial AI attacks, biasin threat models, and regulatory compliance issues must be addressed to ensure ethical and effectiveimplementation. This article explores the role of threat intelligence and predictive analytics in USA cloud security,examining the benefits, challenges, and future directions of AI-driven cybersecurity frameworks. It also evaluatesregulatory considerations, ethical concerns, and the integration of quantum-resistant security solutions. Byleveraging AI and predictive analytics, cloud security infrastructures can proactively mitigate cyber threats,enhance resilience, and safeguard critical digital assets in an increasingly complex threat landscape.
Institution: East Tennessee State University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6911762594