Leveraging AI and machine learning for threat detection and adversarial defense in U.S. cybersecurity

The escalating sophistication of cyber threats against critical U.S. infrastructure necessitates advanced defensive mechanisms that can adapt to evolving attack vectors. This research examines the integration of artificial intelligence (AI) and machine learning (ML) technologies in cybersecurity frameworks, focusing on threat detection capabilities and adversarial defense strategies. Through comprehensive analysis of current implementations across banking, industrial control systems, and network infrastructure, this study demonstrates that AI-driven cybersecurity solutions can achieve detection accuracy rates exceeding 95% while reducing false positive rates by up to 60%. The research identifies key challenges including adversarial attacks against ML models, explainability requirements, and scalability concerns in large-scale deployments. The findings suggest that explainable AI (XAI) frameworks combined with ensemble learning approaches provide the most robust defense against sophisticated cyber threats while maintaining operational transparency required for critical infrastructure protection.