US enterprises are accelerating AI-driven threat detection across endpoints, identities, cloud workloads, and OT/IoT, moving from traditional SIEM/SOC models to advanced analytics using behavior, graph, and language models. Spend on these solutions is expected to grow from ~$6.8B in 2025 to ~$18.9B by 2030. Key improvements include reduced Mean Time to Detect (MTTD) from 28 to 6 hours, and Mean Time to Respond (MTTR) from 19 to 4 hours, with false-positive rates dropping from 18% to 7%. The architecture includes unified telemetry, ML anomaly detection, and SOAR automation, with regulatory compliance for healthcare, finance, and critical infrastructure. AI-driven detection will enhance resilience, reduce breach probability, and satisfy auditors with continuous monitoring.