Secure AI-Driven Identity Infrastructure for Regulated Sectors

Abstract

The regulated industry is facing increased threats to identity as attacks on fleeting clouds, APIs, and mobile devices increase the attack surface. To provide real-time monitoring, adaptive authentication, and auditable compliance, this paper suggests a secure and AI-driven identity infrastructure, a fusion of ML, NLP, and LLM-driven analytics with zero-trust design. The work was inspired by the fact that identity-related breaches are prevalent in most breaches, including the hack of Global data breach victims in 2023 by a total of 43% of all government breaches, which are common, particularly in healthcare, finance, and government ecosystems, where HIPAA, GDPR, and SOX control identity theft. The architecture employs authentication, authorization, entitlement, and evidence in restricted, microservice settings; expels operational telemetry into online feature stores; and implements policy-as-code in a hybrid cloud. Approaches combine scaling risk scoring with gradient-boosting session anomaly sequence models, toxic entitlement graph analytics, and federated learning to minimize data movement. Covering 100,000 sessions in four weeks, the stack outperforms rules-based baselines on metrics including accuracy, recall, F1, ROC-AUC, median arena decision latency, coverage of compliance, newly seen evidence, and false challenges. Findings show that 92 out of 100 fraud attempts were reported caught in 30 seconds (vs. a 60-second baseline), resulting in 31 fewer attempted frauds, a 19-point improvement in coverage of evidence, and the availability of decisions at 99.6% in stressful situations. Up to 35% of fraud was reduced, and the user experience was maintained at a much higher level using adaptive authentication. The study concludes by discussing governance, privacy-saving methods, and a research agenda focused on standard benchmarks, compliance, and adaptive compliance.