AI-Driven Identity Threat Detection and Response Systems for Modern Cloud Security Operations Centers

Abstract

Identity is the new perimeter in cloud-first enterprises, where adversaries increasingly weaponize stolen credentials, malicious OAuth consents, token replay, and over-permissioned roles to traverse control planes and SaaS estates with minimal endpoint noise. In this paper, a telemetry-based Identity Threat Detection and Response (ITDR) architecture integrating identity provider (SSO/OAuth/OIDC), cloud API (AWS/Azure/GCP), endpoint, and SaaS audit log telemetry into a privacy-conscious feature fabric is proposed using AI. Our combination of sequential modelling of session dynamics, graph learning of privilege, relationship abuse, and self-supervised representation learning reveals low-and-slow compromise patterns. An anomaly strength is combined with contextual role criticality of data, device trust, and just-in-time elevation, session isolation, token revocation, and step-up authentication executed by a policy-as-code and SOAR playbooks-based risk layer that is calibrated. Details Implementation summary Streaming feature stores (training/serving parity) Feedback loops (with analyst adjudications) Governance (mapped to ISO 27001, NIST SP 800-207, and GDPR) In hybrid real-world and synthetic tests, the method does better recall at constant low false-positive rates, as well as fivefold trimming alert volume by deduplication and model calibration and with less time-to-detect and time to respond with real-time inference and automated containment. Describe stability to adversarial log poisoning and concept drift, and explainability methods that do not compromise auditability. The findings reveal that AI-based ITDR has the potential to support SOC effectiveness in a material manner without going against the principles of Zero Trust nor regulatory requirements