Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Large Language Models with Cloud Infrastructure Entitlement Management is a revolutionary leap forward in autonomous security operations, tackling the vital challenges of overprivileged access and identity-based vulnerabilities in distributed cloud environments. Contemporary businesses experience unparalleled complexity in digital identities and access permissions, with conventional rule-based systems failing to cope with dynamic multi-cloud architectures. The intersection of artificial intelligence with entitlement management results in smart systems that are able to comprehend natural language policies, scrutinize intricate permission relationships, and make context-aware security decisions in real-time. The advanced transformer-based architectures handle enormous amounts of security data while keeping up behavioral baselines for both human and machine identities across disparate cloud services. Advanced pattern recognition features allow autonomous detection of privilege escalation attempts and anomalous access activity based on multi-dimensional analysis of temporal, spatial, and contextual attributes. Strategies for implementation call for phased deployment methodologies involving orchestrated organizational learning, guided decision-making, and autonomous operations to maintain organizational trust and operational continuity. Security aspects include adversarial attack prevention, data privacy conservation, and recursive governance issues related to administering privileges for artificial intelligence systems themselves. The use of blockchain-based audit trails and privacy-preserving methods ensures compliance with regulations while it keeps autonomous security operations transparent, finally achieving better protection against newer forms of cyber attacks in cloud-native systems.