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@article{201273,
        author = {ARUSREE H.S and Dr. N. Sathyabalaji and M.C. Savithri and L. Dharani},
        title = {AI POWER-DRIVEN THREAT DETECTION AND BEHAVIOUR RISK SCROLING SYSTEM IN CLOUD ENVIRONMENTS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3409-3414},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201273},
        abstract = {Cloud computing continues to evolve with the integration of cutting-edge technologies such as Zero Trust Architecture (ZTA), Confidential Computing, Artificial Intelligence (AI), Blockchain, and Quantum-Resistant Cryptography. These advancements introduce new opportunities and challenges in managing sensitive user data. This paper presents an enhanced framework that integrates modern technologies into measurable, safe, and secure cloud data management. The proposed system leverages AI-driven anomaly detection, blockchain-based immutable auditing, homomorphic encryption, and confidential computing environments to ensure real-time verification, data integrity, and regulatory compliance. The framework introduces dynamic trust scoring and continuous monitoring to eliminate reliance on static security assumptions. Experimental evaluation demonstrates improved resilience, transparency, and scalability with acceptable performance overhead. This approach enables sensitive users to transition from trust-based systems to verifiable and measurable cloud security ecosystems.},
        keywords = {Cloud Security, Zero Trust, Confidential Computing, Blockchain, AI Security, Data Privacy, Quantum Cryptography.},
        month = {May},
        }