Since cloud computing is changing so rapidly, maintaining strong security is now a major issue for companies everywhere. Massive volumes of mixed data are constantly created in cloud environments at every layer, involving virtual machines, containers, storage, identity management and application activities. It is usually not possible for traditional security systems and old monitoring tools to manage vast and changing data flow in real time. Con- ventional methods fail to discover advanced persistent threats, attacks by team members and new vulnerabilities because they do not easily adjust to changing situations. To fix the urgent problem of weak security in cloud sys- tems, this research introduces an AI-powered big data analytics system. The aim is to use artificial intelligence and big data technologies to improve spot- ting threats, marking unusual incidents and reducing risks as they happen. Machine learning and deep learning are used within the system which makes use of distributed processing platforms such as Apache Spark, Hadoop and Kafka. Together, these pieces ensure that a lot of log data and telemetry from hybrid and multi-cloud setups are ingested, worked on and analyzed quickly and efficiently. The proposed solution uses Isolation Forests, Ran- dom Forests, Autoencoders and LSTM networks to spot abnormal activity and risks. They can recognize unusual patterns in network activity, website logs and API usage to find out about possible attacks. It also makes use of natural language processing to study unstructured log data for threats and compares these to the ones listed in external threat intelligence. The archi- tecture is built with a layer using Kafka and Logstash to get data ingested, another using Spark and HDFS for processing and a third for real-time threat analysis and prediction with AI. Information about threats is presented vi- sually in dashboards with the help of Grafana and Kibana, so analysts can easily respond to any threats. Risks are scored with a mechanism that focuses on the worst incidents and those expected to have the biggest impact. Bench- mark datasets such as CICIDS 2017 and UNSW-NB15 are used, along with anonymized real-world activity logs from the cloud, to assess the suggested solution’s robustness. The data suggests that using this technology is more effective and faster than using traditional security approaches. This study has resulted in an AI-based security framework that can handle large enter- prise loads, adaptive security models and affordable implementation paths for the cloud. Thanks to this work, cloud security can now focus on ad- vancing to automating early detection, providing continuous monitoring and implementing automatic steps when needed. Ultimately, the use of AI and big data analytics changes how cloud security functions. This research en- ables systems to detect threats and rate risks in real time, helping to improve the security of today’s cloud networks.

Cloud security, big data analytics, artificial intelligence, real-time threat detection, anomaly detection, machine learning, deep learning, cyber threat intelligence

M. A. Ferrag, L. Maglaras, A. Derhab, and H. Janicke, “A review of the security of distributed ledger technologies,” IEEE Trans. Services Comput., vol. 13, no. 3, pp. 550–563, 2020.

Y. Yuan and F.-Y. Wang, “Blockchain: The state of the art and future trends,” Acta Automatica Sinica, vol. 45, no. 4, pp. 217–223, 2018.

N. A. B. Solochie et al., “Machine learning for cybersecurity: A comprehensive survey,” IEEE Access, vol. 11, pp. 32461–32488, 2023.

A. K. Abeshu and N. Chilamkurti, “Deep learning: The frontier for distributed attack detection in fog-to-things computing,” IEEE Commun. Mag., vol. 56, no. 2, pp. 169–175, 2018.

S. A. Shinde and S. A. Khatoon, “Privacy-preserving federated learning for healthcare systems,” IEEE J. Biomed. Health Inform., vol. 25, no. 4, pp. 1335–1342, 2021.

S. R. Pokhrel and J. Choi, “Federated learning with blockchain for autonomous vehicles: Analysis and design challenges,” IEEE Trans. Commun., vol. 68, no. 8, pp. 4734–4746, 2020.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.

A. M. Tjoa and S. Tjoa, “Cybersecurity in the era of AI: Current challenges and future research directions,” in Proc. 2018 Int. Conf. Cyber Security Prot. Digital Services, 2018, pp. 1–8.

S. A. Shaikh and S. A. Khatoon, “AI and ML in cloud security: Challenges and opportunities,” J. Cloud Comput., vol. 9, no. 1, pp. 1–20, 2020.

P. K. Sharma, S. Rathore, and J. H. Park, “DistBlockNet: A dis- tributed blockchains-based secure SDN architecture for IoT net- works,” IEEE Commun. Mag., vol. 55, no. 9, pp. 78–85, 2017.

National Institute of Standards and Technology, “Post-Quantum Cryptography: NIST’s Plan for the Future,” 2022. [Online]. Avail- able: https://csrc.nist.gov/

M. Al-Rubaie and J. M. Chang, “Privacy-preserving machine learn- ing: Threats and solutions,” IEEE Secur. Privacy, vol. 17, no. 2,

pp. 49–58, 2019.

M. Kantarcioglu and B. Xi, “Adversarial machine learning in cyber- security,” in Proc. IEEE 16th Int. Conf. Data Mining Workshops, 2016, pp. 1305–1310.

R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proc. 22nd ACM SIGSAC Conf. Computer Commun. Security, 2015, pp. 1310–1321.

M. S. Ali, M. Vecchio, M. Pincheira, K. Dolui, F. Antonelli, and

M. H. Rehmani, “Applications of blockchain in the Internet of Things: A comprehensive survey,” IEEE Commun. Surveys Tu- torials, vol. 21, no. 2, pp. 1676–1717, 2019.

T. T. Nguyen and G. Armitage, “A survey of techniques for inter- net traffic classification using machine learning,” IEEE Commun. Surveys Tutorials, vol. 10, no. 4, pp. 56–76, 2008.

A. M. Lone and R. Naaz, “Quantum-safe cryptography: A survey,”

J. Inf. Secur. Appl., vol. 61, p. 102925, 2021.

A. Jain and D. Singh, “AI-powered threat detection for cloud en- vironments: A real-time analytics approach,” J. Cloud Comput., vol. 12, no. 1, pp. 1–15, 2023.

B. Tang and Q. Zhang, “Big data analytics in cloud computing: A survey,” Future Gener. Comput. Syst., vol. 37, pp. 209–220, 2014.

A. Madakam, S. Ramaswamy, and R. Tripathi, “Internet of Things (IoT): A literature review,” J. Comput. Commun., vol. 3, no. 5, pp. 164–173, 2015.

S. Yerra, “Reducing ETL processing time with SSIS optimizations for large-scale data pipelines,” 2025. [Online]. Available: https:

//doi.org/10.55640/ijdsml-05-01-12

S. Yerra, “Optimizing supply chain efficiency using AI-driven pre- dictive analytics in logistics,” 2025. [Online]. Available: https:// ijsrcseit.com/index.php/home/article/view/CSEIT25112475

S. Yerra, “Enhancing inventory management through real-time Power BI dashboards and KPI tracking,” 2025. [Online]. Avail- able: https://ijsrcseit.com/index.php/home/article/view/ CSEIT25112458

S. Yerra, “Leveraging Azure DevOps for backlog management and sprint planning in supply chain,” Journal of Information Systems Engineering and Management, vol. 10, no. 36, pp. f1019–f1023, 2025. [Online]. Available: https://jisem-journal.com/index. php/journal/article/view/6629

S. Yerra and V. L. Middae, “Intelligent workload readjustment of serverless functions in cloud to edge environment,” International Journal of Data Science and Machine Learning, 2025. [Online].

Available: https://doi.org/10.55640/ijdsml-05-01-18

S. Talwar, “Dynamic Just-In-Time app servers with auto- mated access management on AWS,” 2025. [Online]. Available: https://computerfraudsecurity.com/index.php/journal/ article/view/411/280