Enhancing Supply Chain Security with Cloud-Based Threat Intelligence and Risk Management
Abstract
Supply chains face increasing risks from cyber threats, including data breaches, ransomware, and supply chain attacks. This paper presents a cloud-based threat intelligence framework that aggregates data from multiple sources to identify supply chain vulnerabilities and predict potential attacks. The framework employs machine learning models to assess supplier risk profiles, monitor real-time threats, and recommend mitigation strategies. Automated incident response mechanisms enhance threat containment and recovery. Performance tests show improved threat detection rates and faster incident resolution times. The study concludes that cloud-based threat intelligence strengthens supply chain security and resilience.
References
Pindi, V. (2018). NATURAL LANGUAGE PROCESSING(NLP) APPLICATIONS IN HEALTHCARE: EXTRACTING VALUABLE INSIGHTS FROM UNSTRUCTURED MEDICAL DATA. International Journal of Innovations in Engineering Research and Technology, 5(3), 1-10.
Pindi, V. (2019). A AI-ASSISTED CLINICAL DECISION SUPPORT SYSTEMS: ENHANCING DIAGNOSTIC ACCURACY AND TREATMENT RECOMMENDATIONS. International Journal of Innovations in Engineering Research and Technology, 6(10), 1-10.
PINDI, V. (2022). ETHICAL CONSIDERATIONS AND REGULATORY COMPLIANCE IN IMPLEMENTING AI SOLUTIONS FOR HEALTHCARE APPLICATIONS. IEJRD-International Multidisciplinary Journal, 5(5), 11.
Pindi, V. (2021). AI in Dental Healthcare: Transforming Diagnosis and Treatment. International Journal of Holistic Management Perspectives, 2(2).
Pindi, V. (2020). AI in Rare Disease Diagnosis: Reducing the Diagnostic Odyssey. International Journal of Holistic Management Perspectives, 1(1).
Pindi, V. (2018). AI for Surgical Training: Enhancing Skills through Simulation. International Numeric Journal of Machine Learning and Robots, 2(2).
Pindi, V. (2017). AI in Rehabilitation: Redefining Post-Injury Recovery. International Numeric Journal of Machine Learning and Robots, 1(1).
Davuluri, M. (2024). AI in Healthcare Fraud Detection: Ensuring Integrity in Medical Billing. International Machine learning journal and Computer Engineering, 7(7).
Davuluri, M. (2024). AI in Geriatric Care: Supporting an Aging Population. International Numeric Journal of Machine Learning and Robots, 8(8).
Davuluri, M. (2023). AI for Healthcare Workflow Optimization: Reducing Burnout and Enhancing Efficiency. International Numeric Journal of Machine Learning and Robots, 7(7).
Davuluri, M. (2023). AI in Surgical Assistance: Enhancing Precision and Outcomes. International Machine learning journal and Computer Engineering, 6(6).
Davuluri, M. (2022). AI in Mental Health: Transforming Diagnosis and Therapy. International Machine learning journal and Computer Engineering, 5(5).
Davuluri, M. (2021). AI for Chronic Disease Management: Improving Long-Term Patient Outcomes. International Journal of Machine Learning and Artificial Intelligence, 2(2).
Davuluri, M. (2021). AI in Personalized Oncology: Revolutionizing Cancer Care. International Machine learning journal and Computer Engineering, 4(4).
Davuluri, M. (2020). AI-Driven Drug Discovery: Accelerating the Path to New Treatments. International Journal of Machine Learning and Artificial Intelligence, 1(1).
Davuluri, M. (2020). AI in Pediatric Healthcare: Transforming Care for Younger Patients. International Numeric Journal of Machine Learning and Robots, 4(4).
Davuluri, M. (2018). AI in Preventive Healthcare: From Risk Assessment to Lifestyle Interventions. International Numeric Journal of Machine Learning and Robots, 2(2).
Davuluri, M. (2017). AI-Enhanced Telemedicine: Bridging the Gap in Global Healthcare Access. International Numeric Journal of Machine Learning and Robots, 1(1).
Davuluri, M. (2024). An Overview of Natural Language Processing in Analyzing Clinical Text Data for Patient Health Insights. Research-gate journal, 10(10).
Sadaram, G., Karaka, L. M., Maka, S. R., Sakuru, M., Boppana, S. B., & Katnapally, N. (2024). AI-Powered Cyber Threat Detection: Leveraging Machine Learning for Real-Time Anomaly Identification and Threat Mitigation. MSW Management Journal, 34(2), 788-803.
Krishna Madhav, J., Varun, B., Niharika, K., Srinivasa Rao, M., & Laxmana Murthy, K. (2023). Optimising Sales Forecasts in ERP Systems Using Machine Learning and Predictive Analytics. J Contemp Edu Theo Artific Intel: JCETAI-104.
Sadaram, G., Sakuru, M., Karaka, L. M., Reddy, M. S., Bodepudi, V., Boppana, S. B., & Maka, S. R. (2022). Internet of Things (IoT) Cybersecurity Enhancement through Artificial Intelligence: A Study on Intrusion Detection Systems. Universal Library of Engineering Technology, (2022).
Jha, K. M., Velaga, V., Routhu, K. K., Sadaram, G., & Boppana, S. B. (2025). Evaluating the Effectiveness of Machine Learning for Heart Disease Prediction in Healthcare Sector. J Cardiobiol, 9(1), 1.
Maka, S. R. (2023). Understanding the Fundamentals of Digital Transformation in Financial Services: Drivers and Strategic Insights. Available at SSRN 5116707.
Karaka, L. M. (2021). Optimising Product Enhancements Strategic Approaches to Managing Complexity. Available at SSRN 5147875.
KishanKumar Routhu, A. D. P. Risk Management in Enterprise Merger and Acquisition (M&A): A Review of Approaches and Best Practices.
Routhu, KishanKumar & Katnapally, Niharika & Sakuru, Manikanth. (2023). Machine Learning for Cyber Defense: A Comparative Analysis of Supervised and Unsupervised Learning Approaches. Journal for ReAttach Therapy and Developmental Diversities. 6. 10.53555/jrtdd.v6i10s(2).3481.
Chinta, Purna Chandra Rao & Moore, Chethan Sriharsha. (2023). Cloud-Based AI and Big Data Analytics for Real-Time Business Decision-Making. 36. 96-123. 10.47363/JAICC/2023.
