AI-Augmented Continuous Delivery in Regulated Industries: A Compliance-First Strategy
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I1P111Keywords:
AI-Augmented DevOps, Continuous Delivery, Regulatory Compliance, CI/CD, Auditability, Deployment Automation, Healthcare IT, Financial Software, AI in DevSecOps, GxP, SOX, GDPR, HIPAA, ISO 27001, Predictive DeploymentAbstract
In today's fast-paced digital world, healthcare, banking, & military institutions are always being encouraged to come up with new ideas while still following the rules. DevOps teams often have to figure out how to follow strict rules while also making it easy & quick for customers to receive what they need. Artificial intelligence-augmented continuous delivery (CD) is a new way for businesses to speed up deployments while still maintaining security & governance regulations. When smart automation is in the delivery pipeline, Artificial intelligence can run regular compliance checks, find problems, improve audit trails, & help you make deployment decisions based on facts right away. This makes it simple for others to check & write things down, & it also makes the release cycle more open & frequent. Artificial intelligence also helps keep things in order by continually looking back at how well people obeyed the rules in the past. This makes sure that companies are always aware of & respect the rules. This level of accuracy & automation is a game-changer because even one mistake might have enormous legal or financial effects. This paper speaks about how an Artificial intelligence -driven compliance-first strategy might make continuous delivery a safe, scalable, & audit-ready way to get things done that fulfills industry standards & makes it easier to come up with new ideas rapidly
References
1. Zhou, Chuyi, et al. "Trust in AI-augmented design: Applying structural equation modeling to AI-augmented design acceptance." Heliyon 10.1 (2024).
2. Prikshat, Verma, et al. "AI-Augmented HRM: Literature review and a proposed multilevel framework for future research." Technological forecasting and social change 193 (2023): 122645.
3. Allam, Hitesh. “Cloud-Native Reliability: Applying SRE to Serverless and Event-Driven Architectures”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 5, no. 3, Oct. 2024, pp. 68-79
4. Gadde, Hemanth. "AI-Augmented Database Management Systems for Real-Time Data Analytics." Revista de Inteligencia Artificial en Medicina 15.1 (2024): 616-649.
5. Mishra, Sarbaree. “A Reinforcement Learning Approach for Training Complex Decision Making Models”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 3, no. 3, Oct. 2022, pp. 82-92
6. Arugula, Balkishan. “Ethical AI in Financial Services: Balancing Innovation and Compliance”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 5, no. 3, Oct. 2024, pp. 46-54
7. Nookala, G. (2023). Real-Time Data Integration in Traditional Data Warehouses: A Comparative Analysis. Journal of Computational Innovation, 3(1).
8. Eager, Bronwyn, and Ryan Brunton. "Prompting higher education towards AI-augmented teaching and learning practice." Journal of university teaching and learning practice 20.5 (2023): 1-19.
9. Shaik, Babulal. "Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS." Journal of AI-Assisted Scientific Discovery 1.2 (2021): 355-77.
10. Lalith Sriram Datla, and Samardh Sai Malay. “Patient-Centric Data Protection in the Cloud: Real-World Strategies for Privacy Enforcement and Secure Access”. European Journal of Quantum Computing and Intelligent Agents, vol. 8, Aug. 2024, pp. 19-43
11. Guntupalli, Bhavitha. “Data Lake Vs. Data Warehouse: Choosing the Right Architecture”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 4, no. 4, Dec. 2023, pp. 54-64
12. Abdel-Karim, Benjamin M., et al. "How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work." MIS quarterly 4 (2023).
13. Mohammad, Abdul Jabbar. “Chrono-Behavioral Fingerprinting for Workforce Optimization”. International Journal of AI, BigData, Computational and Management Studies, vol. 5, no. 3, Oct. 2024, pp. 91-101
14. Manda, Jeevan Kumar. "Augmented Reality (AR) Applications in Telecom Maintenance: Utilizing AR Technologies for Remote Maintenance and Troubleshooting in Telecom Infrastructure." Available at SSRN 5136767 (2023).
15. Datla, Lalith Sriram. “Optimizing REST API Reliability in Cloud-Based Insurance Platforms for Education and Healthcare Clients”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 4, no. 3, Oct. 2023, pp. 50-59
16. Shaer, Orit, et al. "AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation." Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 2024.
17. Sai Prasad Veluru. “Real-Time Fraud Detection in Payment Systems Using Kafka and Machine Learning”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 7, no. 2, Dec. 2019, pp. 199-14
18. Mishra, Sarbaree, et al. “A Domain Driven Data Architecture for Data Governance Strategies in the Enterprise”. International Journal of Emerging Trends in Computer Science and Information Technology, vol. 3, no. 2, June 2022, pp. 75-86
19. Allam, Hitesh. “Shift-Left Observability: Embedding Insights from Code to Production”. International Journal of AI, BigData, Computational and Management Studies, vol. 5, no. 2, June 2024, pp. 58-69
20. Besiroglu, Tamay, Nicholas Emery-Xu, and Neil Thompson. "Economic impacts of AI-augmented R&D." Research Policy 53.7 (2024): 105037.
21. Immaneni, J. (2021). Securing Fintech with DevSecOps: Scaling DevOps with Compliance in Mind. Journal of Big Data and Smart Systems, 2.
22. Guntupalli, Bhavitha, and Surya Vamshi ch. “Designing Microservices That Handle High-Volume Data Loads”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 4, Dec. 2023, pp. 76-87
23. Dumas, Marlon, et al. "AI-augmented business process management systems: a research manifesto." ACM Transactions on Management Information Systems 14.1 (2023): 1-19.
24. Allam, Hitesh. "Declarative Operations: GitOps in Large-Scale Production Systems." International Journal of Emerging Trends in Computer Science and Information Technology 4.2 (2023): 68-77.
25. Mishra, Sarbaree. “Reducing Points of Failure - A Hybrid and Multi-Cloud Deployment Strategy With Snowflake”. International Journal of AI, BigData, Computational and Management Studies, vol. 3, no. 1, Mar. 2022, pp. 66-7
26. Battina, Dhaya Sindhu. "Ai-augmented automation for devops, a model-based framework for continuous development in cyber-physical systems." International Journal of Creative Research Thoughts (IJCRT), ISSN (2016): 2320-2882.
27. Talakola, Swetha. “Analytics and Reporting With Google Cloud Platform and Microsoft Power BI”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 3, no. 2, June 2022, pp. 43-52
28. Jani, Parth. "UM Decision Automation Using PEGA and Machine Learning for Preauthorization Claims." The Distributed Learning and Broad Applications in Scientific Research 6 (2020): 1177-1205.
29. Nookala, G. (2023). Serverless Data Architecture: Advantages, Drawbacks, and Best Practices. Journal of Computing and Information Technology, 3(1).
30. Manda, Jeevan Kumar. "Privacy-Preserving Technologies in Telecom Data Analytics: Implementing Privacy-Preserving Techniques Like Differential Privacy to Protect Sensitive Customer Data During Telecom Data Analytics." Available at SSRN 5136773 (2023).
31. Shaik, Babulal, and Jayaram Immaneni. "Enhanced Logging and Monitoring With Custom Metrics in Kubernetes." African Journal of Artificial Intelligence and Sustainable Development 1 (2021): 307-30.
32. Bruneliere, Hugo, et al. "AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems." Microprocessors and Microsystems 94 (2022): 104672.
33. Jani, Parth. “AI-Powered Eligibility Reconciliation for Dual Eligible Members Using AWS Glue”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 1, June 2021, pp. 578-94
34. Balkishan Arugula. “Building Scalable Ecommerce Platforms: Microservices and Cloud-Native Approaches”. Journal of Artificial Intelligence & Machine Learning Studies, vol. 8, Aug. 2024, pp. 42-74
35. Abdul Jabbar Mohammad. “Leveraging Timekeeping Data for Risk Reward Optimization in Workforce Strategy”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 4, Mar. 2024, pp. 302-24
36. Lalith Sriram Datla. “Cloud Costs in Healthcare: Practical Approaches With Lifecycle Policies, Tagging, and Usage Reporting”. American Journal of Cognitive Computing and AI Systems, vol. 8, Oct. 2024, pp. 44-66
37. Eramo, Romina, et al. "Aidoart: Ai-augmented automation for devops, a model-based framework for continuous development in cyber-physical systems." 2021 24th Euromicro Conference on Digital System Design (DSD). IEEE, 2021.
38. Vasanta Kumar Tarra. “Claims Processing & Fraud Detection With AI in Salesforce”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 11, no. 2, Oct. 2023, pp. 37–53
39. Nookala, G. (2023). Microservices and Data Architecture: Aligning Scalability with Data Flow. International Journal of Digital Innovation, 4(1).
40. Mohammad, Abdul Jabbar. “Dynamic Labor Forecasting via Real-Time Timekeeping Stream”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 4, Dec. 2023, pp. 56-65
41. Patel, Piyushkumar. "Transfer Pricing in a Post-COVID World: Balancing Compliance With New Global Tax Regimes." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 208-26
42. Alexander, David, and Sandra Anthony. "AI-Augmented Decision Systems for Real-Time Risk Assessment in Cloud Environments." (2024).
43. Guntupalli, Bhavitha. “ETL Architecture Patterns: Hub-and-Spoke, Lambda, and More”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 3, Oct. 2023, pp. 61-71
44. Eramo, Romina, et al. "AI-augmented Automation for Real Driving Prediction: an Industrial Use Case." arXiv preprint arXiv:2404.02841 (2024).
45. Mis45hra, Sarbaree, et al. “Hyperfocused Customer Insights Based On Graph Analytics and Knowledge Graphs”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 4, Dec. 2023, pp. 88-99
46. Jani, Parth. “Integrating Snowflake and PEGA to Drive UM Case Resolution in State Medicaid”. American Journal of Autonomous Systems and Robotics Engineering, vol. 1, Apr. 2021, pp. 498-20
47. Balkishan Arugula. “Personalization in Ecommerce: Using AI and Data Analytics to Enhance Customer Experience”. Artificial Intelligence, Machine Learning, and Autonomous Systems, vol. 7, Sept. 2023, pp. 14-39
48. Chaganti, Krishna Chaitanya. "The Role of AI in Secure DevOps: Preventing Vulnerabilities in CI/CD Pipelines." International Journal of Science And Engineering 9.4 (2023): 19-29.
49. Shaik, Babulal, Jayaram Immaneni, and K. Allam. "Unified Monitoring for Hybrid EKS and On-Premises Kubernetes Clusters." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 649-669.
50. Manda, Jeevan Kumar. "Blockchain-based Identity Management in Telecom: Implementing Blockchain for Secure and Decentralized Identity Management Solutions in." Available at SSRN 5136783 (2024).
51. Abdul Jabbar Mohammad. “Biometric Timekeeping Systems and Their Impact on Workforce Trust and Privacy”. Journal of Artificial Intelligence & Machine Learning Studies, vol. 8, Oct. 2024, pp. 97-123
52. Mishra, Sarbaree. “Incorporating Automated Machine Learning and Neural Architecture Searches to Build a Better Enterprise Search Engine”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 4, no. 4, Dec. 2023, pp. 65-75
53. Abdul Jabbar Mohammad. “Integrating Timekeeping With Mental Health and Burnout Detection Systems”. Artificial Intelligence, Machine Learning, and Autonomous Systems, vol. 8, Mar. 2024, pp. 72-97
54. Suboyin, A., et al. "Transforming Strategy with AI-Augmented Personas for Sustainability, Data Management, Regulatory Frameworks, Healthcare and Compliance." Abu Dhabi International Petroleum Exhibition and Conference. SPE, 2024.
55. Chaganti, Krishna C. "Advancing AI-Driven Threat Detection in IoT Ecosystems: Addressing Scalability, Resource Constraints, and Real-Time Adaptability.
56. Rai, Prakruthi R., Preethi Nanjundan, and Jossy Paul George. "Enhancing industrial operations through AI-driven decision-making in the era of Industry 4.0." AI-Driven IoT Systems for Industry 4.0. CRC Press, 2024. 42-55.
57. Nair, S. S., & Lakshmikanthan, G. (2024). Digital Identity Architecture for Autonomous Mobility: A Blockchain and Federation Approach. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(2), 25-36. https://doi.org/10.63282/49s0p265
