Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence
Abstract
Governance-aligned Generative AI (GenAI) systems enable enterprise-grade GenAI capabilities by embedding governance safeguards directly into GenAI pipelines. Such pipelines instantiating enterprise workflows mitigate GenAI risks in GenAI model choices, foundation datasets, user prompts, and output consumption. Decisions regarding these GenAI pipeline elements should be made with the same formal structures and rigor applied to enterprise decision-making. A wealth of governance-aligned GenAI pipelines applied to enterprise processes would deliver comprehensive enterprise workflow intelligence at a fraction of the cost of traditional GenAI implementations.
For enterprises to harness Generative AI (GenAI) safely, enterprise governance objectives for GenAI must be realised. Candidates address the five enterprise corporate governance objectives—compliance, risk management, strategic alignment, performance and accountability—iteratively for all GenAI pathways; in computer science parlance, the task is to devise a breadth-first search through the enterprise GenAI pipeline graph. A foundational support system is a community-shared catalog of representative governance-aligned pipeline instances that address specific enterprise workflows or aspects thereof, deployed enterprise, domain or solution-wide with appropriate safeguards or controls. Candidate pipelines should instantiate enterprise workflows; the assets used in the pipeline or any GenAI input or output that may pose risk or compliance issues require particular attention. Enterprise workflows are risk scenarios against which GenAI adoption should be justified; pipeline adoption a risk-control measure.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
13256-13268 |
Published |
December 17, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Siva Hemanth Kolla, Ranjith Kumar Peddi (%2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology , Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 13256-13268. https://doi.org/10.15662/IJSRAT.2024.0706012 |
References
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[31] Pamisetty, A. (2023). Integration Of Artificial Intelligence And Machine Learning In National Food Service Distribution Networks. Educational Administration: Theory and Practice, 29 (4), 4979–4994.
[32] Pandiri, L. (2021). Cloud-Based AI Systems for Real-Time Underwriting in Recreational and Property Insurance. International Journal of Science and Research (IJSR), 10(12), 1626-1638.
[33] Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
[34] Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30 (4), 1011–1027.
[35] Kolla, S. K. (2023). Explainable AI and ML Models for Transparent Clinical Decision Support. Journal for ReAttach Therapy and Developmental Diversities, 6, 2444-2460.
[36]Singreddy, S. (2024). Predictive Modeling for Auto Insurance Risk Assessment Using Machine Learning Algorithms. Available at SSRN 5238922.
[37] Pamisetty, V. (2024). AI-Driven Decision Support for Taxation and Unclaimed Property Management: Enhancing Efficiency through Big Data and Cloud Integration. Available at SSRN 5250776.
[38] Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
[39] Ranga Reddy, V. A. (2024). Comparing Batch vs. Streaming Approaches in Healthcare Data Warehousing Environments. Journal of Neonatal Surgery, 13(1), 2287–2309. Retrieved from https://www.jneonatalsurg.com/index.php/jns/article/view/10223
[40] Sheelam, G. K. (2023). Adaptive AI workflows for edge-to-cloud processing in decentralized mobile infrastructure. Journal for Reattach Therapy and Development Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2). 3570ugh Predictive Intelligence.
[41] Kalisetty, S., & Singireddy, J. (2023). Agentic AI in retail: A paradigm shift in autonomous customer interaction and supply chain automation. American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN, 3067-4190.
[42]Amistapuram, K. (2024). Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning. Available at SSRN 6143426.
[43] Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
[44] Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
[45] Inala, R. (2022). Engineering Data Products for Investment Analytics: The Role of Product Master Data and Scalable Big Data Solutions. International Journal of Scientific Research and Modern Technology, 155-171.
[46] Avinash Pamisetty, Vijaya Rama Raju Gottimukkala. (2024). Agentic AI-Driven Multi-Cloud Big Data Architecture For Predictive Demand, Credit Risk, And Inventory Financing In National Food Service Supply Chains. Metallurgical and Materials Engineering, 30(4), 959–975. https://doi.org/10.63278/mme.v30i4.1933
[47] Nandan, B. P. Data Analytics-Driven Approaches to Yield Prediction in Semiconductor Manufacturing. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
[48] Mangala, N. (2021). CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps. Universal Journal of Business and Management, 1(1), 1-18.
[49] Ranjith Kumar Peddi. (2024). AI-Based Workforce Analytics for SLA Governance and Uptime Assurance in Data Centers. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8589–8601. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5361
[50] Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
[51] Pamisetty, V., & Amistapuram, K. Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning.
[52] Pamisetty, A. (2023). Optimizing National Food Service Supply Chains through Big Data Engineering and Cloud-Native Infrastructure.
[53] Pamisetty, A., Adusupalli, B., Mashetty, S., & Singreddy, S. (2024). Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management. Sneha, Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management (December 05, 2024).
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[55] Aitha, A. R. (2024). Generative AI-Powered Fraud Detection in Workers' Compensation: A DevOps-Based Multi-Cloud Architecture Leveraging, Deep Learning, and Explainable AI. Deep Learning, and Explainable AI (July 26, 2024).
[56] Kolla, T. (2024). AI-Powered Data Catalog Systems For Healthcare Data Discovery And Governance. South Eastern European Journal of Public Health, 2296–2311. https://doi.org/10.70135/seejph.vi.7077
[57] Recharla, M., & Chitta, S. AI-Enhanced Neuroimaging and Deep Learning-Based Early Diagnosis of Multiple Sclerosis and Alzheimer’s.
[58] Singireddy, J. (2024). AI-Driven Payroll Systems: Ensuring Compliance and Reducing Human Error. American Data Science Journal for Advanced Computations (ADSJAC) ISSN, 3067-4166.
[59] Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20.
[60] Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
[61] Bandi, V. D. V. K. (2024). AI-Driven Predictive Risk Modeling Architectures for Financial Systems. International Journal Of Finance, 37(3), 54-78.
[62] Keerthi Amistapuram. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950–5958. https://doi.org/10.53555/kuey.v29i4.10965
[63] Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology.
[64] Adusupalli, B., Pandiri, L., & Singireddy, S. (2019). DevOps Enablement in Legacy Insurance Infrastructure for Agile Policy and Claims Deployment. risk, 7(12).
[65] Koppolu, H. K. R., Recharla, M., & Chakilam, C. Revolutionizing Patient Care with AI and Cloud Computing: A Framework for Scalable and Predictive Healthcare Solutions. Pr (y= 1| x)= s (w⊤ x+ b), 1.
[66] Kummari, D. N. (2022). AI-Driven Audit Frameworks For Enhancing Compliance In Modern Manufacturing Systems. Migration Letters, 19, 2150-2177.
[67] Kummari, D. N., & Challa, S. R. Big Data and Machine Learning in Fraud Detection for Public Sector Financial Systems. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI, 10.
[68] Venkata Akhilesh Ranga Reddy. (2021). Challenges in Standardizing Member Eligibility Data Across Multi-Payer Healthcare Ecosystems. International Journal of Medical Toxicology and Legal Medicine, 24(3 and 4), 1–19. Retrieved from https://ijmtlm.org/index.php/journal/article/view/1475
[69] Pandiri, L., & Chitta, S. (2024). Machine Learning-Powered Actuarial Science: Revolutionizing Underwriting and Policy Pricing for Enhanced Predictive Analytics in Life and Health Insurance.
[2] Uday Surendra Yandamuri. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. Zenodo. https://doi.org/10.5281/ZENODO.18095256
[3] Velangani Divya Vardhan Kumar Bandi. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011–1027. https://doi.org/10.63278/mme.v30i4.1938
[4] Kolla, S. K. (2023). Big Data–Driven Machine Learning Frameworks for Clinical Risk Prediction. International Journal of Medical Toxicology and Legal Medicine, 26(3), 44-59.
[5] Chowdhury, R. H. (2021). Cloud-based data engineering for scalable business analytics solutions: designing scalable cloud architectures to enhance the efficiency of big data analytics in enterprise settings. Journal of Technological Science & Engineering (JTSE), 2(1), 21-33.
[6] Meda, R. (2020). Real-Time Data Pipelines for Demand Forecasting in Retail Paint Distribution Networks. Global Research Development (GRD) ISSN, 2455-5703.
[7] Kolla, S. K. (2024). Federated Machine Learning On Big Healthcare Data For Privacy-Preserving Analytics. The Review of Diabetic Studies, 175-190.
[8] Mangalampalli, B. M. (2024). AI-Enhanced Data Governance: Automating Compliance In Healthcare Analytics Platforms. The Review of Diabetic Studies, 191-204.
[9] Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research, 101-122.
[10] Loganathan, R. (2024). GENERATIVE AI-ENABLED COMPLIANCE DOCUMENTATION AND AUDIT TRAIL AUTOMATION FOR GLOBAL DATA CENTER GOVERNANCE. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 487–504. https://doi.org/10.61841/turcomat.v15i3.15512
[11] Ranjith Kumar Peddi (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21. https://doi.org/10.31586/ujscs.2021.1380
[12] Davuluri, P. S. L. N. . (2024). AI-Driven Data Governance Frameworks for Automated Regulatory Reporting and Audit Readiness. Metallurgical and Materials Engineering, 30(4), 996–1010. https://doi.org/10.63278/mme.v30i4.1936
[13] Pamisetty, V., & Amistapuram, K. Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning.
[14] Segireddy, A. R. (2024). Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications. Journal of Computational Analysis and Applications, 33(8).
[15] Mukesh, A., & Aitha, A. R. (2021). Insurance Risk Assessment Using Predictive Modeling Techniques. International Journal of Emerging Research in Engineering and Technology, 2(4), 68-79.
[16] Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
[17] Meda, R. (2020). Designing Self-Learning Agentic Systems for Dynamic Retail Supply Networks. Online Journal of Materials Science, 1(1), 1-20.
[18] Nandan, B. P. (2021). Enhancing Chip Performance Through Predictive Analytics and Automated Design Verification. Journal of International Crisis and Risk Communication Research, 265-285.
[19] Meda, R. (2024). Enhancing Paint Formula Innovation Using Generative AI and Historical Data Analytics. American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN, 3067-4190.
[20] Sheelam, G. K. Power-Efficient Semiconductors for AI at the Edge: Enabling Scalable Intelligence in Wireless Systems. International Journal of Innovative Research in Electrical, Elec-tronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
[21] Kummari, D. N. (2022). AI-driven predictive maintenance for industrial robots in automotive manufacturing: A case study. International Journal of Scientific Research and Modern Technology, 107-119.
[22] Mitta, N. R. (2022). AI-Based Predictive Analytics for Life Insurance Underwriting: Leveraging Machine Learning Models for Mortality Risk Assessment, Policyholder Profiling, and Premium Calculation. American Journal of Data Science and Artificial Intelligence Innovations, 2, 327-362.
[23] Singireddy, J. (2023). Finance 4.0: Predictive analytics for financial risk management using AI. European Journal of Analytics and Artificial Intelligence (EJAAI) p-ISSN, 3050-9556.
[24] Inala, R. (2023). Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 579-606.
[25] Nandan, B. P., & Chitta, S. S. (2023). Machine Learning Driven Metrology and Defect Detection in Extreme Ultraviolet (EUV) Lithography: A Paradigm Shift in Semiconductor Manufacturing. Educational Administration: Theory and Practice, 29(4), 4555-4568.
[26] Recharla, M., Chava, K., Chakilam, C., & Suura, S. R. (2024). Postpartum Depression: Molecular Insights and AI-Augmented Screening Techniques for Early Intervention. International Journal of Medical Toxicology and Legal Medicine, 27(5), 935-957.
[27] Pamisetty, V. (2023). Transforming Community Engagement with Generative AI: Harnessing Machine Learning and Neural Networks for Hunger Alleviation and Global Food Security. Journal for Re Attach Therapy and Developmental Diversities.
[28] Pamisetty, A. (2024). Leveraging Big Data Engineering for Predictive Analytics in Wholesale Product Logistics. Available at SSRN 5231473.
[29] Raghunath Loganathan (2021). Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach. Universal Journal of Computer Sciences and Communications, 1(1), 1-26. https://doi.org/10.31586/ujscs.2021.1377
[30] Reddy, V. A. R. (2023). API-First Design As A Strategy For Healthcare System Interoperability. South Eastern European Journal of Public Health, 224–247. Retrieved from https://www.seejph.com/index.php/seejph/article/view/7128
[31] Pamisetty, A. (2023). Integration Of Artificial Intelligence And Machine Learning In National Food Service Distribution Networks. Educational Administration: Theory and Practice, 29 (4), 4979–4994.
[32] Pandiri, L. (2021). Cloud-Based AI Systems for Real-Time Underwriting in Recreational and Property Insurance. International Journal of Science and Research (IJSR), 10(12), 1626-1638.
[33] Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
[34] Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30 (4), 1011–1027.
[35] Kolla, S. K. (2023). Explainable AI and ML Models for Transparent Clinical Decision Support. Journal for ReAttach Therapy and Developmental Diversities, 6, 2444-2460.
[36]Singreddy, S. (2024). Predictive Modeling for Auto Insurance Risk Assessment Using Machine Learning Algorithms. Available at SSRN 5238922.
[37] Pamisetty, V. (2024). AI-Driven Decision Support for Taxation and Unclaimed Property Management: Enhancing Efficiency through Big Data and Cloud Integration. Available at SSRN 5250776.
[38] Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
[39] Ranga Reddy, V. A. (2024). Comparing Batch vs. Streaming Approaches in Healthcare Data Warehousing Environments. Journal of Neonatal Surgery, 13(1), 2287–2309. Retrieved from https://www.jneonatalsurg.com/index.php/jns/article/view/10223
[40] Sheelam, G. K. (2023). Adaptive AI workflows for edge-to-cloud processing in decentralized mobile infrastructure. Journal for Reattach Therapy and Development Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2). 3570ugh Predictive Intelligence.
[41] Kalisetty, S., & Singireddy, J. (2023). Agentic AI in retail: A paradigm shift in autonomous customer interaction and supply chain automation. American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN, 3067-4190.
[42]Amistapuram, K. (2024). Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning. Available at SSRN 6143426.
[43] Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
[44] Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
[45] Inala, R. (2022). Engineering Data Products for Investment Analytics: The Role of Product Master Data and Scalable Big Data Solutions. International Journal of Scientific Research and Modern Technology, 155-171.
[46] Avinash Pamisetty, Vijaya Rama Raju Gottimukkala. (2024). Agentic AI-Driven Multi-Cloud Big Data Architecture For Predictive Demand, Credit Risk, And Inventory Financing In National Food Service Supply Chains. Metallurgical and Materials Engineering, 30(4), 959–975. https://doi.org/10.63278/mme.v30i4.1933
[47] Nandan, B. P. Data Analytics-Driven Approaches to Yield Prediction in Semiconductor Manufacturing. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
[48] Mangala, N. (2021). CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps. Universal Journal of Business and Management, 1(1), 1-18.
[49] Ranjith Kumar Peddi. (2024). AI-Based Workforce Analytics for SLA Governance and Uptime Assurance in Data Centers. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8589–8601. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5361
[50] Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
[51] Pamisetty, V., & Amistapuram, K. Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning.
[52] Pamisetty, A. (2023). Optimizing National Food Service Supply Chains through Big Data Engineering and Cloud-Native Infrastructure.
[53] Pamisetty, A., Adusupalli, B., Mashetty, S., & Singreddy, S. (2024). Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management. Sneha, Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management (December 05, 2024).
[54] Sheelam, G. K. (2024). Towards autonomic wireless systems: integrating agentic AI with advanced semiconductor technologies in telecommunications. Am. Online J. Sci. Eng., 3(4), 234-256.
[55] Aitha, A. R. (2024). Generative AI-Powered Fraud Detection in Workers' Compensation: A DevOps-Based Multi-Cloud Architecture Leveraging, Deep Learning, and Explainable AI. Deep Learning, and Explainable AI (July 26, 2024).
[56] Kolla, T. (2024). AI-Powered Data Catalog Systems For Healthcare Data Discovery And Governance. South Eastern European Journal of Public Health, 2296–2311. https://doi.org/10.70135/seejph.vi.7077
[57] Recharla, M., & Chitta, S. AI-Enhanced Neuroimaging and Deep Learning-Based Early Diagnosis of Multiple Sclerosis and Alzheimer’s.
[58] Singireddy, J. (2024). AI-Driven Payroll Systems: Ensuring Compliance and Reducing Human Error. American Data Science Journal for Advanced Computations (ADSJAC) ISSN, 3067-4166.
[59] Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20.
[60] Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
[61] Bandi, V. D. V. K. (2024). AI-Driven Predictive Risk Modeling Architectures for Financial Systems. International Journal Of Finance, 37(3), 54-78.
[62] Keerthi Amistapuram. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950–5958. https://doi.org/10.53555/kuey.v29i4.10965
[63] Yandamuri, U. S. AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology.
[64] Adusupalli, B., Pandiri, L., & Singireddy, S. (2019). DevOps Enablement in Legacy Insurance Infrastructure for Agile Policy and Claims Deployment. risk, 7(12).
[65] Koppolu, H. K. R., Recharla, M., & Chakilam, C. Revolutionizing Patient Care with AI and Cloud Computing: A Framework for Scalable and Predictive Healthcare Solutions. Pr (y= 1| x)= s (w⊤ x+ b), 1.
[66] Kummari, D. N. (2022). AI-Driven Audit Frameworks For Enhancing Compliance In Modern Manufacturing Systems. Migration Letters, 19, 2150-2177.
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