International Journal of Research Publications in Engineering, Technology and Management (IJRPETM)
Quantifying the Cost and Risk of Enterprise LLM Adaptation Strategies
Enterprises choosing between prompt engineering, retrieval-augmented generation (RAG) and parameter-efficient fine-tuning are well served by the accuracy literature and poorly served by everything else. Published comparisons establish which technique injects knowledge more effectively in a given domain, but none reports what the alternatives cost to build and run, how they differ in latency, what infrastructure each obliges an organisation to own, or how their risk exposures differ in kind rather than degree. This paper supplies that missing quantitative and operational account. We first consolidate the comparative accuracy evidence and state plainly what it does and does not support. We then develop an adaptation cost of ownership model and derive a closed-form break-even condition for fine-tuning relative to prompting. The condition has three properties that are counter-intuitive and consequential: output-token cost cancels entirely, so the economic case rests only on input tokens removed per request; the break-even volume is inversely proportional to input token price, so the same task amortises very differently on frontier and small models; and it is inversely proportional to the achieved prompt-length reduction, which is a design variable rather than a constant. Under illustrative parameters the break-even sits near 0.64 million requests per month for a 4,000-token prompt, well above the volume of most internal enterprise applications. The practical conclusion is that fine-tuning is a behavioural instrument rather than a cost-reduction strategy, and that RAG lowers cost at no volume relative to the prompt it augments, so its business case must rest on freshness, provenance and accuracy. We close with a latency decomposition showing that decode time dominates, and a risk analysis showing that the archetypes do not sit on a single risk gradient: fine-tuning concentrates data risk, retrieval concentrates system risk, and prompting concentrates assurance risk.
Federated Learning Architecture for Multi-Cloud Financial Analytics with Privacy-Preserving Data Intelligence
The rapid digital transformation of the financial sector has resulted in the generation of massive volumes of sensitive customer and transactional data distributed across multiple cloud platforms. Traditional centralized machine learning approaches often require the transfer of confidential financial data to a single repository, increasing the risk of privacy breaches, regulatory non-compliance, and cyberattacks. Federated Learning (FL) has emerged as a promising distributed learning paradigm that enables collaborative model training without exposing raw data. This study presents a federated learning architecture designed for multi-cloud financial analytics with privacy-preserving data intelligence. The proposed architecture integrates secure aggregation, differential privacy, encryption techniques, and decentralized model synchronization to ensure secure collaboration among financial institutions operating across heterogeneous cloud environments. The architecture supports predictive analytics, fraud detection, credit risk assessment, customer behavior analysis, and anti-money laundering applications while maintaining data confidentiality and regulatory compliance. The research adopts a conceptual design supported by extensive literature analysis and system modeling to evaluate the effectiveness of federated learning in multi-cloud ecosystems. The proposed framework demonstrates improvements in privacy protection, scalability, interoperability, and model accuracy while minimizing communication overhead and security vulnerabilities. The findings suggest that federated learning offers a sustainable and secure solution for intelligent financial analytics in increasingly distributed cloud computing environments
From Posts to Profits: A Systematic Review on How Social Media Influencers Shape Brand Communication and Success
Background: Social media influencers (SMIs) have proved themselves as media stars in the field of brand communication, but little work has yet been done focusing on credibility, authenticity, engagement, or technological aspects of SMIs. However, no systematic review has until now analysed both psychological and emotional SMI characteristics together and investigated the impact of segmentation of the audience on the effectiveness of the brand communication. Methods: This study is a systematic review of the literature done according to the PRISMA 2020 guidelines. The searches were conducted in Scopus, Web of Science, ScienceDirect and Google Scholar databases from January 2015 to December 2022. Moreover, both written (peer-reviewed) and published studies in English in full text that explored the impact of SMI on brand outcomes were included. The Mixed Methods Appraisal Tool (MMAT Version 2018) was used to assess methodological quality. Result: A total of 59 studies met the inclusion criteria and were subsequently synthesised from a total of 72,536 initial records identified. The themes that appeared are credibility and trust as the most important mediator of the brand outcomes; authenticity as the intervening variable that ranged between creating various positive influences depending on the experience; excessiveness as an interfering variable of the impression of credibility when it comes to experiences linked to likeness and trust; virtual influencers as underperforming, compared to human influencers, that it positively moderates the experience when it comes to communication effectiveness. Many times, micro-influencers were able to create more engagement for lower reach. Conclusions: The effectiveness of SMI is influenced by the interactions of credibility, authenticity, audience characteristics, and platform context. This review suggests a new conceptual framework and recommends future research, such as a longitudinal study, cross-cultural evaluation, and AI-powered influences on the environment.
Retrieval-Augmented Generation with Serverless Cloud Computing for Enterprise Knowledge Management and Decision Intelligence
Retrieval-Augmented Generation (RAG) combined with serverless cloud computing represents a transformative approach for improving enterprise knowledge management and decision intelligence by integrating large language models with dynamic organizational information repositories. Traditional enterprise knowledge systems often struggle with fragmented data sources, outdated information, and limited contextual understanding. RAG addresses these limitations by retrieving relevant information from enterprise databases, documents, and knowledge graphs before generating responses through artificial intelligence models. When deployed using serverless cloud architectures, RAG systems gain scalability, cost efficiency, and operational flexibility by dynamically allocating computational resources according to demand. This integration enables organizations to develop intelligent systems capable of providing accurate insights, supporting strategic decisions, and enhancing employee productivity. The research explores the role of serverless RAG frameworks in enterprise environments, focusing on architecture, implementation strategies, knowledge accessibility, and decision-support capabilities. The study adopts a conceptual research methodology based on analysis of existing literature, technological developments, and enterprise AI adoption practices. Findings indicate that serverless RAG solutions can improve organizational intelligence by reducing information retrieval barriers, enhancing contextual accuracy, and enabling real-time decision-making. However, challenges related to data security, model reliability, governance, and integration complexity remain important considerations. The study concludes that the combination of RAG and serverless computing provides a sustainable foundation for next-generation enterprise knowledge ecosystems
Staleness-Bounded Aggregation for Cross-Institutional Federated Clinical Models
Federated learning can also enable development of cross-institutional clinical prediction models which do not require sharing of sensitive information about patients, which can help to improve privacy and regulatory quality. However, the cross institutional deployments are often known to have dissimilar computational attributes, network delays, and imperfect involvement of customers thereby receiving stale model refreshments which are harmful to convergence and predictive speed. The given paper suggests a new Staleness-Bounded Aggregation (SBA) system, where the influence of stalia news that the clients gather with the assistance of the dynamic staleness threshold and weighting according to the recency in the aggregation process on the server is limited. The proposed solution is efficient and balances model freshness and efficiency of communication, though it keeps the advantages of privacy of federated learning. The framework is experimented using non-identical (non-IID) simulated multi-hospital settings with clinical data having varying delay on communications and client availability. The experimental results indicate that converging SBA is quicker, more exact of categorization and more robust than the conventional Federated Averaging and other existing asynchronous aggregation frameworks. Specifically, the specified technique will reduce the adverse effect of slow updates, boost the stability of the most diverse environments, and reduce communication expenses without increasing the privacy concern. These results suggest that staleness-bounded aggregation is a realistic and scalable method of addressing distributed clinical machine learning, which is viable to support cross-institutional cooperation, and will not affect the quality of the predictors of the actual healthcare system. The suggested organization is likely to rely on credible, robust, and safe-next-generation healthcare analytics federated clinical intelligences.
AI-Driven Automated Infrastructure as Code Platform for Scalable Cloud-Native Enterprise Deployments and Operational Resilience
Artificial Intelligence (AI) and Infrastructure as Code (IaC) have emerged as transformative technologies for modern cloud-native enterprise infrastructure by enabling intelligent automation, consistent resource provisioning, operational resilience, and scalable application deployment. Traditional infrastructure management approaches rely heavily on manual configuration, fragmented operational workflows, and reactive maintenance, leading to configuration drift, deployment delays, security vulnerabilities, and increased operational costs. This research proposes an AI-driven automated Infrastructure as Code platform that integrates machine learning, DevOps automation, cloud-native orchestration, policy-as-code, predictive analytics, and continuous monitoring to support scalable enterprise deployments and resilient cloud operations. The proposed framework automates infrastructure provisioning, configuration validation, security compliance, workload optimization, resource scaling, anomaly detection, and disaster recovery through intelligent decision-making. AI models continuously analyze infrastructure telemetry, deployment history, workload behavior, resource utilization, and operational events to predict failures, optimize infrastructure allocation, and recommend proactive remediation strategies. Infrastructure definitions are maintained through version-controlled declarative templates, ensuring consistency across hybrid and multi-cloud environments. Continuous monitoring and governance mechanisms further improve infrastructure reliability, compliance, and service availability. The platform significantly reduces manual intervention, accelerates deployment cycles, minimizes operational risks, enhances cloud security, and optimizes infrastructure performance. Ultimately, the proposed framework supports enterprise digital transformation by providing secure, scalable, self-healing, and intelligent cloud-native infrastructure capable of adapting to dynamic business requirements and evolving operational challenges.
Benchmark-Driven GPU Performance Optimization for Medical Imaging, Genomics, and Large-Scale AI Workloads
This paper illustrates how benchmarking is useful in achieving optimization of the workloads that can be accelerated using GPUs in clinical imaging, genomics studies, and generative AI training. We tested High-Performance Linpack (HPL) tuning, memory throughput optimization, NCCL communication optimization and GPU health validation to clusters of multiple GPUs. Peak floating-point performance of 12.3 TFLOPS to 34.7 TFLOPS was attained in various GPUs. Memory optimizations boosted performance in effective bandwidth up to 1.8-2.2x. In distributed AI workloads NCCL optimization helped to cut communication latency by 35- 42, and memory virtualization trained large models, including VGG-16 (batch size 256), with only 18 percent loss in performance on 12 GB of a GPU. In medical imaging, when 2.1 -3.3 times less time was spent on reconstruction, there was no quality loss, and this was due to the use of the GPU. Genomics processes were almost 166X faster in identifying microRNAs than on a CPU. These findings demonstrate that the optimizations through benchmarking can lead to a reduction in the time-to-diagnosis, training, and cluster utilization in healthcare and AI
Innovation in Medical-Legal Regulatory Review: Improving Quality and Efficiency in Pharmaceutical Product Approvals
Approval of pharmaceutical products hinges on evaluating clinical evidence, the benefit-risk profile in product labeling, the consistency of labeling information with claims, and the appropriate legal framing of product claims. In typical review processes where each factor is treated sequentially during the approval pathway, delays, iterative cycles, and inconsistencies become the norm, hampering patient access and driving up costs. This paper describes several practical methods for redesigning clinical review to reduce inefficiency and enhance quality by: involving medical expertise and legal perspective earlier in the process, leveraging structured decision tools, utilizing targeted digital tools such as text analysis in reviewing content against claims for consistency, and identifying and mitigating core risk points of inefficiency and error within review. The paper reviews innovative approaches already adopted by companies and regulatory agencies, proposes a conceptually organized, integrated, and modular approach that engages medical and legal expertise from the outset, and identifies specific measures of value (e.g., accelerated review cycles, improved label quality) that the framework should provide while upholding high standards for patient safety and legal robustness. The paper discusses concrete implementation steps, meaningful metrics, and persistent challenges to guide industry and regulatory reviewers.
Advancing Pharmaceutical Regulatory Operations through Digital Submission Technologies: A Framework for Global Compliance
This article explores the shift in pharmaceutical regulatory operations from document-based submissions relying on the electronic Common Technical Document of version 3.2.2 to data-based concepts underpinned by eCTD of version 4.0, which is based on the HL7 Regulated Product Submission standard with ISO Identification of Medicinal Products, Substance, Product, Organization, and Referential master data services. The core operational issues inherent in this transition process are then summarized, addressing the multi-year periods of cross-jurisdictional coexistence of both standards, the continual threat of gateway-level technical rejections due to errors in the XML backbone, metadata, and controlled vocabularies, as well as the inconsistent digital maturity of the principal regulatory bodies. A dual data-centric five-layer architecture to facilitate globally consistent compliance is supplemented by a five-level organizational maturity model, illustrative comparative jurisdictional timelines contrasting current requirements and future milestones, and a dual-pipeline implementation methodology which explicitly considers the transitional burden of operating parallel publishing environments. A brief description of the comparative regulatory methodology used and a transparent explanation of the study's limitations, especially regarding shifting agency timelines and the advisory nature of this framework, are provided.
Emerging Applications of Artificial Intelligence and Machine Learning in Modern Earthquake Science
Seismological studies have traditionally relied on classical statistical models and manual interpretation to detect, analyze, and predict earthquake events. However, the growing complexity and volume of seismic data have necessitated more efficient and adaptive approaches. This study explores the integration of artificial intelligence (AI) and machine learning (ML) techniques into seismology. This study highlighted the capacity of AI and ML to revolutionize seismic data processing and interpretation. Majorly, the study reviewed findings on algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), support vector machines (SVMs), and unsupervised clustering methods. Also, AI systems such as WaveCastNet, SCALODEEP, BNGCNN, Cycle-Jnet, SASMEX, and UREDAS were reviewed in areas that improved accuracy in earthquake detection, earthquake predictions, and earthquake analysis
Peter Oluwasayo Adigun, Tobi Titus Oyekanmi
Agentic AI Architectures for Multi-Cloud Enterprise Intelligence and Autonomous Business Process Orchestration
As global enterprises migrate toward heterogeneous multi-cloud environments, traditional rule-based workflow systems prove insufficient for managing non-deterministic, high-velocity business operations. This paper presents a novel architectural framework for Agentic AI designed to achieve cross-cloud cognitive intelligence and autonomous business process orchestration. Modern Agentic AI transcends basic generative capabilities by incorporating multi-step planning, dynamic tool invocation, stateful memory persistence, and inter-agent negotiation protocols. The proposed system utilizes a decentralized, multi-agent orchestration topology that deploys specialized autonomous agents across major cloud providers (AWS, Azure, Google Cloud Platform) using cloud-native microservices and open mesh networks. By embedding an abstraction layer with unified state synchronization, context-aware semantic routing, and continuous policy enforcement, the architecture enables agents to autonomously plan and execute complex end-to-end enterprise workflows without vendor lock-in. Empirical evaluations demonstrate a 54% reduction in cross-cloud process cycle times, a 38% decrease in egress-related operational expenses through intelligent localized routing, and a 99.7% task completion success rate under dynamic environment perturbations. Ultimately, this research provides a scalable, fault-tolerant blueprint for building self-healing, secure, and fully autonomous enterprise intelligence platforms.
A Platform-Independent API Integration Architecture for Scalable Enterprise Commerce Solution
In today's enterprise commerce ecosystems, achieving interoperability in enterprise resource planning (ERP), customer relationship management (CRM), inventory management, fulfillment, pricing and customer intelligence systems becomes more critical than ever as the back-end is becoming more complex and heterogeneous. Traditional integration models often create rigid dependencies between business applications and the underlying operational platforms, which can limit the flexibility of the system's architecture, reduce interoperability, make integration more complex, and present major constraints for enterprise modernization efforts. The restrictions are especially stark when part of a large-scale digital transformation initiative that entails shifting enterprise technology environments and decentralized operational environments The paper describes the platform independent API integration architecture that can support scalable interoperability between enterprise commerce platforms and heterogeneous backend systems by abstracting the details of these integration, defining a set of canonical service models, and building a set of reusable orchestration layers. The proposed architecture in enterprise transformation programs explored in this paper decreased the number of direct integration dependency points by about 80.9%, decreased average deployment time from 14.2 hours to 3.8 hours, and reduced the percentage of integration failures from 20.0% to 2.9% of the deployments as compared to point-to-point baseline. A three-year total cost of ownership (TCO) analysis shows estimated savings of more than 64.4% when replacing legacy integration architecture with the canonical abstraction architecture. These quantitative results validate the architectural model as a scalable, budget-friendly and flexible platform to support enterprise commerce modernization at scale
Integrating Cloud Native Data Engineering with Reinforcement Learning for Autonomous Enterprise Cybersecurity
Cloud-native data engineering and reinforcement learning (RL) are emerging as powerful technologies for developing autonomous enterprise cybersecurity systems capable of detecting, adapting to, and responding to complex cyber threats. Modern enterprises generate enormous volumes of security-related data from cloud applications, networks, endpoints, and digital services. Traditional cybersecurity approaches often struggle to process this information efficiently and respond to rapidly evolving attacks. Integrating cloud-native data engineering enables scalable data collection, processing, and management, while reinforcement learning provides adaptive decision-making capabilities through continuous interaction with dynamic environments. This research explores the integration of cloud-native data engineering architectures with reinforcement learning techniques to establish autonomous cybersecurity frameworks. The study examines how intelligent systems can improve threat detection, automated response, and security optimization by learning from real-time data environments. The research highlights technological opportunities, architectural challenges, and future directions for building resilient cybersecurity ecosystems capable of operating with minimal human intervention
Hybrid CNN-LSTM Forecasting and Demand Response Optimization for Energy Flexibility and Cost Reduction in Grid-Connected Renewable Microgrids
Grid-connected renewable microgrids increasingly rely on accurate short-term forecasting and active demand-side management to reduce operating cost and exploit energy flexibility while maintaining reliable supply. This paper proposes an integrated framework that couples a hybrid convolutional neural network–long short-term memory (CNN-LSTM) forecasting model with a demand response (DR) optimization engine to jointly minimize electricity procurement cost and peak grid exchange in a grid-connected microgrid comprising solar photovoltaics (PV), wind generation, battery energy storage, and flexible/shiftable industrial and commercial loads. The CNN-LSTM model forecasts renewable generation, aggregate load, and real-time/time-of-use electricity prices over rolling 1-hour to 24-hour horizons, extracting local temporal features via convolutional layers before modeling longer-range dependencies with stacked LSTM layers. These forecasts feed a mixed-integer linear programming (MILP)-based demand response optimizer that schedules shiftable loads, curtailable loads, and battery charge/discharge to minimize total cost subject to comfort/production constraints, contractual demand charge limits, and grid import/export bounds. A case study on a representative grid-connected microgrid with 1.8 MW PV, 1.2 MW wind, 1.5 MWh battery storage, and 30% flexible load share demonstrates that the hybrid CNN-LSTM model reduces load and price forecast RMSE by 24.3% and 21.7% respectively compared to a standalone LSTM baseline, and that the integrated forecasting-DR optimization framework achieves an 18.9% reduction in daily electricity cost and a 22.4% reduction in peak grid demand relative to a forecast-agnostic rule-based baseline. These results demonstrate that combining accurate deep-learning-based forecasting with formal DR optimization can substantially improve both the economic performance and grid-friendliness of renewable microgrids without additional hardware investment.
Md Sadik Hassan Arik, Md Samiul Islam, Md Ahsan Habib, Md Sabiruzzaman
DevSecOps for Enterprise Ontology Platforms: Continuous Validation, Security, and Compliance of Semantic Knowledge Systems
Enterprise ontology systems are new platforms that have become the main components of intelligent organizations due to the ability to support semantic interoperability, knowledge integration, and decision-making that is based on AI computing across the heterogeneous enterprise systems. However, with the development of ontologies, and an increase in cybersecurity threats, stringent regulatory imperatives require ontological governance approach to be safe and automated throughout ontology lifecycle. The proposed ontology platform of ontology platforms Secure Semantic DevSecOps Framework (SSDF) in an enterprise is proposed as a stable ontology engineering, automated semantic validation, and security testing, compliance testing and policy-based deployment, which is run as a single pipeline in the proposed paper. It includes ontology version control, automated consistency checking, initial validation in SHACL and subsequent validation in SPARQL, Semantic vulnerability testing, identity and access control, Knowledge graph security, infrastructure as code and continuous compliance auditing, which is consistent with organizational policy and regulatory policy. The AI-based anomaly detection and automated governance systems also maximize semantic integrity, and minimize human effort. By making sure that security and compliance are a critical component of all the software development life, and not an activity performed after the software has been deployed, the ontology model proposed will allow the secure, scalable and survivable development of ontology to be integrated. It may also allow ontology to evolve quickly besides not being able to trade off data quality and semantic consistency or business governance. The framework is a viable platform to establish powerful semantic knowledge systems, that codesigns the digital transformation of organizations, intelligent automation, simplicable AI and interoperable knowledge-based software, which are built upon the cloud-native and hybrid enterprise architects
Modular Ticketing and Issue Resolution Framework Embedded Within Procurement Suites
The procurement suite and external service management systems face a variety of integration problems, which means that they often create operational inefficiencies for organizations. In the article, Modulus Ticketing is explained as a brand new modular ticketing solution with a seamless integration into procurement suites and also solves problems associated with the current systems, such as the complex user setup requirements and ticketing history. There are numerous benefits attached to Modulus, including eliminating repetitive logins, the capability of linking workflows directly into Modulus, automated resolution of tickets during processing, and a significant increase in the amount of time saved per ticket resolved. The benefits of the Modulus Ticketing solution include the elimination of many of the shortcomings of previous (legacy) systems and a scalable solution that will continue to drive improvement in procurement support through a truly unified data approach and application of intelligent/automated data utilization. Other future ambitions in development, such as intake management modules, will support the modular ticketing concept. The article further states that there have been some updated words and phrases that have been revised to have a broader interpretation and can be applied to a larger audience while still remaining consistent with systems such as Ivalua and ServiceNow. The findings of this study suggest that all companies should modernize their procurement support systems and enhance their tools in order to realize the most efficiency and usability from these systems.
Aligning Governance, Data, and Controls: A Practical Blueprint for Bank Remediation Initiatives
This paper presents a structured approach to helping G-SIBs, which have been the subject of multiple compliance orders from both the FRB and OCC since 2010, remediate their compliance problems. This will be accomplished under the direction of a dedicated Remediation Program Manager and the establishment of an 11-pillar framework that contains elements from the FRB SR08-08 policy statements to support a comprehensive approach to sustainability within the areas of risk management, data quality, and compliance oversight, along with enhanced governance and cross-functional collaboration. The methods employed to develop an effective program to remediate regulatory compliance issues included the use of a risk-based triage approach to the prioritization of compliance issues, advanced data analytics to conduct root cause analysis on unresolved issues, and the implementation of a hybrid execution model that employed both Agile and Waterfall methodologies, which resulted in the significant reduction of issues associated with high-risk areas of exposure and improved closure rates. By 2018, the banks had remediated all outstanding regulatory orders from both the FRB and OCC preventing the imposition of substantial fines and ensuring compliance with all regulatory requirements. The overall data architecture that was proposed for reporting compliance remediation results is comprised of a core repository of data and analytical dashboards to monitor compliance status. Future enhancements to the central repository and dashboards will be developed using a cloud-native RegTech and AI/ML-based solution to provide a more proactive solution for addressing complex regulatory compliance issues in the ever-evolving environment of public policy.
Telecom Innovation in Action Modernizing Spectrum Mobile for Growth and Compliance
Spectrum Mobile became a large provider of mobile services in the US after being acquired by charter communications. The speed with which a company can grow increases the complexity of its system, which in turn leads to new challenges. Spectrum Mobile faced a number of challenges: inconsistent releases, delays in testing changes, etc. To overcome these challenges, the QA/DevOps team started working on four key elements within their overall testing procedure: compliance, resiliency, automation, and modernization. Several key initiatives were started as part of this new initiative including the implementation of CI/CD pipelines in the cloud to automate the customer experience; the creation of a "hotfix" lane for addressing critical issues in real time. New technologies such as Blue/Green deployments and service virtualization were utilized to address middleware instability and scalability issues within the company. By leveraging new technology, Spectrum Mobile was able to enhance the speed with which it releases new products and services to customers, improve the availability of their systems, and create a 50% increase in its number of subscribers and protect a large amount of revenue. This case study also illustrates how the modernization of legacy systems will continue to be necessary to maintain competitive advantage in the telecommunications industry. In addition, the case study indicates how cloud-based and AI- based technological advancements will enable Spectrum Mobile to develop automated and scalable architectures in the future. Spectrum Mobile will continue to explore the potential of artificial intelligence (AI), predictive analytics, and sustainable networks to support the evolving needs of society through technology.
Streamlined Claims Adjudication: Observability-Driven Dashboard Intelligence
Significant delays in claims processing for insurance companies are primarily due to human review, data silos and compliance-related issues, which lead to very high levels of fraud and a large number of mistakes made by regional carriers. To remedy this situation, an observability-based architecture is proposed that will integrate technologies such as Apache Spark streaming, MLflow, Snowflake and Grafana to accomplish real-time anomaly detection, as well as KPI visualization. As validated with simulated claims processing, this system would result in a high level of successful straight-through processing for low-complexity claims, accurate detection of fraud relative to the number of claims processed with low levels of bias (the fraud detection rate approaching 100%); and a significant reduction in total time required for claims processing. The key advanced capabilities of the architecture include improved logging, ethical a safeguards against bias and comprehensive auditability. From a business perspective, by providing compliance with audit requirements; faster payments to customers; and lower claim costs, the enterprise can benefit from improved compliance with regulatory and other requirements, improved customer service (due to faster payment), and lower claim costs. Additionally, this system will provide real-time alerts for claims adjusters at peak times of activity and a governance framework for the ethical deployment of AI that can be used to enhance recommendation systems for customer management
Engineering Resilient ETL: PowerCenter Performance Limits and Cloud Migration Pathways
The goal of this project is to modernize BP's OPSD2 Downstream Logistics ETL Solution by moving away from Mainframe DB2 database systems and using Oracle-based DBs and incorporating multiple source data sources through Informatica Mappings using an Agile Governance framework for ETL solutions. This study uses InformaticaPowerCenter's Repository-Integration service architecture to analyze the performance characteristics of InformaticaPowerCenter and find that Read Operations can scale efficiently under high levels of concurrency while Write Operations experience rapid degradations in performance due to the use of exclusive locks on certain tables. Therefore, there is a risk of failure if high- volume deployments are attempted using a Write Operation approach. Performance analysis performed using multiple tools have identified that the write latency is the primary cause of the performance constraint. To alleviate this issue, it is recommended that cache tuning, repository clustering and asynchronous bulk logging be used to meet the operational demands of fuel distribution. Looking forward, OPSD2 intends to evolve into BP's Real-time Net-Zero Analytics Platform by leveraging cloud-native solutions based on Informatica IDMC and Advanced Streaming capabilities.
Handling Criteria-Driven Filtering &Sampling Across Distributed Data Partitions
This paper presents an innovative five-layered architecture for finding equitable representation across the various fragmented sections of large datasets. The architecture employs a set of customizable criteria to group together similar datasets into well-balanced unbiased sample sets; while retaining complete and accurate metadata for all file boundary elements. Important features of this architecture include: a multi-level criteria engine which is capable of processing accepted and rejected streams from a variety of sources; a stratified sampling process which effectively eliminates the presence of partition skew; and a quality assurance mechanism that provides an efficient means to capture and report performance-related metrics. Benchmark partitioning results indicate a significant shift toward overall balance ratio improvements; with improvements of approximately 1.75 to 1.12 ratio, as well as an overall reduction in volume anomaly of 87% (from ±25% down to ±3.2%), and total absence of schema drift occurred at a cost of less than 5% of the overall dataset processing costs to the author(s). By implementing this method, the processing of large datasets has been made much more reliable than previously possible, while simultaneously minimising bias and maximising the ability to provide efficient audit trial capabilities. Future enhancements to the architecture will focus on providing distributed execution capabilities in conjunction with streaming integration into enterprise data systems
Enhancing Cloud-Native Cyber Defense using Deep Learning for Predictive Threat Intelligence and Adaptive Incident Response
The rapid adoption of cloud-native technologies has transformed modern information technology by enabling organizations to develop, deploy, and manage applications using microservices, containers, Kubernetes orchestration, and serverless computing. While these technologies improve scalability, flexibility, and operational efficiency, they also introduce increasingly sophisticated cybersecurity challenges that traditional security approaches struggle to address. Cyber threats targeting cloud-native environments continue to evolve, requiring intelligent, proactive, and adaptive defense mechanisms capable of identifying attacks before they cause significant damage. Deep learning has emerged as a promising solution for predictive threat intelligence because of its ability to analyze vast volumes of structured and unstructured security data, identify hidden attack patterns, and forecast potential cyber threats. Furthermore, adaptive incident response powered by artificial intelligence enables automated detection, prioritization, containment, and remediation of security incidents with minimal human intervention. This essay explores the integration of deep learning techniques into cloud-native cybersecurity frameworks to strengthen predictive threat intelligence and adaptive incident response capabilities. It reviews existing literature, identifies research gaps, and proposes a comprehensive research methodology for designing and evaluating an intelligent cloud-native cyber defense framework. The proposed approach aims to improve threat prediction accuracy, response efficiency, system resilience, operational scalability, and cybersecurity decision-making while reducing incident response time and enhancing protection against continuously evolving cyber threats
AI-Driven Incident Response Using Collaborative Cyber Security Analytics for Enterprise Cloud Environments
Modern enterprise cloud environments face increasingly sophisticated, multi-vector cyberattacks that easily bypass traditional perimeter-based defenses and isolated Security Information and Event Management (SIEM) platforms. The velocity and scale of cloud-native threats necessitate an evolution from manual, siloed threat detection toward automated, intelligence-driven mitigation architectures. This paper proposes a comprehensive, privacy-preserving framework titled "AI-Driven Incident Response Using Collaborative Cyber Security Analytics for Enterprise Cloud Environments." By combining federated learning paradigms, graph neural networks (GNNs), and automated orchestration engines, the framework aggregates threat intelligence across heterogeneous, multi-tenant cloud ecosystems without exposing sensitive proprietary logs. The architecture constructs dynamic attack-path graphs from multi-source telemetry, uses spatial-temporal neural models to detect lateral movement and zero-day exploits, and deploys deep reinforcement learning agents to execute real-time containment protocols. Empirical evaluations on multi-cloud benchmark environments demonstrate that this collaborative framework reduces Mean Time to Detect (MTTD) by 64%, accelerates Mean Time to Respond (MTTR) by 78%, and decreases false-positive alerts by 41% compared to traditional centralized security operational frameworks. Ultimately, this research provides a scalable, privacy-conscious blueprint for autonomous cloud security operations, enabling collective defense capabilities across distributed enterprise cloud infrastructures.
Building Explainable AI Integrated Virtualized Enterprise Platforms for Intelligent Adaptive Cybersecurity Operations
The rapid digital transformation of enterprises has significantly increased the complexity of cybersecurity environments, requiring intelligent, adaptive, and transparent security solutions. Virtualized enterprise platforms have become the foundation of modern organizational infrastructures by enabling scalable computing, cloud integration, and flexible resource management. However, these platforms also introduce sophisticated cyber threats that conventional security mechanisms struggle to detect and mitigate effectively. Explainable Artificial Intelligence (XAI) has emerged as a promising approach for enhancing cybersecurity by combining advanced machine learning capabilities with interpretable decision-making processes. This study explores the development of Explainable AI integrated virtualized enterprise platforms that support intelligent adaptive cybersecurity operations. The proposed framework emphasizes transparency, trust, accountability, and real-time threat response while maintaining high detection accuracy. By integrating explainable models with virtualization technologies, organizations can improve incident detection, risk assessment, automated response, and regulatory compliance without sacrificing operational efficiency. The study adopts a qualitative and conceptual research methodology supported by an extensive review of recent scholarly literature on artificial intelligence, explainable AI, virtualization, and cybersecurity. The findings suggest that integrating explainable AI into virtualized enterprise environments enhances situational awareness, strengthens cyber resilience, improves human–AI collaboration, and enables informed security decision-making. The proposed approach provides a strategic foundation for developing secure, transparent, scalable, and adaptive enterprise cybersecurity ecosystems capable of addressing evolving digital threats
Generative Artificial Intelligence–Powered Enterprise Multi-Cloud Infrastructure for Intelligent Workload Optimization
Modern enterprise IT environments increasingly rely on heterogeneous multi-cloud architectures to achieve high availability, prevent vendor lock-in, and optimize operational costs. However, managing distributed resources across disparate public and private cloud environments introduces significant complexity regarding dynamic workload placement, resource allocation, latency minimization, and cost governance. Traditional rule-based and static telemetry systems fail to adapt to real-time execution anomalies and fluctuating user demands. This paper presents a novel framework integrating Generative Artificial Intelligence (GenAI) into multi-cloud control planes for intelligent, predictive workload optimization. By synthesizing high-dimensional telemetry streams, workload dependencies, and pricing APIs into contextual multimodal embeddings, GenAI models predict utilization spikes, generate adaptive infrastructure topology configurations, and synthesize automated infrastructure-as-code (IaC) deployment policies in real time. The proposed framework employs transformer-based architecture combined with reinforcement learning from operational feedback to continuously refine resource balancing across AWS, Azure, and Google Cloud Platform. Experimental validation demonstrates that GenAI-driven orchestration reduces multi-cloud operational expenditure by 28%, decreases cross-cloud latency by 34%, and mitigates service-level agreement (SLA) breaches by 42% compared to conventional heuristic methods. This research establishes a scalable blueprint for autonomous, self-healing, and cost-aware enterprise cloud management powered by generative AI paradigms.
Zero-Downtime Modernization of Enterprise Platforms: Migration Patterns and Operational Considerations
Modernizing enterprise platforms without service interruption is a persistent architectural challenge, especially when legacy systems are deeply coupled to business operations, data stores, and external interfaces. Full replacement strategies often introduce excessive delivery risk, long timelines, and operational disruption. This paper presents a zero-downtime modernization architecture based on incremental migration, controlled traffic routing, coexistence between legacy and modern services, phased data transition, and explicit rollback capability. The proposed model combines a façade or gateway layer, progressive cutover practices, dual-run data synchronization, health-based release decisions, and staged decommissioning. The architecture is intended for enterprise platforms where availability, continuity, and controlled operational change are critical requirements. The paper discusses migration seams, transitional architecture, release safety, data synchronization, rollback boundaries, and modernization sequencing. It argues that zero-downtime modernization should be treated as an operational design discipline rather than a deployment tactic. Rather than emphasizing platform replacement as a one-time event, the paper frames modernization as a governed progression of reversible steps that preserve business continuity while enabling architectural renewal. The resulting model supports lower migration risk, earlier value realization, and more sustainable long-term platform evolution.
Rule Externalization for Enterprise Validation Platforms: Architecture and Design Considerations
Enterprise validation platforms often evolve under changing business policies, compliance obligations, and operational constraints. In many organizations, validation logic is initially embedded directly within application code, database procedures, or channel-specific service flows. While such an approach may accelerate early delivery, it typically reduces maintainability, complicates auditing, weakens explainability, and increases the cost of policy change. This paper presents a generalized architecture for rule externalization in enterprise validation platforms, with emphasis on separation of concerns across intake systems, canonical data transformation, orchestration, rule evaluation, audit tracing, and outcome routing. The proposed design is intended to be reusable across domains such as financial services, healthcare, insurance, logistics, and other workflow-intensive enterprise environments. The paper discusses architectural trade-offs related to rule granularity, versioning, performance, traceability, and governance. It also examines how external verification steps can be integrated without tightly coupling third-party dependencies to core validation logic. Rather than treating rule engines as isolated implementation tools, the paper frames rule externalization as a platform capability that improves policy agility, cross-channel consistency, and operational resilience. The resulting model offers a practical approach for building scalable, explainable, and maintainable validation systems in heterogeneous enterprise architectures
Monolith-to-Microservices Migration in Enterprise Operations Platforms: A Workflow-Oriented Architecture
Monolith-to-microservices migration is widely practiced in enterprise engineering but rarely well characterized in the literature. Engineering teams often treat the migration as a refactoring exercise, when in reality it is a workflow re-architecting exercise, and the difference determines whether the migration improves or harms the operations platform. This article develops a workflow-oriented treatment of monolith-to-microservices migration in enterprise operations platforms. We position the migration as a workflow decomposition problem rather than a code decomposition problem; we identify the five workflow archetypes that dominate enterprise operations platforms (long-running approvals, batch orchestration, event-driven state transitions, human-in-the-loop review, and time-sensitive material flows), and show how each archetype maps to a distinct decomposition pattern: strangler-fig with bounded context for long-running approvals, saga orchestration for business-process flows, event-carried state transfer for cross-domain propagation, dual-write bridging for human-in-the-loop review, and shadow-write reconciliation for time-sensitive material flows. We then examine the migration mechanics that determine whether the re-architecting succeeds in production: workflow-aware dependency analysis, contract-first API design, idempotent compensation, gradual traffic shifting with feature-flagged routing, and observability that surfaces in-flight workflow state. We close with a phased migration framework that connects migration decisions to workflow properties rather than to service boundaries drawn prematurely. The result is a practitioner’s map for migrating enterprise operations platforms in a way that preserves correctness during the migration while improving the architecture that the platform leaves behind
Integrated Artificial Intelligence Framework for Enterprise Cloud Modernization and Intelligent Threat Management Systems
Enterprise organizations are rapidly adopting cloud computing to improve operational efficiency, scalability, and digital transformation. However, cloud modernization introduces complex cybersecurity challenges that require intelligent, adaptive, and automated defense mechanisms. Artificial Intelligence (AI) has emerged as a transformative technology capable of enhancing cloud modernization while strengthening threat detection, incident response, and risk management. This study proposes an integrated artificial intelligence framework that combines cloud modernization strategies with intelligent threat management systems to achieve secure, resilient, and efficient enterprise cloud environments. The framework incorporates machine learning, deep learning, natural language processing, behavioral analytics, and predictive security models to automate infrastructure management and identify sophisticated cyber threats in real time. The proposed approach emphasizes continuous monitoring, intelligent workload optimization, zero-trust security principles, and automated incident response to improve organizational resilience against evolving cyberattacks. The research adopts a qualitative methodology supported by an extensive review of existing literature, industry best practices, and conceptual framework development. The findings indicate that integrating AI into enterprise cloud ecosystems significantly enhances resource utilization, minimizes operational risks, reduces security incidents, and supports regulatory compliance. Furthermore, AI-driven threat intelligence enables organizations to proactively identify vulnerabilities and mitigate emerging attacks before they affect critical business operations. The proposed framework offers a comprehensive foundation for secure cloud modernization and sustainable digital transformation across diverse enterprise environments
Reimagining Enterprise SaaS Products with AI: An Architect's Perspective on Infusing AI Use Cases Without Breaking the Product
Most enterprise SaaS "AI" features are bolted onto existing screens rather than reasoned into the workflows they are meant to improve, producing a predictable pattern: a launch announcement followed by a quiet plateau in adoption. This article argues that durable value comes from reimagining specific, currently manual workflows end-to-end, not from adding AI surfaces, and that multi-tenant SaaS products carry a distinct risk profile - cross-tenant data leakage, autonomous actions inside a customer's live account, and erosion of the trust the subscription relationship depends on - that generic enterprise AI governance does not address. The article proposes a risk-tiered autonomy model scoped to tenant isolation, a guardrail architecture for actions taken inside customer accounts, a four-step implementation roadmap (tenant-aware knowledge base, retrieval-augmented generation pipeline, model testing, integration and deployment), and a consolidated checklist for product and engineering leaders reimagining SaaS workflows around AI.