International Journal of Research Publications in Engineering, Technology and Management (IJRPETM)
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.
Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security
Modern digital enterprises demand highly adaptive, scalable, and resilient software ecosystems to handle complex data streams and evolving threat vectors. This paper presents a holistic framework for designing intelligent enterprise platforms by converging machine learning (ML) driven API engineering with cloud-native security paradigms. Traditional API architectures and security protocols rely heavily on static configurations and reactive rule-matching, which fail to accommodate the dynamic nature of distributed microservices and serverless architectures. By embedding predictive analytics, behavioral profiling, and unsupervised anomaly detection directly into the API gateway and container orchestration layers, our framework creates an autonomous ecosystem capable of self-optimization and proactive threat mitigation. We explore the architectural blueprints required to implement real-time payload inspection, intelligent rate-limiting, predictive zero-trust access control, and continuous security posture management. The findings indicate that integrating intelligence at the architectural core reduces operational latency, significantly decreases the time to detect sophisticated cyber-attacks, and enhances resource utilization across multi-cloud environments. Ultimately, this research provides a comprehensive technical roadmap for enterprise architects aiming to transition from legacy, deterministic systems to cognitive, self-defending, and intelligent digital platforms.
Building Cyber Resilient Enterprise Architectures through Machine Learning Driven API Security and DevSecOps Automation
The rapid digital transformation of enterprises has significantly increased the adoption of cloud computing, microservices, application programming interfaces (APIs), and continuous software delivery pipelines. While these technologies enhance organizational agility and scalability, they also introduce complex cybersecurity challenges that conventional security mechanisms struggle to address. This study explores the role of machine learning-driven API security and DevSecOps automation in building cyber resilient enterprise architectures capable of preventing, detecting, responding to, and recovering from sophisticated cyber threats. Machine learning algorithms facilitate intelligent anomaly detection, behavioral analysis, threat prediction, and automated incident response by continuously learning from evolving attack patterns. Simultaneously, DevSecOps integrates security practices throughout the software development lifecycle, enabling automated vulnerability assessment, secure code analysis, compliance verification, and continuous monitoring without compromising development speed. The integration of these technologies strengthens enterprise resilience by reducing attack surfaces, improving visibility across distributed systems, and enabling proactive risk management. The research adopts a qualitative approach supported by an extensive review of current academic literature, industry frameworks, and cybersecurity best practices. The findings demonstrate that combining artificial intelligence, API security analytics, and DevSecOps automation significantly enhances organizational capability to withstand cyberattacks while maintaining business continuity. The study concludes that intelligent security automation represents a strategic foundation for developing adaptive, resilient, and future-ready enterprise architectures
Threat DetectionSecure Software Development
Event-Driven Enterprise Applications with Apache Kafka and Resilient Cloud-Native Architecture
Increasingly, the current business environment is requiring more real-time, scaled and fault tolerant application designs with the ability to accommodate large volume event in distributed cloud environments. Monolithic and request-based systems have been found to have issues with latency, lack of scalability and resilience when it comes to working with ever-generated enterprise data. The study introduces an Event-Driven Enterprise Application Framework that is based on an Apache Kafka and a Resilient Cloud-Native Architecture to facilitate the processing of events in a reliable, asynchronous and intelligent manner. The suggested framework is a combination of event producers, Apache Kafka clusters, distributed message topics, stream processing engines, microservices, API gateways, Kubernetes-based container orchestration, service mesh, cloud-native databases, distributed caching, observability services, and automated recovery mechanisms. The architecture uses event sourcing, Command Query Responsibility Segregation (CQRS), schema management, fault tolerance, horizontal auto-scaling, and AI-assisted monitoring to enhance the responsiveness of the system, its operational reliability, and resource usage. Cloud-native technologies provide dynamic workload management, self-healing deployments, continuous integration and continuous deployment (CI/CD) and secure communication between distributed services. Additionally, full-scale monitoring, distributed tracing, and predictive analytics allows detecting anomalies and optimization of performance proactively. The proposed architecture provides a versatile and strong foundation of enterprise applications utilized in the domain of finance and healthcare, retail, manufacturing, logistics, and IoT ecosystems in cases when low-latency event processing and high system availability are essential. The framework illustrates how the event-driven principles and cloud-native resiliency can contribute greatly to the agility of the enterprise, operational efficiency, fault tolerance, and business continuity as part of the contemporary digital transformation initiatives.
Cognitive Enterprise Platforms for Business Intelligence Leveraging Artificial Intelligence for Cloud Computing and Intelligent Automation
Modern corporations struggle to extract meaningful strategic value from highly fragmented, multi-cloud datasets while contending with the rigid constraints of traditional, rule-based analytics. This paper introduces an architectural blueprint for Cognitive Enterprise Platforms that natively fuse Business Intelligence (BI) with Artificial Intelligence (AI) and cloud computing infrastructures to drive advanced, intelligent automation loops. Unlike standard BI systems that rely exclusively on historic descriptive reporting, cognitive enterprise platforms replicate human-like reasoning, semantic context parsing, and predictive logic to uncover complex, hidden market patterns and operational anomalies. By utilizing hyper-scalable, cloud-native data fabrics alongside autonomous machine learning pipelines, this architecture changes enterprise data repositories from passive data lakes into self-optimizing, context-aware information engines. Furthermore, we evaluate the implementation of event-driven, intelligent automation workflows (often called Agentic Process Automation) that autonomously convert prescriptive insights into localized operational actions. The proposed multi-layered design bridges the classic gap between strategic decision-making and rapid transactional execution across diverse corporate domains. Ultimately, our research shows that deploying cognitive architectures significantly increases data processing efficiency, slashes decision latencies, and provides the scalable agility required for long-term digital corporate survival.
Event-Driven Architecture in Long-Running Enterprise Validation Workflows
Long-running enterprise validation workflows — vendor onboarding, KYC, claims adjudication, regulatory filings, procure-to-pay — routinely span hours to weeks, cross multiple systems, and involve dozens of validation steps whose outcomes cannot be predicted in advance. Request/response and synchronous orchestration models buckle under these properties: they hold connections open across human waits, they retry timeouts as if they were failures, and they force retry semantics onto business steps that were never designed to be idempotent. Event-Driven Architecture (EDA), paired with durable execution, is the pattern that these workflows structurally require, not merely a performance optimization. This article treats EDA in the specific context of long-running validation workflows and identifies the architectural elements that distinguish a system that survives production from one that quietly corrupts state. We define five architectural building blocks — the event backbone, the workflow orchestrator, the validation-step service pattern, the compensation and saga path, and the observability surface — and derive their design constraints from the workflow properties. We then present an end-to-end reference architecture that composes the five blocks, illustrate it in a vendor-onboarding worked scenario, and discuss the trade-offs against synchronous orchestration and pure choreography alternatives. The intended reader is the enterprise architect or platform engineer who must decide whether to adopt EDA, how deeply to commit to durable execution, and where the operational cost of the resulting system will fall.
Cloud Native Enterprise Architecture for Intelligent Utility Transformation Predictive Monitoring and Regulatory Governance
The transformation of utility organizations through cloud-native enterprise architecture has become essential for achieving operational intelligence, scalability, resilience, and regulatory compliance. Modern utility systems require advanced digital platforms capable of managing complex infrastructure, large-scale data processing, real-time monitoring, and evolving governance requirements. This research explores a cloud-native enterprise architecture framework that integrates intelligent utility transformation, predictive monitoring, artificial intelligence (AI), machine learning (ML), and regulatory governance. The proposed approach leverages microservices, container orchestration, cloud computing, data analytics, and automated governance mechanisms to enhance utility operations. Predictive monitoring enables early identification of equipment failures, service disruptions, and operational risks by analyzing real-time sensor data, application metrics, and historical performance patterns. Regulatory governance ensures compliance through automated auditing, security controls, policy enforcement, and transparent reporting mechanisms. The integration of AI-driven analytics supports intelligent decision-making, resource optimization, and improved customer service management. The framework addresses major utility challenges including legacy infrastructure limitations, cybersecurity threats, operational inefficiencies, and regulatory complexity. By adopting cloud-native enterprise architecture, utility organizations can achieve higher agility, improved reliability, and sustainable digital transformation. This research highlights the importance of combining cloud technologies, intelligent monitoring, and governance strategies to create secure, adaptive, and future-ready utility ecosystems.
Unified Enterprise Intelligence through AI Cloud Native Integration, Zero Trust Architecture and Intelligent Governance
Unified Enterprise Intelligence represents a transformative approach to modern organizational management by integrating artificial intelligence, cloud-native technologies, zero trust security principles, and intelligent governance frameworks into a cohesive digital ecosystem. Contemporary enterprises operate within complex environments characterized by rapidly expanding data volumes, distributed computing infrastructures, evolving cybersecurity threats, and increasing demands for real-time decision-making. Traditional enterprise architectures often struggle to provide the agility, security, and intelligence required to address these challenges. This research explores how the convergence of AI-driven analytics, cloud-native integration, zero trust architecture, and intelligent governance enables organizations to develop adaptive, secure, and data-driven operational models. The study examines the theoretical foundations, technological components, implementation strategies, and governance implications associated with unified enterprise intelligence. A comprehensive research methodology based on qualitative analysis, conceptual framework development, and evaluation of existing academic and industry perspectives is applied to understand the relationship between intelligent technologies and enterprise transformation. The research highlights that successful enterprise intelligence requires not only advanced technological adoption but also strategic alignment, ethical governance, cybersecurity maturity, and organizational capability development. The findings suggest that integrated AI-cloud-zero trust ecosystems can enhance operational efficiency, strengthen resilience, improve decision accuracy, and establish sustainable competitive advantages in the digital economy.
Architecting Adaptive Enterprise Intelligence through Secure Cloud Platforms and AI Driven Intelligent Decision Systems
The rapid advancement of digital technologies has transformed enterprise operations by enabling organizations to adopt intelligent, data-driven decision-making systems supported by secure cloud computing platforms. Adaptive enterprise intelligence integrates artificial intelligence (AI), machine learning, big data analytics, cloud infrastructure, cybersecurity, and business intelligence to create responsive organizational ecosystems capable of adapting to dynamic market conditions. Secure cloud platforms provide scalable computing resources, real-time data accessibility, and cost-efficient infrastructure, while AI-driven intelligent decision systems facilitate predictive analytics, automation, and strategic planning. This study explores the architecture of adaptive enterprise intelligence by examining the integration of secure cloud technologies with AI-enabled decision support mechanisms. The research investigates the essential architectural components, including cloud security frameworks, data governance, AI algorithms, distributed computing, and enterprise knowledge management. A qualitative research methodology based on secondary data analysis is employed to synthesize findings from scholarly literature, industry reports, and technological frameworks. The study demonstrates that secure cloud environments significantly enhance organizational agility, operational resilience, and innovation capabilities while ensuring confidentiality, integrity, and availability of enterprise data. Furthermore, AI-driven decision systems improve strategic responsiveness through real-time analytics and intelligent automation. The findings contribute to a comprehensive understanding of adaptive enterprise intelligence architecture and provide recommendations for organizations seeking sustainable digital transformation, competitive advantage, and secure intelligent enterprise ecosystems.
Advanced Enterprise Computing Architecture for AI Driven Cloud Migration Secure Data Governance and Intelligent Cyber Resilience
The rapid evolution of cloud computing, accelerated by artificial intelligence (AI), has redefined organizational infrastructures, prompting a shift toward Advanced Enterprise Computing Architecture. This research explores the integration of AI-driven cloud migration, secure data governance, and intelligent cyber resilience frameworks to address the vulnerabilities inherent in modern digital transformations. As enterprises migrate legacy systems to hybrid and multi-cloud environments, automated orchestration tools optimize workload distribution, reduce operational overhead, and enhance scalability. Concurrently, data governance must adapt to ensure continuous compliance, utilizing automated data classification, zero-trust architectures, and cryptographic protocols to protect sensitive assets across distributed networks. Furthermore, the escalation of sophisticated cyber threats necessitates a transition from reactive security measures to intelligent cyber resilience. By embedding machine learning algorithms directly into the security architecture, organizations can achieve autonomous threat detection, real-time anomaly analysis, and self-healing system recovery. This paper synthesizes these three pillars—migration, governance, and resilience—into a cohesive, unified architectural paradigm. Ultimately, this comprehensive framework provides global enterprises with a strategic blueprint to maximize operational efficiency, maintain regulatory compliance, and guarantee business continuity amidst an increasingly hostile and dynamic digital threat landscape
Designing Modular AI Architectures for Enterprise Scale: From Monolithic Models to Composable AI Systems
Enterprises adopting machine learning have moved beyond isolated proof of concept models towards production systems that must integrate with established platforms such as customer relationship management (CRM), enterprise resource planning (ERP) and telecom operations and business support systems (OSS/BSS). This paper argues that the dominant obstacle to value realisation is architectural rather than algorithmic. We review the limitations of the monolithic model pattern, in which a single large model is trained, deployed and maintained as one indivisible unit, and we set out a reference architecture for modular, composable AI systems. The proposed architecture organises capability into independently deployable model services, a shared data foundation with a feature store and lineage tracking, and an operations control plane that automates integration, testing, monitoring and governance. We map established software engineering practice, namely microservices, continuous delivery and design patterns for machine learning, onto the constraints of enterprise AI, and we discuss integration with CRM, ERP and telecom stacks through an API and event driven serving layer. A qualitative evaluation compares the monolithic and modular approaches across maintainability, scalability, governance and time to change. The analysis indicates that modularisation reduces coupling and shortens the path from experimentation to a governed, observable production system, at the cost of an increased operational surface area that the control plane is designed to manage
Next Generation Enterprise Computing Through Hybrid AI FinOps Blockchain and Healthcare Analytics
The rapid evolution of enterprise computing has transformed organizational strategies by integrating intelligent technologies that enhance operational efficiency, financial governance, data security, and healthcare service delivery. Next-generation enterprise computing combines Artificial Intelligence (AI), Financial Operations (FinOps), blockchain technology, and healthcare analytics into a unified framework capable of supporting sustainable digital innovation. AI enables intelligent automation, predictive analytics, and data-driven decision-making across enterprise environments, while FinOps provides financial accountability and optimized cloud resource utilization to reduce operational costs. Blockchain technology strengthens trust through decentralized, transparent, and immutable transaction management, ensuring secure data exchange among multiple stakeholders. Healthcare analytics leverages large-scale clinical and operational datasets to improve patient outcomes, optimize resource allocation, and support evidence-based healthcare management. The integration of these technologies creates an adaptive digital ecosystem that improves organizational resilience, regulatory compliance, cybersecurity, and business agility. This paper proposes a hybrid enterprise computing framework that combines AI-driven intelligence, FinOps governance, blockchain-enabled trust, and healthcare analytics to address the growing demands of digital transformation. The framework emphasizes scalable cloud infrastructure, secure information sharing, predictive decision support, and sustainable innovation while minimizing operational risks and financial inefficiencies. The proposed approach demonstrates how interdisciplinary technological convergence can enable intelligent enterprises to achieve long-term competitiveness, environmental sustainability, improved healthcare services, and resilient digital ecosystems capable of addressing future technological and organizational challenges
Enhancing Clinical Data Reliability through Predictive Machine Learning-Driven Intelligent DevOps Frameworks
The rapid digitization of healthcare systems has generated enormous volumes of clinical data from electronic health records, medical imaging systems, wearable devices, laboratory platforms, and healthcare information networks. Ensuring the reliability, accuracy, availability, and security of this data has become a critical challenge for healthcare organizations. Traditional approaches to clinical data management often struggle with data inconsistencies, system failures, delayed error detection, and difficulties in maintaining continuous integration across complex healthcare environments. Predictive machine learning-driven intelligent DevOps frameworks provide an advanced solution by combining artificial intelligence, automation, continuous monitoring, and adaptive operational practices to enhance clinical data reliability. These frameworks utilize machine learning algorithms to predict potential data quality issues, detect anomalies, optimize system performance, and automate corrective actions before failures affect healthcare operations. The integration of DevOps principles with predictive analytics enables seamless collaboration between development, operations, and clinical technology teams while improving scalability and resilience. This research explores the role of intelligent DevOps frameworks in strengthening clinical data reliability through predictive machine learning techniques. It examines architectural approaches, data governance mechanisms, automation strategies, and predictive models that support reliable healthcare information systems. The study proposes a methodological framework for evaluating the effectiveness of machine learning-enhanced DevOps environments in improving clinical data integrity, operational efficiency, and patient-centered healthcare outcomes
Integrated Model of Strategic Alignment and Project Portfolio Management Success
This paper addresses a critical gap in project management literature by integrating two distinct research traditions: strategic alignment and project portfolio management (PPM) success. While strategic alignment research offers a precise typology of alignment types (e.g., intellectual, operational, cross-domain), it has not connected these to portfolio outcomes. Conversely, PPM success research has identified validated mechanisms driving portfolio success (e.g., management quality, business case control) but has not explored how different alignment types influence these mechanisms. Prior integration attempts have treated strategy or alignment as aggregate variables, overlooking the nuanced internal structure of alignment. This study develops a conceptual model that maps specific strategic alignment types onto validated PPM success mechanisms, categorized by their organizational level of operation (strategic translation, execution, boundary-spanning). Four testable propositions are derived, offering a more granular theoretical vocabulary for researchers and a diagnostic-to-decision pathway for practitioners. The model shifts the research question from whether alignment predicts portfolio success to which type of alignment predicts which success mechanism under specific conditions