The fast interwoven of the world of artificial intelligence (AI) and cloud computing changes the manner in which the business ecology is created, grown, and maintained. This article discusses a Abstract- The rapid integration of artificial intelligence (AI) and cloud computing is transforming how enterprise ecosystems are created, scaled, and sustained. This paper presents an AI-integrated cloud enterprise platform designed to support entrepreneurship and innovation through intelligent automation, scalable digital infrastructure, data-driven decision-making, and adaptive service orchestration. The proposed framework combines cloud-native architecture with AI services such as predictive analytics, recommendation systems, adaptive resource management, and decision intelligence to improve operational agility and innovation support. Unlike traditional enterprise systems, the platform provides real-time guidance, dynamic workflow adaptation, personalized support for entrepreneurs, and seamless integration with third-party tools and APIs. The framework also supports rapid prototyping, experimentation, collaboration, and market responsiveness within entrepreneurial ecosystems. Key architectural aspects such as multi-tenant cloud environments, security, privacy, scalability, interoperability, and governance are also discussed. The proposed work contributes a unified and adaptive platform that integrates decision intelligence, service orchestration, and innovation enablement within a single scalable architecture.
Shandilya Avadhanam Venkat Krishna Sastry, Pasuluri Bindu Swetha, S. Kishore et al.· International Conference on...· 0 citations
Legacy enterprise systems continue to support critical business operations, but many of these systems are monolithic, tightly coupled, difficult to scale, and vulnerable to security risks. These limitations reduce their ability to adapt to modern digital environments that require flexibility, reliability, faster deployment, and continuous security. This paper presents a structured framework for cloud-native modernization of legacy enterprise systems by integrating Artificial Intelligence and DevSecOps practices. The proposed framework supports gradual transformation through microservice decomposition, containerization, API-based interoperability, and hybrid integration, allowing organizations to modernize existing systems without major disruption to business processes. Artificial Intelligence is used to support intelligent code analysis, dependency mapping, anomaly detection, workload optimization, and migration planning. These AI-driven capabilities help identify risks, reduce manual effort, and improve decision-making during modernization. DevSecOps practices are integrated into the software development lifecycle through automated CI/CD pipelines, vulnerability scanning, compliance validation, and continuous monitoring. This ensures that security is not treated as a final-stage activity but is continuously applied from development to deployment and post-migration operations. The framework also addresses important migration concerns such as data integrity, system resilience, interoperability, and governance in hybrid cloud environments. Data consistency is maintained through controlled synchronization, validation procedures, and API-led integration between legacy and modernized components. The proposed model improves operational efficiency, deployment agility, security readiness, and system reliability by combining cloud-native architecture with intelligent automation and continuous security enforcement. This study provides a systematic modernization approach that connects legacy enterprise systems with scalable, secure, and future-ready cloud-native architectures.
Shandilya Avadhanam Venkat Krishna Sastry, Pasuluri Bindu Swetha, S. Kishore et al.· International Conference on...· 0 citations
The rapid evolution of enterprise systems toward cloud-native environments has introduced significant improvements in scalability and flexibility, while also increasing architectural complexity. Traditional solution architectures struggle to handle dynamic workloads, heterogeneous infrastructures, and real-time decision-making requirements. This paper proposes an AI-driven enterprise solution architecture designed to enhance scalability, resilience, and intelligent orchestration in cloud-native systems. The framework integrates artificial intelligence across multiple layers, including resource provisioning, service orchestration, anomaly detection, and adaptive scaling. Unlike conventional rule-based approaches, the architecture leverages data-driven intelligence to optimize system performance and resource utilization while maintaining reliability. Key components include microservices-based design, container orchestration, event-driven communication, and AI-enabled control mechanisms. The architecture emphasizes modularity, interoperability, and continuous learning to ensure adaptability across diverse enterprise applications. Security and governance are incorporated following DevSecOps practices. The proposed solution effectively addresses operational inefficiencies, latency issues, and scalability bottlenecks, providing a robust foundation for next-generation intelligent enterprise systems.
Shandilya Avadhanam Venkat Krishna Sastry, Pasuluri Bindu Swetha, S. Kishore et al.· 2026 6th International Confe...· 0 citations
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