Engineering The Future Ai Augmented Devsecops And Cloud Native Platforms For The Enterprise 2025
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Engineering the Future: AI-Augmented DevSecOps and Cloud-Native Platforms for the Enterprise 2025
Author: Author:1-Chandrakanth Devarakadra Anantha, Author:2-Dr Priyanka Kaushik
language: en
Publisher: RAVEENA PRAKASHAN OPC PVT LTD
Release Date:
PREFACE The rapid evolution of technology has fundamentally altered how enterprises operate, with a significant shift towards cloud-native platforms and AI-powered tools. The convergence of artificial intelligence (AI) and DevSecOps (Development, Security, and Operations) has brought about a new era in enterprise technology, one that emphasizes automation, scalability, and security in every layer of the development lifecycle. “Engineering the Future: AI-Augmented DevSecOps and Cloud-Native Platforms for the Enterprise” explores this transformative intersection, offering a comprehensive guide to understanding and leveraging AI and cloud-native technologies to drive innovation, efficiency, and security within the enterprise ecosystem. At its core, this book delves into how AI can augment DevSecOps practices to foster a more secure, agile, and efficient development pipeline. By integrating AI into the DevSecOps process, organizations can achieve enhanced automation, proactive threat detection, and real-time insights, making it easier to develop and deploy secure applications in increasingly complex cloud environments. AI-powered solutions can detect vulnerabilities, optimize workflows, and automate compliance checks, allowing development teams to focus on innovation without sacrificing security. As businesses embrace cloud-native architectures, where microservices and containerization enable greater flexibility and scalability, the need for AI to facilitate seamless operations across distributed systems becomes ever more critical. The enterprise landscape has witnessed an unprecedented shift towards cloud-first strategies, which have revolutionized the way applications are developed, deployed, and maintained. Cloud-native platforms enable enterprises to accelerate their digital transformation, providing the agility to rapidly scale and innovate while ensuring robust security measures are embedded into every stage of the development lifecycle. Cloud-native technologies, such as Kubernetes, containerization, and serverless architectures, have become essential building blocks for modern enterprise applications. However, with this new paradigm come complex challenges in managing infrastructure, maintaining security, and ensuring smooth integration across diverse environments. This book offers insights into how AI-augmented DevSecOps practices can address these challenges, enabling organizations to stay ahead in an increasingly competitive and fast-paced business world. The synergy between AI and cloud-native platforms is particularly evident in the areas of continuous integration and continuous delivery (CI/CD), where AI-driven tools can enhance deployment efficiency and reduce human errors. By automating repetitive tasks, AI-powered systems free up valuable developer time, allowing them to focus on higher-value activities that directly contribute to business growth. Furthermore, AI’s predictive capabilities enable proactive decision-making, identifying potential bottlenecks, vulnerabilities, or failures before they affect production environments. This is especially important as enterprises adopt multi-cloud and hybrid cloud strategies, where seamless integration, monitoring, and security across various cloud platforms are critical to maintaining operational continuity. Security is at the forefront of every conversation in the world of DevSecOps, particularly as cyber threats become more sophisticated and persistent. AI plays a vital role in strengthening security frameworks by automating threat detection, identifying abnormal patterns, and responding to incidents in real-time. The integration of AI into security processes within DevSecOps workflows helps organizations address vulnerabilities faster and more efficiently, reducing the window of opportunity for attackers. This book examines how AI can enhance traditional security measures, enabling organizations to secure their cloud-native applications against ever-evolving threats. As enterprises continue to evolve in the digital age, the role of AI in augmenting DevSecOps and cloud-native platforms will only grow more pivotal. Organizations that embrace these technologies will be better positioned to innovate at scale while ensuring their applications remain secure and resilient. This book is designed for IT leaders, product managers, developers, and security professionals who are seeking to navigate the complexities of AI, DevSecOps, and cloud-native technologies. Whether you are looking to integrate AI into your DevSecOps pipeline, adopt cloud-native architectures, or enhance your enterprise’s security posture, “Engineering the Future” provides the necessary tools, frameworks, and strategies to succeed in this rapidly evolving landscape. In the pages that follow, you will gain a deeper understanding of how AI can drive automation and intelligence in DevSecOps practices, how cloud-native platforms are transforming enterprise IT operations, and how organizations can seamlessly integrate these technologies to build the secure, scalable, and agile applications of tomorrow. Welcome to the future of enterprise technology—one where AI and cloud-native platforms work hand in hand to drive innovation, security, and operational excellence. Authors
AI-Driven Enterprise Architecture: From Data Engineering to Generative AI 2025
Author: Author:1- Bhanuvardhan Nune, Author:2-Dr. Gaurav Kumar
language: en
Publisher: RAVEENA PRAKASHAN OPC PVT LTD
Release Date:
PREFACE In the rapidly evolving landscape of technology, enterprises are increasingly turning to artificial intelligence (AI) to drive innovation, efficiency, and growth. The integration of AI into enterprise architecture has shifted from a trend to an essential strategy for businesses looking to maintain a competitive edge. AI-Driven Enterprise Architecture: From Data Engineering to Generative AI is written to explore the transformative impact of AI across all layers of enterprise systems, from data engineering and analytics to innovative generative AI technologies that are reshaping industries. In today’s digital age, businesses face an explosion of data that is often unstructured, decentralized, and sold. For AI to truly revolutionize enterprise systems, there must be a solid architecture that not only supports large-scale data processing but also enables the seamless integration of AI technologies into every corner of the organization. This book takes a comprehensive approach to AI-driven enterprise architecture, focusing on the technical, strategic, and operational challenges and opportunities associated with AI adoption. The journey from data engineering to generative AI requires a solid foundation of data management and processing capabilities. The book begins by discussing the critical importance of data engineering, the practice of building robust systems for collecting, storing, and transforming data into actionable insights. Understanding how to build and maintain efficient data pipelines, databases, and data lakes forms the backbone of AI integration in an enterprise. This foundational understanding sets the stage for deploying machine learning (ML) models and AI-driven tools, which require sophisticated infrastructure to function on a scale. The integration of machine learning and AI models into enterprise architecture is the central focus of this book. As businesses recognize the value of AI in improving decision-making, automation, and customer experiences, this book guides readers through how to implement AI across multiple enterprise functions. From predictive analytics and automation to natural language processing (NLP) and computer vision, we will examine how these AI technologies interact with existing enterprise systems to create smarter, more efficient business operations. One of the most exciting and rapidly advancing fields in AI is generative AI—a technology that can create new data, designs, or content based on learned patterns. Generative AI tools like GPT-3, DALL-E, and stable diffusion models are now being used to generate text, images, code, and even video. The power of these models lies in their ability to produce new, high-quality content that can be harnessed for marketing, customer engagement, product development, and innovation. This book explores how generative AI fits within the broader enterprise architecture and how businesses can leverage these capabilities to unlock new value streams, foster creativity, and enhance productivity. AI-Driven Enterprise Architecture: From Data Engineering to Generative AI is designed for business leaders, data engineers, architects, and AI practitioners who are looking to understand the potential of AI in their organizations. Through real-world case studies, best practices, and technical insights, this book aims to provide a holistic view of how AI-driven enterprise architecture can deliver long-term strategic value. The book also delves into the challenges and ethical considerations of AI implementation, particularly with regard to data privacy, algorithmic bias, and governance, ensuring that AI is deployed responsibly and sustainably. As businesses embrace AI technologies, it is clear that the future of enterprise architecture will be driven by data-centric, AI-powered models that allow organizations to be more adaptive, responsive, and innovative. This book offers a roadmap for navigating that future, helping organizations transform their architecture to support the AI-driven, intelligent enterprise of tomorrow. We invite you to embark on this journey through the evolving world of AI-driven enterprise architecture, where the combination of data engineering, machine learning, and generative AI is shaping the future of businesses across the globe. Authors
Enterprise Reinvented: AI, Cloud, and Data at Scale 2025
Author: Author:1- Souvari Ranjan Biswal, Author:2-Dr. Nagaraj S
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
PREFACE In an era defined by digital disruption, enterprises face a singular imperative: to harness the synergistic power of artificial intelligence, cloud computing, and data at unprecedented scale. “Enterprise Reinvented: AI, Cloud, and Data at Scale” emerges from this landscape as both a strategic manifesto and a practical playbook, guiding leaders, architects, and technologists through the seismic shift from monolithic legacy systems to adaptive, intelligence-driven platforms. Rather than viewing AI, cloud, and data as discrete initiatives, this book treats them as deeply intertwined pillars of business reinvention—each amplifying the others to unlock agility, resilience, and transformative insight. We begin by exploring the tectonic forces reshaping the modern enterprise: the exponential growth of data volumes, the maturation of containerized and serverless cloud architectures, and the democratization of machine learning through open-source frameworks and managed services. In these opening chapters, you will discover how strategic alignment between data governance, platform engineering, and AI-driven innovation sets the stage for truly scalable outcomes—from real-time customer personalization and predictive maintenance to autonomous supply chains and intelligent risk management. Subsequent sections dive into the pragmatic mechanics of building “AI-ready” cloud platforms: designing data fabrics that ensure quality, lineage, and compliance; implementing cloud-native architectures that support burst-to-edge workloads; and establishing ML Ops pipelines for continuous model training, validation, and deployment. Case studies drawn from industries as diverse as manufacturing, financial services, and healthcare illustrate how leading organizations navigate governance, security, and cost-optimization challenges while accelerating time-to-value for analytic and AI use cases. Finally, the book offers a forward-looking perspective on the next frontier: how emerging paradigms—such as distributed AI at the edge, digital twins of business processes, and federated learning ecosystems—will redefine the contours of enterprise scale. We also examine the organizational and cultural shifts required to sustain this transformation: cross-functional “platform teams,” data-literate leadership, and an experimentation mindset that balances rigorous risk management with audacious, data-driven ambition. Authors