Governance-First AI

Board Authority. Architectural Precision. Governed AI at Enterprise Scale.
1. Auflage 2026
ISBN: 979-8-8688-3117-1
Verlag: APRESS L.P.

Buch, Englisch, 259 Seiten, Format (B × H): 155 mm x 235 mm

As Generative AI shifts from early excitement to real-world implementation, organizations face a critical crossroads. Executives demand rapid returns on investment, yet deployment is frequently stalled by risk, compliance, and governance barriers, and the rise of unmanaged Shadow AI systems has elevated the stakes.  confronts this tension head-on, offering a practical, execution-ready guide for leaders seeking to move beyond hype and into secure, scalable adoption.

This book bridges the gap with a unified, governance-first approach that enables enterprises to operationalise Generative AI confidently and responsibly. The authors present a cohesive framework for achieving Certified Operational Velocity, helping organisations evolve from experimental pilots to compliant, production-grade systems. At its core, the book translates stringent governance principles—including platform engineering and platform-as-a-product thinking—into fifteen concrete architectural patterns. These blueprints address the full spectrum of enterprise AI challenges, from retrieval-augmented generation (RAG) deployment to context management, legacy system integration, and the auditable controls required for autonomous Agentic AI systems. Each chapter delivers step-by-step guidance for building secure, measurable, and future-proof AI capabilities.

By introducing a unified governance-first architecture and pairing it with repeatable, production-ready use cases, becomes the missing guide for unlocking AI’s true impact. It equips organizations to achieve transformative productivity gains—without compromising compliance—while preparing for the era of safe, autonomous AI.

What you will learn:

• Implement a unified, governance-first architectural framework to reliably scale Generative AI from POC experiments to fully compliant enterprise systems.
• Apply 15 production-ready architectural blueprints to unlock significant value and achieve 70–80% efficiency gains across core enterprise use cases.
• Enforce essential technical governance controls—such as DLAC, retrieval-time access checks, and immutable audit logs—to ensure complete auditability and legal defensibility.
• Design and deploy autonomous Agentic AI systems with strict safeguards, including policy-as-code enforcement, accountability layers, and robust human-in-the-loop protocols.

Who this book is for:

The book is designed for C-suite leaders—including CDOs, CTOs, CIOs, CISOs—along with Enterprise Architects and Directors of AI/ML Engineering who are responsible for scaling AI across the enterprise. It also serves Compliance Officers, Risk Managers, and senior ML engineers deploying LLMs in highly regulated sectors such as finance, pharma, manufacturing, and legal. Readers should have foundational knowledge of enterprise IT or cloud architecture and a basic understanding of Generative AI and LLMs.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Hossain, M Maruf

Ponnusamy, Ahilan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Betriebssysteme Linux Betriebssysteme, Open Source Betriebssysteme
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme

Inhaltsverzeichnis

.- Chapter 1: Building Future-Proof AI.
.- Chapter 2: From Chatbot Hype to Enterprise Reality.
.- Chapter 3: Architecting for Governance and Scale.
.- Chapter 4: Governing Access and Lineage.
.- Chapter 5: Operationalising Quality and Accuracy with LLMOps.
.- Chapter 6: The Efficiency Model.
.- Chapter 7: The Compliance Model.
.- Chapter 8: Data Integrity and Optimisation.
.- Chapter 9: The Agentic Paradigm Shift.
.- Chapter 10: Intelligent Agents for Core Business Functions.
.- Chapter 11: Multi-Domain Advanced Automation.
.- Chapter 12: Roadmap to AI Maturity.
.- Appendices.
.- Appendix A: EDS Logging and Explainability Standard — Mandatory 18-Field Log Schema.
.- Appendix B: Glossary of Terms.
.- Appendix C: EDS Governance Readiness Self-Assessment.

Über Autor(innen)

Dr M Maruf Hossain, named among the 2024 Global Top 100 Innovators in Data and Analytics, is Australia’s leading Fractional Chief AI Officer (CAIO), providing strategic leadership in artificial intelligence to boards, consulting partnerships, and technology firms. He holds a PhD in Artificial Intelligence from the University of Melbourne, complemented by GAICD governance credentials.

Dr Hossain’s executive experience spans the enterprise and public sectors. As Chief AI Strategist at 42 Consulting.AI, he supports organisations worldwide in building sustainable AI capabilities. Previously, as Vice President of Data Science at ANZ Bank, he led large-scale AI adoption across banking operations. His earlier roles with Credit Clear, Telstra Global, IBM Global Business Services, Infosys Consulting, and the Australian Government involved establishing Data and AI Centres of Excellence and embedding responsible AI frameworks across regulated industries.

Author of several research papers, Dr Hossain is recognised for aligning AI strategy with measurable organisational and governance outcomes.

Ahilan Ponnusamy currently works as a specialist for Application Platform at Red Hat APAC. He enjoys working with customers on Hybrid cloud architectures and cloud-native application development and delivery practices. He previously completed a Master of Computer Applications degree at Madurai Kamaraj University in India. His work history includes Philips CE in Eindhoven, Netherlands; BEA Technologies as a member of Customer Centric Engineering and support in India and the USA; Pre-sales Tech-lead for the cloud platform team at Oracle USA; Principal platform engineer at VMware; and Global Architect at Dell Technologies Singapore.