RESPONSIBLE AI ENGINEERING: Security, Privacy, Explainability and Governance of Intelligent Systems

Artificial intelligence has moved from research laboratories into the everyday machinery of society. Models now help decide who receives a loan, which patient is seen first in an emergency department, what news a citizen reads and which job applicant is called for an interview. Large language models draft legal letters, write software and act as autonomous agents that browse the web, send emails and execute transactions on our behalf. With this reach comes responsibility. An AI system that is accurate on a benchmark but unfair to a minority group, leaks personal data, can be manipulated by a single crafted sentence or fabricates citations with total confidence is not a finished engineering product. It is a liability waiting to surface.
This book is written for the people who build, test, deploy and govern such systems. Its central argument is simple: responsible AI is an engineering discipline, not a slogan. Fairness, explainability, privacy, security and accountability can be specified as requirements, designed into architectures, measured with metrics, verified with tests and monitored in production, exactly like latency, availability or cost. The chapters that follow translate high-level ethical principles into concrete methods, code, checklists and governance processes that engineering teams can adopt on Monday morning.
Who This Book Is For
The primary audience is advanced undergraduate and postgraduate students of computer science, data science, artificial intelligence and information security, together with practitioners working as machine learning engineers, security engineers, product managers, risk officers and policy professionals. Readers should be comfortable with basic Python and introductory machine learning. No prior background in law, ethics or cryptography is assumed; every concept is introduced from first principles and illustrated with worked examples.
How the Book Is Organised
The eighteen chapters progress from foundations to practice. Chapters 1 and 2 establish the vocabulary of responsible AI and the lifecycle and risk-management methods that frame everything else. Chapters 3 to 5 address fairness and explainability: how bias enters a system, how it is measured and mitigated, and how model behaviour can be explained to developers, regulators and affected people. Chapters 6 to 8 cover privacy-preserving machine learning, adversarial machine learning and the security of the wider ML supply chain and infrastructure, including hardware-level threats to edge devices.
Chapters 9 to 13 focus on generative AI and large language models, which have introduced a new class of risks: hallucinations, prompt injection, the difficulty of evaluating open-ended outputs, the design of guardrails and the containment of AI agents that hold real permissions in real systems. Chapters 14 to 17 turn to human oversight, deepfakes and content provenance, responsible deployment and incident response, and the regulatory and governance landscape, including the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001 and India's AI Governance Guidelines. Chapter 18 brings the material together into a blueprint for an organisational responsible AI programme and five capstone projects. Four appendices provide consolidated engineering checklists, a glossary, worked answers to selected review questions and a guide to datasets and tools, followed by a list of references.