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Assessing AI Resilience
Evidence, Methods and Measurement of Recovery
This book is 100% completeLast updated on 2026-11-11
As global legislation and procurement rules increasingly mandate that high-level AI systems must "recover from incidents", the critical challenge has shifted from conceptual policy to verifiable evidence. Assessing AI Resilience bridges this gap by establishing a mathematically grounded, evidentiary framework for evaluating how complex and agentic AI systems recover their operational condition. Drawing on international standards and rigorous signal-processing principles, this 660-page monograph provides policymakers, enterprise risk leaders, and assessors with the actionable methods, governance guardrails, and measurement models required to turn compliance into defensible engineering discipline.
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About
About the Book
This book examines AI resilience through the assessment of recovery. Its conceptual foundation is ISO/IEC 22989:2022, which defines resilience as the ability of a system to recover operational condition quickly following an incident. The focus is on assessing this ability, and not on engineering systems to become more resilient. The central concern is what observations and measurements of recovery can establish, and how the resulting conclusions should be interpreted. The book covers assessment context, recovery objectives and conditions, evidence sources and quality, analytical and simulation-based methods, empirical and human-centred approaches, recovery measures, measurement uncertainty, and interpretation of results. It covers assessment across AI application contexts, documented cases, governance, and the limitations of the assessment.
A central principle throughout the book is that evidence must be distinguished from the claims it supports. No single metric captures the complete operational condition of an AI system, and findings obtained under specified conditions do not guarantee future behaviour. The strength and scope of a resilience claim must remain proportionate to the evidence, the conditions examined, and the assessment's limitations.
The intended readership includes AI researchers, system developers, software and systems engineers, resilience assessors, risk professionals, auditors, and regulators. The book provides a common conceptual and methodological basis for examining recovery, interpreting measurements, and communicating findings precisely.
Author
About the Author
Sridharan Sankaran
Sridharan Sankaran has over three decades of engineering, software systems architecture, and research experience spanning both conventional software engineering and artificial intelligence. Beginning his career in 1989, his foundational engineering background includes tenure as an R&D engineer with the Space Applications Center, Indian Space Research Organisation (ISRO), where he was involved in camera electronic circuits for satellites and conducted rigorous assessments of satellite circuit reliability, robustness, and resilience.
Over his subsequent industry career, he directed enterprise architecture transformations, large-scale systems modernisation programs, and research-to-product commercialisation across international delivery hubs in the United States, the United Kingdom, Singapore, Australia, and India. He previously served as Head of AI Ethics, and Standards at Tata Consultancy Services (TCS), where he established and chaired committees that looked at global standardisation initiatives, assurance workflows, and auditable risk frameworks across production AI systems.
He is an active expert delegate and contributor to international AI standardisation within ISO/IEC JTC 1/SC 42 (Artificial Intelligence) and the national mirror body — Bureau of Indian Standards (BIS LITD 30). His AI standardisation work centers on translating abstract safety, governance, and recovery principles into testable, mathematically defensible technical specifications. These contributions address the trustworthiness of AI systems, including reliability, verification and validation, resilience assessment, fairness outcomes, and ethical and societal concerns.
An accomplished researcher, his recent publications span journals and conferences including IEEE Transactions on Artificial Intelligence, IEEE Signal Processing Letters, AI and Society, AI and Ethics, addressing explainable reinforcement learning, spectral signal forensics, runtime verification, and socio-technical frameworks. He holds a Master of Science (by Research) in Computer Science and Engineering from the Indian Institute of Technology Madras and a Bachelor of Engineering in Electronics and Communication from the University of Madras.
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