AI Security Engineering by Ashish Rajan (.ePUB)+
File Size: 10 MB
AI Security Engineering: Design, Build, and Secure Dependable AI Systems by Ashish Rajan
Requirements: .ePUB, .PDF reader, 10 MB
Overview: Design, Build, and Secure Dependable AI Systems Across the Enterprise Lifecycle. AI is rapidly becoming part of core enterprise systems but most security programs were not designed for systems that are probabilistic, adaptive, and increasingly autonomous. AI Security Engineering provides a foundational, engineering-first playbook for designing, operating, and scaling secure AI systems across the enterprise lifecycle. AI security engineering focuses on designing, building, and operating secure AI systems across their entire life cycle. Rather than treating models as isolated components, it considers the full operational stack surrounding modern AI deployments, including data pipelines, model infrastructure, orchestration layers, retrieval systems, agent frameworks, and the cloud environments in which these systems operate. Like other engineering disciplines that emerged as technology evolved such as software engineering, site reliability engineering, and AI engineering—AI security engineering focuses on building systems that remain secure as they grow in scale, complexity, and autonomy. This book aims to provide the mental models, frameworks, and practical guidance needed to secure AI systems in real-world environments. Whether you are a security architect designing guardrails for enterprise AI deployments, an engineer building secure Machine Learning pipelines, or a technology leader responsible for governance and risk management, the principles outlined here are intended to help you approach AI security as a structured engineering discipline rather than as a collection of isolated controls. CISOs seeking strategic clarity for AI security investments, security architects designing resilient systems, and engineers responsible for operating AI in production will find this book a durable reference for building dependable AI systems at enterprise scale. This book will also be valuable for AI and Machine Learning practitioners building or deploying AI systems in production environments. Data scientists, Machine Learning engineers, and AI platform teams increasingly need to understand how their systems interact with enterprise infrastructure, data governance requirements, and security controls. As AI deployments grow in complexity and connectivity, security can no longer be treated as a downstream concern.
Genre: Non-Fiction > Tech & Devices
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