Search results for "Offensive Security Using Python"
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Reinforcement Learning for Cyber Operations
A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing, a team of distinguished researchers delivers an incisive and practical discussion of reinforcement learning (RL) in cybersecurity that combines intelligence preparation for battle (IPB) concepts with multi-agent techniques. The authors explain how to conduct path analyses within networks, how to use sensor placement to increase the visibility of adversarial tactics and increase cyber defender efficacy, and how to improve your organization’s cyber posture with RL and illuminate the most probable adversarial attack paths in your networks. Containing entirely original research, this book outlines findings and real-world scenarios that have been modeled and tested against custom generated networks, simulated networks, and data. You’ll also find: A thorough introduction to modeling actions within post-exploitation cybersecurity events, including Markov Decision Processes employing warm-up phases and penalty scalingComprehensive explorations of penetration testing automation, including how RL is trained and tested over a standard attack graph constructPractical discussions of both red and blue team objectives in their efforts to exploit and defend networks, respectivelyComplete treatment of how reinforcement learning can be applied to real-world cybersecurity operational scenarios Perfect for practitioners working in cybersecurity, including cyber defenders and planners, network administrators, and information security professionals, Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing will also benefit computer science researchers.
R 4,396.00
AI Security Engineering
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. Written by Ashish Rajan, a CISO advising Fortune 500 organizations on AI security, this book focuses on security for AI systems not AI used as a security tool. It examines how AI changes traditional security assumptions and how organizations must adapt their architectures, pipelines, governance models, and operating practices to manage AI risk effectively in production environments. Rather than focusing on tools or point solutions, AI Security Engineering applies security engineering principles to modern AI systems. It covers AI threat models, secure AI pipelines, runtime detection and incident response, governance and compliance at scale, and the emerging challenges of agentic and multi-model systems. Readers are guided through how to evolve existing security programs to support AI-enabled applications without slowing delivery or becoming the “department of no”. This book shows how to: Design and operate secure AI systems across their full lifecycle from architecture and delivery to operations and scaleDefend production AI applications against adversarial, emergent, and agent-driven threatsEmbed governance and risk controls into AI pipelines using engineering-first approachesOperate AI security in dynamic environments, including third-party and agentic systemsBalance regulatory compliance, system performance, and engineering velocity 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.
R 1,527.00
