Gibbs Marcel (20 results)
Language: English
Published by Independently Published, 2026
Series: Book 2 of 4 - The Practical AI Systems for Small Business Owners 2026 Series
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Science, Technology, Human Values ; Volume 12 Issue 2 Spring 1987 No. 59
La Follette, Marcel Chotkowski (editor) ; Janet M. Fitchen, Sal Restivo, Philip T. Shepard, Christopher Hamlin, Bruce V. Lewenstein, John C. Burham, John E. Sauer, Ronald D. Gibbs, Wolfgang Krohn, Peter Weingart and Rachelle Hollander
Published by John Wiley & Sons, New York, 1987
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- First Edition
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Softcover. First Edition, First Printing. Book condition is Very Good, bound in wraps. Some rubbing and edgewear to exterior. Text is clean and unmarked. ; 4to. 11"h x 8 1/2"w. Contents: "Cultural Aspects of Environmental Problems: Individualism and Chemical Contamination of Groundwater" by Janet M. Fitchen "Science Studies-What… Is to be Done" by Sal Restivo "How Not to Presume: Toward a Descriptive Theory of Ideology in Science and Technology Controversy" by Philip T. Shepard and Christopher Hamlin "Was There Really a Science "Boom"?" by Bruce V. Lewenstein "Peer-Reviewed Grants in U.S. Trade Association Research" by John C. Burham, John E. Sauer, and Ronald D. Gibbs Commentaries : "Nuclear Power as a Social Experiment- -European Political "Fall Out" from the Chernobyl Meltdown" by Wolfgang Krohn and Peter Weingart "In a New Mode-Ethics and Values Studies at the NSF" by Rachelle Hollander.
Language: English
Published by Independently published, 2026
Series: Book 2 of 4 - The Practical AI Systems for Small Business Owners 2026 Series
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Taschenbuch. Condition: Neu. Neuware - What happens after your AI agents are deployed Most organizations focus on building, securing, testing, and governing agentic AI systems. Far fewer are prepared for the challenges that emerge when autonomous agents begin making decisions, invoking tools, accessing sensitive resources, and c…ollaborating with other agents in real production environments.How do you detect dangerous behavior before it causes damage How do you stop a compromised agent in real time How do you contain failures, enforce policies at runtime, and maintain control as autonomous systems operate at machine speed Practical Runtime Security and Defense for Agentic AI Systems provides a hands-on blueprint for continuously protecting autonomous AI agents and multi-agent systems after deployment. Designed for security engineers, AI platform teams, DevSecOps professionals, architects, and enterprise technology leaders, this book focuses on the operational security controls required to defend agentic AI systems in production.Rather than relying on theory, this book shows you how to implement practical runtime defense strategies, automated response mechanisms, behavioral monitoring pipelines, containment workflows, and self-healing security controls that work in modern enterprise environments.Inside, you will learn how to: - Build runtime security architectures for autonomous AI agents and multi-agent systems- Implement policy enforcement, guardrails, and real-time action controls- Establish behavioral monitoring and anomaly detection capabilities- Deploy automated containment, isolation, and kill-switch mechanisms- Create agent control towers and runtime observability pipelines- Design self-healing security workflows and autonomous defense systems- Investigate incidents using traceability, telemetry, and forensic analysis- Integrate runtime security into AgentOps, DevSecOps, and CI/CD pipelines- Strengthen operational resilience across cloud, hybrid, and multi-agent environments- Scale continuous protection strategies for enterprise AI platformsAs agentic AI systems become more capable, runtime security is rapidly becoming one of the most important disciplines in enterprise AI. Organizations need more than prevention, they need the ability to detect, respond, recover, and adapt while autonomous systems are actively operating.Whether you are securing a single AI agent or managing a large-scale multi-agent ecosystem, this book provides the practical architectures, implementation patterns, operational playbooks, and proven defense strategies needed to maintain control in production.If you are ready to move beyond static security controls and build resilient, continuously protected AI systems, this book will help you create the runtime defense capabilities modern enterprises require.Get your copy today and learn how to protect autonomous AI systems where it matters most, while they are running.
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Taschenbuch. Condition: Neu. Neuware - How do you secure an application that can rewrite its own execution logic at runtime As organizations rapidly deploy autonomous AI agents with direct write access to database engines, internal networks, and cloud infrastructure, traditional perimeter defense models fall short. When your sof…tware moves from deterministic code to probabilistic reasoning loops, how do you stop an adversary from hijacking your entire enterprise platform Advanced Threat Modeling and Red Teaming for Agentic AI Systems provides the definitive, production-first blueprint to secure non-deterministic software deployments. This comprehensive technical guide skips basic introductory concepts to deliver hard-hitting offensive engineering strategies, automated vulnerability testing pipelines, and zero-trust defensive hardening patterns. It addresses the unique architectural realities of modern multi-agent systems, shifting your security posture from reactive prompt filtering to code-enforced, continuous validation across your entire cluster fabric.What advantages will you gain by engineering your security framework from the ground up This manual equips platform architects, security engineers, and DevSecOps teams with the exact technical skills required to build an automated, self-healing defensive lifecycle.By implementing the production-grade methodologies detailed inside, you will acquire the skills to: - Build stateful autonomous red team agents to programmatically simulate complex, multi-turn adversarial cascades.- Embed declarative automated fuzzing and prompt regression scanners directly into containerized CI/CD pipelines as mandatory release quality gates.- Inject semantic faults and behavioral noise to evaluate multi-agent swarm resilience and configure robust infrastructure circuit breakers.- Decouple tool execution privileges from volatile model logic using relationship-based access control models.- Harden human-in-the-loop approval interfaces against context-spoofing and parameter manipulation exploits.- Establish weighted risk quantification frameworks that translate probabilistic alignment flaws into deterministic business risk metrics.Do not wait for a critical semantic breach to expose your core infrastructure. Secure your autonomous workflows, harden your multi-agent networks, and enforce strict policy-as-code gates today.Buy your copy now to deploy production-grade defense validation across your enterprise platform.
- Softcover
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Taschenbuch. Condition: Neu. Neuware - Deploying AI agents is no longer the hardest part.Keeping them governed, compliant, observable, and secure in production is.As organizations rapidly adopt autonomous agents, multi-agent systems, and AI-powered workflows, many discover that traditional governance and security models were nev…er designed for systems that can make decisions, invoke tools, access sensitive data, and collaborate with other agents autonomously.How do you enforce policies at runtime How do you maintain continuous compliance across thousands of agent actions How do you create audit trails that satisfy regulators, security teams, and enterprise stakeholders How do you detect and contain risky agent behavior before it becomes a business problem Practical Agentic AI Governance, Compliance, and Runtime Security provides a hands-on blueprint for building governance and runtime protection capabilities that work in real-world enterprise environments.Rather than focusing on theory, this book shows you how to implement governance controls, compliance automation, runtime policy enforcement, observability pipelines, audit frameworks, and operational security mechanisms that can scale across modern AI platforms.Inside, you will learn how to: - Build governance control planes for autonomous agents and multi-agent systems- Implement policy-as-code using modern authorization and policy frameworks- Establish continuous compliance programs aligned with leading industry standards- Create immutable audit trails and enterprise-grade traceability pipelines- Deploy runtime guardrails and enforcement mechanisms for agent actions- Monitor agent behavior and detect anomalies in real time- Integrate governance and compliance controls into CI/CD workflows- Automate evidence collection, reporting, and audit preparation- Build incident response and recovery procedures for agent-driven environments- Scale governance programs across cloud, hybrid, and multi-tenant architecturesWhether you are a security engineer, AI platform architect, DevSecOps professional, governance specialist, or technology leader, this book provides practical guidance, implementation patterns, operational playbooks, and proven architectures for governing agentic AI systems at enterprise scale.If your organization is building the next generation of autonomous AI systems, this book will help you move beyond deployment and establish the governance, compliance, and runtime security foundations required for long-term success.Build systems that are not only intelligent, but also auditable, compliant, trustworthy, and continuously protected.
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Language: English
Published by Independently Published, 2026
Series: Book 2 of 4 - The Practical AI Systems for Small Business Owners 2026 Series
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Paperback. Condition: new. Paperback. Practical AI Guide for Small Business Owners 2026: Save 20+ Hours a Week with Simple AI Strategies for Operations, Marketing, and Customer Service - No Coding RequiredAre you a small business owner spending too many hours every week answering the same questions, writing social media posts, f…ollowing up with leads, handling scheduling, and keeping up with admin work?You are not alone. For many small business owners, the real problem is not a lack of effort. It is the constant flow of repetitive tasks that eat up time, energy, and focus.In 2026, AI has become one of the easiest ways to work faster, stay organized, and reduce manual work without hiring extra staff or learning complicated software.Practical AI Guide for Small Business Owners 2026 shows you exactly how to use simple AI tools to save 20+ hours a week without learning to code.Inside this practical, beginner-friendly guide, you will learn how to use AI to improve the areas that take up the most time in a small business: - Customer Service - write faster, more professional replies, handle complaints calmly, and create reusable response templates- Marketing and Social Media - generate post ideas, captions, and simple content plans in minutes- Sales and Lead Generation - improve product descriptions, write better follow-up messages, and qualify leads more effectively- Daily Operations - simplify scheduling, repetitive admin work, and basic tracking for inventory or services- Finance Basics - organize expenses, improve invoice communication, and spot simple financial insights more clearlyThis book is written in plain English and designed for busy owners who want practical help, not technical explanations.Inside, you will find: - clear step-by-step guidance- ready-to-copy AI prompts- real-world examples- before-and-after samples- simple action steps you can use right away- a full 30-Day AI Action Plan- a Ready-to-Use Prompt Library for customer service, marketing, sales, operations, and financeWhether you run a local service business, retail shop, online business, freelance operation, or one-person brand, this book will help you use AI in a simple, realistic way that saves time and makes your business easier to manage.You do not need coding skills. You do not need a technical background. You just need practical systems that help you get more done with less stress.If you are ready to stop wasting hours on repetitive work and start using AI to run your business more efficiently, this book will show you how to start. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Condition: new. Paperback. How do you secure an application that can rewrite its own execution logic at runtime? As organizations rapidly deploy autonomous AI agents with direct write access to database engines, internal networks, and cloud infrastructure, traditional perimeter defense models fall short. When your sof…tware moves from deterministic code to probabilistic reasoning loops, how do you stop an adversary from hijacking your entire enterprise platform?Advanced Threat Modeling and Red Teaming for Agentic AI Systems provides the definitive, production-first blueprint to secure non-deterministic software deployments. This comprehensive technical guide skips basic introductory concepts to deliver hard-hitting offensive engineering strategies, automated vulnerability testing pipelines, and zero-trust defensive hardening patterns. It addresses the unique architectural realities of modern multi-agent systems, shifting your security posture from reactive prompt filtering to code-enforced, continuous validation across your entire cluster fabric.What advantages will you gain by engineering your security framework from the ground up? This manual equips platform architects, security engineers, and DevSecOps teams with the exact technical skills required to build an automated, self-healing defensive lifecycle.By implementing the production-grade methodologies detailed inside, you will acquire the skills to: Build stateful autonomous red team agents to programmatically simulate complex, multi-turn adversarial cascades.Embed declarative automated fuzzing and prompt regression scanners directly into containerized CI/CD pipelines as mandatory release quality gates.Inject semantic faults and behavioral noise to evaluate multi-agent swarm resilience and configure robust infrastructure circuit breakers.Decouple tool execution privileges from volatile model logic using relationship-based access control models.Harden human-in-the-loop approval interfaces against context-spoofing and parameter manipulation exploits.Establish weighted risk quantification frameworks that translate probabilistic alignment flaws into deterministic business risk metrics.Do not wait for a critical semantic breach to expose your core infrastructure. Secure your autonomous workflows, harden your multi-agent networks, and enforce strict policy-as-code gates today.Buy your copy now to deploy production-grade defense validation across your enterprise platform. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Condition: new. Paperback. Modern enterprises are rapidly deploying autonomous AI agents that can access data, call APIs, trigger workflows, and coordinate with other systems at machine speed. But here is the critical challenge: how do you secure systems that think, act, and delegate on their own?Without a strong secu…rity foundation, agentic AI systems introduce serious risks, unchecked tool access, credential leakage, prompt injection attacks, privilege escalation through delegation chains, and silent data exposure across enterprise infrastructure. Traditional security models were not designed for autonomous systems operating with dynamic decision-making capabilities.Practical Zero Trust Security for Agentic AI Systems delivers a production-focused approach to solving this problem using modern Zero Trust architecture principles. This book shows you exactly how to design, implement, and secure autonomous AI systems in real enterprise environments. You will learn how to apply identity-first security, enforce least-privilege execution, and build runtime policy controls that continuously validate every action an AI agent performs.Rather than theory, this book focuses on implementation patterns used in modern cloud-native and enterprise platforms. You will work with practical architectures involving workload identities, policy-as-code, secure orchestration, and runtime enforcement across single-agent and multi-agent systems.By the end of this book, you will be able to confidently design and secure production-grade agentic AI systems using techniques such as: Non-human identity management for AI agents using modern IAM principlesSecure multi-agent workflows with controlled delegation and trust boundariesFine-grained authorization using relationship-based access control models like OpenFGASecure secrets management and credential isolation using tools like VaultRuntime enforcement using policy engines, sandboxing, and execution gatesProtection against prompt injection, tool abuse, and agent escalation attacksEnterprise integration with Kubernetes, service meshes, and API/AI gatewaysContinuous observability, auditing, and incident response for autonomous systemsIf you are building or securing AI agents in production, this book gives you the architectural clarity and hands-on implementation strategies needed to move from experimental systems to hardened enterprise infrastructure.Secure your copy of Practical Zero Trust Security for Agentic AI Systems and start building autonomous AI systems that are powerful, controlled, and production-ready. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Condition: new. Paperback. What happens after your AI agents are deployed?Most organizations focus on building, securing, testing, and governing agentic AI systems. Far fewer are prepared for the challenges that emerge when autonomous agents begin making decisions, invoking tools, accessing sensitive resources, and co…llaborating with other agents in real production environments.How do you detect dangerous behavior before it causes damage? How do you stop a compromised agent in real time? How do you contain failures, enforce policies at runtime, and maintain control as autonomous systems operate at machine speed?Practical Runtime Security and Defense for Agentic AI Systems provides a hands-on blueprint for continuously protecting autonomous AI agents and multi-agent systems after deployment. Designed for security engineers, AI platform teams, DevSecOps professionals, architects, and enterprise technology leaders, this book focuses on the operational security controls required to defend agentic AI systems in production.Rather than relying on theory, this book shows you how to implement practical runtime defense strategies, automated response mechanisms, behavioral monitoring pipelines, containment workflows, and self-healing security controls that work in modern enterprise environments.Inside, you will learn how to: Build runtime security architectures for autonomous AI agents and multi-agent systemsImplement policy enforcement, guardrails, and real-time action controlsEstablish behavioral monitoring and anomaly detection capabilitiesDeploy automated containment, isolation, and kill-switch mechanismsCreate agent control towers and runtime observability pipelinesDesign self-healing security workflows and autonomous defense systemsInvestigate incidents using traceability, telemetry, and forensic analysisIntegrate runtime security into AgentOps, DevSecOps, and CI/CD pipelinesStrengthen operational resilience across cloud, hybrid, and multi-agent environmentsScale continuous protection strategies for enterprise AI platformsAs agentic AI systems become more capable, runtime security is rapidly becoming one of the most important disciplines in enterprise AI. Organizations need more than prevention, they need the ability to detect, respond, recover, and adapt while autonomous systems are actively operating.Whether you are securing a single AI agent or managing a large-scale multi-agent ecosystem, this book provides the practical architectures, implementation patterns, operational playbooks, and proven defense strategies needed to maintain control in production.If you are ready to move beyond static security controls and build resilient, continuously protected AI systems, this book will help you create the runtime defense capabilities modern enterprises require.Get your copy today and learn how to protect autonomous AI systems where it matters most, while they are running. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
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£ 37.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Paperback. Condition: new. Paperback. Deploying AI agents is no longer the hardest part.Keeping them governed, compliant, observable, and secure in production is.As organizations rapidly adopt autonomous agents, multi-agent systems, and AI-powered workflows, many discover that traditional governance and security models were neve…r designed for systems that can make decisions, invoke tools, access sensitive data, and collaborate with other agents autonomously.How do you enforce policies at runtime?How do you maintain continuous compliance across thousands of agent actions?How do you create audit trails that satisfy regulators, security teams, and enterprise stakeholders?How do you detect and contain risky agent behavior before it becomes a business problem?Practical Agentic AI Governance, Compliance, and Runtime Security provides a hands-on blueprint for building governance and runtime protection capabilities that work in real-world enterprise environments.Rather than focusing on theory, this book shows you how to implement governance controls, compliance automation, runtime policy enforcement, observability pipelines, audit frameworks, and operational security mechanisms that can scale across modern AI platforms.Inside, you will learn how to: Build governance control planes for autonomous agents and multi-agent systemsImplement policy-as-code using modern authorization and policy frameworksEstablish continuous compliance programs aligned with leading industry standardsCreate immutable audit trails and enterprise-grade traceability pipelinesDeploy runtime guardrails and enforcement mechanisms for agent actionsMonitor agent behavior and detect anomalies in real timeIntegrate governance and compliance controls into CI/CD workflowsAutomate evidence collection, reporting, and audit preparationBuild incident response and recovery procedures for agent-driven environmentsScale governance programs across cloud, hybrid, and multi-tenant architecturesWhether you are a security engineer, AI platform architect, DevSecOps professional, governance specialist, or technology leader, this book provides practical guidance, implementation patterns, operational playbooks, and proven architectures for governing agentic AI systems at enterprise scale.If your organization is building the next generation of autonomous AI systems, this book will help you move beyond deployment and establish the governance, compliance, and runtime security foundations required for long-term success.Build systems that are not only intelligent, but also auditable, compliant, trustworthy, and continuously protected. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
