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AI Strategy and Security: A Roadmap for Secure, Responsible, and Resilient AI Adoption

Name: AI Strategy and Security: A Roadmap for Secure, Responsible, and Resilient AI Adoption
Author: Donnie W. Wendt
ISBN: 9798868817335

by Donnie W. Wendt

September 2025

Intermediate to advanced

214 pages

5h 48m

English

Apress

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AI Strategy and Security
Table of Contents
About the Author
About the Technical Reviewer
1. Strategy Development
Developing a Vision for AI IntegrationAI to Enable DifferentiationAI for Market ExpansionAI for Process OptimizationWorkforce OptimizationResearch the MarketAlign with Business ObjectivesStrategic WorkshopsParticipantsDefining the Organization’s Vision for AIExplore ScenariosSummarySample AI Initiatives StrategyExecutive SummaryVision StatementStrategic Objectives and AI InitiativesImplementation RoadmapResource AllocationSuccess MetricsRisk Management and Responsible AI
2. Preparing for AI Adoption: Assess AI Readiness
Technical CapabilitiesScalable InfrastructureInfrastructure ModelsScalability and Cost ConsiderationsCompliance and Data GovernanceCloud Adoption MaturityFlexibility and ScalabilityManaged AI ServicesCurrent UsageCloud-Native ToolsIn-House ExpertiseHybrid and Multi-Cloud ApproachesCost OptimizationData Privacy and LocalizationAI Development ToolsAI-Friendly Integrated Development Environments (IDEs)Version Control SystemsAutomated Testing and Validation ToolsData Preparation and Management ToolsModel Monitoring and Maintenance ToolsOperationalizationMachine Learning Operations (MLOps)MLOps PlatformsScalability TestingContinuous Integration and Deployment (CI/CD) PipelinesPerformance OptimizationData ReadinessData Quality and AccessibilityBreaking Down Data SilosData GovernanceData Readiness AssessmentPersonnel Skills and CapabilitiesCultural ReadinessBuilding the AI PipelineIdentifying AI Use CasesAssessing Candidate AI Use CasesPrioritizing AI Use CasesProofs of Concept and Failing FastMaintaining the AI PipelinePerforming Algorithmic Impact AssessmentsHuman Involvement and OversightSummary
3. The Team
Building the TeamChief AI Officer/AI StrategistAI ArchitectAI EngineerData ScientistData EngineerData Scientist Versus Data EngineerMLOps EngineerDomain ExpertAI Project ManagerAI Ethics and Compliance OfficerAI Security ArchitectAI Security EngineerAI Security TesterAI Security Roles: Comparing Architects, Engineers, and TestersThe Talent PipelineRecruiting and Growing the PipelineContinuous Learning and Skill DevelopmentA Word on Job Loss – A Difference of PerspectiveSummary
4. Securing AI
AI-Specific Attack VectorsData PoisoningModel PoisoningBackdoor AttacksExploratory and Evasion AttacksPrivacy AttacksInstruction AttacksSupply Chain Attacks: Shared Datasets and Pre-trained ModelsAgentic AI ThreatsAI-Specific Security MeasuresData Preparation and HandlingData Provenance and LineageAI Data SecurityRobust LearningModel and Data Change ManagementAPI SecuritySecuring Agentic AISecuring LLM IntegrationLLM FirewallsInput Validation and FilteringConstrain Model BehaviorBias and Harmful Content FilteringLLM RoutersAI Testing and MonitoringAdversarial AI Development and TestingChallenger/Champion ModelsModel Behavior MonitoringDrift AnalysisMitigation MappingSummary
5. AI Governance and Risk Management
Establishing the AI Governance StructureAI Governance Roles and ResponsibilitiesEstablish AI Risk StrategyAccountability and IncentivesLeadership SupportRegulatory Compliance OversightStakeholder EngagementCentralized, Federated, and Hybrid AI Governance ModelsAI Governance PoliciesEnterprise AI StandardsAI Usage Guidelines and Ethical PrinciplesOngoing EnforcementAI Risk ManagementAI InventoryRisk Assessment and Mitigation ProcessKnowledge Sharing and TrainingContinuous MonitoringThird-Party AI Risk ManagementPrivacy and AIData MinimizationPrivacy-Enhancing TechnologiesPrivacy-Preserving Model TechniquesUser Data Rights and TransparencyUnderstanding AI System FailuresBrittlenessHallucinationsEmbedded BiasCatastrophic ForgettingUncertaintyFalse Positives and NegativesSummary
6. Responsible AI
Core Risks and Harms Posed by AI SystemsHarms to Individuals and GroupsHarms to SocietyHarms to OrganizationsEcosystem-Level HarmCharacteristics of Trustworthy AI SystemsHuman-Centric AITransparency and ExplainabilityRobustness, Security, and SafetyAccountabilityInclusive Growth, Sustainable Development, and Well-BeingEthical DesignEthical PrinciplesPrivacy by DesignBias MitigationEthical Impact AssessmentSummary

7. Regulations, Standards, and Frameworks
Key Regulations Impacting AI Development and UseEU AI ActGeneral Data Protection Regulation (GDPR)OECD AI Principles for Trustworthy AICalifornia Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA)HIPAA and HITECHAI-Specific Standards and FrameworksISO/IEC 42001: Artificial Intelligence Management System (AIMS)NIST AI Risk Management Framework (AI RMF)HUDERIA: Human and User Data Exposure Risk Impact AssessmentDatabricks AI Security FrameworkCybersecurity Standards and FrameworksNIST SP 800-53ISO/IEC 27001NIST Cybersecurity Framework (CSF)PCI-DSSNIST Risk Management Framework (RMF)SummaryRegulations, Standards, and Frameworks
8. Operationalizing AI
Deploying AIMachine Learning Security OperationsScalability and ResilienceData and Feature Engineering OperationsManaging the Cost of AI WorkloadsCompliance and Operational GovernanceCollaboration and CommunicationExplainability and InterpretabilityResponsible AI PracticesOrganizational Change ManagementThird-Party and Open-Source Risk ManagementSummary
9. Continuous Improvement
Monitoring and MaintenanceModel Lifecycle Management and RetrainingFeedback Loops and Human-in-the-Loop OptimizationDetermining ROI and Post-Deployment EvaluationResponsible Decommissioning and Sunset PlanningSummary
10. AI As a Way of Doing Business
An AI-Ready CultureEducation and AI LiteracySkills and Capability DevelopmentEncourage Experimentation and InnovationAI Center of Excellence (CoE)Executive Sponsorship and VisionCross-Functional CollaborationIntegrate AI into Business ProcessesChange CommunicationQuality AssuranceHuman-AI Collaboration FrameworkChange ManagementOrganizational Context ConsiderationsEthical Risk ManagementBehavioral and Cultural MetricsShadow AI and Citizen Development GovernanceIncentives and Recognition SystemsLong-Term Workforce Transformation PlanningSummary
11. The AI Adoption & Management Framework
Evaluate: Aligning AI with StrategyGovern: Embedding Responsible, Ethical, and Compliant AIInnovate: Driving AI ExperimentationSecure: Protecting AI Systems from Cyber ThreatsOperate: Operationalizing AI Systems at ScaleIntegrate: Making AI Part of the Organizational DNASummary
Index

Content preview from AI Strategy and Security: A Roadmap for Secure, Responsible, and Resilient AI Adoption

D. W. WendtAI Strategy and Securityhttps://doi.org/10.1007/979-8-8688-1733-5_5

5. AI Governance and Risk Management

Donnie W. Wendt¹

Organizations that integrate AI into their operations should implement a comprehensive AI governance and risk management framework. Establishing such a framework requires aligning the organization’s policies and procedures with widely accepted standards, such as the NIST AI RMF (RMF), OECD AI Principles, and ISO guidelines. An effective governance program will ensure ongoing compliance with applicable regulations and industry standards, such as the EU AI Act, GDPR, HIPAA, and PCI DSS. However, governance ...

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AI Strategy and Security: A Roadmap for Secure, Responsible, and Resilient AI Adoption

by Donnie W. Wendt

5. AI Governance and Risk Management

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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