book

AI Management Framework : Practical Solutions for Ethical AI Deployment and Continuous Improvement

Name: AI Management Framework : Practical Solutions for Ethical AI Deployment and Continuous Improvement
Author: John Kyriazoglou
ISBN: 9798868815362

by John Kyriazoglou

August 2025

Intermediate to advanced

405 pages

8h 44m

English

Apress

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AI Management Framework
Book Review by Dr. Martha Beck
OverviewObjective
Prologue: Can Computers Think?
Introduction to Artificial IntelligenceAnalysis of the Terms and Concepts of “Thought”Do Computers Think?Impact of Artificial IntelligenceRecommendationAI Management System: ContentsAI Ecosystem Journey
Disclaimer
Table of Contents
About the Author
Part I: Foundation
Foundation
1. AI Management System
AI Management SystemComponent 1: Corporate Philosophical FrameworkComponent 2: Legal and Regulatory EnvironmentComponent 3: AI Implementation ApproachPhase A (AI Preparation)Goal and Objectives of Phase ASteps of Phase A (AI Preparation)Phase B: AI Management FrameworkGoal and Objectives of Phase B (AI Management Framework)Phase B-Part 1 (AI Governance) ObjectivesPhase B-Part 2 (Data Governance) ObjectivesPhase B-Part 3 (IT Governance) ObjectivesSteps of Phase B (Part B-1: Establish AI Governance Controls)Steps of Phase B (Part B-2: Establish Data Governance Controls)Steps of Phase B (Part B-3: Establish IT Governance Controls)Phase C: AI Systems DevelopmentGoal and Objectives of Phase CSteps of Phase C (AI Systems Development)Phase D: AI Systems OperationGoal and Objectives of Phase D (AI Systems Operation)Steps of Phase D (AI Systems Operation)Phase E: AI Infrastructure and Systems AssessmentGoal and Objectives of Phase E (AI Infrastructure and Systems Assessment)Steps of Phase E (AI Infrastructure and Systems Assessment)Component 4: AI Support ToolsRecommendationsConclusionAnnex 1: AI Risk CatalogueAnnex 2: AI ControlsAnnex 3: AI Management System Products
Part II: AI Ethics Framework
AI Ethics Framework
2. The Seven Ancient Greek Wisdom Principles
Principle 1: TemperancePrinciple 2: FaithPrinciple 3: JusticePrinciple 4: HarmonyPrinciple 5: FriendshipPrinciple 6: Kalokagathia (Goodness and Kindness)Principle 7: CourageOutcome, Results, and OutputsRecommendationsConclusion

3. Corporate Philosophy Statements and Policies
CPP01: AI Vision, AI Mission, and AI Values StatementsCPP02: Conflict of Interest PolicyCPP03: AI Ethical Standards PolicyAnnex 1: Harmony MnemonicCPP04: Corporate Ethics PolicyCPP05: Corporate Social Responsibility PolicyCPP06: Corporate Diversity PolicyCPP07: Environment Protection PolicyCPP08: Data Ethics PolicyPart A: Principles of Data Ethics for AIPart B: Data Ethics Principles for Personal DataReview and ChangesOutcomes, Results, and OutputsRecommendationsConclusion
Part III: AI Regulations and Standards
AI Regulations and Standards
4. AI Global Standards and Guidelines
ISO/IEC 42001 AI Management SystemISO 42001 Standard Responsible AI PrinciplesAustralia’s AI Ethics PrinciplesNIST AI Risk Management FrameworkUniversal Guidelines for Artificial IntelligenceThe OECD AI PrinciplesEU AI ActEU AI Act System Development PrinciplesMicrosoft Responsible AI Standard PrinciplesGoogle Responsible AI PracticesIBM Responsible AI PracticesOutcomes, Results, and OutputsRecommendationsConclusion
5. Major Data Protection Laws and Privacy by Design Framework Principles
GDPR (for EU)LGPD (for Brazil)Privacy by Design Principle FrameworkOutcomes, Results, and OutputsRecommendationsConclusion
6. Digital and Secure AI System Development Guidelines
Digital Nations Charter2EU Digital Rights and PrinciplesThe Principles for Digital DevelopmentThe Guidelines for Secure AI System DevelopmentSecure Design GuidelinesSecure Development GuidelinesSecure Deployment GuidelinesSecure Operation and Maintenance GuidelinesOutcomes, Results, and OutputsRecommendationsConclusion
Part IV: AI Infrastructure Management
AI Infrastructure Management
7. Prepare the Organization for AI
Phases of the AI Implementation ApproachGoal and Objectives of Phase A (AI Preparation)Steps of Phase A (AI Preparation)Step A1: Conduct AI Needs and Gap AnalysisStep A2: Carry Out AI Regulations and Standards AnalysisStep A3: Perform AI Readiness and Awareness AssessmentStep A4: Establish AI Steering CommitteeStep A5: Appoint AI Project ManagerStep A6: Draft AI BudgetStep A7: Educate Board and Senior ManagementProducts of Phase A (AI Preparation)Phase A (AI Preparation) Recommended Assurance TasksConcluding Remarks
8. Establish AI Governance Framework
Phases of the AI Implementation ApproachGoal and Objectives of Phase B (AI Management Framework, Part 1: AI Governance Structure)Steps of Phase B (AI Management Framework, Part 1: AI Governance Structure)Step B1-1: Ensure Corporate Governance Controls OperationStep B1-2: Establish AI Governance Roles and ResponsibilitiesStep B1-3: Establish AI Governance FrameworkStep B1-4: Develop and Issue AI StrategyStep B1-5: Establish AI Third-Party ControlsStep B1-6: Determine AI Ethical Principles and ValuesStep B1-7: Educate and Train Employees on AIProducts of Phase B (AI Governance)Phase B (AI Governance) Recommended Assurance ActionsConcluding RemarksAnnex 1: Insider Threats Action PlanAnnex 2: IT Sabotage Prevention Plan
9. Establish Data Governance Framework
Phases of the AI Implementation ApproachGoal and Objectives of Phase B (AI Management Framework, Part 2: Data Governance Structure)Steps of Phase B (AI Management Framework, Part 2: Data Governance Structure)Step B2-1: Establish Data Governance FunctionStep B2-2: Establish Data Governance Policies and ProceduresStep B2-3: Establish Big Data Management Policies and ProceduresStep B2-4: Manage Corporate Data StorageStep B2-5: Locate and Organize AI DataStep B2-6: Manage Business Records and IntelligenceStep B2-7: Protect Intellectual Property (IP) AssetsProducts of Phase B (AI Data Governance)Phase B (Data Governance) Recommended Assurance ActionsConcluding RemarksAnnex 1: AI Data Protection GuidelinesAnnex 2: Enterprise Data Repository Management Procedure
10. Manage IT Governance and Privacy Controls
Phases of the AI Implementation ApproachGoal and Objectives of Phase B (AI Management Framework, Part 3: IT Governance Controls)Steps of Phase B (AI Management Framework, Part 3: IT Governance Controls)Step B3-1: AI Computer Infrastructure ManagementStep B3-2: Information Security and Privacy Policies and Procedures ManagementStep B3-3: Vulnerabilities and Malware ManagementStep B3-4: Robotic Accounts ManagementStep B3-5: Human Accounts ManagementStep B3-6: Account Provisioning and Revocation ManagementStep B3-7: Password Controls ManagementStep B3-8: Segregation of IT Duties ImplementationStep B3-9: Third-Party Access Controls ImplementationStep B3-10: IT Change ManagementStep B3-11: AI Solutions Inventory ManagementStep B3-12: IT Resource ManagementStep B3-13: IT Knowledge Retention ManagementStep B3-14: IT Configuration ManagementStep B3-15: Backup and Recovery Process ManagementIT Governance and Privacy ProductsIT Governance Recommended Assurance ActionsConcluding Remarks
Part V: AI Systems Development and Operations
AI Systems Development and Operations
11. Develop AI Systems
Phases of the AI Implementation ApproachGoal and Objectives of Phase C (AI Systems Development)Steps of Phase C (AI Systems Development)Step C1: Establish AI Development TeamStep C2: Select AI Project to Be Developed and RunStep C3: Establish AI System OwnerStep C4: Perform AI System Regulatory Risk AnalysisStep C5: Develop AI SystemStep C6: Check AI System’s Hypotheses, Algorithms, and ModelStep C7: Document Business Case for AI SystemStep C8: Document Business Processes for AI SystemStep C9: Include Internal Application Controls in AI SystemStep C10: Include “Explainability by Design” in AI SystemStep C11: Include Security and Privacy Controls in AI SystemStep C12: Test AI SystemProducts of AI Systems DevelopmentAI Systems Development Recommended Assurance ActionsConcluding RemarksAnnex 1: User/Business Needs for AI Project “Manage Emails”Annex 2: AI Systems Development and Operation MethodologyPhase 1: AI Needs AnalysisPhase 2: AI System Analysis, Design, and DevelopmentPhase 3: AI System Deployment and OperationPhase 4: AI System MaintenanceAnnex 3: Human Aspects Improvement GuidelinesAnnex 4: AI System Test Procedure
12. Operate AI Systems
Phases of the AI Implementation ApproachGoal and Objectives of Phase D (AI Systems Operation)Steps of Phase D (AI Systems Operation)Assurance Stage: Steps D1–D4Step D1: Review the IT Operational Aspects of AI SystemsStep D2: Review the Quality of AI SystemsStep D3: Review the Auditability of AI SystemsStep D4: Review Application Controls and Logging of AI SystemsDeployment StageStep D5: Operate (Deploy) the AI SystemStep D6: Monitor Performance of AI SystemsStep D7: Manage Results and Errors of AI SystemsStep D8: Manage User Requests of AI SystemsStep D9: Manage AI Incidents and HazardsReview StageStep D10: Carry Out Post-Implementation Review of AI SystemsProducts of AI Systems OperationAI Systems Operation Recommended Assurance ActionsConcluding RemarksAnnex 1: Example of an EU Act Assessment QuestionnaireAnnex 2: UK Government AI Assurance and Governance Standards
Part VI: AI Infrastructure and Systems Assessment
AI Infrastructure and Systems Assessment
13. Develop AI Infrastructure and Systems Audit Process
Phases of the AI Implementation ApproachGoal and Objectives of Phase E (AI Infrastructure and Systems Assessment)Steps of AI Infrastructure and Systems AssessmentIntroduction to AI AuditAI Auditing Challenges, Opportunities, and RisksAI ChallengesAI OpportunitiesAI RisksAI Audit ObjectivesAI Audit ProcessStage 1: AI Audit Planning and PreparationStage 2: AI Audit ExecutionStage 3: AI Audit ReportingStage 4: AI Audit Follow-UpStage 5: AI Audit Project ManagementRecommendationsConclusionAnnex 1: AI Compliance DocumentationAnnex 2: AI Audit AreasAnnex 3: Example of Auditing Issue 7 (Data Privacy and Protection Controls) in AI Audit Area 5 (AI Data Management)Annex 4: Physical and Environmental Security Checklist
14. Assess Corporate Governance and HR Management Issues
Phases of the AI Implementation ApproachGoal and Objectives of Phase E (AI Infrastructure and Systems Assessment)Steps of AI Infrastructure and Systems AssessmentStep E1: Assess Corporate GovernanceIssue 1: AI Requirements, Standards, and Rules ComplianceIssue 2: AI EthicsIssue 3: AI Audit ProcessIssue 4: Corporate StrategyIssue 5: Corporate Ethical Rules and ValuesIssue 6: AI GuidelinesIssue 7: IP ProtectionIssue 8: Corporate Knowledge Retention ManagementStep E2: Assess HR ManagementIssue 1: AI Management Roles and ResponsibilitiesIssue 2: Human Resource Requirements ManagementIssue 3: Human CultureIssue 4: AI Awareness and TrainingIssue 5: Personnel Responsibilities and SkillsIssue 6: Basic or Minimum IT and Digital Required SkillsIssue 7: Intermediate or Second-Level IT and Digital SkillsIssue 8: Advanced or Third-Level IT and Digital SkillsRecommendationsConclusion
15. Assess AI Governance and AI Data Management Issues
Phases of the AI Implementation ApproachGoal and Objectives of Phase E (AI Infrastructure and Systems Assessment)Steps of AI Infrastructure and Systems AssessmentStep E3: Assess AI GovernanceIssue 1: AI Governance FrameworkIssue 2: AI StrategyIssue 3: AI Program ManagementIssue 4: AI Risks and Controls FrameworkIssue 5: Human OversightIssue 6: AI Benefits ManagementIssue 7: AI Language and Concept DefinitionsIssue 8: AI Expectations ManagementIssue 9: AI Operating ModelIssue 10: AI Solution Exit StrategyIssue 11: AI Awareness and TrainingIssue 12: AI Third-Party ControlsIssue 13: AI Incident ManagementStep E4: Assess AI Data ManagementIssue 1: AI Data Governance Roles and ResponsibilitiesIssue 2: Data Governance Policies and ProceduresIssue 3: Data QualityIssue 4: Completeness and Accuracy of DataIssue 5: Big Data ManagementIssue 6: Processing Records DocumentationIssue 7: Data Privacy and Protection ControlsRecommendationsConclusion
16. Assess AI Models and AI System Development Issues
Phases of the AI Implementation ApproachGoal and Objectives of Phase E (AI Infrastructure and Systems Assessment)Steps of AI Infrastructure and Systems AssessmentStep E5: Assess AI Model and Hypotheses ManagementIssue 1: AI Hypotheses GovernanceIssue 2: AI Algorithms GovernanceIssue 3: AI Model GovernanceIssue 4: AI Model ValidationStep E6: Assess AI System DevelopmentIssue 1: AI Architecture and DevelopmentIssue 2: Business Case Documentation for AI Systems/SolutionsIssue 3: AI Design and Development MethodologyIssue 4: Business Process DocumentationIssue 5: AI Application ControlsIssue 6: AI System/Solution Testing MethodologyIssue 7: “Explainability by Design” in AI Systems/SolutionsIssue 8: “Security by Design” in AI Systems/SolutionsIssue 9: “Privacy by Design and by Default” in AI Systems/SolutionsIssue 10: AI System/Solution OwnershipIssue 11: AI System/Solution Regulatory Risk AnalysisRecommendationsConclusion
17. Assess AI System Operation Issues
Phases of the AI Implementation ApproachGoal and Objectives of Phase E (AI Infrastructure and Systems Assessment)Steps of AI Infrastructure and Systems AssessmentStep E7: Assess AI System OperationIssue 1: Critical IT ControlsIssue 2: AI Computer InfrastructureIssue 3: IT Operational Aspects of AI Systems/SolutionsIssue 4: Quality of AI Systems/SolutionsIssue 5: Auditability of AI Systems/SolutionsIssue 6: Application Processing Controls and Logging of AI Systems/SolutionsIssue 7: AI Systems/Solutions Operation (Deployment)Issue 8: AI Systems/Solutions Operation MonitoringIssue 9: AI Systems/Solutions Results Review and Error ManagementIssue 10: AI Systems/Solutions Error ManagementIssue 11: AI Systems/Solutions User Requests ManagementIssue 12: AI Incidents and Hazards ManagementIssue 13: Post-Implementation Review of AI Systems/SolutionsIssue 14: Business Value of AI Systems/Solutions EvaluationIssue 15: AI Systems’/Solutions’ Benefits Management EvaluationIssue 16: AI Systems’/Solutions’ Exit Strategy EvaluationRecommendationsConclusion
18. Assess IT Governance Issues
Phases of the AI Implementation ApproachGoal and Objectives of Phase E (AI Infrastructure and Systems Assessment)Steps of AI Infrastructure and Systems AssessmentStep E8: Assess IT GovernanceIssue 1: AI Computer Infrastructure ManagementIssue 2: Information Security and Privacy Policies and Procedures ManagementIssue 3: Vulnerabilities and Malware ManagementIssue 4: Robotic Accounts ManagementIssue 5: Human Accounts ManagementIssue 6: Account Provisioning and Revocation ManagementIssue 7: Password Controls ManagementIssue 8: Segregation of IT Duties ImplementationIssue 9: Third-Party Access Controls ImplementationIssue 10: IT Change ManagementIssue 11: AI Systems/Solutions Inventory ManagementIssue 12: IT Resource ManagementIssue 13: IT Knowledge Retention ManagementIssue 14: IT Configuration ManagementIssue 15: System SoftwareIssue 16: Backup and Recovery Process ManagementRecommendationsConclusion
Epilogue
End Notes
Prologue: Can Computers Think?Chapter 1: AI Management SystemChapter 2: The Seven Ancient Greek Wisdom PrinciplesChapter 3: Corporate Philosophy Statements and PoliciesChapter 4: AI Global Standards and GuidelinesChapter 5: Major Data Protection Laws and Privacy by Design Framework PrinciplesChapter 6: Digital and Secure AI System Development GuidelinesChapter 7: Prepare the Organization for AIChapter 8: Establish AI Governance FrameworkChapter 9: Establish Data Governance FrameworkChapter 10: Manage IT Governance and Privacy ControlsChapter 11: Develop AI SystemsChapter 12: Operate AI SystemsChapter 13: Develop AI Infrastructure and Systems Audit ProcessChapter 14: Assess Corporate Governance and HR Management IssuesChapter 15: Assess AI Governance and AI Data Management IssuesChapter 16: Assess AI Models and AI System Development IssuesChapter 17: Assess AI System Operation IssuesChapter 18: Assess IT Governance IssuesAppendix 1: AI GlossaryAppendix 6: AI Risk Assessment ProcedureAppendix 10: AI Third-Party ControlsAppendix 12: AI Data Governance JobsAppendix 27: AI Trustworthiness Assessment QuestionnaireAppendix 28: AI Algorithmic Impact AssessmentAppendix 31: AI Model Development ProcedureAppendix 32: AI Results Verification ProcedureAppendix 33: AI Error Correction ProcedureAppendix 34: IT Change Management ProcedureAppendix 35: AI User Support ProcedureAppendix 36: AI Incident Reporting PolicyAppendix 37: AI Incident Response ProcedureAppendix 38: AI System Post-Implementation Review QuestionnaireAppendix 39: AI Audit Findings Report ExampleEpilogue
Additional Resources
AI-Related BooksPublished Works of John Kyriazoglou
Index

Content preview from AI Management Framework : Practical Solutions for Ethical AI Deployment and Continuous Improvement

Epilogue

Marvin Minsky: “If you understand something in only one way, then you really don’t understand it at all. The secret of what anything means to us depends on how we’ve connected it to all the other things we know.”¹

According to recent research, most companies are still in the early stages of AI adoption.² While AI investment is rising and innovation is accelerating, too many leaders risk falling into the trap of complacency, satisfied with early wins rather than pursuing the transformative potential AI offers. The real challenge isn’t just adopting AI as a differentiator – companies must ensure their AI strategies are actionable at every level of the business, turning bold visions into sustainable, transformative outcomes.

But what are the ...

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AI Management Framework : Practical Solutions for Ethical AI Deployment and Continuous Improvement

by John Kyriazoglou

Epilogue

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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