AI Governance Playbook : How to Secure, Control, and Optimize Artificial Intelligence Initiatives.

Format:

Book

Author/Creator:

Smallwood, Robert F.

Language:

English

Subjects (All):

Artificial intelligence.

Corporate governance.

Physical Description:

1 online resource (353 pages)

Edition:

1st ed.

Place of Publication:

New York : Bloomsbury Academic & Professional, 2026.

Summary:

Artificial intelligence can evoke fears that evil robots are taking over the world. AI governance is key to making sure that never happens.

Contents:

Cover

Half Title

Title Page

Copyright Page

Contents

Preface

Thesis

Purpose

Goals

Chapter 1: AI's Great Potential-and Governance Challenges

What Is AI?

Mathematics: The Universal Language

The Appeal of GAI

AI Wins in Daily Life

Emerging Consumer-Facing AI Apps

The Tremendous Potential of AI Programs to Build Business Value

To Maximize Value and Reduce Risks, AI Programs Must Be Governed

Issues in Training AI Models

Overcoming AI Model Training Challenges

Introducing AI Governance

The Role of Governments, Business, Researchers, Society, and Users

Apply Proven Information Governance Models and Best Practices to AI Governance

Key Points

Key Glossary Terms

Chapter 2: AI Governance Standards, Frameworks, and Models

Creating Guardrails

Need for AI Governance Standards, Frameworks, and Models

Benefits of AI Governance Standards, Frameworks, and Models

Beyond the Need for Technical Standards

Relevant ISO Standards for AI Governance

Relevant IEEE Standards for AI Governance

AI Standards from National Bodies

Emerging Agentic AI Standards

Leading Agentic AI Standards and Protocols

Company-Specific Frameworks and Approaches

NIST Risk Management Framework

Presidential Executive Order and NIST

An Overview: Frameworks, Guidelines, and Principles for AI Governance

AI and Information Governance Models

Anticipating AI Litigation: Leveraging the IG Reference Model into the e-Discovery Reference Model

Business Case for Leveraging the Information Governance Reference Model into the e-Discovery Reference Model for AI Governance in Litigation Preparedness

AI Governance Models

AI Establishments and Organizations

Key Glossary Term

Chapter 3: AI Governance in the Lifecycle of AI Development.

1. Problem Definition and Project Initiation

2. Data Acquisition and Collection

3. Data Preparation: Exploration and Preprocessing

4. Model Selection and Training

Steps in the Training and Validation Phase

5. Model Evaluation: Interpretability and Explainability in the AI Development Lifecycle

Testing and Quality Assurance

AI System Testing

6. Model Deployment

User Training and Support

AI Model Disgorgement

Understanding Model Disgorgement and Destruction

Post-Deployment Monitoring and Evaluation

Conclusion

7. Monitoring and Maintenance

Key Requirements for Post-Deployment

Steps in the Monitoring and Maintenance Process

8. Model Retirement

Notes

Chapter 4: The Role of Records and Information Management in AI Governance

The Role of RIM in AI Governance

The Role of RIM in the AI Systems Lifecycle

Records Management Business Rationale

Detailed RIM Role in AI Governance

Record Retention and Disposition

Information Lifecycle Management

Compliance and Legal Requirements

Data Quality and Integrity

Collaboration with AI Development Teams

Documentation and Auditing

Information Risk Management and Mitigation

Audit Trails

Tracking Technological Changes

Education and Training

Ethics and Responsible AI

Key Elements: Change Management, and Continuous Improvement

The Use of AI in Records Management

Chapter 5: Legal and Regulatory Challenges in AI Governance

Introduction

The Revised 2006 and 2015 Federal Rules of Civil Procedure Changed Everything

Leveraging the IG Reference Model and e-Discovery Reference Model provide a foundational structure for AI Governance.

Seven Basic Steps of the E-Discovery Process

The Role of Legal Professionals in AI.

AI Lifecycle and the Role of Legal

1. Problem Definition

2. Data Collection

3. Data Preparation

5. Model Evaluation

8. Retirement

The Responsibilities of the Legal Function in AI Systems Development

More Detail on the Role of Legal in AI Systems

Compliance with Data Protection and Privacy Laws

Compliance with Anti-discrimination Laws

Intellectual Property (IP) Management

Contractual Agreements

Ethical and Responsible AI

Risk Management

Data Governance

Audit and Monitoring

Regulatory Engagement

Training and Awareness

Incident Response Planning

Compliance: The Structural Role of Legal in AI Deployment

Incorporating Compliance into AI Governance Framework

Importance of Data Privacy in AI

Model Privacy

Bias and Fairness

Employment and Labor Laws

Algorithmic Accountability and Transparency

Data Sovereignty

Integrating Compliance into AI Systems - Compliance by Design

Chapter 6: Risk Management for AI Implementations

Positive and Negative Risks

Managing Risk Through the AI Lifecycle

1. Problem Definition and Program Initiation

The Risk Identification, Evaluation, and Mitigation Process

Risk Identification

Risk Assessment

Legal and Ethical Considerations

Documentation and Transparency

Model Validation and Testing

Employee Training and Awareness

Crisis Response Plan

Continuous Monitoring

Chapter 7: The Role of Privacy in AI Governance

Introduction to AI and Privacy.

Data Privacy: The Bedrock of AI Governance

The Role of Privacy Professionals in AI

Engagement with Authorities on Data Privacy

AI Lifecycle and the Role of Data Privacy

Incorporating Data Privacy into an AI Governance Framework

Role of Data Privacy in Building Trust

Trust, Data Subject Rights, and Consent Management

Informed Consent and Data Usage

Clearly define the purposes for which the data will be used.

Develop Clear and Concise Privacy Notices

Implement Consent Management

Privacy Rights - Data Access and Control

Integrating Privacy into AI Systems - Privacy by Design

Compliance with Privacy and Data Protection Laws

Link between AI Failures and Privacy Requirements

Data Privacy Breaches in AI

Summary

Detailed Guidance: Privacy and AI Data Control

Conduct a Thorough Inventory of the Data Used in AI Systems.

Classify data based on sensitivity and privacy implications.

Data Minimization and Purpose Limitation

Informed Consent

Data Security Measures

Data Subject Access Rights

Employee Training on Data Privacy

Regular Audits and Assessments

Continuous Improvement

Chapter 8: The Role of Cybersecurity in AI Governance

The Role of Cybersecurity in the AI System Development Lifecycle

Threat Modeling

Fundamentals of Threat Modeling

Unique Security Considerations for AI

Best Practices for AI Threat Modeling

Data Security

Securing Data in AI Systems

Data Encryption

Access Management

Infrastructure Protection

Continuous Threat Detection Monitoring

Compliance.

Access Controls for AI Systems

Key Concepts

Data Classification - Organizing data by sensitivity/criticality levels to determine security controls

Governance Considerations

Access Control Methods

1. Technical Controls

Authentication

Role-Based Access Control (RBAC)

Attribute-Based Access Control (ABAC)

Access Control Lists (ACLs)

Network Security

Encryption

Operating System Controls

2. Administrative Controls

Data Classification

Access Control Policies

Access Provisioning and Reviews

Access Policy Enforcement

Zero Trust Security

Least Privilege Access

Temporal Access Controls

Emergency Access Procedures

Regulatory Compliance

Audits

3. Physical Controls

Cryptographic Best Practices for Protecting Sensitive Data in AI Systems

Identifying and Classifying Sensitive Data

Choosing Strong Encryption Algorithms

Implementing Secure Key Management

Applying Data-at-Rest and Data-in-Transit Encryption

Documenting Cryptographic Configurations and Training Personnel

Conducting Cryptographic Audits and Seeking External Expertise

Cryptographic Controls for Responsible AI Governance

Establishing a Robust Data Access Monitoring Methodology for AI Systems

Defining Data Access Monitoring

Looking Ahead

Version Control for AI Model Reproducibility and Security

Version Control Systems Overview

Benefits for AI Systems

Leveraging Version Control for AI Governance and Security

Key Takeaways

Machine Learning Framework Updates and Patches for Security

What Are Machine Learning Frameworks and Libraries?

Why Patch Management Matters for AI Systems

Secure AI Model Deployment

Containerization

Identity and Access Management (IAM)

Deployment Pipelines

Encryption.

Monitoring, Logging, and Auditing.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

979-88-8184-314-4

9798765160183

OCLC:

1579273307