Short Course

Secure AI Adoption: Strategy, Governance, and Risk

Name: Secure AI Adoption: Strategy, Governance, and Risk
Brand: CodeRed

Designing, governing, and securing enterprise AI through strategy, risk management, and operational excellence.

4 hr 39 m of video content

Intermediate

Validation of Completion Included

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$99.99

Overview What You Will Learn Curriculum Instructor

Course Overview

Artificial Intelligence is rapidly transforming how organizations innovate, automate processes, and make strategic decisions across every industry. However, successful AI adoption requires more than implementing advanced technologies—it demands a strong foundation in strategy, governance, risk management, and security. This course provides cybersecurity leaders, technology professionals, and decision-makers with a practical framework for building secure, responsible, and resilient AI programs that align with business objectives while addressing emerging risks.

This course begins by introducing the fundamentals of AI strategy, organizational readiness, and the key challenges associated with enterprise AI adoption. You will then explore governance models, risk management frameworks, and security principles based on the guidance presented in AI Strategy and Security: A Roadmap for Secure, Responsible, and Resilient AI Adoption (Wendt, 2025). The course also examines industry-recognized frameworks such as the NIST AI Risk Management Framework (AI RMF) and ISO/IEC 42001, covering responsible AI governance, security controls, compliance, operational resilience, and best practices for deploying, managing, and scaling AI systems across the enterprise.

By the end of this course, you will be able to develop AI strategies, establish effective governance and security frameworks, assess organizational readiness, and implement trustworthy AI solutions that support secure, responsible, and resilient enterprise AI adoption.

What You Will Learn

Develop an AI strategy aligned with organizational goals and risk tolerance.
Assess enterprise readiness for secure AI adoption.
Design governance structures that balance innovation with security and compliance.
Identify and mitigate threats unique to machine learning and generative AI systems.
Apply responsible AI principles, including fairness, transparency, and accountability.
Operationalize AI programs using MLSecOps and secure deployment practices.
Implement lifecycle management strategies for continuous improvement.
Integrate AI initiatives into business operations and organizational culture.

Program Curriculum

Why Organizations Need an AI
Defining Purpose: Aligning AI with Mission, Value, and Risk
Strategic Planning for Secure AI Adoption
Avoiding the Technology-first Trap in AI Adoption
Case Study: When Strategy Fails Without Governance
Chapter 1 Quiz

Evaluating Organizational Readiness for Secure AI Adoption
Infrastructure Readiness: Scaling for AI
Data Readiness: The Foundation of Successful AI
Workforce Readiness: Skills, Roles, and Cultural Transformation
Building a Sustainable AI Pipeline for Long-term Success
Chapter 2 Quiz

Why AI Systems Fail: Lessons for Governance Leaders
Designing Effective AI Governance Structures
Establishing Policies, Standards, and Guardrails for Responsible AI
Managing AI Risk Across the Lifecycle
Privacy, Trust, and Accountability in AI Programs
Case Study: Governance Gaps and Responsible AI Failures
Chapter 3 Quiz

Training-stage Threats: Poisoning, Manipulation, and Data Risks
Inference-stage Threats: Evasion, Prompt Attacks, and Abuse
Defending AI Systems: Mitigation Strategies Beyond Traditional Security
AI Security Testing and Assurance for Enterprise Trust
Never Forget the Fundamentals: Cybersecurity Still Matters
Chapter 4 Quiz

Understanding the Risks and Harms Posed by AI Systems
Trustworthy AI Characteristics: Human-centric, Transparent, and Robust
Ethical Design and Responsible AI Assessment
Privacy by Design and Human-centric AI
Case Study: Ethical Failures in Automated Decision Systems
Lab: Data Minimization to Mitigate Bias
Chapter 5 Quiz

Turning Strategy into Execution: Operationalizing Secure AI
MLSecOps Foundations: Building Secure AI Pipelines
Monitoring, Logging, and Governance in Production Environments
Managing Scale, Cost, and Operational Risk
Case Study: Operationalizing AI Securely
Chapter 6 Quiz

Navigating the Global AI Regulatory Landscape
AI Standards and Frameworks
Cybersecurity Standards Supporting AI Security Programs
Mapping AI Governance to Enterprise Risk and Compliance Structures
Chapter 7 Quiz

Monitoring AI Systems for Performance, Risk, and Trust
Human Feedback Loops and Adaptive Governance
Measuring Value: ROI, Risk Reduction, and Operational Impact
Responsible Model Retirement and Lifecycle Governance
Chapter 8 Quiz

Building an AI-ready Organization from the Inside Out
Executive Sponsorship and Leading AI Transformation
Cross-functional Collaboration Between Security, Data, and Business Teams
Preparing the Workforce for Human-AI Collaboration
Case Study: Overcoming Organizational Resistance to AI
Chapter 9 Quiz

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Instructor

Donnie W. Wendt

Dr. Donnie Wendt, author of The Cybersecurity Trinity and AI Strategy and Security, is a leading authority in AI security with over 30 years of experience in software development, cybersecurity, and AI operationalization. As a retired Principal Security Researcher at Mastercard, he advanced AI-driven defense strategies and explored emerging cyber threats. Now an advisor to Whiteglove AI and Styrk.ai, he promotes responsible AI innovation. A dedicated educator at Columbus State University, Donnie empowers future cyber defenders through hands-on learning. He holds a Doctorate in Computer Science (Information Security) and continues to shape the secure future of AI and cybersecurity.

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