Will McMillan
William is an experienced cybersecurity professional and Microsoft-certified engineer with deep, practical knowledge of AI integration within security operations, threat detection, and automation workflows.
Courses
Developing Custom AI Tools
Custom AI tools introduce non-determinism, external dependencies, and the risk of excessive agency. Traditional IT controls are necessary but not sufficient. You will learn to set trust boundaries, constrain tool scopes, and enforce latency budgets that match business SLAs.
Getting Started in AI
In this practical course, you'll learn how to treat AI prompts and outputs like governed assets. Mitigate risks like prompt injection, protect sensitive data, and apply enterprise-grade controls to your workflow. Master the art of validating outputs and measuring cost/accuracy to scale AI safely. Secure your output. Protect your data.
Secure AI Research
Learn to align AI research with secure principles to ensure due care and faster approvals. You will prioritize adversarial risks using MITRE and OWASP LLM Top 10, design data protection controls for privacy and integrity, build an evaluation process, select safe integration patterns, and communicate risk to review boards with audit-ready artifacts.
AI For Incident Responders
This course will teach you how to use AI copilots safely and responsibly in real-world investigations. You will learn how to combine human judgment with AI speed, apply governance frameworks, identify AI-specific threats, and measure tangible improvements in performance and accuracy.
AI Best Practices
Artificial Intelligence is reshaping how organizations make decisions, build products, and serve customers. Yet for every success story, there are many AI projects that fail compliance reviews, stall before launch, or create new risks because teams lacked clear, actionable best practices. This course was created to bridge that gap.
Machine Learning Lifecycles
Every successful AI initiative, regardless of size or complexity, follows a repeatable process known as the machine learning (ML) lifecycle. Learn the phases of a typical ML lifecycle and why adopting one is the single most important factor in delivering reliable, ethical, and cost-effective AI solutions.
Types of AI
Artificial Intelligence is not a single system or tool. It's a collection of different methods that learn, reason, and solve problems in unique ways. Each type of AI has its own strengths, limitations, and data requirements. Learn how these types differ, when to use each, and what tradeoffs are most important in production systems.
Data Model Basics
Before any algorithm is trained or deployed, its success depends on one thing: the shape and structure of the data feeding it. This course establishes the foundation for understanding why data modeling (how information is organized, related, and represented) is the most critical stage of any AI or machine learning (ML) project.
William is an experienced cybersecurity professional and Microsoft-certified engineer with deep, practical knowledge of AI integration within security operations, threat detection, and automation workflows.
He's designed and delivered technical training for enterprise environments covering topics such as AI-driven incident response, secure AI adoption, and data model governance.He has applied AI extensively within cybersecurity operations, particularly in threat detection, SOC automation, and incident response enrichment. His experience includes integrating GPT-based models and Azure OpenAI into Microsoft Sentinel workflows to summarize incident data, flag anomalies, and assist analysts with triage. He's also developed AI-assisted scripts for phishing analysis, log correlation, and vulnerability prioritization using Python and REST APIs. He has also contributed to AI risk assessments and data handling controls aligned with secure AI principles (prompt safety, model governance, and auditability).