1.1 – Welcome to the AI Risk Course Lesson

Lesson Overview

This welcome lesson sets the tone for a practical approach to AI risk. The goal is simple: accelerate value while managing downside risk. You will get a clear mental model for AI risk, checklists you can act on, and talking points that align legal, security, and product teams. The course anchors every topic to the CIA triad. Confidentiality connects to data safety. Integrity links to output quality. Availability speaks to operational resilience. With this anchor, you can see how each risk affects what matters most.

Each video relies on one of two organizing lenses. The first is People, Process, and Technology to cover the three aspects of organizational control. The second is Document, Evaluate, Mitigate to guide risk handling from discovery to mitigation. You will map risks across the AI lifecycle, including problem framing, data sourcing, model selection, integration, deployment, and monitoring. This keeps controls landing where they matter instead of getting lost in theory.

Expect pragmatic metrics that help you decide what to do next. You will track risk, exposure, value, and time to mitigation. Good looks like measurable controls, clear ownership, periodic testing, and well documented expectations, not perfect systems. If you are tired of vague principles, this course keeps things concrete and useful.

Who This Is For

This lesson is for anyone who needs to align on AI risk without getting buried in jargon. It helps teams agree on what matters, where to act, and how to show progress with simple measures.

• Product managers and owners who need to balance speed and safety
• Security leaders who want clear controls and testable expectations
• Legal and compliance partners who need common language with product and engineering
• Data and AI practitioners who need practical guardrails across the lifecycle
• Operations and support teams who care about reliability and recovery

Where This Fits in a Workflow

Use this lesson as your starting point to establish shared language and expectations. Before designing controls or drafting policies, align on how risks map to confidentiality, integrity, and availability. Then choose the right lens for your task. Use People, Process, and Technology when you are defining who does what and how. Use Document, Evaluate, Mitigate when you are handling a specific risk from discovery to closure.

Examples:

• Planning a new AI feature: map risks across problem framing, data sourcing, and model selection. Decide owners, controls, and tests before integration and deployment.
• Reviewing an existing AI system: trace risks across deployment and monitoring. Set metrics for exposure, value, and time to mitigation to guide improvements.

Technical & Workflow Benefits

Many teams handle AI risk with ad hoc debates and scattered policies. That approach leads to unclear ownership, uneven controls, and slow response when issues surface. This course offers a simpler, more structured way to work. By anchoring to confidentiality, integrity, and availability, you can connect each risk to a concrete impact on data safety, output quality, or operational resilience.

Using People, Process, and Technology helps you place controls where change actually happens. Using Document, Evaluate, Mitigate helps you move from discovery to mitigation with a clear record of decisions. Tracking risk, exposure, value, and time to mitigation turns discussions into measurable progress. This reduces rework, speeds decisions, and improves consistency.

Use cases where this approach stands out:

• Launching an AI feature on a tight timeline while keeping measurable controls and clear owners
• Running a production AI system with periodic testing and documented expectations that survive staffing changes

Practice Exercise

Pick one AI project your team is working on now. If you do not have one, choose a realistic scenario such as a text classification model or a retrieval feature for internal documents.

• Step 1: Map the lifecycle. Write a short note on each stage: problem framing, data sourcing, model selection, integration, deployment, and monitoring. List one risk you see at each stage.
• Step 2: Anchor to the CIA triad. For each risk, mark whether it primarily affects confidentiality, integrity, or availability. Add a one sentence reason. Use the phrasing data safety, output quality, or operational resilience if that helps clarity.
• Step 3: Decide what to measure. For the two highest priority risks, note risk, exposure, value, and a target time to mitigation. Assign a clear owner and set a simple periodic testing plan.

Reflection: Compare your notes against what good looks like. Where are controls measurable, ownership clear, testing periodic, and expectations well documented? What is your first change to move closer to that standard?

Course Context Recap

This is the starting point for the AI Risk Management course. You now have the shared language and structure we will use across the series, along with a clear picture of what good looks like. Next, you will get a quick primer on confidentiality, integrity, and availability so you can apply the triad consistently across every stage of the AI lifecycle. Continue through the course to see these ideas put to work with practical checklists, metrics, and team alignment steps.