Certified: The ISACA AAIR Audio Course

In this episode, we focus on a part of A I risk work that is easy to underestimate until something goes wrong: training people in a way that actually changes what they do. Many organizations run training as a one-time event, like checking a box, but risk training only matters if it sticks when someone is busy, stressed, or trying to move fast. With A I, that challenge is bigger because the technology can feel magical, and people may trust it too much, fear it too much, or misunderstand what it is doing behind the scenes. The goal is not to turn everyone into an expert, because that is unrealistic, but to make sure each group of people knows what they must know to make safe decisions. We are going to break this down in a beginner-friendly way by focusing on who needs what training, why their needs differ, and how to make the learning durable over time. If you can plan training that sticks, you reduce accidents, improve consistency, and build a culture where A I risks are noticed early instead of discovered after harm happens.

Before we continue, a quick note: this audio course is a companion to our course companion books. The first book is about the exam and provides detailed information on how to pass it best. The second book is a Kindle-only eBook that contains 1,000 flashcards that can be used on your mobile device or Kindle. Check them both out at Cyber Author dot me, in the Bare Metal Study Guides Series.

A good way to start is by defining what we mean by A I risk training, because beginners sometimes think it is only about rules like do not paste secrets into a chatbot. That is part of it, but effective training has three layers that work together in real life: basic understanding, practical behavior, and good judgment under uncertainty. Basic understanding is knowing what A I can and cannot do, including common failure modes like confident wrong answers. Practical behavior is knowing what to do in normal situations, like how to handle sensitive data, how to verify outputs, and how to report concerns. Good judgment is what you use when the situation is new and you cannot follow a simple script, like deciding whether an output is trustworthy enough to act on. Training that sticks aims for all three, but the balance changes depending on the audience. A person building systems needs deeper understanding than a person casually using them, and a leader making decisions needs a clear mental model more than technical detail. When you train everyone the same way, it usually sticks with nobody.

The next step is recognizing that different roles touch A I in different ways, which means different risks show up in their day-to-day actions. Some people interact with A I as users, meaning they ask questions, generate content, or rely on suggestions, and their risks involve misuse, over-trust, and accidental data exposure. Some people design the experience, meaning they decide how the system behaves, and their risks involve building unsafe defaults and shaping how much people rely on the system. Some people manage data, meaning they decide what data flows into the system, and their risks involve privacy, bias, and quality problems that become baked into outcomes. Some people secure systems, meaning they protect access and detect abuse, and their risks involve new attack paths and subtle manipulation. Some people oversee compliance and governance, meaning they define rules and evidence, and their risks involve gaps between policy and reality. Training works best when it follows the path of real work, so you teach the moments where someone’s decision can create or reduce risk. If you can name those moments, you can plan training that sticks.

For brand-new learners, it helps to separate training audiences into simple groups without getting lost in job titles. One group is everyday users, meaning people who use A I tools as part of their work, like drafting text, summarizing information, or getting suggestions. Another group is builders, meaning people who create, integrate, or tune A I features, even if they are not researchers. Another group is reviewers and approvers, meaning people who sign off on use cases, releases, or risk decisions. Another group is responders, meaning people who deal with incidents, complaints, or failures. Leadership is a special audience because leaders set priorities, budgets, and risk appetite, and their decisions shape everything else. The same training deck cannot serve all these groups, because the same words mean different things depending on responsibilities. Training that sticks respects those differences and uses them as the organizing idea. You are not trying to teach everything; you are trying to teach the right things to the right people so that risk decreases in practice.

Everyday users need training that is clear, short enough to remember, and focused on real behavior, because their risk comes from normal use. They need to understand that A I output can be wrong even when it sounds confident, and they need a habit of verification for anything important. They need to understand what data should never be entered, such as secrets or personal details, and they need to understand that deleting a message on the screen is not the same as removing it from every system behind it. They also need to recognize when a request is inappropriate, like using A I to generate content that breaks policy or misleads people. Another important user lesson is to understand the difference between assistance and authority, meaning the system can help you think, but it should not replace your responsibility for the outcome. Training for users should include simple examples of failure and a simple method for responding, like pause, verify, and escalate when something feels off. If user training does not change daily habits, it did not stick.

Builders need deeper training because they shape the system, and their choices set the boundaries that users will live inside. They need to understand data risks, including how data quality and labeling can influence outcomes and how hidden bias can enter through seemingly harmless sources. They need to understand how models can fail, including hallucinations, unsafe recommendations, and sensitivity to unusual inputs, because those failures must be expected and managed. They need to understand security risks that are specific to A I, like misuse of interfaces, manipulation of prompts, and unintended access through integrations. Builders also need training on documentation and traceability, because when something goes wrong, you will need to know what changed, why it changed, and what assumptions were made. Another builder lesson is about guardrails, meaning technical and process constraints that reduce harm, such as limiting actions the model can trigger and requiring human review for high-impact decisions. For builders, training sticks when it becomes part of design habits and review routines, not when it is just remembered trivia.

Reviewers and approvers need training that helps them ask better questions and recognize risk signals, because they often decide whether the system is allowed to go live. They should be able to interpret claims about model performance and understand that accuracy is not a single number that applies everywhere. They need to understand the idea of context, meaning a model can perform well on average but fail badly for certain groups or certain situations, which can create fairness and safety issues. They should know what evidence to request, such as testing results, monitoring plans, and clear boundaries for use. They also need to understand accountability, meaning who is responsible for what, because approval without ownership leads to confusion during incidents. Approver training should also include common traps, such as approving a system based on impressive demos instead of real use conditions. Training sticks for approvers when it becomes a consistent decision checklist in their minds, not a one-time class they forget.

Responders need training that prepares them for messy, time-sensitive events, because A I incidents are often confusing and fast-moving. They need to recognize different incident types, such as privacy leakage, harmful content generation, unsafe recommendations, or silent degradation where performance drifts over time. They also need to understand containment options at a high level, like limiting access, turning off certain features, or switching to safe fallbacks, without requiring them to memorize technical steps. Communication is also part of their training, because incidents involve explaining what happened to stakeholders, and that explanation must be accurate and calm. Responders need a shared vocabulary so that security, privacy, legal, and product can coordinate quickly without arguing about definitions. Another key responder skill is evidence thinking, meaning they know what to preserve, what to document, and what questions to ask the builders and vendors. Training sticks for responders when it is practiced, because under stress people do what they have rehearsed, not what they vaguely remember.

Leadership training is often overlooked or treated as a short briefing, but leaders shape whether training for everyone else is possible and respected. Leaders need a clear mental model of A I risk that avoids extremes, because the two most common leadership mistakes are blind enthusiasm and blanket fear. They should understand that A I risk is not only about cyberattacks; it is also about reliability, fairness, privacy, and trust. They need to know what resources are required for responsible use, such as monitoring, governance, and incident readiness, because without resources, safety becomes wishful thinking. Leaders also need to understand tradeoffs, such as speed versus assurance, and they need to be comfortable making those tradeoffs explicit rather than pretending there are no costs. Another leadership lesson is incentives, meaning people will follow what leaders reward, so leaders must reward careful behavior, not only fast delivery. Training sticks at the organizational level when leaders consistently reinforce the same expectations through decisions, not just speeches.

Now that the audiences are clearer, we can talk about what makes training stick, because stickiness is about memory and habit, not about making slides more colorful. Training sticks when it is repeated in small pieces, connected to real tasks, and reinforced by the environment people work in. If a user learns a rule but the tool interface encourages the opposite behavior, the environment will win. If a builder learns about risk but release deadlines punish careful testing, deadlines will win. Stickiness comes from repetition, from practice, and from feedback when mistakes happen. It also comes from making training relevant to the learner’s role, because relevance increases attention and attention increases retention. A common misconception is that longer training means better training, but for many roles, short focused training repeated over time is more effective than a single long session. When you plan training, you are really planning habit formation.

Another factor that makes training stick is using examples that feel close to the learner’s reality, because beginners remember stories and situations better than abstract warnings. An everyday user might remember a story where a model confidently gave a wrong answer that caused a mistake, and that memory can trigger a verification habit later. A builder might remember an example where a small data change caused a surprising behavior change, and that memory can trigger better version control and testing. An approver might remember a case where a system performed well in testing but failed in real-world context, and that memory can trigger better questions. A responder might remember an incident where confusion about roles slowed containment, and that memory can trigger clearer playbooks and contact paths. Examples should be simple and realistic, not dramatic, because dramatic examples can make learners think the risk is rare. Training should normalize the idea that small mistakes are common and preventable. When learners recognize their own world in the examples, they are more likely to apply the lessons.

It is also important to build training around common misconceptions, because misconceptions are like traps that pull people back into risky behavior. One misconception is that the model is neutral because it is mathematical, when in reality it reflects data choices and objectives. Another misconception is that adding a warning message makes a system safe, when in reality the design may still encourage over-trust. Another misconception is that privacy is only about names and addresses, when in reality context and combinations of details can identify people. Another misconception is that security is a separate layer added at the end, when in reality many A I risks come from product design and data handling decisions. Another misconception is that if a vendor provides the A I, risk is the vendor’s problem, when in reality your organization still carries responsibility. Training that sticks directly confronts these misconceptions and replaces them with simple, accurate mental models. If you do not address misconceptions, people will fill gaps with assumptions, and assumptions create risk.

Finally, planning training that sticks means planning how you will know whether it worked, because confidence is not evidence. You can watch for behavior changes, such as fewer risky inputs, more consistent verification, better reporting of issues, and stronger documentation. You can also watch for improved decision quality, such as approvers asking the right questions and builders including monitoring and guardrails by default. You should treat training as part of a larger system, where policy, tooling, leadership reinforcement, and incident learning all connect. When an incident or near-miss occurs, you can fold that lesson back into training so the organization improves over time instead of repeating the same mistakes. For beginners, the key takeaway is that A I risk training is not about making people afraid; it is about making them capable. When you match training to roles, connect it to real work, and reinforce it over time, you create learning that stays with people when it matters most.