Certified: The ISACA AAIR Audio Course

In this episode, we’re going to make AI feel understandable by taking it apart into four everyday ideas: models, data, training, and inference. A lot of anxiety around AI comes from the feeling that it is a black box that only mathematicians can grasp, and that feeling is exactly what creates risk in organizations, because people either blindly trust the box or completely avoid it. You do not need advanced math to understand what is happening at a high level, and you do not need to be an engineer to spot when a system is being used in an unsafe way. What you need is a clean mental picture of how AI systems learn from examples and then produce outputs that look like decisions, predictions, or generated content. Once you have that picture, a lot of other topics in this course will start to make sense, including why errors happen, why bias shows up, and why monitoring matters after deployment. By the end, you should be able to explain these basics to someone else using plain language and a simple analogy, without turning it into a technical lecture.

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.

Start with the word model, because it is the core object people are talking about when they say AI. A model is a kind of learned pattern, stored in a form a computer can use, that takes input and produces output. If that sounds abstract, think of it like a very complicated recipe that was not written by a person line by line, but instead was learned by being shown many examples of inputs and the outputs we want. In a simple case, the input might be an email and the output might be spam or not spam. In another case, the input might be a set of customer behaviors and the output might be a risk score. In a generative case, the input might be a prompt and the output might be a paragraph of text. The model is the thing that transforms one into the other, and it does that transformation based on patterns it learned, not based on human common sense.

Now we need to talk about data, because the model does not come from nowhere. Data is the collection of examples the system learns from, and those examples are what shape how the model behaves. If you feed the model training examples that are clean, relevant, and representative of the real world where it will be used, you improve the chance that it will behave well. If you feed it examples that are messy, biased, outdated, or missing important types of cases, you increase the chance of failure. Data can include numbers, text, images, audio, or records of past decisions. Data can also include labels, which are the answers that tell the system what the correct output should be during learning, like marking which emails were actually spam. One important beginner insight is that data is not just a technical ingredient; it is a business and governance ingredient, because it can include sensitive information, and it can encode unfair patterns from the past.

Training is the learning process where the model is built using data. In plain language, training is the phase where the system is shown examples and adjusts itself to get better at producing the desired output. It does this by comparing its output to the correct answer, measuring how wrong it was, and then changing internal settings to reduce that error. You can picture training like practicing an instrument: you play, you hear what is off, you correct, and you repeat many times. In many AI systems, this repetition happens across huge numbers of examples, and the model gradually becomes better at matching patterns. The details of the math are not the point here; the point is that training is optimization based on past examples, not understanding. The model does not know why a pattern is meaningful, and it does not know the real-world context unless that context is captured in the data and learning process.

Inference is what happens after training, when the model is used to produce outputs for new inputs. Inference is the moment the AI system is asked a question, given a new case, or prompted for a response, and it generates an output based on what it learned. This is the part people experience as AI in daily life, because it is where the model performs. If training is learning from a textbook, inference is taking the test, except the model is taking the test again and again on new inputs. During inference, the model is applying learned patterns to situations it has not seen exactly before, and that is why uncertainty exists. It might encounter a new kind of customer behavior, a new slang term, a new product, or a new policy rule that did not exist in the training data. When that happens, the model still produces an output, because it is designed to always produce something, and that can create the illusion of confidence. Understanding inference helps you understand why AI can produce plausible answers that are still wrong.

It helps to connect these four ideas with a single story so you can keep them straight. Imagine you want an AI system that helps categorize support tickets by urgency. The data would be a history of tickets and what urgency level was assigned in the past. Training would be the process of showing the model many examples of tickets and teaching it to predict the urgency label that matches past decisions. The model would be the resulting learned pattern that can take a ticket and output a predicted urgency level. Inference would be when a new ticket arrives today and the model assigns an urgency level to help the support team. This example also shows why risk is involved: if the past labels were inconsistent, the model learns inconsistency, and if urgent cases were rare, the model might under-detect them. The system can still be useful, but only if you understand its limits and place it into a process with appropriate oversight.

Another plain-language concept that matters is the idea of features, even if you never use that word on the exam. A feature is simply a piece of information used to help make a prediction, like the words in an email, the time of day, or the location of a transaction. In some systems, features are obvious, like the text in a message. In other systems, features are less obvious, like patterns of behavior over time. What matters for beginners is that the model is not reading meaning the way a human does; it is using measurable signals to connect input to output. If a feature is correlated with something sensitive, like a demographic attribute, the model may indirectly learn patterns that create unfair outcomes even if nobody intended it. This is one reason AI risk is closely tied to data and governance, because the choice of data and features can have ethical and legal consequences. When you hear debates about bias, a large part of that debate is about what the model learned from signals in the data.

You should also understand that there are different kinds of models, and you only need a high-level picture to follow the rest of the course. Some models are used for classification, which means placing something into categories, like spam or not spam. Some are used for regression, which means predicting a number, like a price estimate or a probability score. Some are used for ranking, which means ordering items by relevance, like which search results appear first. Some are used for generation, which means producing new content like text, images, or summaries based on patterns. The risks differ by type because the outputs differ, but the core mechanics of model, data, training, and inference still apply. Beginners sometimes assume AI is always a chatbot, but many AI systems are quiet scoring engines that influence decisions without ever speaking. Those quiet systems can carry serious risk because people do not realize they are relying on them.

There is also a useful difference between traditional software rules and learned behavior that you should be able to explain in plain terms. In traditional software, a developer writes explicit instructions, like if this condition is true, do this action. In AI systems, the model’s behavior is shaped by learning from examples, so you get a system that acts like it has rules, but those rules are not written as simple human-readable statements. This makes the system harder to explain, harder to predict in edge cases, and sometimes harder to debug. It also means two systems trained on different data can behave differently even if they are intended to do the same job. For risk management, this matters because you cannot assume a model will behave well just because it behaved well in one test environment. You need evidence, monitoring, and governance around how it is used.

Because this course is about AI risk, we should also talk about what people mean by the black box problem without making it sound mystical. The black box idea is simply that the internal reasoning of the model is not always easy to interpret, especially for complex models. You can see the input and output, but you may not be able to point to a simple explanation for why a specific output happened. That can be fine for low-impact tasks, like suggesting a next word while you type, because the harm of a wrong suggestion is small. But for high-impact tasks, like decisions about safety, eligibility, or legal obligations, lack of explainability becomes a risk. If you cannot explain why the system did what it did, you cannot easily defend it, audit it, or correct it when it fails. This is one reason governance often includes requirements for documentation and oversight, because those create alternative forms of accountability when the model itself is hard to interpret.

Another beginner misunderstanding is assuming training makes a model correct in some permanent way, like learning a fact that never changes. In reality, training reflects the past, and the world changes. Customer behavior changes, language changes, fraud tactics change, and business processes change. When the world changes, the patterns the model learned can become less accurate, which is often called drift. You do not need the technical details of drift yet, but you should understand the intuition: if the model learned from yesterday’s world, it may struggle in tomorrow’s world. This is why monitoring is part of responsible AI use, and why organizations need to regularly check whether the system still performs as expected. Without monitoring, the model can quietly degrade while people keep trusting it, and that is a classic pathway to harm. The lesson is that AI systems are not set-and-forget tools; they are living dependencies that require ongoing attention.

We should also address the idea of confidence, because AI outputs often sound confident even when they are uncertain. Many models produce scores or probabilities, and those numbers can be misread as certainty. Generative systems can produce fluent language that feels authoritative, which can trick humans into treating it like a knowledgeable person. As a beginner, your safest assumption is that AI outputs are suggestions that require context and validation, not guarantees. The organization’s job is to decide when validation is required, what kind of validation is appropriate, and who is accountable for outcomes. This is where AI risk becomes operational: it is about how humans and processes treat the output. If the process treats the output as final, the risk is higher than if the process treats it as one input among several with human review. Understanding this relationship between output and reliance is central to responsible use.

To help you keep these ideas clean, you can use a simple mental model: AI systems learn patterns from data during training, store those patterns in a model, and then use the model during inference to produce outputs for new inputs. Almost every AI risk discussion you will encounter can be traced back to a problem in one of those steps or a problem in how humans rely on the output. If the data is flawed, the model learns flawed patterns. If training is poorly designed, the model may optimize for the wrong goal. If inference happens in a new environment, the output may become less reliable. If humans treat the output as unquestionable, small errors can become big harm. This is not a technical framework; it is a plain-language checklist for thinking clearly. With that checklist, you can ask better questions about evidence, documentation, and oversight without pretending you are an engineer.

As we close, the main goal of this episode is to remove the fog and replace it with a simple, sturdy explanation you can carry forward. A model is the learned pattern that maps input to output, data is the examples and signals that shape that pattern, training is the learning process that adjusts the model to fit those examples, and inference is the moment the model is used in real life to produce outputs for new situations. Once those four terms feel natural, you can understand why AI can be powerful and risky at the same time, because it can act intelligently without truly understanding. That insight sets you up for the next topics, where we explore where AI goes wrong and how organizations manage that risk through governance, assessment, and monitoring. If you can explain these basics in plain English, you are already doing one of the most important A A I R skills: translating complexity into clarity. Keep these four words in your mind, and you will find that the rest of the course becomes easier to follow.