Certified: The ISACA AAIA Audio Course

In this episode, we make three stages of an A I model’s life feel clear and audit-ready: training, validation, and inference. Beginners often hear these words as if they are technical steps that only engineers care about, but auditors care because these stages are where claims are created, checked, and then used to influence real decisions. If you can describe these stages in plain language, you can also identify what evidence should exist at each stage, what risks tend to appear, and what questions an auditor should ask first. Training is where the model learns, validation is where the model is checked, and inference is where the model is used in the real world. That sounds simple, but the details matter because many failures happen when an organization confuses these stages or treats validation as optional. For an exam, you want to be able to hear a scenario and immediately know which stage it is describing, because that tells you what controls and artifacts should be present. When you can do that, you answer faster and with more confidence because your reasoning stays organized.

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.

Training is the stage where the model is taught patterns using data, which means training is always a story about data choices and learning objectives. In supervised learning, the data includes labels that represent the desired output, and the model adjusts itself until it can predict those labels well. In other learning types, the model learns patterns or actions based on different forms of feedback, but the key idea remains that training is where behavior is shaped. From an audit perspective, training raises questions about where the data came from, whether the data was allowed to be used, whether it represents the environment the model will operate in, and whether it contains hidden bias or sensitive information. Training also raises questions about scope, because what you teach a model is tied to what you want it to do, and unclear scope leads to unclear outcomes. Another audit concern is reproducibility, which means whether the organization can explain and repeat how the model was trained if questions arise later. If a model is trained in a messy, undocumented way, the organization may not be able to prove what changed or why, which weakens accountability. Training is therefore not just a technical process, it is a governance process because it embodies decisions that affect risk.

A useful way to think about training evidence is to imagine what you would need to prove that the training process was responsible. You would want documentation that describes the use case and the training objective, because the objective determines what the model is trying to optimize. You would want a record of the data sources and how data was prepared, because preparation choices can change outcomes dramatically. You would want clarity about who approved the data use and who had access, because access controls and permissions are part of responsible handling. You would want an explanation of how the training data relates to the real-world environment, because training on a narrow slice of reality can create a model that fails in important cases. You would also want notes on assumptions, such as what time period the data covers or what populations are represented. This is all audit-friendly because it is about traceability, ownership, and controlled decision making. Training is where the model’s worldview is formed, and auditors want to know whether that worldview is appropriate and well governed.

Now move to validation, which is the stage where an organization checks whether the model’s learned behavior is acceptable before it is relied on. In plain language, validation is like a safety inspection, but with an important twist: you must test the model on data it did not see during training to get a realistic sense of how it will behave on new cases. Validation is not just about a single accuracy number, because accuracy can hide important weaknesses. A model can be accurate overall while failing in specific subgroups, rare events, or high-impact edge cases. From an audit perspective, validation raises questions about what metrics were chosen, why those metrics matter for the use case, and what thresholds define acceptable performance. It also raises questions about fairness, reliability, and robustness, meaning whether the model behaves consistently under different conditions. Validation is also where you test how outputs will be interpreted and used, because a model that produces a score requires rules for what different score ranges mean. If an organization validates the model but never defines decision thresholds and escalation rules, the system can still be risky in operation. Validation therefore sits at the boundary between technical checking and governance readiness.

To think like an auditor during validation, focus on whether validation answers the question: is this system fit for purpose. Fit for purpose means it performs well enough, behaves safely enough, and aligns with documented requirements for this specific use case. That means evidence should include test results, but also include evidence that testing was designed to match real conditions. For example, if the model will be used on live customer behavior, testing should reflect the diversity and messiness of real customer data, not just clean historical samples. If the model will affect people, validation should include checks for disparate outcomes and clear documentation of how those checks were performed. If the model will be used in a changing environment, validation should include plans for monitoring and revalidation over time. Another key audit question is whether validation is independent and credible, meaning it is not just the same team confirming their own work without oversight. Independence can be formal or practical, but the principle is that validation should not be a rubber stamp. On the exam, when you see validation concepts, the best answers often emphasize evidence, defined thresholds, and testing aligned to real-world use.

Now consider inference, which is the stage where the trained and validated model is actually used to produce outputs for new inputs. Inference is where the model meets reality, and it is where model risk becomes business risk because outputs influence decisions and actions. Inference can happen in different patterns, such as real-time scoring of a transaction, batch processing of a set of cases overnight, or interactive generation of responses in a user-facing tool. Regardless of the pattern, inference introduces a crucial audit concern: operational context. The model may have been trained and validated under certain assumptions, but inference occurs in a live environment with changing data, changing behaviors, and sometimes unexpected inputs. This is where issues like data drift and model drift can appear, because the distribution of inputs can shift away from what the model learned. Inference is also where security, privacy, and access control matter, because the model may process sensitive information and its outputs may reveal more than intended. Auditors care about inference because this is where monitoring, incident handling, and human oversight should show up clearly. A model that is safe in a lab can become unsafe in production if inference is poorly governed.

An auditor-focused breakdown of inference asks three practical questions. First, how are inputs collected, checked, and protected before the model sees them, because garbage in can produce garbage out and sensitive data can be mishandled. Second, how are outputs presented and used, because users can over-trust outputs if interfaces make them look authoritative or if instructions are unclear. Third, what happens when outputs are wrong, uncertain, or harmful, because every system needs exception handling and escalation paths. This is where monitoring becomes central, because monitoring is how you notice performance changes, abnormal patterns, or rising error rates. It is also where audit trails matter, because you need logs to reconstruct what happened when an incident occurs. Another inference concern is version control, meaning knowing which model version produced which output at which time, because updates can change behavior. If an organization cannot trace outputs to model versions, accountability becomes weak and troubleshooting becomes slow. Inference is therefore not just the model running, it is a controlled operational process.

A common beginner confusion is thinking of training, validation, and inference as a straight line you complete once, like building a project and then never touching it again. In reality, these stages form a cycle because models are often retrained, revalidated, and redeployed as data and requirements evolve. That cycling introduces governance needs such as change management, approvals, and documentation for each iteration. Another confusion is assuming that strong validation guarantees safe inference, when inference can fail due to environmental changes or unexpected user behavior. Another confusion is assuming that monitoring is part of validation, when monitoring is primarily an inference-stage responsibility that continues after deployment. The audit mindset is to treat each stage as having its own goals and its own evidence requirements. Training evidence shows responsible learning choices, validation evidence shows fit for purpose, and inference evidence shows controlled use and ongoing oversight. On the exam, if you can separate these stages, you can often spot the correct answer because it targets the stage where the described gap actually exists.

Let’s bring this to life with a scenario that stays simple. Imagine a hospital uses an A I model to help prioritize which patients should be seen first in a crowded emergency department. Training would involve historical patient data and outcomes, and audit questions would include whether the data reflects a representative population and whether sensitive information was handled appropriately. Validation would involve testing whether the model correctly prioritizes cases under realistic conditions, including whether it performs consistently across different patient groups and whether the organization defined acceptable error thresholds. Inference would involve the live use of the model in the emergency department workflow, and audit questions would include how staff use the output, what human review exists, how exceptions are handled, and how performance is monitored over time. If a problem occurs, such as the model consistently under-prioritizing a particular group, the auditor must determine whether the root cause is training data bias, inadequate validation, or inference-stage misuse and lack of oversight. Each stage offers different kinds of evidence and different remediation strategies. This scenario shows why auditors care about stage clarity: you cannot fix what you cannot locate.

Another audit-relevant point is that organizations sometimes misuse stage labels to sound more mature than they are. They may say they validated the model when they only tested it briefly on the same data used for training, which is not a credible check. They may claim inference is monitored when they only track system uptime and not outcome quality, which misses the point of model oversight. They may claim training is controlled while data sources are undocumented or permissions are unclear, which creates governance risk. Certification exams often include answer choices that reward you for recognizing these weaknesses, even if the scenario sounds confident. The safe audit response is to ask for evidence that each stage was performed properly and that it produced traceable artifacts. If you see a scenario where validation seems weak, the best answer is often to strengthen independent testing and define performance thresholds. If you see a scenario where inference governance seems weak, the best answer is often to implement monitoring, escalation, and audit trails tied to model versions and outcomes. Stage-based reasoning helps you choose the most responsible next step.

You should also understand that stage boundaries can blur in modern systems, especially those that adapt or learn continuously, because learning can occur during inference through feedback loops. Even if you do not call it reinforcement learning, a system might adjust behavior based on user feedback or outcomes, which means inference can feed into training decisions. For auditors, the key is to ensure that any updates are controlled, documented, and evaluated before they change high-impact behavior. That means the organization needs a clear policy for when retraining occurs, who approves it, how validation is repeated, and how changes are deployed. Beginners can handle this by remembering a simple principle: any change that could affect decisions should be treated as a change that requires governance and evidence. If the system adapts, oversight must adapt too. The exam may test whether you recognize that continuous change increases the need for monitoring and change control, not decreases it. Stage thinking still applies, but you must be alert to loops and updates.

As we wrap up, keep the audit-friendly narrative in your mind: training shapes the model’s behavior based on data and objectives, validation checks whether that behavior is acceptable for the intended use, and inference is the controlled use of the model in the real world where monitoring and accountability are critical. Each stage has its own typical risks and its own evidence expectations, and audits often succeed or fail based on whether those expectations are clear. When you can describe these stages simply, you can also explain why a model that looks impressive might still be risky if validation is weak or if inference is poorly governed. In the next episode, we will shift toward translating business goals into A I requirements you can audit, because requirements are the bridge between what an organization wants and what you can verify. For now, practice identifying stage language in any scenario you hear, because the moment you know the stage, you know what to ask for and what good oversight should look like.