Certified: The ISACA AAIA Audio Course

In this episode, we focus on a risk area that beginners often overlook because it sounds like plumbing, yet it is one of the most common sources of real-world failures: how an A I system interacts with other systems and how it depends on upstream inputs. An A I model does not float in space, and it does not only depend on its own code. It depends on data feeds, interfaces, identity systems, workflows, and downstream consumers that use its outputs. When any part of that chain changes, the A I system’s behavior or its impact can change, sometimes quietly. Auditors care because a system can be well designed and still become risky if dependencies are unstable, undocumented, or poorly monitored. Task 3 is about evaluating impacts, and system interactions are a major impact category because they affect reliability, security, privacy, and the ability to trace and control decisions. For brand-new learners, the goal is to develop a mental habit of asking, what does this A I system rely on, what relies on it, and what happens when those dependencies shift. Once you can do that, you can spot risks that are invisible if you only look at the model’s accuracy.

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 practical way to start is to define upstream dependencies in plain language. Upstream dependencies are the systems and processes that provide inputs to the A I system, such as databases, sensors, forms, log streams, third-party feeds, and even human-entered information. These dependencies matter because they shape what the model sees and therefore what it outputs. If upstream data becomes delayed, incomplete, inconsistent, or biased, the model can produce worse outputs even if the model has not changed. Upstream dependencies also include data transformation steps, where raw data is cleaned, combined, or summarized before reaching the model, because transformation choices can introduce errors or hide assumptions. Auditors evaluate upstream dependencies because upstream issues can look like model failures when they are actually pipeline failures. This matters for accountability because the responsible fix might be improving data validation and monitoring rather than retraining the model. Beginners should remember that most A I systems are only as reliable as their inputs, and inputs are often more fragile than organizations admit. Evaluating upstream dependencies is therefore a way of evaluating the reliability of the entire decision chain.

Next, define system interactions, because an A I system is often a component within a larger architecture, not a standalone tool. System interactions include how the A I system connects to other services through Application Programming Interface (A P I) calls, how it reads and writes data, how it authenticates, and how it integrates into workflows like ticket routing or approvals. These interactions create dependencies in both directions. The A I system depends on upstream systems for data and on platform services for identity, logging, and availability. Downstream systems and people depend on the A I outputs to make decisions, trigger actions, and prioritize work. Auditors care because every interaction point is a potential failure point and a potential control gap. An interaction can fail due to outages, changes in data formats, changes in permissions, or changes in business workflows. An interaction can also create security and privacy risks if data is shared more widely than intended or if access is not properly controlled. Evaluating interactions is therefore a core part of evaluating impact, because impact is shaped by how the system is embedded in a broader environment.

A key audit concept here is the difference between direct dependencies and indirect dependencies. Direct dependencies are obvious, like a model reading customer data from a customer database. Indirect dependencies are less obvious, like the database depending on a data ingestion process that pulls from a third-party source, or the model depending on a feature calculation that depends on another system’s clock settings or data definitions. Indirect dependencies matter because they are often undocumented, which means they become surprises during incidents. An A I system might appear stable, but its inputs might actually depend on a fragile chain that breaks during system upgrades or vendor changes. Auditors evaluate indirect dependencies by asking how data is produced, transformed, and validated end to end, not just where it is stored. This connects to traceability, because if you cannot trace an input back to its source and its transformation steps, you cannot confidently evaluate its quality. It also connects to change management, because any change in an upstream dependency can become a hidden model change. Beginners can handle this by remembering a simple principle: when a model depends on a number, a label, or a text field, it depends on the meaning of that information, not just its existence.

Data semantics are one of the most important upstream risks, and semantics simply means what the data actually means. A field called status might mean something different in different systems, or it might change meaning after a process update. A label like fraud might reflect confirmed fraud, suspected fraud, or simply a case that triggered investigation, and those meanings are not interchangeable. If the meaning shifts, the model’s learned patterns become misaligned with reality, which can create unfair outcomes or operational errors. Auditors care because semantic drift can happen without any visible system outage, and it can be difficult to detect unless monitoring is designed to catch it. Requirements should therefore include data definitions and ownership, because someone must be responsible for maintaining consistent meaning. When evaluating a solution, an auditor would ask whether key fields are defined, whether definitions are shared across systems, and whether changes to definitions are governed. This is part of impact evaluation because semantic issues can affect decisions at scale. Beginners should recognize that data can be present and still be wrong if its meaning is unclear or unstable.

Another major dependency risk is timing, because A I systems often assume data arrives within a certain window and represents the current state of the world. If upstream data is delayed, the model might make decisions based on stale information. In a low-impact use case, stale data might cause minor inconvenience, but in a high-impact use case it can cause serious harm, such as failing to detect an urgent issue or prioritizing the wrong action. Auditors evaluate timing by asking about data freshness, update frequency, and how the system behaves when data is missing or late. They also ask whether the A I system distinguishes between unknown and negative, because missing data is not the same as a true value of zero. A classic beginner misunderstanding is assuming the model can always handle missing or delayed data gracefully. In reality, the system might fill missing values in ways that change outcomes, or it might fail silently and still produce outputs. Audit logic pushes toward explicit requirements and controls for data freshness, missing data handling, and alerting when upstream feeds degrade. Timing is therefore a dependency impact you should always consider, because it affects both model quality and operational safety.

Security and privacy impacts also flow through system interactions, because the A I system can become a new pathway for sensitive data to move or be exposed. If the A I system pulls sensitive fields from multiple sources and aggregates them, it can create a concentrated target for attackers. If model outputs are shared widely, they can reveal information indirectly, such as predicting a sensitive attribute or exposing patterns about individuals. Interactions through A P I connections can introduce risks if authentication is weak, if tokens are mismanaged, or if permissions are broader than necessary. Auditors evaluate these risks by asking what data is accessed, who can access it, how access is logged, and how least privilege is enforced. They also evaluate whether the system’s design minimizes unnecessary data movement, because reducing data movement reduces risk. This is not about implementing security controls yourself, it is about verifying that the organization considered data exposure and built enforceable safeguards. For beginners, the key idea is that adding an A I component often changes the data flow map, and data flow changes are security and privacy changes that must be governed.

System interactions can also create resilience and availability impacts, which matter because organizations often rely on A I outputs once they are embedded in workflows. If a workflow depends on a model to route tickets or approve steps, model downtime can slow operations or cause backlogs. Auditors evaluate whether there is a fallback plan, such as a manual process or a default routing rule, and whether that fallback is documented and tested. They also evaluate whether dependencies are single points of failure, such as relying on one external service without redundancy. Another resilience question is how the system behaves under load, because high demand can degrade response time and affect decision timeliness. A model that is accurate but slow may still create harm if it delays critical actions. Operational monitoring should therefore include not only model quality metrics but also system health metrics that reflect whether the service is usable. For beginners, it helps to remember that availability is a form of safety in many contexts, because unavailable systems force rushed workarounds. Evaluating interactions includes evaluating what happens when the A I component is not there.

A particularly tricky impact area is feedback loops, where the model’s outputs influence the environment that generates the model’s future inputs. For example, if a fraud model flags transactions, investigators may focus on those flags, which increases the confirmation of fraud in flagged categories and decreases investigation elsewhere. That can bias future training data and reinforce certain patterns. If a support routing model directs certain customers to certain agents, the interaction patterns and resolution outcomes can change, influencing future data. Auditors care because feedback loops can create self-fulfilling patterns that look like improved performance while actually narrowing the system’s perspective. They can also create fairness issues if certain groups are consistently treated differently based on the model’s influence on process decisions. Evaluating feedback loops requires asking whether the organization understands how model outputs change behavior and whether monitoring accounts for that dynamic. It also requires governance decisions about when and how retraining occurs so that the system does not learn from distorted signals. Beginners can grasp this by remembering that A I systems can shape the data they later learn from, and that shaping can be unintentional and risky if not controlled.

Change management is another key area because upstream dependencies and system interactions change over time due to upgrades, policy changes, vendor updates, and business process redesign. A model can behave differently if an upstream system changes a field format, or if a workflow changes how data is entered. Auditors evaluate whether there is a process to assess the impact of changes on the A I system, including whether changes trigger revalidation and whether versions are tracked. They also evaluate whether there is coordination between teams, because A I systems often sit at the intersection of multiple groups that may not communicate well. If an upstream team changes data without notifying the A I owners, the model can degrade silently. This is why governance requirements should include dependency ownership and change notification expectations. It is also why monitoring should be designed to detect unusual input patterns, not just unusual outputs. Beginners should remember that the A I system is part of a living environment, and living environments change. Task 3 evaluation is partly about recognizing that change itself is a risk driver.

Let’s ground these ideas in a simple scenario. Imagine an A I system that prioritizes maintenance tickets for a fleet of delivery vehicles based on sensor readings and past repair history. Upstream dependencies include sensor data, maintenance logs, and vehicle usage records. If sensors fail or calibration shifts, inputs become misleading. If maintenance logs are incomplete or inconsistently entered, the model learns and operates on an inaccurate history. System interactions include how the model pulls data, how it writes priority scores into the ticketing system, and how dispatch teams act on those scores. Downstream impacts include which vehicles are repaired first and whether safety issues are addressed promptly. If a ticketing system update changes a field meaning or format, the model may misinterpret priority and create unsafe outcomes. Auditors would ask about data validation, monitoring for sensor anomalies, fallback procedures when data is missing, and governance for changes across the sensor platform and ticketing system. This scenario shows that dependency impacts are not theoretical, they determine whether the system can be trusted in real conditions.

As we close, the main idea is that evaluating A I impacts on system interactions and upstream dependencies is a way of evaluating whether the A I solution is stable, secure, and governable within the organization’s environment. Upstream data quality, semantics, timing, and transformations shape model behavior. System interactions create security, privacy, resilience, and traceability impacts that can amplify risk at scale. Feedback loops and change management can quietly shift outcomes over time, making monitoring and governance essential. Task 3 expects you to recognize these dependency-driven impacts and to ask for evidence and controls that address them, such as documented data definitions, dependency ownership, input validation, monitoring, fallback plans, and change impact assessments. In the next episode, we will evaluate A I impacts on the environment and operational sustainability, which expands the idea of impact beyond systems and into resource use and long-term operational responsibility. For now, practice asking two questions whenever you hear an A I proposal: what must this system depend on to work, and what else will change because this system now exists.