Certified: The ISACA AAIA Audio Course

In this episode, we focus on a problem that sounds abstract until it causes real damage: policy gaps that let A I behave in production in ways nobody intended and nobody is truly managing. New learners often assume that if an organization has an A I policy, then the policy naturally covers what matters. The reality is that policies frequently have holes, and those holes become pathways for risky behavior, especially when systems move from experiments into everyday use. A gap might be a missing rule, a vague requirement that cannot be followed consistently, or a missing procedure that should turn the policy into action. A I makes gaps more dangerous because it can scale quickly, it can be reused in new contexts, and it can behave differently as data and users change. The goal today is to learn how to spot the gaps that most often lead to unmanaged behavior in production, and to develop a mindset that treats policy review as a way to predict where surprises will appear.

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 useful starting point is to define what unmanaged A I behavior means, because it is not always a dramatic failure. Unmanaged behavior can be as simple as a tool being used for a purpose it was never evaluated for, or outputs being treated as trustworthy without oversight. It can also be a system slowly expanding its reach, such as being used by more teams, with more data, in more decisions, without any new review. Sometimes unmanaged behavior shows up as inconsistent outcomes, where the system behaves one way in testing and another way in real use. Sometimes it shows up as invisible decisions, where the system influences choices but no one can later explain how or why. A policy gap is what allows these patterns to happen without triggering a required review, a required approval, or a required control. Beginners should think of policy gaps like missing guardrails on a road, because the road might be safe most days, but the guardrails matter when something unexpected happens. The purpose of identifying gaps is to put guardrails where the risk actually is.

One of the most common gaps is unclear scope, where the policy does not clearly define what counts as A I or what systems are covered. If the policy focuses only on custom-built machine learning models, it may ignore A I features embedded in third-party products or cloud services. If the policy focuses only on production deployments, it may ignore pilots, prototypes, and internal experiments that can quietly become production-like because people depend on them. If the policy focuses only on one business unit, other units may adopt A I tools without realizing they are supposed to follow the same rules. When scope is unclear, teams can honestly believe the policy does not apply to them, which creates unmanaged behavior by default. A good gap-spotting habit is to ask whether the policy covers third-party tools, internal assistants, and any automated decision support that influences people or outcomes. If the policy language leaves room for interpretation, the safest assumption is that some teams will interpret it in the loosest way possible. That is how unmanaged behavior begins.

Another frequent gap is missing classification, meaning the policy does not define how to distinguish low-risk A I use from high-risk A I use. Without classification, the organization either applies heavy governance to everything, which people will avoid, or applies light governance to everything, which leaves high-impact systems unmanaged. A classification approach usually defines what triggers stronger requirements, such as use cases that affect individuals, safety, financial decisions, employment decisions, or essential services. It can also include triggers like using sensitive data, operating at large scale, or having limited explainability. Beginners should notice that classification is not about perfect categories; it is about having a predictable way to decide when extra scrutiny is required. If a policy lacks classification, it often cannot consistently require risk assessments, approvals, or monitoring for the systems that need them most. That means high-impact production behavior can occur under the same minimal rules as a harmless internal tool. When you identify that gap, you are identifying a core reason unmanaged behavior is likely.

A third gap is weak requirements for intended use and prohibited use, which is where production misuse quietly grows. Many policies say use A I responsibly but never define what responsible use means in terms of boundaries. If intended use is not documented, teams cannot prove what the system was designed to do. If prohibited use is not defined, teams may stretch the tool into decisions that were never evaluated. For example, a tool built to summarize feedback might later be used to recommend actions that affect customers, or a tool built for brainstorming might later be used to draft official communications without review. These shifts may feel small day to day, but they change risk dramatically. Beginners should learn to spot whether the policy requires documenting intended use, requires approval to change intended use, and defines at least some prohibited or restricted use cases. Without those requirements, production behavior can evolve faster than governance, and the organization loses control of what the system is actually doing.

Data-related gaps are among the most dangerous because data is the fuel that shapes behavior. A policy might mention privacy generally but fail to require documented data sources, ownership, and purpose for A I training and operation. It might fail to address data minimization, meaning teams might collect or reuse more data than needed. It might fail to address retention and deletion, meaning training datasets could be kept indefinitely without justification. It might fail to address data lineage, meaning no one can trace where training data came from or whether it was authorized. In production, these gaps can lead to A I systems using data in ways that violate expectations, contracts, or laws. Beginners should also recognize that data gaps can create quality problems, not just privacy problems. If a policy does not require basic data quality checks and documentation, a system can drift into unreliable outputs because the data feeding it changed. Unmanaged behavior often begins as unmanaged data practices.

A major operational gap is missing change control requirements, which allows a system to become a different system without governance noticing. A I systems can be updated, retrained, connected to new data sources, or repurposed for new users, and each of these changes can create new risk. If a policy does not define what counts as a material change, teams may treat changes as routine and skip reassessment. If it does not require approvals for certain changes, people may implement them quickly to solve short-term problems. Beginners should notice that material change is not only about code changes; it can include changes in user population, decision context, or the kinds of outputs relied upon. When change control is missing, production behavior becomes a moving target, and monitoring and governance fall behind. A gap-spotting approach is to ask whether the policy requires reassessment when the system’s inputs, outputs, users, or purpose change. If that trigger is absent or vague, unmanaged behavior is likely.

Monitoring gaps are another common root cause, because without monitoring, an organization cannot see what the system is doing in production. A policy might require safe use but fail to require ongoing measurement of performance, errors, or harmful outcomes. It might fail to require logging and traceability, meaning the organization cannot later reconstruct why a system made a particular output. It might fail to require alerting and escalation when certain risk signals appear. Beginners can think of this as running a school bus route without checking whether the bus is on time, whether it is safe, or whether it is being used appropriately. The route may work most days, but when something goes wrong, the lack of monitoring becomes a crisis. In A I governance, monitoring is what turns risk management into a living practice rather than a one-time review. If monitoring requirements are missing or optional, production behavior can drift and harm can accumulate unnoticed. Identifying that gap means recognizing that governance needs feedback loops.

A related gap is the absence of incident response procedures specific to A I behavior, which leaves teams unsure how to act when harms appear. Many organizations have general security incident response, but A I issues can include non-security problems like harmful outputs, unfair outcomes, or misleading advice. If the policy does not define what counts as an A I incident and does not require reporting and escalation, people may treat issues as minor bugs and ignore them. If the policy does not define who can pause or restrict a system during an incident, teams may delay action because they fear making the wrong call. Beginners should notice that unmanaged behavior often persists because no one owns the response process. A policy gap can be as simple as failing to define who is accountable for investigating A I-related complaints and who decides on remediation. When the response path is unclear, problems linger, and the system continues operating in a risky state. That is unmanaged behavior by design.

Vendor and third-party service gaps are also frequent, especially because many A I capabilities come from outside providers. If a policy focuses only on internal development, teams may adopt external A I tools without proper evaluation of data handling, contractual obligations, or security expectations. Procedures might be missing for vendor assessment, meaning there is no consistent way to evaluate whether a provider meets requirements. There might be no rules for what data can be sent to a provider, which can lead to accidental exposure of sensitive information. Beginners should understand that third-party A I services can be just as impactful as internal models, and often more difficult to control. If policy language does not explicitly cover third-party use, unmanaged behavior may grow quickly because people can enable features with a few clicks. Identifying this gap means looking for explicit requirements around vendor approval, data sharing limits, and ongoing oversight. Without those, production behavior can be shaped by external systems in ways the organization cannot fully see or manage.

Training and awareness gaps are quieter but highly influential, because a policy that people do not understand becomes a policy that is not followed. A policy might require careful use of A I outputs, but if users are not trained to recognize limitations, they may over-trust results. A policy might prohibit certain uses, but if people do not know the prohibition exists, they may violate it unintentionally. Procedures might not exist for role-based training, meaning developers, reviewers, and end users all receive the same generic message, which does not match their responsibilities. Beginners should also recognize that awareness must be reinforced over time, because A I tools change and new people join teams. If a policy lacks concrete training requirements, unmanaged behavior can appear simply because people are improvising. A gap-spotting check is to ask whether the policy requires training, whether training is tracked, and whether training content is tied to actual allowed and prohibited uses. Without that, production behavior becomes whatever people believe is acceptable, not what the policy intends.

Another category of gaps involves accountability and documentation, because unmanaged behavior thrives where ownership is unclear and evidence is missing. A policy might say governance exists but fail to define who owns risk acceptance, who owns controls, and who approves deployments. It might fail to require documented approvals, meaning production use happens without a clear go-live decision. It might allow exceptions but fail to require documenting them, making exceptions invisible. Beginners should remember that if it is not recorded, it is difficult to prove, and it is difficult to manage consistently. Documentation is not about bureaucracy; it is how an organization keeps track of what it has agreed to and what it is doing. If accountability and documentation requirements are missing, production behavior can become a collection of informal practices that vary by team and shift over time. That is unmanaged behavior, even if everyone is trying to be responsible. Identifying these gaps helps you recommend practical fixes like clearer roles, required sign-offs, and decision trails.

The overall skill is learning to read a policy with a detective mindset, looking for where the policy fails to trigger action at the moments risk changes. Those moments include moving from pilot to production, changing data sources, changing intended use, expanding to new users, responding to complaints, and adopting third-party tools. If policy language does not create clear requirements at those moments, the organization will be surprised by what the system is doing in production. Beginners should notice that policy gaps rarely announce themselves. They appear as convenience, ambiguity, and silence, and then they become risk at scale. A good policy closes the most dangerous gaps by defining scope, classification, intended and prohibited use, data rules, change control, monitoring, incident response, vendor oversight, training, and accountability. You do not need to fix every possible gap at once, but you do need to identify the gaps most likely to create unmanaged behavior in the systems that matter most.

The central takeaway is that unmanaged A I behavior in production is often not the result of bad intent, but the result of missing or weak policy controls that fail to keep pace with real-world use. Policy gaps around scope, risk classification, use boundaries, data governance, change management, monitoring, incident response, vendor use, training, and accountability create openings where systems evolve without oversight. When those openings exist, production behavior can drift into higher impact, higher exposure, and higher harm without anyone making a conscious decision to accept that risk. Learning to identify these gaps is therefore a form of prevention, because you are spotting where governance is likely to fail before it fails publicly. When you can point to a specific gap and explain how it could lead to unmanaged behavior, you are doing the kind of practical oversight that keeps A I systems aligned with safety, compliance, and organizational intent over time.