Certified: The ISACA AAISM Audio Course

In this episode, we focus on a reality that surprises many beginners: even well-designed controls can become less effective over time, not because anyone did something wrong, but because the system around them changes. A I systems evolve through model updates, data refreshes, new integrations, and shifting user behavior, and at the same time the threat landscape changes as attackers learn what works. If you treat controls as permanent, you will eventually protect yesterday’s system while today’s system quietly drifts into new risk. Reviewing and tuning controls is the habit that keeps risk treatment alive, because it is how you notice that a control is no longer strong enough, or no longer appropriate, or no longer aligned with how the A I system is actually being used. Beginners sometimes assume tuning is only about performance, but tuning security controls is about maintaining safety, reliability, and trust. The goal is to build a loop where controls are assessed, adjusted, and proven effective again, so change does not accumulate into vulnerability.

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 review is a deliberate check that asks whether a control is still doing what it was designed to do. The control might still exist, but existence is not the same as effectiveness. A logging control might be enabled, but if it captures the wrong events or if no one reviews alerts, it is not effective. An access control might be configured, but if permissions have expanded over time or if service accounts are shared, it may no longer enforce least privilege. A safety filter might still run, but if user behavior has shifted or the model has been updated, the filter might miss new patterns of unsafe output. A review is how you compare the control’s current behavior to its intended purpose. For beginners, the key is to understand that reviews are not blame exercises, they are calibration exercises, like checking that a smoke detector still works and still has batteries.

Tuning is what you do after review, and it means adjusting the control so it better matches current risk and current system behavior. Tuning can involve tightening a rule, adding a new check, changing thresholds, expanding coverage to new components, or simplifying a control that is too noisy to be useful. In A I systems, tuning often happens because models and data change. A model update can change the kinds of outputs users receive, which can change what needs to be detected or constrained. A new data source can increase the sensitivity of the system, requiring stronger access and retention controls. A new integration can expand the attack surface, requiring new monitoring and new identity restrictions. Tuning is therefore a normal part of responsible operations. Beginners should view tuning as maintenance that prevents small misalignments from becoming major incidents.

One reason review and tuning are essential in A I is model drift and behavior drift. Drift means the model’s performance or behavior changes over time, sometimes because the environment changes, sometimes because updates alter the model, and sometimes because user patterns shift. Drift can show up as accuracy slipping on certain tasks, unsafe edge cases becoming more common, or outputs becoming inconsistent in ways that confuse users. When drift occurs, the risks you originally treated may reappear in new forms. Controls must therefore be reviewed in the context of drift, meaning you ask whether validation checks still catch the failure modes you care about and whether operational monitoring detects changes in output quality and safety. Beginners often assume drift is only a data science issue, but drift is a security issue too because it affects trust and can create opportunities for misuse. A control that was effective against one pattern of unsafe behavior may not catch a new pattern after drift.

Data changes create another strong reason to review and tune controls, because data is both powerful and fragile. Data pipelines may introduce new fields, new sources, or new retention practices over time, often driven by business needs. A small data change can create a big security change if it introduces sensitive personal information where previously the system used only general information. Data changes can also affect training outcomes, which can affect model behavior in ways that impact safety. Controls around data access, data minimization, masking, and retention must therefore be reviewed whenever data scope changes. For beginners, a useful rule of thumb is that a change in what data the system touches is almost always a change in risk. If risk changes, controls should be reviewed and tuned rather than assuming the old control set still fits.

Threat changes also demand control tuning because adversaries adapt. If a system becomes widely used or becomes a valuable target, attackers will experiment and discover which prompts, inputs, or pathways are most effective at bypassing safeguards. They may learn to exploit weak identity management, abuse rate limits, or use repeated queries to extract sensitive details. They may also target the pipeline rather than the model, trying to tamper with training data or inject malicious changes into deployments. This adaptation means that controls must not be static, because static controls become predictable. Review processes should therefore include learning from industry incidents, internal near misses, and changes in observed attack patterns. Beginners should understand that attackers are not impressed by your past success; they care about what works today. Tuning is how you stay ahead of predictable exploitation of stale defenses.

Monitoring controls are one of the most important areas for review and tuning because monitoring is the feedback loop that tells you what is happening. Monitoring can fail in two common ways: it can be too quiet or too noisy. If it is too quiet, important events are missed, and misuse goes undetected. If it is too noisy, people ignore alerts, and the control becomes meaningless. In A I systems, noise can come from normal variations in user input, and quiet failure can come from not logging the right context to interpret behavior. Tuning monitoring means adjusting what is captured, what triggers alerts, and how alerts are routed and handled. It also means checking whether monitoring is aligned to trust boundaries and data flows, so you see what happens where risk is highest. For beginners, the key is to see monitoring as a living control that must be tuned to be both sensitive and practical, because an ignored alarm is the same as no alarm.

Access control is another area that naturally drifts and therefore requires review. Permissions tend to grow, because it is easy to grant access to solve a short-term problem and harder to remove access later without breaking workflows. A I systems often involve many roles, including developers, analysts, operators, and business users, plus service accounts that integrate systems. Over time, those roles can blur, and service accounts can accumulate privileges beyond what they need. Reviewing access controls means checking whether least privilege still holds and whether identities still match current responsibilities. Tuning access controls may involve narrowing permissions, separating duties more clearly, rotating credentials, or replacing shared accounts with traceable identities. Beginners should remember that access reviews are not about distrusting people; they are about recognizing that systems change and that old access is a quiet form of risk.

Safety controls and content restrictions also need review and tuning, especially when models are updated or when the system is deployed into new contexts. A model used internally for drafting might later be used in customer interactions, which changes what safety expectations are appropriate. The same model might be connected to different data sources, which can change what sensitive information is at risk of exposure. User populations can also change, bringing different types of prompts, different languages, or different levels of adversarial behavior. Reviewing safety controls means examining whether the model still refuses inappropriate requests, whether it handles sensitive topics responsibly, and whether it avoids leaking private information through outputs. Tuning may involve adjusting prompts, adding stronger constraints, changing how outputs are reviewed, or narrowing capabilities in high-risk areas. Beginners should understand that safety is not set once, because usage context is part of safety.

Change management controls are a special case because they protect the act of changing the system itself. If change management is weak, then tuning efforts can accidentally introduce new vulnerabilities, or updates can bypass review during urgent situations. Reviewing change controls means checking whether model updates, data changes, and configuration changes follow the required approvals and testing, and whether evidence of those checks exists. Tuning change controls may involve tightening thresholds for review, requiring additional validation for certain changes, or improving rollback processes so changes can be reversed safely. In A I systems, the need for rollback can be more frequent because behavior changes may only become clear under real usage. Beginners should see change management as the safety cage around innovation, because it allows improvement while preventing uncontrolled risk. When change controls are reviewed and tuned, the organization can move quickly without losing governance.

A mature review and tuning process also relies on signals and metrics, because you need data to decide whether a control is working. Signals can include the number and severity of incidents, the number of near misses, the rate of false alarms, the time it takes to respond, and the trend of model validation results over time. For A I, signals can also include shifts in output quality, increased reports of unsafe behavior, or patterns of repeated probing that suggest adversarial interest. Metrics are not the goal, but they provide a shared language for discussing whether controls need adjustment. Beginners sometimes feel intimidated by metrics, but you can think of them as simple questions like, are we seeing fewer risky events, and are we catching issues sooner. Review and tuning use these signals to decide where to invest effort, because not every control needs adjustment at the same frequency. High-risk controls and rapidly changing components should be reviewed more often than stable, low-risk areas.

It is also important to understand that tuning does not always mean tightening. Sometimes a control is too strict and causes workarounds, and workarounds create hidden risk. If a system is frustrating to use, people may copy data into unapproved tools or create shadow processes to get work done. That is why tuning should consider human behavior and operational realities. A control that is slightly less strict but widely followed can be safer than a strict control that people bypass. This does not mean sacrificing safety; it means designing controls that achieve safety through adherence rather than through unrealistic expectations. Beginners should remember that controls exist to shape behavior, and behavior is part of system security. A tuned control is one that balances risk reduction with usability and operational feasibility.

To close, reviewing and tuning A I security controls as models, data, and threats change is the practice that keeps risk treatment alive over time. Reviews compare current control effectiveness to intended purpose, and tuning adjusts controls so they fit the current system and current risk landscape. Model drift, data changes, and evolving threats are the forces that make static controls unreliable. Monitoring, access management, safety safeguards, and change management are common areas where drift shows up and where tuning can prevent incidents. When review and tuning are continuous and evidence-driven, controls become resilient, and the organization stays aligned with governance even as technology evolves. That is what Task 12 is aiming for: not perfect controls once, but controls that remain effective because you revisit them, improve them, and keep them matched to reality.