Certified: The ISACA AAISM Audio Course

In this episode, we’re going to move past the urgent scramble of containment and focus on the phase that separates a temporary fix from real safety: removing the root cause and bringing systems back online in a way you can trust. When an incident calms down, it can be tempting to declare victory as soon as the obvious bad behavior stops, but that is exactly when hidden weaknesses can remain and cause a repeat incident later. With A I systems, the root cause might be a misconfigured integration, overly broad access, unsafe data flow, a brittle policy filter, or a change that quietly altered model behavior. Recovery is not just turning things back on, because turning things back on without confidence can reintroduce the same risk under a new name. By the end, you should understand what eradication means, how to find and remove root causes without guesswork, and how to recover in a controlled way that rebuilds trust.

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.

Eradication means eliminating the specific condition that made the incident possible, not just reducing its symptoms. Symptoms are what you observe, such as unsafe outputs, suspicious access patterns, or sensitive data appearing where it should not. Root causes are deeper, such as an approval process that allowed a risky data source to be connected, a permission model that granted too much access, or an incomplete filter that failed to catch certain kinds of content. Beginners sometimes think a root cause is always a single bug, but in security it is often a chain of contributing factors that line up at the wrong time. Eradication is about breaking that chain so the same pattern cannot repeat easily. This is also why eradication is closely tied to learning, because you are not only fixing one incident, you are strengthening the system against a class of incidents.

To eradicate effectively, you need a disciplined approach to understanding causality, which is the relationship between what changed and what happened. A helpful technique is Root Cause Analysis (R C A), which is a structured way to trace from an observed outcome back to the conditions that produced it. After the first mention, we will refer to this as R C A. R C A does not mean blaming a person, and it does not mean searching for a single dramatic mistake, because complex systems rarely fail for only one reason. In A I incidents, R C A often involves examining how inputs were handled, what data the model could access, how safety checks worked, what logs were captured, and how changes were approved and deployed. The purpose of R C A is to replace speculation with evidence so the eradication work targets what truly matters.

A core challenge in A I incident eradication is separating model behavior from system behavior, because the model is usually only one component in a wider pipeline. The model might generate a problematic output, but the root cause might be that the system fed it sensitive context, or that the system allowed unsafe requests to reach it, or that the system delivered its output into an inappropriate channel. Similarly, suspicious access might look like a model issue, but the root cause might be an identity control weakness that allowed an account to be abused. If you focus only on the model, you can miss the surrounding failures that will remain after recovery. A good eradication mindset treats the incident as a pipeline failure, then identifies which pipeline links were weakest. That is how you prevent repeating the same incident even if you swap models or change vendors.

One common root cause category is access and privilege, because many incidents begin with someone having capabilities they should not have. In an A I context, this can mean an account that can query sensitive data sources, a service account that can invoke high risk tools, or an admin role that can change system instructions without review. Eradication here often includes removing unnecessary privileges, tightening role definitions, and improving approval and review for access changes. Another aspect is revoking or rotating credentials that may have been exposed, because continued use of questionable credentials can keep an attacker present even after containment. A beginner should understand that access eradication is not only about kicking out an attacker, it is about ensuring the system’s permission boundaries match the organization’s actual trust boundaries. When permission boundaries are correct, misuse becomes harder and mistakes become less damaging.

Another common root cause category is data flow, which is how information enters, moves through, and leaves the A I system. Many A I incidents revolve around sensitive data being pulled into prompts, stored in logs, or included in outputs, even when no one intended that to happen. Eradication here can include narrowing what data sources can be queried, limiting the fields that can be retrieved, reducing the amount of context passed into the model, and applying stronger checks before data is included. It can also include adjusting how outputs are stored and shared so that even if an unsafe output occurs, its exposure is limited. For beginners, the main idea is that data flow controls create safety boundaries, and eradication often means redesigning those boundaries to be more restrictive and more intentional. If you can prevent sensitive data from entering the model context, you reduce a whole class of leakage risk.

Safety controls and policy enforcement can also be root causes when they are incomplete, misconfigured, or too easily bypassed. For example, a filter might only catch obvious sensitive patterns and miss indirect identifiers, or it might refuse certain requests but allow slightly reworded variants. In other cases, the control might work but be placed too late in the pipeline, such as checking outputs after they have already been sent somewhere risky. Eradication here can include improving detection logic, adjusting where checks occur, and adding defense in depth so multiple controls cover the same risk in different ways. It can also include tightening exception processes so that allowing a bypass requires review and expiration rather than becoming permanent. Beginners should recognize that safety controls are part of the system’s contract with users, and when they fail, trust erodes quickly. Eradication aims to restore that contract with stronger boundaries and clearer behavior.

Change management is another root cause category that shows up often in A I incidents, because A I systems evolve quickly and small changes can have large effects. A model version update, a new prompt template, a new integration, or a new data source can quietly shift behavior, and the incident may be the first time anyone notices. Eradication in this case may require rolling back a change, revalidating a deployment pipeline, and improving predeployment reviews and testing so risky changes are caught earlier. It may also require improving documentation so teams understand what changed and why, which helps avoid repeated mistakes. For beginners, the key lesson is that not every incident is a villain story, because sometimes the villain is complexity combined with speed. If changes are not tracked and reviewed carefully, the system becomes unpredictable, and unpredictability is a security risk.

Once you believe you have identified the root cause, you need to validate that belief with evidence, because eradication based on a wrong cause can waste time and leave the real issue untouched. Validation can include checking whether the incident behavior stops after the suspected cause is removed, or whether related indicators change in expected ways. For example, if you remove a risky integration and the suspicious outputs stop immediately, that strengthens the hypothesis that the integration was involved. If you tighten access for a specific account and the probing activity stops, that suggests the account was central to misuse. Validation also includes checking for alternate paths, such as whether another integration could still pull the same data, or whether another account has the same privilege. Beginners should remember that attackers and failure modes often have redundancy, and you should assume that if one path exists, there may be others. Validation is how you find and close those hidden parallel paths before recovery.

Eradication work should be performed in a controlled way that preserves evidence and avoids creating new outages, which is why documentation and change discipline still matter even after containment. When you remove access, change configurations, or alter data flows, you should record what changed and when, because those changes affect your ability to interpret logs and timelines. Controlled eradication also means coordinating with the right owners, because a security team may not own every component of an A I pipeline. An engineering owner may need to implement code changes, a data owner may need to adjust data access, and an identity owner may need to adjust permissions. For beginners, it is important to see eradication as a coordinated project, not a single heroic fix. Coordination reduces the chance that someone unintentionally reintroduces risk by making a conflicting change.

Recovery is the phase where you restore normal operations, but safe recovery is about confidence, not speed. If you rush recovery, you can reopen the same risk pathways or reveal that eradication was incomplete. Safe recovery usually involves bringing systems back in stages, starting with the least risky functions and gradually restoring higher risk capabilities as evidence supports readiness. For A I systems, this might mean restoring basic functionality while keeping sensitive data access disabled, or allowing a smaller user group first while monitoring for early signals. Recovery also includes verifying that monitoring and alerting are functioning, because you want strong visibility during the period when the system is returning to normal. Beginners should think of recovery like reopening a building after a fire, where you check structural safety before inviting everyone back in. The building might look fine, but you need evidence that it is safe.

Verification is the bridge between eradication and recovery, and it answers the question of whether the system is behaving within expected boundaries again. Verification can include checking that safety controls are triggering appropriately, checking that access controls match the new intended permissions, and checking that data flows no longer include restricted paths. It can also include checking that the model’s outputs are stable and that any earlier unsafe behavior is no longer observed under similar conditions. Verification should be evidence based and should include monitoring for signs of recurrence, because a single test is rarely enough. For beginners, it helps to understand that verification is not perfection, because you cannot prove a system is invulnerable, but you can prove that the specific failure pattern is addressed and that guardrails are stronger than before. Verification gives you the confidence to recover without gambling.

A mature recovery also considers the human side, because users may have lost trust and may change their behavior in risky ways if communication is unclear. If a system was paused or restricted, users may seek alternatives, including unapproved A I tools, which can introduce new risks. Recovery planning should include clear messaging about what is restored, what remains limited, and what users should do if they see suspicious behavior. This communication should be factual and should avoid speculation, but it should also be supportive and practical. Beginners should see that recovery is partly about restoring service and partly about restoring reliable expectations. If users do not know what is safe to do, they will improvise, and improvisation is where mistakes and shadow usage grow.

After recovery, the work is not truly done until lessons learned are translated into durable improvements, because eradication of a single root cause does not automatically fix the surrounding ecosystem. Durable improvements might include tightening governance around A I integrations, improving training to reduce accidental sensitive data entry, improving monitoring to detect probing sooner, and improving review processes for changes that affect model context. They might also include updating incident playbooks so future responders know what evidence to collect, what containment steps are safest, and what triggers require escalation. For beginners, it is helpful to recognize that incidents are expensive teachers, and the only way to get value from that pain is to reduce the chance of repetition. A strong security program treats every incident as both a problem to fix and a signal about where the system design needs to mature. This is how organizations get steadily safer instead of repeatedly surprised.

As we close, the main idea is that containment stops the immediate harm, but eradication removes the conditions that allowed the harm, and recovery restores operations with evidence and confidence. Effective eradication uses disciplined R C A to identify root causes without guesswork, then validates fixes so you do not reopen the same pathways. Safe recovery brings systems back in controlled stages, verifies boundaries and monitoring, and communicates clearly so people do not create new risks through uncertainty. In A I incidents, root causes often involve access, data flows, safety controls, and change processes, which means fixes must address the pipeline, not just the model. When you approach eradication and recovery as careful, evidence driven phases, you turn an incident from a temporary crisis into a long term strengthening event. That is how you rebuild trust, reduce repeat incidents, and operate A I systems safely over time.