Certified: The ISACA AAISM Audio Course

In this episode, we’re going to focus on how investigations begin, because the first hour of an incident often determines whether the rest of the work is smooth or chaotic. New learners sometimes imagine incident investigation as a long, detective style process that happens after everything is already broken, but real investigations start with uncertainty and incomplete information. The challenge is to collect the right evidence quickly before it disappears, changes, or becomes harder to trust. With A I systems, evidence can be scattered across prompts, outputs, data pipelines, access logs, and downstream services, which makes speed and organization even more important. By the end, you should understand what evidence is, why it must be protected, how to avoid common mistakes that ruin investigations, and how to think clearly when the first signals arrive.

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.

An incident is a security event that threatens confidentiality, integrity, or availability in a way that demands a coordinated response. For A I systems, incidents might involve sensitive data exposure through prompts or outputs, misuse that bypasses controls, tampering with models or data, or unsafe outputs that cause real harm. Investigation is the process of figuring out what happened, how it happened, what was affected, and what you need to do next. A beginner should remember that investigation is not about guessing or blaming, and it is not about proving your favorite theory. Investigation is about building a credible story from evidence, and credible means you can explain it, support it, and defend it. Evidence is anything that helps you reconstruct that story, and in cybersecurity, evidence is also what you use to justify decisions to others later.

The phrase collecting the right evidence fast is important because not all information is equally valuable and not all information stays available. Some evidence is volatile, meaning it can disappear quickly, like temporary logs, short retention buffers, running process state, and live connection information. Other evidence can be overwritten, like rolling log files or rotating telemetry. Even evidence that persists can become less trustworthy if it is changed without control, such as if someone edits records or reprocesses data. In A I systems, prompt and response content can also change due to privacy scrubbing, retention policies, or system updates. That is why investigators prioritize capturing what is most at risk of loss first, while also making sure they do not accidentally contaminate what they collect. Speed does not mean rushing blindly; it means knowing what is perishable and acting deliberately.

Before collecting anything, you need a calm initial frame: what is the suspected harm, and what is the suspected path the harm took. This is not a final conclusion, it is a working hypothesis that guides what evidence to grab first. If the suspected harm is data leakage, the suspected path might be prompts containing sensitive data, outputs revealing sensitive data, or downstream storage of those interactions in a place attackers could access. If the suspected harm is misuse, the suspected path might be repeated probing prompts, suspicious account activity, or unusual access patterns to model features. If the suspected harm is integrity compromise, the suspected path might be changes to model versions, changes to data pipelines, or unauthorized updates to system instructions. A working hypothesis helps you avoid collecting random data and missing the critical pieces that make the incident understandable.

One of the first evidence categories is identity and access evidence, which answers who did what and whether they should have been able to do it. This includes authentication logs, session records, account creation events, privilege changes, and records of access to the A I system and its connected data sources. In A I incidents, access evidence is especially important because misuse can come from legitimate accounts that are compromised or misused. You want timestamps, source locations, client applications, and any signs of unusual behavior like rapid repeated requests or access from unexpected networks. You also want to capture recent changes, such as newly granted permissions or newly enabled integrations, because many incidents occur shortly after changes. For beginners, the main idea is that access evidence helps you scope the incident: which accounts, which environments, and which systems are involved.

Another crucial category is interaction evidence, meaning the actual requests and responses associated with the A I system. This includes prompts, uploaded content, model outputs, and metadata linking them, such as request identifiers and timestamps. Interaction evidence can reveal whether sensitive data was entered, whether the model was coaxed into disallowed behavior, and what the system actually returned. It can also show patterns, such as repeated similar prompts that indicate probing or attempts to bypass controls. When collecting interaction evidence, you must be careful about privacy and access control, because this evidence may contain sensitive information itself. The investigator’s goal is not to spread the sensitive content around the organization, but to preserve it securely so the incident can be understood. If you cannot see what the system was asked and what it answered, you are investigating in the dark.

System and application logs are another category, and they often provide the timeline that ties everything together. For A I systems, this can include logs from the model hosting service, the application layer that receives requests, the data pipeline that prepares inputs, and any output delivery components. These logs can show errors, timeouts, changes in configurations, and unusual traffic patterns. They can also show when safety filters triggered, when outputs were blocked, and whether any safeguards were bypassed. Beginners sometimes assume logs are just technical noise, but logs are the memory of a system, and without memory you cannot reconstruct events. The key is to collect the logs that create a continuous story across components, rather than collecting isolated snippets that cannot be correlated.

Evidence also includes configuration and change history, because many incidents are enabled by changes rather than by spontaneous failures. This category includes records of model version updates, changes to system prompts or instructions, changes to safety policies, changes to data sources, and changes to integration settings. It also includes who approved and deployed those changes, and when. In A I systems, a small configuration change can have large behavioral effects, such as changing what data the model can access or altering how outputs are filtered. If an incident involves unexpected behavior, change history is often the fastest way to find the cause, because you can ask what was different yesterday compared to today. For beginners, this is an important mindset shift: do not just ask who attacked us, also ask what changed that made attack or failure possible.

Downstream impact evidence is sometimes forgotten, but it is essential for understanding the scope of harm. Many A I systems are connected to other systems that take action based on outputs, such as sending messages, updating records, making recommendations, or triggering workflows. If an A I output caused an incorrect action, you need evidence of what action occurred, who received it, and what data it affected. If an output was delivered externally, you need evidence of where it went and whether it can be recalled or corrected. Downstream evidence helps answer how bad the incident is and what the priority should be. It also helps you decide whether the problem is contained within the A I system or whether it spread into business operations. For new learners, the lesson is that an incident is not just what the model said, it is what the organization did because of what the model said.

Now we need to address a foundational rule of investigations: protect the integrity of evidence. Integrity means evidence stays accurate and unaltered from the moment you capture it. This matters because decisions will be made based on this evidence, and later you may need to prove that your conclusions were justified. Basic integrity practices include limiting who can access collected evidence, keeping records of when evidence was collected and by whom, and avoiding actions that overwrite or modify logs. Another integrity practice is to capture copies, not originals, when possible, so the system can continue operating while you preserve what you need. Beginners sometimes think integrity is only a legal concern, but it is also a practical concern, because if evidence becomes untrustworthy, your investigation stalls. In A I incidents, integrity also applies to prompts and outputs, because if content is altered by redaction or formatting changes after the fact, you may lose the exact context needed to understand what happened.

A common investigation failure is focusing too quickly on a single theory and collecting only evidence that supports it. This is called confirmation bias, and it is especially dangerous during stressful incidents. A better approach is to collect broad enough evidence to test multiple explanations, such as misuse by an external attacker, misuse by an insider, accidental misuse by a normal user, or drift and misconfiguration. For example, if the incident looks like data leakage, it could be a user pasting sensitive data into a prompt, or it could be an integration pulling sensitive data automatically, or it could be a model revealing cached information due to a configuration error. Each explanation points to different evidence, and collecting evidence early lets you eliminate possibilities. For beginners, the habit is to treat early beliefs as guesses and to use evidence to correct them quickly.

Another common failure is collecting evidence but not being able to connect it across systems, which turns the investigation into a pile of unrelated facts. Correlation is the skill of linking events using shared identifiers like timestamps, request IDs, user IDs, session IDs, and system component names. If you cannot correlate, you cannot build a reliable timeline, and without a timeline you cannot confidently say what happened first and what happened because of it. In A I incidents, correlation is critical because the pipeline can include many steps, and each step may log different details. That is why collecting metadata is just as important as collecting content, because metadata often provides the links. A strong evidence collection plan captures enough metadata to let you trace a request from entry to output to downstream action.

Speed also depends on being ready before an incident happens, even though this episode is focused on investigation in the moment. Readiness means logging is enabled, retention is adequate, and access to investigation tools is preapproved for the right roles. It also means there are clear procedures for how to capture data, where to store it securely, and how to document what was done. When readiness is missing, teams waste precious time arguing about access or discovering that logs were not retained. In A I systems, readiness may also include decisions about how prompts and outputs are stored, how sensitive content is handled, and how model changes are tracked. A beginner should understand that fast evidence collection is not just a skill, it is a design choice made in advance. Good systems make it easy to investigate, and poor systems make it hard.

To wrap up, investigating A I security incidents starts with collecting the right evidence quickly and carefully, because early evidence is perishable and later evidence may be incomplete. The right evidence usually includes access records, prompt and response interactions, system and application logs, configuration and change history, and downstream impact records. Good investigation protects the integrity of evidence, avoids locking onto one theory too early, and prioritizes correlation so events can be connected into a timeline. Speed comes from knowing what is volatile, having readiness built into the system, and using a calm working hypothesis to guide what you capture first. When you practice these habits, investigations become clearer, faster, and more defensible, which reduces harm and builds trust in the security program.