Certified: The ISACA AAIA Audio Course

In this episode, we focus on what many people consider the hardest part of auditing, even though it is also the part that creates the most value: writing findings that actually make sense, hold up under scrutiny, and lead to fixes that reduce risk. A finding is not just a complaint, and it is not a vague statement that something could be better. A strong A I finding is a compact, defensible story that connects four things in a way readers can follow: what caused the problem, what risk it creates, what evidence supports the claim, and what remediation would realistically address it. Beginners often think findings are simply lists of issues, but teams do not fix lists; they fix problems they can understand, measure, and own. A I makes this even more important because people can argue about model behavior, definitions of correctness, and what counts as acceptable uncertainty. The goal here is to teach you how to write findings that avoid hand-waving, avoid overpromising certainty, and still deliver a clear message that executives and teams can act on.

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 finding starts with a clear condition, which means describing what you observed as the current state. This is where many weak findings fail, because they use general words like inadequate, insufficient, or unclear without describing what was actually missing or broken. A clear condition is specific, but it is not overly technical, and it is framed so a reader can picture what is happening. For example, instead of saying monitoring is insufficient, you might describe that drift thresholds are not defined, alerts are not reviewed on a schedule, and there is no documented escalation when drift signals appear. That condition is concrete, and it can be verified. When you write a condition, you also make sure it is scoped, meaning it applies to the system or process you audited, not to every A I system in the organization unless you have evidence for that broader claim. A good condition statement is like a photograph: it captures what is true right now, in a way that is hard to misinterpret.

Cause explains why the condition exists, and it matters because remediation that ignores cause often fails. If the condition is that approvals are inconsistent, the cause might be that roles are unclear, policy requirements are not communicated, or the workflow allows bypassing gates. If the condition is that testing evidence is incomplete, the cause might be that the team lacks a defined test plan, the schedule is too compressed, or ownership is split across teams and nobody coordinates. In A I settings, cause can include issues like unclear business objectives, poor data governance, or misunderstanding about what the model is capable of doing. Beginners sometimes assume cause must be a single root cause, but real environments often have multiple contributing factors. The key is to identify the most meaningful causes that, if addressed, would reduce recurrence. Cause should also be written carefully to avoid blaming individuals, because audit findings should focus on process, design, and controls, not on personal criticism.

Risk explains what could happen because of the condition, and it is where you connect the finding to business impact. Risk is not a generic sentence that says this could lead to problems; it should describe realistic consequences in a way that matches the system’s purpose. If monitoring is weak, risk might include undetected drift leading to declining decision quality, customer harm, and delayed incident response. If access controls are weak, risk might include unauthorized model changes, data leakage, and inability to prove integrity of decisions. In A I, risk often includes safety, fairness, privacy, compliance, operational continuity, and reputational harm. A strong risk statement avoids exaggeration, but it also avoids minimizing, because both extremes undermine credibility. The risk statement should be plausible given the evidence and should reflect the organization’s context, such as whether the A I is customer-facing or internal, and whether it influences high-impact decisions or low-impact convenience tasks.

Evidence is what makes a finding defensible, because auditing is not about opinions. Evidence can be documents, records, logs, interviews, or observations of system behavior, but the key is that evidence must support the condition and, when possible, the cause. Beginners often confuse evidence with explanation, so they write long paragraphs that argue the point rather than pointing to what was verified. A better approach is to state what you reviewed and what you found, such as that a policy requires a specific review step, but the last several releases lacked recorded approvals, or that monitoring dashboards exist but there is no record of scheduled review or incident tickets triggered by alerts. Evidence should also show you looked for counter-evidence, meaning you tried to verify whether controls did operate, not just whether they failed. This is part of audit-grade skepticism and fairness. When evidence is weak, you do not fill the gap with confidence; you write the finding in a way that matches what you can support, often focusing on missing controls or missing evidence rather than making claims about outcomes you cannot prove.

Remediation is the recommended fix, and it must be tied directly to cause, not just to the condition. If the cause is unclear ownership, remediation might include defining roles, assigning accountable owners for approvals and monitoring review, and updating workflows so responsibilities are clear. If the cause is bypassable gates, remediation might include enforcing gates in the release process and requiring documented exceptions with approval. If the cause is missing baselines for drift detection, remediation might include defining baseline metrics, setting thresholds, and establishing a review cadence with escalation triggers. Remediation should also be realistic, meaning it considers the organization’s constraints and avoids asking for impossible perfection. An auditor is not writing an engineering design, but they are describing what change would close the control gap and how to verify the change occurred. A strong remediation statement makes it easy for a team to say, we did that, and here is the proof.

A useful way to keep findings coherent is to ensure each part connects logically to the next without jumping. The condition describes the gap, the cause explains why it exists, the risk explains why it matters, the evidence proves it, and the remediation addresses the cause in a verifiable way. If any link is weak, the finding becomes easier to challenge. For example, if you state a risk that is unrelated to the condition, readers will feel the finding is exaggerated. If you recommend remediation that does not address the cause, teams may implement changes that look good but do not actually fix the problem. Beginners can think of this as building a chain where each link must hold. The strongest findings often feel simple because they avoid extra claims and focus on what matters most. Simplicity here is not superficial; it is disciplined writing that leaves little room for confusion.

A I findings require extra care with language because of uncertainty and the temptation to make sweeping claims. If you did not test the model for bias, you should not state the model is biased, but you can state that bias testing is missing and that the organization lacks evidence to support fairness claims. If you observed a few harmful outputs, you should not necessarily claim the system is unsafe in all cases, but you can state that harmful outputs were observed in controlled sampling and that safeguards and escalation are not sufficient. The point is to match confidence to evidence and to avoid turning possibility into certainty. This careful phrasing protects credibility and also helps teams focus on what is fixable. A well-written A I finding often centers on governance and control gaps because those are measurable and can be corrected, even when model behavior is complex and context-dependent.

Another common challenge is distinguishing between design issues and operational issues, because remediation is different for each. A design issue might be that the model choice does not align with business objectives or that the system lacks an appropriate human oversight mechanism. An operational issue might be that the organization has a policy requiring oversight but does not actually perform it consistently. Both can create risk, but the fixes differ. If the problem is design, you may need to change the system architecture, the model, or the decision process. If the problem is operation, you may need to change training, ownership, scheduling, and enforcement. Good findings make this distinction clear so teams do not waste time fixing the wrong layer. Auditors also look for whether the organization is treating design problems as if they are operational, such as adding more monitoring to a fundamentally misaligned use case. A finding that correctly identifies the layer of the problem is far more likely to produce meaningful risk reduction.

Findings also need to respect scope and materiality, meaning they should focus on issues that matter and are supported by the audit work performed. Beginners sometimes write findings that are technically correct but not meaningful, like minor formatting issues in documentation, while missing the bigger control failures that create real risk. Materiality in A I audits often tracks impact, meaning issues affecting high-impact models deserve more attention. It also tracks recurrence and control health, meaning a repeated failure to follow approvals is more concerning than a single isolated slip. A strong finding shows why it matters and why it deserves priority. It does not require dramatic language; it requires a clear link to risk and evidence. When you prioritize well, you also build trust with stakeholders because they see you are focused on protecting the organization, not policing minor details.

It is also helpful to understand how findings should anticipate the natural pushback that occurs when people feel criticized. Teams might say the control exists but they did not document it, or that the issue was a one-time situation, or that the model is working fine in practice. A strong finding prepares for that by grounding statements in evidence and by explaining why missing evidence is itself a risk, because controls that cannot be demonstrated are hard to trust. If the team claims it is a one-time issue, the auditor can point to whether similar gaps appeared elsewhere or whether the process allows recurrence. If the team says the model works fine, the auditor can emphasize that governance is about being able to prove and sustain performance, not just being lucky today. This approach keeps the discussion professional and focused on risk reduction. The finding becomes a starting point for improvement rather than a personal argument.

To write findings that lead to action, you also need to describe what success looks like after remediation. This is where verification comes in, because audits do not end at recommendations; they end when risk is reduced in a way that can be shown. Success criteria might include updated policies, clear role assignments, evidence of approvals on subsequent releases, monitoring review records, incident triggers that produce tickets, or periodic reports showing stable performance. The key is that success criteria are observable and tied to the control gap. This prevents a common failure where teams implement cosmetic changes that do not change behavior. When you include success criteria, you make it easier for follow-up audits to confirm progress. You also help executives see that remediation is not just effort; it is measurable improvement.

Writing A I findings that tie cause, risk, evidence, and remediation together is about disciplined reasoning expressed in clear, defensible language. The condition states what is wrong in a specific and scoped way, cause explains why it happened, risk shows why leaders should care, evidence proves the claim, and remediation targets the cause with verifiable outcomes. A I adds complexity through uncertainty and debate, so strong findings avoid overclaiming and focus on control gaps and missing evidence when direct conclusions are not supportable. The best findings are often the ones that feel straightforward, because they are built from careful observation and precise connections rather than broad statements. When you master this style, your reports become more than summaries; they become practical tools that help teams fix the right problems and help leaders see risk in a way that is actionable and credible. That is the essence of audit reporting that actually changes outcomes.