Certified: The ISACA AAIA Audio Course

In this episode, we take a common frustration and turn it into a practical skill: A I policies often sound noble, but the words are so broad that no one can prove whether the policy is being followed. Beginners usually read a policy statement like we ensure fairness or we protect privacy and assume it must be good because it sounds responsible. The problem is that broad promises are not automatically enforceable, and they are not automatically measurable. When an audit happens, or when leadership asks whether a system is compliant, the organization needs criteria that can be tested with evidence. Testable criteria turn a policy from a collection of values into a set of verifiable requirements. This is not about making policies harsh or overly complicated, and it is not about catching people doing the wrong thing. It is about converting vague language into specific expectations so teams know what to do, and so the organization can demonstrate it is doing what it promised. By the end, you should be able to hear a vague policy statement and naturally ask the questions that turn it into something that can be checked.

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.

The first idea is understanding what makes language vague in the first place. Vague language often uses words that sound strong but do not specify an action, an owner, a timeframe, or evidence. Words like appropriate, reasonable, best effort, where feasible, and as needed can be useful when you truly cannot be precise, but they can also become loopholes. Another form of vagueness is using goals without describing the method, such as maintain transparency or reduce bias without saying how you will evaluate whether transparency exists or whether bias is reduced. Beginners can think of this like a teacher saying do your best on the exam without explaining what topics will be tested or what a good answer looks like. You might try hard, but you cannot prove you met the expectation because the expectation is not defined. In A I governance, that lack of definition becomes risk, because different teams interpret the same policy differently. The goal of testable audit criteria is to eliminate interpretation where it matters most, so behavior becomes consistent and verifiable.

A good way to start converting vague language is to treat every policy statement as a claim that must be supported by observable facts. If the policy claims the organization protects personal data, then an auditor should be able to ask what controls exist, where they are applied, and what records show they are working. If the policy claims the organization uses A I responsibly, then an auditor should be able to ask which uses are permitted, which are prohibited, and what approvals are required. Turning a claim into criteria often begins with simple questions: Who is responsible for making this true. What must happen, and when. What evidence proves it happened. What happens if it did not happen. Beginners sometimes worry that this makes governance too strict, but it actually makes governance fairer. When expectations are clear, people are judged against the same standard, and teams can plan their work without guessing. Clarity also makes it easier to improve because gaps are easier to identify.

One of the most useful habits is to translate vague adjectives into measurable conditions. For example, a policy might say A I systems must be secure, but secure can mean many different things. Testable criteria would specify required protections, such as access control standards, logging requirements, monitoring expectations, and incident reporting timelines. A policy might say A I outputs must be accurate, but accurate depends on the context and the decision impact. Testable criteria would specify what metrics are used to evaluate accuracy, what thresholds must be met before deployment, and how frequently performance is re-evaluated. A policy might say systems must be transparent, but transparency could range from a brief notice to detailed documentation. Testable criteria would specify when disclosures are required, what information disclosures must include, who approves them, and how updates are managed. The pattern is always the same: replace general qualities with specific actions and checks. That turns a feeling into an audit condition.

To make this practical, consider a vague policy statement such as we will assess A I risks before deploying systems. That sounds good, but it is not yet testable because it does not define what counts as an assessment, who performs it, or what triggers the requirement. Testable criteria might include that every A I system must have a documented risk assessment before first production use, that the assessment must include specific categories such as privacy, security, fairness, and operational impact, and that a designated reviewer must sign off. The criteria could also include that high-impact systems require a higher level of review and formal risk acceptance. The key is that an auditor can verify the presence of the assessment document, verify the required sections exist, verify the sign-off exists, and verify the timing relative to deployment. Beginners should notice how the criteria do not require you to guess whether an assessment happened; you can check it. The criteria also guide teams by telling them what the assessment must contain. This is how policy becomes operational.

Another common vague area is permitted and prohibited use, where policies say things like do not use A I in ways that could cause harm. The phrase could cause harm is vague because almost anything could cause harm under some conditions. Testable criteria require defining what kinds of harm are in scope and what types of uses are restricted. For example, criteria might specify that certain high-impact decisions require human review, or that certain categories of personal data cannot be used for training without specific approvals. Criteria might also define that certain uses are prohibited entirely, such as making final decisions about individuals without safeguards, depending on organizational context and legal obligations. The testability comes from the ability to classify a use case, check whether it falls into a restricted category, and verify that required safeguards and approvals were applied. Beginners should understand that clarity here reduces accidental misuse, because people know the boundaries. Without clear criteria, teams may unintentionally cross a line because the policy language did not draw it. Testable criteria draw the line in a way that can be enforced consistently.

Privacy language is a place where vagueness often hides serious compliance gaps, so it is a good area to practice translation. A policy might say we respect privacy and handle data responsibly, but that does not tell you what data is allowed, how it is minimized, or how it is retained. Testable criteria might require that data sources for A I training are documented with ownership and purpose, that personal data use is justified and approved, and that retention periods are defined and enforced. Criteria might require that access to training data and model outputs is limited to approved roles, and that logs exist to support investigation if misuse occurs. Criteria might also require that individuals’ rights are addressed when applicable, such as having a process to respond to certain requests about personal data use. The test is whether the organization can produce documentation, approvals, and records that show these steps occurred. Beginners should notice that the criteria do not need to cite laws to be useful; they simply define what responsible privacy behavior looks like in practice.

Fairness language is another place where policies often sound inspiring but remain untestable. A policy might say we will avoid bias, but bias is a broad concept and it can be measured in many ways. Testable criteria start by defining when fairness review is required, such as for systems that affect individuals in high-impact contexts. Criteria then define what evidence must exist, such as a documented evaluation of outcomes across relevant groups, and what process exists for addressing issues found. It may also include requirements for human oversight, escalation, and monitoring for drift in outcomes over time. The key is to define what will be checked and how often. If criteria require that fairness evaluation results are documented and reviewed by a designated role, an auditor can verify the presence of those records. Beginners should also understand that testable criteria do not guarantee perfect fairness; they guarantee that the organization has a consistent method for assessing and addressing fairness risk. Consistent method is what governance can realistically enforce.

Security and safety language also benefits from translation into concrete criteria. A policy might say A I systems must be protected against misuse, but misuse could include many scenarios, from unauthorized access to malicious manipulation of inputs. Testable criteria might require role-based access control, require monitoring for abnormal usage patterns, and require an incident response process that includes A I-specific scenarios. Criteria might require that changes to models or prompts are reviewed and approved, and that versions are tracked so the organization can identify what changed when. Criteria might also require that third-party A I services are assessed and approved before use, including contract and data handling requirements. The testability comes from evidence like access control records, monitoring reports, change approvals, and vendor assessment records. Beginners should see how criteria connect to real controls, because a policy that cannot be backed by controls is not enforceable. Controls are how policy becomes true.

A big part of turning policy into audit criteria is defining evidence types, because otherwise teams may record things inconsistently. Evidence can include approvals, risk assessments, testing results, monitoring logs, training records, incident reports, and meeting minutes that capture decisions. Testable criteria often specify what evidence must exist for a given requirement, such as a sign-off, a documented report, or a stored record. Beginners should pay attention to where evidence lives and who owns it, because evidence that is scattered and hard to retrieve is evidence that might not exist when needed. Audit criteria should also specify timing, because evidence created after the fact is weaker. For example, an assessment completed after deployment may not satisfy a requirement that it occurs before production use. Timing can be tested by comparing dates in records. If criteria include periodic review, timing can be tested by checking whether reviews occurred on schedule. This is how audit criteria create accountability without relying on memory.

Another useful translation habit is replacing soft verbs with hard verbs that require action. Soft verbs include encourage, support, promote, and consider, because they do not compel specific behavior. Hard verbs include require, document, approve, verify, and review, because they imply a clear action that can be checked. Beginners may worry hard verbs make policies too rigid, but the right balance is to use hard verbs for high-risk obligations and allow flexible language for low-risk areas where strictness would create unnecessary friction. Audit criteria should focus on what must be consistent for safety and compliance, such as high-impact approvals, data handling rules, and incident response requirements. You can still allow flexibility in how teams meet the requirements, but the requirements themselves should be clear. The phrase teams must document and obtain approval before high-impact deployment is testable, while teams should consider documenting risks is not. Converting soft language into hard commitments is a core step in making governance auditable.

It is also important to account for exceptions, because policies that pretend exceptions never happen become unrealistic and ignored. A good policy can allow exceptions, but testable criteria must specify how exceptions are requested, approved, recorded, and reviewed. The criteria should define who can grant an exception and under what conditions, and it should define whether exceptions have expiration dates or required compensating controls. Beginners can think of this like a school allowing a student to take an exam at a different time, but only with approval and documentation, so it is fair to everyone. In A I governance, exceptions might include temporary use of a new data source or a pilot allowed under restricted conditions. If exceptions are undocumented, they become invisible risk. Audit criteria make exceptions visible and manageable, which is one of the biggest benefits of being testable. When exceptions are tracked, the organization can learn where rules may need adjustment or where teams need more support.

As you translate policies into audit criteria, you should also check for alignment, because criteria must match the intent of the policy and the realities of the organization. If criteria are too strict to follow, people will work around them, and auditability will suffer. If criteria are too loose, they will not control risk. The best criteria are specific, evidence-based, and proportional to impact. Beginners should notice that proportionality is a safety principle as much as a convenience principle. High-impact systems that affect individuals need stronger criteria, more evidence, and more review. Low-impact internal tools may need lighter requirements, but still require basic data handling and security safeguards. When criteria are proportional, governance becomes sustainable, and sustainable governance is what stays in place long enough to matter. The process of translation is therefore not only about precision, but also about designing requirements people will actually follow.

The main takeaway is that vague A I policy language becomes powerful when it is converted into testable audit criteria that specify actions, owners, timing, and evidence. Testable criteria replace words like fair, secure, and transparent with concrete requirements like documented assessments, approved data sources, defined thresholds, required reviews, and recorded sign-offs. They also define how exceptions work and how ongoing monitoring proves that controls remain effective over time. When criteria are clear, teams can implement them consistently, and the organization can demonstrate compliance and responsibility with confidence. This is how governance moves from inspirational statements to operational reality, and it is how an organization protects itself and the people affected by A I outcomes. If you can listen to a policy statement and instinctively ask what must happen, who must do it, and how we prove it, you are already thinking like someone who can make A I governance real and auditable.