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In this episode, we’re going to turn governance and risk boundaries into something that actually changes daily behavior: a practical AI policy. Beginners sometimes imagine policies as long documents that sit in a folder and never get read, and unfortunately many organizations treat them that way. A good AI policy is different, because it functions like a set of clear, simple rules that help people make safe choices without needing to be experts. It tells employees what they can do, what they can do only with approval, and what they must not do at all. It also sets expectations about data, accountability, and oversight so AI use does not become a collection of private experiments hidden inside teams. Policies matter because AI tools are easy to access and easy to misuse, even unintentionally, and once risky habits spread, they are hard to undo. If you are new to this field, your most important takeaway is that policies are not about controlling people for its own sake; they are about protecting the organization and the people it serves by making risk boundaries clear. By the end, you should be able to explain why AI policies need allowed, restricted, and prohibited categories, what kinds of rules belong in each, and how a policy supports defensible decision-making.

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.

Start by understanding what a policy is and what it is not. A policy is a formal statement of intent and rules that the organization expects people to follow, and it is meant to be stable enough to guide behavior across many situations. A policy is not a detailed instruction manual, and it is not a technical configuration guide. Instead, it answers questions like who is allowed to use AI tools, what kinds of tasks are appropriate, what data can be used, what approvals are required, and what responsibilities people have when they rely on AI output. A policy also defines consequences for violating the rules, because rules without consequences are just suggestions. In the AI context, a policy helps reduce risk by preventing high-impact misuse, controlling data exposure, and ensuring oversight processes are followed. It also reduces confusion by creating shared language, so teams are not inventing their own definitions of responsible use. When the policy is clear, employees can make safer choices quickly, which is critical because AI tools often appear in day-to-day workflows.

The allowed, restricted, and prohibited structure is a practical way to make a policy usable for beginners and for busy employees. Allowed means activities that are generally permitted because the risk is low and the organization has decided the benefit outweighs the risk under standard safeguards. Restricted means activities that may be permitted, but only with additional controls, approvals, or oversight, usually because the impact is higher or the uncertainty is greater. Prohibited means activities that are not permitted because the risk is too high, the harm is unacceptable, or the organization cannot manage the risk within its constraints. This structure is powerful because it mirrors how people think, and it reduces the chance that employees interpret vague language as permission to do whatever seems efficient. It also supports consistent enforcement because it gives managers and governance teams a clear basis for decisions. A policy without these categories often becomes ambiguous, and ambiguity is exactly what creates shadow AI behavior. For exam thinking, you should recognize that the best policies make boundaries explicit and tied to risk appetite and tolerance.

Now let’s make the allowed category concrete, because beginners need to see what low-risk AI use looks like. Allowed use often includes tasks where AI assists with drafting, summarizing, or organizing information that is not sensitive and that will be reviewed by a human before it leaves the organization. It can also include internal brainstorming that does not involve confidential data, such as generating ideas for a presentation outline or rephrasing an internal message. Allowed use can include translating non-sensitive text or improving grammar, again with human review. The defining feature is that the AI output is not directly making a high-impact decision and the input data does not include sensitive or protected information. Allowed use still requires basic responsibility, such as not treating AI output as guaranteed correct and not representing AI-generated content as verified fact without checking. The policy should also remind users that the organization remains accountable for what it publishes and for how it treats customers and employees. Allowed does not mean carefree; it means low risk under standard precautions.

Restricted use is where most of the governance value sits, because restricted activities are often where the organization wants the benefit but must control the risk. Restricted use commonly includes any AI system that influences decisions affecting customer rights, employee opportunities, financial outcomes, safety outcomes, or legal obligations. It can also include the use of sensitive data, such as personal data, protected health information, payment data, or confidential business information. It can include use of AI tools that connect to external services or vendors, where data leaves the organization’s control. Restricted use might include deploying a model into production, integrating AI into a core business process, or using AI to generate customer-facing content at scale. The policy should tie restricted use to an approval process, which could include risk assessment, privacy review, security review, legal review, and governance committee oversight for high-impact cases. Restricted use should also require documentation, monitoring plans, and clear ownership, because those are what make the use defensible. The key beginner insight is that restricted does not mean no; it means yes only under conditions that reduce harm.

Prohibited use is often where organizations protect themselves from the most severe or least manageable risks. Prohibited use might include using AI to make final decisions in high-stakes areas without human review, especially when explanations are required or when the impact on individuals is significant. It might include using AI for covert surveillance, manipulation, or generating deceptive content, because those uses can create serious ethical, legal, and trust harm. It often includes submitting confidential or sensitive information to public AI tools that are not approved, because that can create data exposure and privacy violations. It can include using AI to generate content that impersonates individuals or misrepresents official communications, because that can be deceptive and damaging. Prohibited use can also include bypassing governance processes, such as deploying an AI system into production without review or ignoring required monitoring and documentation. The goal is to define prohibitions that match the organization’s risk appetite, legal obligations, and values, not to create a long list of unrealistic bans. A small number of clear, enforceable prohibitions is usually more effective than a long list that nobody can remember.

A strong AI policy also needs to address data handling, because data is often the fastest path to harm. Even in allowed use cases, employees need to understand that some data should never be entered into AI tools unless explicitly approved. This includes sensitive personal data, confidential business data, and information that could cause harm if exposed. The policy should also make clear that data minimization matters, meaning people should use the smallest amount of data needed to accomplish the task. Another important concept is that the policy should clarify whether AI tools retain data, use it for training, or share it with third parties, because those behaviors affect risk. Employees often assume AI tools are private, but many tools are designed to learn from inputs or store them for service improvement unless configured otherwise. Even if the details are handled by technical teams, the policy should set the expectation that only approved tools and approved data use patterns are permitted. This is less about technical detail and more about preventing accidental disclosure through casual behavior.

Policies also need to address responsibility for output, because AI tools can produce confident content that is wrong, biased, or inappropriate. A practical rule is that humans remain responsible for the decisions and communications they make using AI assistance. That means an employee cannot excuse harm by saying the tool wrote it. It also means employees should review AI-generated content before sharing it externally, especially when it involves customers or legal commitments. The policy can require that AI outputs used in high-impact contexts be validated through defined review steps, such as human approval or cross-checking with authoritative sources. It can also require that certain AI uses include transparency, such as informing users when content was AI-assisted, depending on the organization’s obligations and values. Again, the purpose is not to punish people; it is to prevent overreliance and to ensure accountability is not diluted. In risk terms, this is about controlling automation bias and preserving human judgment where it matters.

Another practical element is clarifying ownership and escalation inside the policy itself, so people know where to go when they have questions. If the policy says restricted use requires approval, it should also make clear who grants that approval and what process to follow. If the policy prohibits certain data use, it should clarify who can grant exceptions, if exceptions are allowed at all, and what documentation is required. If employees suspect an AI tool is producing harmful outputs, the policy should define how to report it and what happens next. Without this guidance, policies become frustrating because people cannot comply even when they want to. For beginners, this is a key lesson: a usable policy includes both rules and the practical pathways for compliance. Policies that only say do not do this, without providing the path to do the right thing, often fail. Governance works when it is both clear and actionable.

We should also discuss enforcement in a way that feels realistic, because a policy that cannot be enforced is not a boundary, it is a wish. Enforcement includes training and awareness so employees understand the rules and the reasons behind them. It includes manager support so teams are not pressured to violate policy for speed. It includes technical controls where appropriate, like limiting access to unapproved tools or preventing data exfiltration, but policy itself is still needed because not every misuse can be technically blocked. Enforcement also includes consequences for violations, which can range from coaching for accidental misuse to formal action for repeated or intentional violations. The point is not punishment; it is consistency, because inconsistent enforcement teaches people that rules are optional. A policy becomes credible when employees see that leadership supports it and that governance teams respond to issues promptly. Credibility reduces shadow AI because people believe the approved path will work.

A common misconception is that policies must be extremely detailed to be effective, but over-detail can backfire. AI changes quickly, tools evolve, and overly specific policies become outdated and ignored. A strong policy stays focused on principles and boundaries that remain stable, such as impact-based oversight, data protection, human accountability, and prohibited high-risk behaviors. Detailed requirements can live in supporting standards and procedures that can be updated more frequently. This separation helps keep the policy readable and enforceable while still allowing the organization to adapt. For exam thinking, this is important because the best approach often distinguishes between policy level intent and standards level detail. If you see an answer choice that tries to solve policy needs by listing technical configurations, it is often mixing levels in a way that is less effective. The policy should set the rule, and supporting standards should define how the rule is implemented.

To close, practical AI policies are one of the most effective tools for reducing AI risk because they translate governance into daily decisions. The allowed, restricted, and prohibited structure makes boundaries easy to understand, easy to communicate, and easier to enforce. Allowed use focuses on low-impact tasks with non-sensitive data and human review, restricted use covers higher-impact or higher-uncertainty activities that require approval, documentation, and monitoring, and prohibited use blocks activities that create unacceptable harm or cannot be managed within constraints. A strong policy also addresses data handling, human responsibility for outputs, and clear pathways for compliance, reporting, and escalation. When policies are clear and supported by leadership, they reduce shadow AI and help teams innovate within safe boundaries rather than guessing. This policy foundation sets up the next step, which is building standards for responsible AI, where we define expectations around ethics, fairness, transparency, and oversight in a way that can be applied consistently across use cases.