How to Set Rules That AI Must Always Follow

Why Rules Come First

Before you configure AI agents, train them on data, or give them access to systems, you need rules. Rules establish the outer boundaries of what the AI can do. Everything else, including learned behaviors, goals, and operational preferences, operates within those boundaries. Without rules, you are relying on the AI to make good judgment calls on its own, which is exactly what governance is designed to prevent.

The most important characteristic of an AI rule is that it is permanent. The AI cannot decide to override a rule because it thinks a different approach would produce better results. A rule that says "never share customer email addresses in chat responses" holds even if the AI believes sharing the email would be helpful. This permanence is what makes rules trustworthy.

Types of Rules to Define

Data Handling Rules

These rules control what the AI can do with information. Common examples include never exposing personally identifiable information in outputs, never sending customer data to third-party services without explicit approval, never storing sensitive information in logs or temporary files, and always anonymizing data before using it in reports. Data handling rules are especially important for organizations in regulated industries where mishandling data carries legal consequences.

Action Boundary Rules

These rules define what the AI is and is not allowed to do. They might include never publishing content without human review, never sending communications to customers without approval, never deleting records from production databases, and never making changes to systems outside designated working hours. Action boundaries prevent the AI from taking steps that could cause harm or that exceed the authority you intended to give it.

Communication Rules

If your AI interacts with customers, partners, or the public, communication rules define the tone, boundaries, and limitations of those interactions. These might include never making promises about pricing or timelines, never providing legal or medical advice, always identifying itself as an AI when asked, and always escalating complaints above a certain severity to a human team member.

Operational Rules

Operational rules govern how the AI manages its own work. Examples include always logging every action taken and the reasoning behind it, never working on more than a defined number of tasks simultaneously, always completing validation checks before finalizing output, and always flagging situations where confidence is below a defined threshold.

How to Write Effective AI Rules

Good AI rules share three qualities: they are specific, testable, and unambiguous. A rule that says "be careful with customer data" is too vague to enforce. A rule that says "never include customer email addresses, phone numbers, or account IDs in AI-generated content" is specific enough that the system can check compliance on every operation.

Write rules as clear prohibitions or requirements. "Never" and "always" are the keywords of effective rules. Avoid conditional language like "try to" or "when possible" because those create judgment calls that undermine the purpose of having rules in the first place.

Start with a small set of critical rules and expand over time. Most organizations find that 10 to 20 well-defined rules cover the majority of risk scenarios. Each rule should address a specific category of harm you want to prevent. If you find yourself writing dozens of highly specific rules, you may need to restructure your AI's access rather than adding more rules.

Rules vs. AI Learned Behaviors

Rules and learned behaviors serve different purposes. Rules are your constraints, set by humans, enforced permanently. Learned behaviors are patterns the AI discovers through experience, like noticing that customers asking a certain question usually need a specific type of help. Both are valuable, but they must never conflict. When they do, rules always win.

This hierarchy is critical. An AI that can override its own rules is an AI you cannot trust with autonomous operation. The governance system must enforce this hierarchy automatically. For more on how these two systems interact, see What Is the Difference Between AI Rules and AI Suggestions.

Reviewing and Updating Rules

Rules are permanent in the sense that the AI cannot change them, but humans can and should review them periodically. As your AI takes on new responsibilities, you may need new rules. As your business changes, existing rules may need updating. Schedule quarterly reviews of your AI rule set. Check whether any rules are too restrictive for current operations, whether any new risk categories have emerged, and whether existing rules have been triggered and how they performed.