What does AI enforcement mean?

AI enforcement means applying written requirements to AI systems while they are operating. These requirements can come from regulations, company policies, or security controls.

What is the difference between AI enforcement and AI governance?

AI enforcement turns written requirements into controls that can be applied when an AI system is in use. For example, a control might check whether a customer-facing message makes a prohibited claim, includes a required disclosure, or contains information that should not be shared. Each requirement is enforceable at the point when it applies. AI governance covers the broader policies and processes an organization uses to manage AI. AI enforcement focuses on specific rules within that framework and how they are carried out. For example, governance might establish standards for using AI in customer communications, while enforcement applies those standards to the content being produced.

What is the difference between real-time AI compliance and AI governance?

AI governance covers the broader policies and processes an organization uses to manage AI. AI enforcement focuses on specific rules within that framework and how they are carried out. For example, governance might establish standards for using AI in customer communications, while enforcement applies those standards to the content being produced.

What is runtime AI enforcement?

Runtime enforcement applies requirements while an AI system is operating. This allows an issue to be addressed when it occurs instead of relying only on review afterward. For example, an AI-generated message can be checked after it is created but before it reaches the recipient. Any issue can then be handled while the message is still in the send path.

Is AI enforcement the same as AI guardrails?

No. Guardrails generally place boundaries around AI behavior, such as identifying content that is unsafe or outside an organization's guidelines. AI enforcement is focused on applying specific written requirements and determining what happens when they are not met. For example, a guardrail might stop an AI assistant from generating abusive language, while enforcement might make sure a financial-services chatbot includes a disclosure required by company policy.

What kinds of requirements can AI enforcement apply?

AI enforcement can be used for regulations, company policies, and security controls. The exact requirements depend on what the AI system does and the environment in which it is used. For example, a specific rule may govern claims made in customer communications, mandate certain disclosures, restrict the use of sensitive information, or identify situations that need human review.

Resources / Glossary

What Are AI Guardrails?

The terms behind the Enforcement Runtime,
and what they mean.

Abstraktní pozadí s přechodem teplých zlatavých tónů do černé barvy.

What is an Enforcement Runtime?

AI guardrails are filters and classifiers wrapped around a model's inputs and outputs to catch unsafe or off-policy content. Guardrails detect and flag. They do not read a regulation, and they do not fix the message.

Guardrails help developers set boundaries around how an AI application behaves. They can inspect requests before a model processes them and check generated responses before users see them. Depending on their design, these checks can identify harmful content, sensitive information, or attempts to manipulate the model. The application can then decide whether to allow, flag, or reject the content.

How it works

Four stages. From setting boundaries to handling flagged content.

Define

Developers establish the types of content or behavior the application should detect, such as harmful requests, sensitive information, or suspicious instructions.

Check Input

Guardrails inspect incoming requests before they reach the model. They can flag content that falls outside the application's defined boundaries.

Check Output

After the model generates a response, guardrails inspect the output for potential problems before the application sends it to the user.

Act

The application handles flagged content according to its configuration. It may reject the request, withhold the response, or send the content for further review.

Guardrails can use pattern matching, classifiers, and other detection methods. Their effectiveness depends on the risks they are designed to identify and how reliably those checks perform.

What it is not

The limits of AI guardrails, and how they differ from other controls.

Not a guarantee of accuracy.

A response can pass a guardrail check and still be incorrect.

Not a complete defense against prompt injection.

Guardrails cannot guarantee detection of every attack.

Not a replacement for access controls

Enforcement can intervene.

Not an enforcement runtime.

Guardrails typically check for defined risks; ZeroDrift applies written rules to outgoing AI messages and records each enforcement verdict.

Why it matters

Detecting unsafe content is only one part of controlling AI behavior.

Art. 14

Human oversight requirements for high-risk AI systems.

Source: EU AI Act, Article 14

Art. 15

Accuracy, robustness, and cybersecurity requirements for high-risk AI systems.

Source: EU AI Act, Article 15

Guardrails help developers identify problematic content before an AI application processes a request or returns a response. But detecting unsafe content does not establish whether a generated message satisfies every applicable written rule. Organizations may also need controls that determine what happens when a message violates a regulation or company policy. ZeroDrift addresses that need through runtime enforcement, applying written rules to AI-generated communications before they are sent.

FAQ

Common questions.

What are AI guardrails used for?

AI guardrails help developers identify content or behavior that falls outside an application's defined boundaries. Common uses include detecting harmful requests, sensitive information, prompt injection attempts, and inappropriate model responses. The checks depend on the risks the application needs to address.

How do AI guardrails work?

Guardrails inspect model inputs, outputs, or both. They can use predefined patterns, classifiers, or other methods to detect potential problems. When a check flags content, the application can reject the request, withhold the response, or send it for review.

Can AI guardrails prevent hallucinations?

Some guardrails can help identify unsupported or inconsistent claims, but they cannot guarantee factual accuracy. A response may pass every configured check and still be wrong. Applications that require reliable factual answers may need additional verification against trusted information.

Can AI guardrails prevent prompt injection?

Guardrails can help detect suspicious instructions in user requests or external content. However, they cannot guarantee that every attack will be detected. Effective protection also depends on how an application handles untrusted inputs, permissions, and access to tools.

What is the difference between AI guardrails and an enforcement runtime?

AI guardrails are generally used to detect unsafe or off-policy content. An enforcement runtime applies written rules to AI-generated messages and determines what happens before they are sent. ZeroDrift returns a Pass, Fix, Block, or Escalate verdict and records the decision as evidence.

Can AI guardrails and runtime enforcement work together?

Yes. Guardrails can check inputs and outputs for risks such as harmful content or suspicious instructions. Runtime enforcement can separately apply written regulations, company policies, and security controls to outgoing messages. The two approaches serve different purposes and can work together.