AI policy enforcement for GenAI workflows: are your controls enough?

TL;DR: Enterprises are moving AI into production faster than they can govern it, and Lasso Security argues that runtime policy enforcement must inspect prompts, retrieved context, outputs, and tool calls as they happen, not after the fact, to manage data exposure, misuse, and compliance risk. The governing assumption is already broken: traditional policy models expect stable boundaries and predictable execution, while GenAI changes behavior with context and downstream actions.

NHIMG editorial — based on content published by Lasso Security: AI Policy Enforcement to Protect Data, Models & Enterprise Systems

Questions worth separating out

Q: How should security teams enforce AI policy at runtime?

A: Security teams should enforce AI policy in a separate control layer that inspects prompts, retrieved context, outputs, and tool calls before a response or action completes.

Q: Why do traditional IAM controls fail for GenAI workflows?

A: Traditional IAM controls assume a stable subject, a bounded action, and a predictable execution path.

Q: What do organisations get wrong about AI policy enforcement?

A: The most common mistake is treating AI policy as a document, a prompt rule, or a design-time approval step.

Practitioner guidance

• Move policy enforcement into the runtime path Inspect prompts, retrieved context, outputs, and tool calls in-line so allow, block, redact, or constrain decisions happen before completion.
• Replace static rules with context-based decisions Use identity, role, query intent, data classification, and conversation state to decide whether an AI response is acceptable.
• Constrain model and tool combinations by workflow Limit which models, plugins, APIs, and retrieval sources can be used for each business process.

What's in the full article

Lasso Security's full article covers the operational detail this post intentionally leaves for the source:

• Streaming inspection logic for prompts, outputs, and tool calls in production AI pipelines
• Examples of context-based policy decisions across different roles and data classes
• Framework mapping for NIST AI RMF, ISO/IEC AI governance standards, and the EU AI Act
• Practical patterns for logging enforcement rationale for audit and compliance review

👉 Read Lasso Security's analysis of AI policy enforcement for GenAI workflows →

AI policy enforcement for GenAI workflows: are your controls enough?

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