LLM Governance Framework: 90-Day Implementation Guide

Implementation Guide

LLM Governance Framework: A 90-Day Implementation Plan

An LLM governance framework is the set of policies, identity controls, and runtime enforcement mechanisms that determine how large language models and AI agents access data, invoke tools, and operate in production. A workable implementation sequences policy and risk definition, agent identity, least-privilege permissioning, and runtime enforcement, typically over about 90 days, followed by continuous audit logging rather than a single compliance document.

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Architecture Considerations for Runtime Enforcement

Five design decisions that determine whether a governance framework actually enforces policy in production, rather than describing it on paper.

  1. 1

    Agent Identity

    Establish a distinct identity and permission model for AI agents, separate from the human users who initiate or oversee them, so access can be scoped and audited independently.

  2. 2

    Tool-Call Enforcement Points

    Place policy enforcement at the tool-call and runtime layer rather than relying on model-level prompting or fine-tuning, since those controls cannot reliably prevent excessive agency once an agent is operating.

  3. 3

    MCP and Connector Scoping

    Scope MCP or equivalent tool-connector configurations narrowly for each agent-tool pairing, following least-privilege principles, and validate third-party servers for authentication before granting production access.

  4. 4

    Centralized Audit Logging

    Log agent actions, tool invocations, and decision outputs in a centralized system to support post-hoc review and compliance evidence rather than relying on documentation alone.

  5. 5

    IAM and SIEM Interoperability

    Connect governance tooling to existing identity and security infrastructure instead of building a separate AI-specific silo, so agent activity remains visible within established security operations.

Evaluation Criteria for Governance Capability

Use these questions to assess whether a governance approach, homegrown or vendor-provided, actually meets the bar for runtime enforcement.

  • Does the approach assign agents a distinct identity separate from end users, scoped per task?
  • Are least-privilege permissions enforced at runtime, not only reviewed at design time?
  • Are MCP or other tool-connector integrations authenticated and permission-scoped before production use?
  • Does the system generate audit logs sufficient to demonstrate enforcement, not just policy existence?
  • Can the approach support a phased rollout from policy definition through pilot enforcement to full production scope?

Defining the LLM Governance Framework

An LLM governance framework is the set of policies, identity structures, and enforcement mechanisms that determine how large language models and AI agents access data, invoke tools, and take action inside an enterprise environment. Frameworks such as NIST's AI Risk Management Framework (AI RMF 1.0), published in January 2023, organize this work into four functions: Govern, Map, Measure, and Manage. ISO/IEC 42001:2023 extends this into a formal AI management system standard, specifying requirements for establishing, maintaining, and improving governance over time rather than treating it as a static exercise.

For enterprises deploying LLMs and AI agents, governance means more than publishing an acceptable-use policy or completing a risk assessment. It requires defining how each model or agent is identified, what permissions it holds, what actions it can take through tool calls, and what evidence exists afterward to demonstrate that policy was actually enforced. The EU AI Act, in force since August 2024, adds a regulatory layer with risk-tiered obligations and phased compliance deadlines, reinforcing that governance frameworks need to produce auditable operational evidence, not only documentation.

Why Policy Documents Alone Do Not Govern Agent Behavior

OWASP's Top 10 for LLM Applications identifies risk categories directly relevant to agentic systems, including prompt injection, insecure output handling, and excessive agency. Excessive agency describes what happens when an LLM agent is granted broad permissions or unchecked tool-calling capability, a risk that a written policy alone cannot prevent. Static, pre-deployment review, such as approving a model card or completing a use-case questionnaire, does not account for how an agent behaves once it is calling tools, retrieving data, or interacting with other agents in production.

This is where Anthropic's Model Context Protocol (MCP), released in November 2024, becomes relevant to governance planning. MCP standardizes how AI applications connect to external tools and data sources by separating clients, servers, and hosts, but the protocol itself does not enforce authorization or permission scoping. That responsibility falls to the implementing organization. Security discussion following MCP's release has focused on authenticating MCP clients and servers and on the risk of over-permissioned or malicious MCP servers being connected to production agents. A governance framework that stops at policy definition will not catch these runtime risks.

A 90-Day Implementation Roadmap

  • Policy Definition and Risk Mapping: Establish governance scope aligned to NIST AI RMF's Govern and Map functions. Inventory existing and planned LLM and agent use cases, classify them against OWASP's LLM risk categories, and define a distinct identity model for AI agents separate from human users. Catalog any MCP or third-party tool connectors under consideration for authentication and permission review before production.
  • Least-Privilege Permissioning and Pilot Enforcement: Define and assign least-privilege permissions for each agent-tool pairing, scoping data and action access to what a specific task requires. Validate MCP servers or equivalent tool integrations for authentication and permission handling. Pilot runtime policy enforcement and monitoring on a limited set of use cases rather than rolling out enterprise-wide.
  • Runtime Enforcement and Audit Readiness: Expand runtime enforcement from pilot to production scope, with policy enforcement points at the tool-call layer rather than relying solely on model-level prompting. Centralize logging of agent actions, tool invocations, and outputs to support NIST AI RMF's Measure and Manage functions and to produce evidence for frameworks such as ISO/IEC 42001. Integrate logging with existing IAM and SIEM systems.

Core Components of Operational Governance

The four capabilities referenced throughout this roadmap, summarized for reference.

Agent Identity