Governance in AI Controller

This document explains how AI Controller helps organizations implement effective governance for their LLM usage. AI Controller provides robust oversight, controls, and compliance mechanisms that organizations need for responsible AI implementation.

What is AI Governance?

When organizations deploy artificial intelligence systems, they need a structured approach to manage them properly. AI governance creates this structure through policies, processes, and tools. For large language models (LLMs), good governance ensures that AI systems:

• Run securely and responsibly
• Follow all relevant regulations
• Adhere to organizational policies
• Maintain proper oversight and accountability
• Implement effective risk management controls

AI Controller offers a complete governance framework built specifically for organizational LLM deployments, giving you the tools to manage AI systems confidently and responsibly.

Governance Framework Overview

AI Controller takes a layered approach to governance that covers all aspects of LLM management:

flowchart TD
    subgraph GF[AIC Governance Framework]
        subgraph AC[Access Control Governance]
            AC1[Rules Engine]
            AC2[User Management]
            AC3[API Key Control]
            AC4[Authentication & Event Logging]
        end

        subgraph CU[Content & Usage Governance]
            CU1[Usage Monitoring]
            CU2[Cost Management]
            CU3[Request Routing]
        end

        subgraph AL[Auditing & Compliance]
            AL1[Comprehensive Logging]
            AL2[Usage Metrics]
            AL3[Audit Reports]
        end
    end

    classDef access fill:#6A5ACD,stroke:#333,stroke-width:1px,color:#fff
    classDef content fill:#3CB371,stroke:#333,stroke-width:1px,color:#fff
    classDef audit fill:#FF7F50,stroke:#333,stroke-width:1px,color:#fff
    classDef component fill:#F5F5F5,stroke:#333,stroke-width:1px,color:#000

    class GF framework
    class AC access
    class CU content
    class AL audit
    class AC1,AC2,AC3,AC4,CU1,CU2,CU3,AL1,AL2,AL3 component

These governance layers work together to form a complete control system that satisfies various organizational needs. Think of it as building a house: access control forms the foundation, content and usage governance builds the walls, and auditing places the roof that protects everything underneath.

Core Governance Components

AI Controller includes several key components that create effective governance:

Rules Engine

The Rules Engine serves as the foundation of AI Controller's governance capabilities by controlling access to LLM resources. It determines which users and groups can access specific models based on configurations you define.

With the Rules Engine, you can implement policies that:

• Control which users can access which models
• Apply model and provider restrictions
• Create pattern-based access control for specific use cases

Picture the Rules Engine as a traffic control system, directing users and requests to appropriate destinations while blocking unauthorized paths. For implementation details and examples, see the Rules Engine documentation.

User & Group Management

AI Controller's user and group management system provides identity-based governance through:

• Role-based access control (RBAC) for administrative functions
• Group-based permissions for model access
• Separation of duties for administrative tasks

These controls ensure that users only have access to the LLM capabilities appropriate for their roles and responsibilities, similar to how different departments in an organization have access to different resources based on their needs.

API Key Management

API keys in AI Controller create application-level governance through:

• Specific permissions and restrictions
• Provider associations
• Key lifecycle management
• Usage tracking and auditing

The API Key Management feature provides a secure way to authenticate applications while maintaining governance controls.

Comprehensive Logging

AI Controller's logging system creates a detailed audit trail that supports governance through complete request and response logging.

The system includes authentication event logging, which tracks all user authentication activities such as:

• Successful and failed login attempts
• Password changes and resets
• API key authentications

Authentication logs provide critical security information for identifying potential unauthorized access attempts, account misuse, and compliance verification.

This comprehensive logging capability helps organizations demonstrate compliance with internal policies and external regulations. The logs record all activities for future reference and review. For more information on how logging supports governance, see the Logging and Monitoring documentation.

User Access Control

You can control user access to models and providers by:

• Assigning users to groups with specific access permissions
• Monitoring and auditing user activities

This approach creates clear boundaries around sensitive AI capabilities, much like how organizations restrict access to sensitive data or systems.

Authentication Governance

AI Controller's authentication governance provides security controls through:

• Comprehensive authentication event logging
• Visibility into login patterns and potential security issues
• Audit trails for compliance requirements
• Tracking of all access-related events

This enhanced authentication monitoring helps identify unusual patterns that may indicate account compromise or misuse, providing an additional security layer for your AI systems.

Application Access Control

For applications, AI Controller governance includes:

• API keys with specific provider associations
• Rules that restrict model access by API key
• Detailed logging of API key usage

This system allows organizations to control how applications interact with AI models, creating appropriate guardrails for automated systems.

Implementing Access Policies

Access policies can be implemented through various approaches:

1. Group-based access: Create groups like "AI Researchers" with access to specialized models
2. Project-based access: Create groups for specific projects with appropriate scopes
3. Progressive access: Begin with limited access and expand based on demonstrated needs

Organizations can mix these approaches to create access systems that match their specific requirements and organizational structure.

Cost Governance

AI Controller helps organizations manage the financial aspects of LLM usage:

Usage Monitoring

Track usage across users, groups, and applications:

• Usage by model and provider
• Request volume and patterns
• Usage trends over time

The Cost Management feature provides tools for optimizing AI spending while maintaining appropriate governance controls. Think of this as a financial dashboard for your AI investments, showing where resources are allocated and how they're being used.

Cost Optimization

Optimize costs through governance mechanisms:

• Route requests to cost-appropriate models
• Implement caching for common queries
• Monitor and minimize unnecessary usage

These capabilities allow organizations to balance cost considerations with performance and capability requirements, making the most efficient use of AI resources.

Compliance Frameworks

AI Controller supports various compliance requirements through its governance features:

Regulatory Compliance

Features that support regulatory requirements include:

• Comprehensive audit logging
• Access controls and authorization
• Data protection mechanisms
• Policy enforcement through rules

These tools help organizations demonstrate compliance with regulations that govern AI usage, creating documented evidence of responsible AI management.

Risk Management

Governance tools for risk management include:

• Model access restrictions
• User activity monitoring
• Content filtering capabilities
• Usage anomaly detection

Together, these capabilities help organizations identify and mitigate risks associated with AI usage, creating a safer environment for AI deployment.

Internal Policies

Support for organizational policies includes:

• Custom rule configurations
• Access control frameworks
• Usage monitoring and reporting
• Policy-based routing

Organizations can implement their specific policies and standards through these tools, creating governance systems that reflect their values and requirements.

Governance Tools Reference

Key AI Controller interfaces for governance:

Tool	Location	Purpose
Rules Management	Admin -> Rules	Configure access rules
User Management	Admin -> Users	Manage user access
Group Management	Admin -> Groups	Configure group membership
API Key Management	Admin -> API Keys	Manage application access
Logs	Admin -> Logs	Review audit and usage logs
Provider Management	Admin -> Providers	Configure provider access
Dashboard	Admin -> Dashboard	Overview of system usage

Related Documentation

• Rules Engine
• API Key Management
• Logging and Monitoring
• Cost Management
• Access Control
• Performance Optimization

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Updated: 2025-05-27