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A Deep Dive into Enterprise Access Control for GenAI Solutions

Workshop
Are your GenAI systems enforcing access controls with the same rigor as your core enterprise platforms?

LLM-driven workflows introduce new interaction patterns around prompts, data, and roles that can quickly outgrow traditional access control assumptions. 
To win, your GenAI solutions must apply enterprise-grade identity, role-based access, and governance controls that scale safely with adoption.

The Challenge

As GenAI systems spread across teams, access control gaps become difficult to detect and harder to correct. 
• Inconsistent identity standards: GenAI tools bypass or loosely integrate with established enterprise identity and access frameworks. 
• Overly broad permissions: Prompts, data sources, and model capabilities are exposed without sufficient role-based or granular controls. 
• Limited auditability: Access logs exist but are not structured or reviewed to detect suspicious or inappropriate usage. 
These weaknesses increase data exposure risk, complicate compliance, and limit confidence in scaling GenAI across the enterprise. 

Our Solution

In this hands-on workshop, your team designs and evaluates enterprise-ready access control patterns for GenAI solutions through applied exercises. 
• Establish identity and access control standards aligned to enterprise IAM practices. 
• Design role-based access models tailored to LLM interactions and usage patterns. 
• Implement granular controls over prompts, data access, and model capabilities. 
• Analyze access logs to identify suspicious behavior and misuse patterns. 
• Define approaches for automating access governance as GenAI usage scales. 

Area of Focus

Establishing Identity and Access Control Standards 
Designing Role-Based Access for LLM Interactions 
Implementing Granular Controls Over Prompts and Data 
Auditing Access Logs for Suspicious Activity 
Automating Access Governance for Scalability 

Participants Will

• Apply consistent identity and access standards across GenAI solutions. 
• Design role-based access models that reflect real LLM interaction patterns. 
• Enforce fine-grained controls over sensitive prompts and data sources. 
• Audit and interpret access logs to detect anomalous or risky behavior. 
• Leave with scalable access governance patterns for enterprise GenAI adoption. 

Who Should Attend:

Security ArchitectPlatform EngineersRisk and Compliance Leaders

Solution Essentials

Format

 Virtual or in-person 

Duration

8 hours 

Skill Level

Intermediate 

Tools

IAM concepts, access control models, logging examples, and guided design exercises 

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