Lani Leuthvilay

February 13, 2025

Introduction

Generative AI (GenAI) is actively reshaping how enterprises extract business insights and drive operational efficiencies. At the heart of this transformation is Retrieval-Augmented Generation (RAG), a technique that powers Large Language Models (LLMs) with real-time data. However, as enterprises rush to implement GenAI, they face serious data security and access control challenges – especially in the context of new gaps emerging within a Zero Trust architecture. 

In this blog, we’ll explore these challenges and shed some light on the OWASP Top 10 for LLM Applications, which spell out access control vulnerabilities and is a perfect example of how Policy-based Access Control (PBAC) offers a practical, scalable solution.

Understanding the Emerging Threat Landscape: Cracks in the RAG Pipeline

Integrating LLMs with internal documents and data sources via RAG is like giving your AI a live feed to your organization’s brain. It fuels contextually relevant and domain-specific outputs, making your LLM smarter and more responsive. But here’s the catch: this constant interaction with sensitive or proprietary data introduces vulnerabilities that can’t be ignored. Think of it as opening new doors into your data vault – these doors must be carefully guarded.

The key challenges include:

• Data Leakage: Without the proper controls, RAG systems might inadvertently expose sensitive information to unauthorized users.
• Unauthorized Access: Employees (or even malicious insiders) with excessive permissions could potentially abuse LLMs to poke around in areas they shouldn’t.
• Compliance Risks: Regulations for financial services, GDPR and HIPAA demand strict data governance, a major headache in dynamic LLM environments.

Traditional access control methods, including Role-Based Access Control (RBAC), simply can’t keep up with the pace and scale of RAG systems. The need for dynamic, granular, and centralized control is evident.

Addressing OWASP Top 10 for LLM Application Vulnerabilities with PBAC

The OWASP Top 10 for LLM Applications provides a framework for understanding and mitigating the security risks associated with LLMs, three of which can be addressed by PBAC. 

Here’s a detailed look at the relevant categories in the OWASP Top 10 and how they relate to authorization:

• LLM02: Sensitive Information Disclosure: This vulnerability arises when LLMs inadvertently expose sensitive information to unauthorized users. In the context of access control, this means that the system lacks proper mechanisms to filter or redact sensitive data based on user permissions. A robust access control system should ensure that users only see the data they are authorized to view, preventing the leakage of confidential information.
• LLM08: Vector and Embedding Weaknesses: LLMs rely on vector embeddings to represent and retrieve information. If unauthorized users can manipulate or access these embeddings, they could potentially gain access to sensitive data or bypass intended access restrictions. Proper access control mechanisms are essential to protect the integrity and confidentiality of vector embeddings.
• LLM10: Unbounded Consumption: This vulnerability manifests as a security issue when a lack of access control on LLM usage allows malicious actors to consume excessive resources. Effective access controls should limit usage based on user roles, groups, and other attributes to prevent abuse and ensure resource availability for authorized users.

By implementing robust access controls, organizations can proactively address these OWASP Top 10 vulnerabilities and build more secure RAG pipelines. 

The PBAC Advantage in GenAI and LLM Applications

Policy-Based Access Control (PBAC) offers a more agile and extensive approach to authorization. PBAC dynamically evaluates access requests, taking into account identity attributes (i.e. human and non-human identities) – be it role, groups, data sensitivity, data tags, environmental context, and more. It’s about making intelligent, real-time decisions based on the complete picture of the access.

PBAC addresses data access control at three specific points in the RAG pipeline:

• Securing Query Input: Before a query even reaches the LLM, PBAC verifies whether the user has the right to ask that question in the first place.
• Controlling Data Retrieval: PBAC ensures that AI-driven tools only access data, or documents, in RAG systems they’re authorized to see, applying granular policies to control access to sensitive information.
• Masking and Filtering Generated Responses: PBAC dynamically removes or masks sensitive information from LLM responses, ensuring that users only see what they’re allowed to see in the final output.

With Dynamic Authorization via PBAC, you can enforce least privilege principles, adapt to evolving security requirements, and ensure every access decision is precise and compliant.

Centralized Policy Management: The Power of Unified Control

Managing access controls across the sprawling enterprise ecosystem can feel like herding cats. Centralized policy management brings order to the chaos, consolidating all your access policies in one place, giving you a unified view of who can access what and under what conditions. It’s about creating a single source of truth for authorization – and with RAG systems integrating multiple sources for documents and data the need for unified controls has never been greater nor more critical to maintain a robust security posture.

PlainID: Securing GenAI Applications with PBAC

PlainID empowers enterprises to confidently embrace GenAI by providing dynamic, fine-grained authorization. The PlainID Platform provides a PlainID GenAI Authorizer, which seamlessly integrates into RAG pipelines, providing real-time, identity-driven access decisions.

Here’s how your PlainID helps your enterprise elevate its GenAI security posture – our solution provides:

• Input Authorization: Secures query inputs, ensuring users can only ask questions within their authorized scope, preventing unauthorized data extraction attempts.
• RAG Authorization: Controls data retrieval, restricting AI-driven tools from accessing unauthorized data and documents from RAG systems and enforcing fine-grained policies with PlainID’s Policy Decision Point (PDP).
• Output Authorization: Enforces response generation, ensuring the LLM-generated response displayed to the user aligns with permissions and prevents further exposure of unauthorized insights.
• Centralized Policy Management: Enables policies to be authored and managed from a single unified control plane across applications, API gateways, microservices, and the data layer. This ensures the extensibility of enforcement coverage for the tech stack while applying identity-security to access for both human and non-human identities (NHI).

Building a Secure Foundation for AI-Driven Innovation with PlainID

LLM and GenAI security must ensure that both the user and the AI agent acting on their behalf can only access permitted data, preventing unauthorized exposure. PlainID’s Policy-Based Access Control (PBAC) enforces dynamic authorization by tying AI-driven access to the user’s identity, role, and attributes, ensuring AI agents (NHIs) don’t exceed the user’s permissions. 

Additionally, centralized policy management applies these controls consistently across APIs, data sources, and AI access points, reducing risk and simplifying compliance.

Key Takeaways

To truly unlock the potential of GenAI, enterprises must embrace authorization as a strategic business enabler, not a roadblock. By implementing dynamic, fine-grained access control with PlainID’s PBAC, organizations can safely and confidently navigate the OWASP Top 10 for LLM Applications risks, safeguard sensitive information, and drive AI-driven innovation.

Ready to take control of your GenAI security? Contact the PlainID team today to explore tailored solutions for your enterprise.