Using Identity to Secure AI, Not Just the Other Way Around

In recent months, the identity industry has been buzzing with excitement over how artificial intelligence (AI) can enhance identity and access management (IAM). From smarter anomaly detection to automated policy generation, AI is steadily being embedded into IAM platforms.

But there's a flip side of the coin that’s not getting nearly enough attention:

What does using identity to secure AI systems, especially enterprise-grade large language models (LLMs), look like?

As AI becomes embedded in internal tools, customer support platforms, and data access layers, the need to secure AI interactions with the same rigor we apply to any sensitive system is clear and urgent.

The Problem: AI Knows a Lot, Maybe Too Much

In many enterprise scenarios, employees are now interacting with AI systems that are connected to internal data sources. That’s powerful and also risky.

What’s currently missing from most LLM implementations is fine-grained access control that answers two essential questions:

1. What data or functionality should this user be allowed to request?
2. What parts of the AI-generated response should this user be allowed to see?

If we don’t enforce controls at both levels, we run the risk of accidental data leakage, privilege escalation, or even regulatory violations.

The Solution: Identity-Driven Access to AI

A natural, standards-based approach to solving this problem starts with using both OAuth 2.0 and OpenID Connect (OIDC) and Policy Based Access Control (PBAC) as they are technologies already trusted for secure authentication and authorization within enterprise applications.

Here’s how the architecture could look.

Step 1: Authenticate with OIDC

The employee logs in via an OAuth 2.0 Authorization Server or OIDC Provider. Upon successful authentication, they receive an access_token and an id_token, both of which contain rich identity information and custom claims, such as role, department, and clearance level.

Step 2: Dynamic Policy-Based Authorization using Policy Based Access Control or PBAC

PBAC solutions like PlainID’s can be used to secure LLMs by enabling policy-based access control across all three critical stages: prompt, retrieval, and response. At the prompt stage,

PlainID can evaluate whether a user is authorized to ask a particular type of question based on roles, attributes, or context.

During the retrieval phase, it can enforce fine-grained access to data sources by leveraging JSON and SQL authorizers tied to policies that align with data labels and metadata. And in the response stage, policies can be applied to determine whether specific entities or regions referenced in the output are within the user's access rights, ensuring consistent identity-aware enforcement from input to answer.

Here’s how it works:

• Policy Administration Point: In the context of LLM workflows, Policy Administration Point (PAP) functionality empowers organizations to define granular policies that span prompt submission, information retrieval, and response delivery. These policies can specify which users are allowed to ask particular types of questions, what categories of data they are permitted to access, and how that data should be handled within generated responses. By incorporating context such as user roles, locations, and business units, organizations can build deterministic policies that govern even the most dynamic and non-linear LLM interactions.
• Policy Information Point: The Policy Information Point (PIP) gathers real-time contextual information about both the user and the data involved throughout the LLM pipeline. It can integrate with data catalogs, vector databases, and entity recognition tools to ensure that data is accurately labeled, whether it’s retrieved for RAG enrichment or embedded in a generated answer. This metadata, combined with user attributes like region or clearance level, allows policies to remain dynamically aware and precisely aligned with compliance and data governance requirements.
• Policy Decision Point: As LLM workflows evolve at runtime, the Policy Decision Point (PDP) evaluates each request, whether it’s to issue a prompt, retrieve supporting documents, or deliver a response, against the defined access control policies. It determines whether the user is authorized to engage with the content in question and, critically, which parts of the retrieved or generated information they are permitted to see. This ensures that access decisions are not just reactive, but proactive and consistent across the entire interaction lifecycle.
• LLM Access Enforcement: When you implement PBAC with PlainID and their Authorizers, these decisions are enforced across all stages of the LLM pipeline. In the prompt stage, this may mean blocking certain questions. In the RAG stage, it filters retrieved data through SQL or JSON-based policies. And in the response stage, it ensures that generated outputs are scanned and redacted as needed based on policy outcomes.

This multi-layered enforcement approach helps organizations secure LLM applications without compromising performance or flexibility. By integrating these PBAC components, you have a dynamic and robust solution to safeguard against vulnerabilities, ensuring that access through the many egresses of your ecosystem are protected.

What makes the PlainID PBAC solution unique is that although there are a few different avenues to protecting LLM as mentioned above, all of this is boiled down to managing a few policies. The policies themselves aren't dependent on which type of enforcement (or authorizer) is used in the solution.

4 Benefits of This Architecture

1. Zero trust-ready: Every request is evaluated individually, based on dynamic identity attributes.
2. Least privilege enforcement: Users only see what they’re supposed to.
3. Policy flexibility: Rules can be updated without code changes.
4. Auditable and explainable: Every decision is traceable.

The Bottom Line

The LLM revolution is exciting, but just like any other critical system, it needs proper identity and access controls. As identity professionals, we should be thinking beyond how AI can help IAM, and start focusing on how IAM can help secure AI.

Integrating standards like OAuth 2.0 and OIDC with a PBAC solution like PlainID’s creates a strong, flexible framework for enterprise-grade AI security before the next breach forces the conversation.