Putting access controls around ChatGPT: data masking for AI coding agents (on AWS)

How can you let ChatGPT write code against your AWS resources without exposing production secrets? Data masking is the essential control that prevents those secrets from ever reaching the model.

Many teams embed large language model (LLM) agents directly into CI pipelines, granting them the same IAM role that developers use. The agent can issue AWS CLI commands, spin up containers, or query databases. If the model is prompted with a request that contains a credential, that secret can be echoed back in logs, error messages, or API responses. The result is a silent data leak that is hard to detect because the LLM operates behind the same network path as a normal user.

Why data masking matters for AI coding agents

Data masking is the practice of replacing or redacting sensitive fields in a response before they reach the requester. For an AI coding agent, masking prevents the model from learning or re‑using production keys, passwords, or personally identifiable information (PII) that appear in API payloads. Without masking, the model can inadvertently embed those values in generated code, configuration files, or chat history, creating a downstream risk of credential sprawl.

The missing control in a direct integration

When an LLM talks directly to AWS services, the request travels from the model to the service endpoint over TLS. The identity of the caller is verified by the service, but the traffic is not inspected for policy violations. The setup therefore fixes authentication – the model has a valid token – yet it leaves three critical gaps:

• The service response is delivered unchanged to the model, so any secret in the payload is exposed.
• There is no audit trail that records which model query triggered which AWS operation.
• There is no real‑time approval step for risky commands such as iam:CreateUser or ssm:StartSession.

These gaps exist because the enforcement point is missing. Authentication alone cannot guarantee that a request complies with your data‑protection policies.

Introducing a gateway in the data path

Placing a Layer 7 gateway between the AI agent and the AWS APIs creates a single, inspectable boundary. hoop.dev terminates the TLS connection, validates the OIDC token, and then forwards the request to the target service using its own credential. Because every packet passes through hoop.dev, it becomes the only place where policy enforcement can happen.

How data masking works in hoop.dev

When hoop.dev receives a response from an AWS service, it parses the protocol payload – for example, the JSON body of a GetSecretValue call. It then applies a masking rule that you define – typically a pattern that matches secret keys, passwords, or any field marked as sensitive in your schema. hoop.dev replaces those values with a placeholder before sending the data back to the LLM. Because the model never sees the raw secret, it cannot incorporate it into generated code.

All masking decisions are driven by the identity claims in the OIDC token, so you can grant different masking policies to different service accounts or AI workloads. hoop.dev also records the full request and the masked response, providing an audit log that can be used for compliance reviews.

Getting started with hoop.dev

To try this approach, deploy hoop.dev using the official Docker Compose quick‑start. The compose file provisions a network‑resident agent, configures OIDC authentication, and enables masking out of the box. After deployment, register an AWS connection, attach a masking policy that targets secret fields, and point your ChatGPT‑powered coding agent at the hoop.dev endpoint instead of the raw AWS API.

Detailed deployment steps, policy syntax, and example masking rules are covered in the getting‑started guide. The learn section also explains how to tune approval workflows and session recording for AI agents.

For the full open‑source implementation, see the GitHub repository.

FAQ

Does masking affect the functionality of the AWS service?

No. hoop.dev only masks fields that are marked as sensitive. The underlying service receives the original request and processes it normally; only the response sent back to the model is altered.

Can I audit which AI prompt triggered a privileged AWS call?

Yes. Because hoop.dev records every session, you can query the audit log to see the exact prompt, the request payload, and the masked response. This provides full visibility for security reviews.

Is hoop.dev compatible with other LLM providers?

The gateway operates at the protocol level, so any client that can speak the AWS API – whether it is ChatGPT, Claude, or a custom model – can be routed through it. You only need to configure the OIDC identity that the model uses.