Putting access controls around Cursor: session recording for AI coding agents (on AWS)

When an AI coding assistant runs unchecked, a single erroneous suggestion can rewrite production code, expose secrets, or trigger costly cloud spend. Without session recording, teams cannot prove who approved a change, investigate a breach, or meet audit requirements. The hidden expense is not just the immediate outage, it’s the loss of confidence in automated development pipelines.

Most teams that experiment with Cursor on AWS simply grant the agent a static IAM role or access key and let it talk directly to the underlying services. The connection bypasses any central control point, so every command, file write, or database query disappears into the cloud logs, if they appear at all. Engineers end up with a black box: the AI generated code, the cloud executed it, and no one can replay the exact sequence of actions.

Why session recording is a non‑negotiable control for AI coding agents

Session recording captures a verbatim, time‑stamped stream of every request and response that passes between the AI agent and the target service. For Cursor, that means seeing the exact prompts, generated code snippets, and the subsequent CLI or API calls that deploy the code. With a complete record, security teams can:

• Trace the origin of a change back to a specific AI suggestion.
• Detect accidental exposure of credentials in generated scripts.
• Provide auditors with concrete evidence of who, what, and when.
• Replay sessions to reproduce bugs or verify compliance post‑mortem.

However, simply enabling a logger on the AWS side does not guarantee that every AI‑driven interaction is captured. The agent still initiates the connection, and any privileged credential it holds can be used outside of a controlled path. The missing piece is a choke point that can observe, record, and enforce policy on every wire‑level exchange.

Precondition: a controlled request path without built‑in audit

What we need is a way to force every Cursor request to travel through a component that can apply session recording before the request reaches the target service. The identity system (OIDC or SAML) can tell us who the user is, and least‑privilege IAM roles can limit what the agent can do, but those pieces alone do not provide an immutable audit trail. Without a gateway, the request still goes straight to the AWS endpoint, leaving session data unrecorded and any inline guardrails unenforced.

In other words, the problem is solved only when the request is forced through a data path that can observe and persist the full conversation. That data path must sit between the AI agent and the AWS service, be transparent to the client, and be able to enforce policies such as session recording, command blocking, or approval workflows.

hoop.dev as the enforced data path for Cursor

hoop.dev is a Layer 7 gateway that sits between identities and infrastructure. When an AI coding agent like Cursor connects through hoop.dev, the gateway terminates the protocol, inspects the traffic, and then forwards it to the AWS service on behalf of the agent. Because the gateway is the only point that sees the clear‑text request and response, it can record the entire session without exposing credentials to the agent.

hoop.dev records each session, stores the replayable stream in a durable backend, and makes the logs searchable for audit and forensics. The recording happens automatically once the session‑recording feature is enabled for the Cursor connection, so engineers do not need to instrument their code or modify the AI client.

The gateway also respects the identity presented by the user or service account. It validates OIDC or SAML tokens, extracts group membership, and applies any additional policies such as just‑in‑time approval before allowing a potentially dangerous command to proceed. Because the enforcement happens in the data path, the agent never sees the underlying credential, and any attempt to bypass the gateway is blocked.

How to enable session recording for Cursor with hoop.dev

At a high level, the workflow looks like this:

1. Deploy the hoop.dev gateway in the same network segment as the resources the Cursor agent will access. The official getting started guide walks you through a Docker Compose or Kubernetes deployment.
2. Register a new connection that represents the Cursor AI service. The connection definition includes the target endpoint (for example, an AWS Lambda that runs generated code) and the credential that hoop.dev will use to authenticate to AWS.
3. Configure OIDC authentication so that engineers and CI pipelines receive short‑lived tokens that hoop.dev can verify. Group membership can be used to restrict which users are allowed to invoke the Cursor connection.
4. Enable the session‑recording flag for the connection in the hoop.dev UI or via the management API. From that point on, every request that passes through the gateway is captured.
5. Access recorded sessions through the hoop.dev console or export them for integration with SIEMs and compliance tooling. The learn page provides details on query options and retention policies.

The architecture ensures that the AI agent never talks directly to AWS; it always goes through hoop.dev, which is the only place where the session can be recorded and any policy applied.

FAQ

What exactly is stored in a recorded session?

hoop.dev captures the full request and response payloads at the protocol level. For a Cursor‑driven CLI call, this includes the original prompt, the generated code snippet, and the subsequent AWS API calls that execute the code. Sensitive fields can be masked inline according to policy, ensuring that secrets do not appear in long‑term storage.

Can I replay a session to reproduce a bug?

Yes. Because the gateway records the exact byte stream, you can replay the session in a sandbox environment to see the same outcome the AI agent produced. This is useful for debugging, post‑incident analysis, and demonstrating compliance to auditors.

Does session recording affect latency?

The additional hop introduces a small amount of overhead, but hoop.dev is designed to operate at wire‑protocol speed. In most workloads the latency impact is negligible compared to the benefits of having an immutable audit trail.

Ready to add reliable session recording to your Cursor AI workflow? Explore the open‑source repository on GitHub and follow the quick‑start instructions to get hoop.dev up and running in minutes.