When a nested agent silently forwards queries to BigQuery, missing logs can cost your organization compliance penalties and lost forensic insight, breaking the audit trail.
Nested agents are processes that act on behalf of an original user, often to batch work, run scheduled jobs, or translate API calls. They receive a credential from a primary service, then open their own connection to the data warehouse. From the perspective of the downstream system, the request appears to come from the agent, not the human who initiated the workflow.
This indirection creates two audit challenges. First, the original identity is stripped away, so the system that records activity only sees the agent’s service account. Second, the agent may execute many statements in a single session, mixing privileged and routine queries, making it hard to attribute each statement to the right owner.
Why audit trail matters with nested agents
Regulators and internal security teams rely on a complete audit trail to answer questions such as:
- Who accessed sensitive tables and when?
- Did a particular analyst trigger a data export that later appeared in a breach?
- What sequence of commands led to an unexpected cost spike?
When a nested agent obscures the true initiator, those answers become speculative. Gaps in the trail also weaken incident response – you may have to replay logs from multiple sources, reconcile timestamps, and still be unsure which user caused the problem.
What to watch for
To keep the audit trail trustworthy, monitor the following signals:
- Identity leakage. Verify that every query that reaches BigQuery carries a claim about the original user, either in a header, a session variable, or a dedicated audit field. If the claim is missing, flag the transaction for review.
- Session granularity. Ensure that the agent does not batch dozens of unrelated statements into a single logged session. Fine‑grained logging helps you map each statement back to its source workflow.
- Approval checkpoints. For high‑impact operations – such as creating external tables, exporting data, or altering access controls – require an explicit human approval step before the agent can proceed.
- Replay capability. Store enough metadata to reconstruct the exact sequence of commands, including timestamps, parameters, and the originating identity.
These controls must sit on the data path, because the downstream system itself cannot infer the missing context. Relying on the agent’s own logs is unsafe: a compromised agent could tamper with its own records.
How hoop.dev solves the audit trail problem
hoop.dev acts as a Layer 7 gateway that sits between any identity – human or service – and the BigQuery endpoint. By routing every connection through hoop.dev, the gateway becomes the sole place where enforcement can happen.
When a request arrives, hoop.dev validates the OIDC token, extracts the original user’s claims, and injects that information into the BigQuery session. Because hoop.dev is the data path, it records each command, the associated identity, and the exact time it was executed. The gateway also supports just‑in‑time approval for privileged queries, inline masking of sensitive result columns, and session replay for forensic analysis.
In practice, the workflow looks like this:
- A user or upstream service obtains an OIDC token from the corporate IdP.
- The token is presented to hoop.dev when opening a BigQuery connection.
- hoop.dev verifies the token, maps groups to access policies, and forwards the request to BigQuery using its own credential.
- Every statement passes through hoop.dev, which records the full audit trail and applies any required guardrails.
This architecture guarantees that the audit trail remains complete and tamper-evident, even when nested agents are involved.
By placing a single, identity‑aware proxy in front of BigQuery, you regain confidence that every nested‑agent operation is accounted for, approved when necessary, and fully replayable for audit purposes.
Getting started
Deploy the gateway with the official getting‑started guide. The quick‑start includes OIDC configuration, a sample BigQuery connection, and default policies for masking and approval. For deeper policy customization, see the learn section, which explains how to define per‑user rules, set up just‑in‑time workflows, and enable session replay.
FAQ
Q: Does hoop.dev store any data from BigQuery?
A: hoop.dev only records metadata about each session – timestamps, identities, and the commands themselves. Result rows are streamed to the client; the gateway can mask fields in‑flight but does not persist the raw data.
Q: Can I use hoop.dev with existing service accounts?
A: Yes. The gateway holds the credential that talks to BigQuery, while the calling service presents its own OIDC token. This separation ensures the service never sees the downstream credential.
Q: How does hoop.dev handle high‑throughput workloads?
A: The gateway is designed for Layer 7 traffic and can be horizontally scaled. Audit‑trail records are written to an external store of your choice, allowing you to keep up with large query volumes.
Explore the open‑source repository on GitHub to see the full implementation and contribute.
