A forensic analyst can replay every command an autonomous tool‑using agent issued, see exactly what data it touched, and verify that no secrets were leaked.
Most modern pipelines rely on agents that run code, invoke cloud CLIs, or call internal APIs without a human at the keyboard. In practice these agents often authenticate with long‑lived service accounts or static API keys that are baked into CI/CD jobs, scripts, or container images. Because the credential is shared across many jobs, a single compromise gives an attacker unrestricted access to databases, Kubernetes clusters, or remote hosts.
When an incident occurs, the first question is "who did what?" The answer is usually "the agent" – a vague label that provides no insight into the exact sequence of commands, the data returned, or whether a secret was exfiltrated. Without a dedicated audit trail, investigators must rely on scattered logs from the target systems, which may be incomplete, rotated, or already tampered with. The result is a forensic dead‑end: you know an agent was involved, but you cannot reconstruct the precise activity.
Adding identity and least‑privilege is not enough for forensics
Organizations have begun to mitigate the risk by issuing each agent a distinct OIDC or SAML token and by tightening IAM policies so that the token only permits the actions required for a specific job. This step eliminates the shared‑secret problem and reduces the blast radius of a compromised token.
However, the token‑based approach still leaves the core forensic gap untouched. The agent still connects directly to the target service, meaning the service itself is the only place that sees the traffic. No central point records the request, masks sensitive fields, or forces a human approval before a dangerous operation runs. If the target service does not emit detailed audit logs, or if those logs reside on the same host that the attacker now controls, the attacker can erase the forensic trail.
hoop.dev places enforcement in the data path
Enter hoop.dev. It is a Layer 7 gateway that sits between the agent and the infrastructure it accesses. By proxying every connection – whether it is a PostgreSQL query, a kubectl exec, or an SSH session – hoop.dev becomes the sole point that inspects, records, and alters traffic.
Because hoop.dev resides in the data path, it can provide the forensic controls that were missing before:
- Session recording. hoop.dev records each command and its result, providing a replayable log for investigators to view the agent’s behavior step by step.
- Inline data masking. hoop.dev redacts sensitive fields such as passwords, tokens, or personal identifiers in real time, ensuring audit logs never expose secrets while preserving analysis context.
- Just‑in‑time approvals. hoop.dev routes high‑risk operations to a human reviewer before execution, creating an explicit approval record that becomes part of the forensic evidence.
- Command blocking. hoop.dev halts dangerous commands – for example, “DROP DATABASE” or “kubectl delete node” – preventing destructive actions from ever reaching the target.
- Identity‑aware policy. The gateway reads the OIDC token presented by the agent, matches it to group membership, and enforces per‑user or per‑service policies, tying the forensic record to a concrete identity.
All of these outcomes exist only because hoop.dev is the gateway that all traffic must traverse. If the gateway were removed, the agent would again connect directly to the target, and the forensic guarantees would disappear.
How the forensic workflow looks with hoop.dev
- An agent obtains an OIDC token from the corporate IdP.
- The agent initiates a connection to a database, Kubernetes API, or SSH host through hoop.dev.
- hoop.dev validates the token, applies the appropriate policy, and begins recording the session.
- hoop.dev masks any response that contains a configured sensitive field before writing it to the audit log.
- If the request matches a high‑risk rule, hoop.dev pauses execution and forwards the request to an approval workflow.
- After the session ends, hoop.dev stores the complete log for later replay and attaches the approval decision to the record.
Investigators can now query the audit store, filter by agent identity, replay the exact terminal output, and verify that no secret was ever written to an untrusted destination.
Getting started
You deploy hoop.dev by running the provided Docker Compose file or installing the Helm chart in a Kubernetes cluster. The getting‑started guide walks you through the steps to register a resource, configure OIDC authentication, and enable masking for common secret patterns. For deeper insight into the masking and audit capabilities, see the learn section of the documentation.
FAQ
Q: Does hoop.dev store the raw data that it masks?
A: No. The gateway redacts configured fields before persisting the log, so the stored record never contains the original secret.
Q: Can I use hoop.dev with existing CI pipelines?
A: Yes. Agents simply point their client (psql, kubectl, ssh, etc.) at the hoop.dev endpoint instead of the target host. No changes to the application code are required.
Q: How does hoop.dev ensure the audit logs themselves are trustworthy?
A: Because the logs are written by the gateway that sits in the data path, they cannot be altered by a compromised target. You can export the logs to any external storage solution you trust for long‑term retention.
By placing forensic controls at the gateway level, hoop.dev turns opaque tool‑using agents into fully observable participants in your security ecosystem.
