DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to keep a metadata-only audit trail of agent calls with Aifw

Use the dashboard and audit logs to review what AI agents sent and received through the gateway.

Who it is forA security or compliance lead who must show a regulator or internal reviewer how AI agents used models.

The problemWithout a central record of agent model calls, reviewers cannot reconstruct what happened or prove policy was followed.

The outcomeEach agent call is recorded as a metadata-only audit event that can be reviewed in the dashboard and exported.

What you need first

  • Docker and Docker Compose on a host you control
  • The repository cloned locally so docker-compose.yml is available
  • API keys for the LLM backends you plan to register in the Model Inventory

Steps

  1. Run docker compose up -d in the folder that contains docker-compose.yml.
  2. Send a few test requests through the gateway so transactions are recorded.
  3. Open the observability dashboard and review the recorded transactions.
  4. Open the audit logs section and review the metadata-only events.
  5. Configure the encrypted audit export to your SIEM over syslog with TLS, as described in the audit logs documentation. (inferred, check the README)

Prompt for your agent

In the securetron-gh/aifw repo, start the gateway with `docker compose up -d` and send three test requests through it. Report how many transactions appear in the audit logs for <agent-name>, and verify each entry contains metadata and not full prompt text.

Caveat. The README lists encrypted SIEM export under Enterprise, so confirm which export options your edition includes before relying on them.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page