How Do I Deploy AI Evidence Collection at Il5 Il6 Without Network Egress
Engineers often ask how do i deploy ai evidence collection at il5 il6 without network egress. You do this by running local AI models inside your isolated boundary with signed bundles. Defense systems at Impact Level 5 and Impact Level 6 block all public internet calls. Your team must ship all software, model weights, and dependencies through an approved diode. Once inside the enclave, the AI agent queries local databases and system APIs. It collects audit logs, checks system state, and writes e
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Engineers often ask how do i deploy ai evidence collection at il5 il6 without network egress. You do this by running local AI models inside your isolated boundary with signed bundles.
Defense systems at Impact Level 5 and Impact Level 6 block all public internet calls. Your team must ship all software, model weights, and dependencies through an approved diode. Once inside the enclave, the AI agent queries local databases and system APIs. It collects audit logs, checks system state, and writes evidence files to local storage.
How do i deploy ai evidence collection at il5 il6 without network egress
First, you package the AI model and agent runner on an outside build network. You scan the container images for known flaws and sign them with your key. Next, you move those signed artifacts across the air gap or data diode. You load the images into a private registry that sits inside the secure enclave.
Inside the enclave, the AI agent runs on local compute nodes with GPU hardware. It does not call external cloud endpoints to process prompts or read system logs. Instead, the agent talks to a local model server on the internal network. It reads state data from internal databases, Kubernetes clusters, and cloud management APIs.
The agent maps the collected log records to your specific security control framework. It then writes the structured evidence reports into an internal audit storage for auditors.
Security tradeoffs of static roles in isolated enclaves
Many teams assume an air gap protects them from internal data exposure risks. That belief leads engineers to give the AI agent broad, static database roles. Static keys and wide roles create serious problems during an active collection session.
A local AI agent with a full read role can query every table. If a prompt tells the agent to check user activity, it pulls raw rows. Those rows often hold emails, phone numbers, card numbers, or national IDs. Without query controls, that sensitive data enters the model context window and audit files.
Evidence Collection Automation + AI Agent Security: architecture patterns and best practices
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Static roles also cannot stop a faulty agent from running a bad write command. Even inside an IL5 or IL6 network, you need strict controls on every query. You must check what the agent runs and mask sensitive fields in the results. Data masking rewrites matched fields like national IDs before they reach the local model.
Operational steps for air-gapped audit workflows
Running AI agents without network egress requires a clear plan for model updates. You cannot pull new model weights or agent code on the fly. Your team must test prompt changes and agent logic in a staging lab first.
Keep your local models small and focused on parsing logs and structured text. Smaller models need less hardware and run faster inside constrained government server racks. Schedule the agent to run collection jobs during low-traffic windows to save compute power. Store every prompt, query, and output log in a local tamper-proof database. Auditors can then review the exact queries the agent ran to build each report.
Check database and API queries on the wire
Identity tools, roles, and network rules decide who opens a session. Setup alone cannot check what each query does or mask sensitive values in a response once a session starts. When workflows touch Postgres, MySQL, SQL Server, MongoDB, ClickHouse, Kubernetes APIs, SSH, or internal HTTP APIs, hoop.dev runs beside those services as an open-source proxy, a sidecar for your Postgres or API. It inspects requests coming in and results going back on the wire.
Your login details and client tools stay as they are while hoop.dev guardrails refuse bad deletes or drops before they run. On responses, hoop.dev data masking rewrites matched sensitive values in memory. Furthermore, hoop.dev holds SOC 2 Type II, is MIT licensed, and writes every query and decision to an audit trail that generates evidence for HIPAA, GDPR, and PCI DSS. See the source on GitHub.
Frequently asked questions
Can an AI agent collect audit evidence without calling external model APIs?
Yes, you can run open-weights models on local hardware inside your secure enclave. The agent sends all prompts to the local model server over the internal network.
How do we update the AI agent inside an IL6 network?
You build and scan a new container bundle on your external staging system. Then you transfer the signed bundle across your approved cross-domain solution into the enclave.
How do we keep sensitive data out of the collected evidence files?
You apply field masking rules to database queries before results reach the AI agent. Masking rewrites matched emails, phone numbers, and national IDs in the query results.