Keep sensitive media inside your own environment

How DeepFAIC Edge brings media analysis to your infrastructure, and what to consider when designing the boundary around it.

A detection workflow begins with a file, but the architecture decision begins with a question: where is that file allowed to go? For internal recordings, customer submissions, and sensitive conversations, the answer can shape the entire system.

DeepFAIC Edge is a single-machine runtime that runs the API, metadata, cache, and detection inference inside your environment as a managed Docker Compose stack. That gives teams a concrete place to draw their media processing boundary.

Put the boundary around the workflow

Keeping inference local is useful, but privacy depends on the surrounding workflow too. Map the path from upload to analysis to deletion. Include temporary files, application logs, backups, and the systems that retrieve results.

A practical design has three responsibilities:

• Ingestion: place media into the runtime's uploads directory with access limited to the submitting service and the runtime.
• Analysis: create a job using the file's container-visible path, then retrieve progress and results through the authenticated Edge API.
• Retention: define how long input files and derived records should remain, and which process removes them.

The Edge runtime provides the analysis layer. Your deployment still owns access controls, storage policy, and the services around it.

Treat local paths as an interface

Edge accepts an input_path when creating a job. A file visible to your application is not automatically visible at the same path inside the container. Establish one staging location and one mapping into the runtime.

For example, an application can copy a submission into the host's uploads directory and pass a path under /var/lib/deepfaic/uploads/ to the API. Preserve a separate application record that connects the submission to the returned job ID. This avoids coupling your product's filenames to the runtime's internal job storage.

For the complete working example, follow Detect Impersonations with DeepFAIC Edge.

Separate progress from evidence

Polling is a user experience concern; the final record is an evidence concern. The partial job endpoint is a lighter way to watch a job move through its lifecycle. Once processing completes, retrieve the full record for the scores, thresholds, coverage, and available chunk or frame analysis.

Keep operational logs small. A job ID, lifecycle status, and elapsed time are often enough to investigate a stalled request. Copying raw media or full result payloads into every log stream creates additional places where sensitive information can accumulate.

Plan for offline operation

The Edge documentation describes an offline runtime. Scoring profile bundles are delivered as files, verified on import, and explicitly selected by jobs. The runtime does not need to contact DeepFAIC cloud to import and use those bundles.

Decide how your team will receive approved updates, move them into the environment, validate them, and roll them out. Offline deployments benefit from a documented maintenance process just as much as connected services do.

Make retention an explicit product decision

Deleting a job record and removing its files are different operations. Edge documents soft deletion and hard deletion separately. Choose the behavior that matches your retention policy, and verify it against your own storage and backup configuration.

Local deployment gives you control over the processing boundary. A complete privacy design also explains who can submit media, who can inspect results, and when the evidence leaves your storage.