Implementation Guide for: DevSecOps / ML Engineer

Integrating security shouldn't require hardcoding interceptors into your Python ML pipeline or slowing down CI/CD. Spectorn uses an architecture designed for zero friction: Universal Sidecars, configuration-as-code (YAML), and sub-millisecond C/Rust performance.

<1ms

LATENCY

Shield (C DMZ) adds negligible latency, maintaining API SLAs.

YAML

CONFIG-AS-CODE

Declarative `spectorn.yaml` easily versioned in Git alongside ML code.

Binary

DEPLOYMENT

Static binaries (Go/Rust/C) with no massive Python dependencies.

Universal Sidecar Integration

The Sentinel Shield is deployed as a lightweight sidecar container (Kubernetes) or a background Go-binary process. It seamlessly proxies API traffic to your internal LLM instances (like vLLM or Ollama), transparently intercepting threats without any code changes to your application layer.

Reproducible CI/CD BuildsRun `spectorn scan --ci` inside GitHub Actions or GitLab CI to automatically execute red-team scans against staging models before production release.
Metrics & ObservabilityNative Prometheus metrics export and syslog integration out of the box. Easily plug Spectorn telemetry into Grafana and your existing centralized logging stacks.