AI DevSecOps: the pipeline has to know
it’s shipping a model.
CI/CD, security, and governance for AI systems — where the deployment artefact is probabilistic, the test suite is an evaluation harness, and the compliance surface is materially larger than it was for the microservice it replaced.
Your CI/CD pipeline can’t test a model
A traditional deployment pipeline runs unit tests, integration tests, and a security scan, and promotes on green. That pipeline cannot evaluate a model, because the model’s correctness is statistical rather than binary, its failure modes are distribution-dependent rather than input-dependent, and its security surface includes prompt injection, data poisoning, and output toxicity — none of which a SAST scanner knows how to find.
Most teams discover this gap after the first model reaches production and the question shifts from “does it work?” to “is it getting worse, is it safe, and can we prove either to an auditor?”
What we do
- AI-aware CI/CD pipelines. Evaluation gates that score quality, safety, bias, and cost before promotion. A model that fails its eval does not ship — a pipeline rule, not a meeting.
- Security for AI systems. Prompt injection testing, output filtering, data-poisoning controls, model isolation, and the red-team exercises that find the vulnerabilities a scanner cannot.
- Governance in the pipeline. Model cards, risk classification, and audit evidence generated automatically as a by-product of deployment — not assembled the week before the audit.
- Continuous monitoring. Drift detection, quality degradation alerts, cost tracking, and latency monitoring with defined escalation and rollback triggers.
- Supply chain security. Model provenance, dependency scanning for ML libraries, and the controls that prevent a compromised upstream model from reaching production undetected.
Tell us what’s stuck.
First conversation is a working session with an engineer, not a pitch.