AI CI/CD Security Review
Determine whether AI applications can move from source code to production securely.
We help engineering and security teams determine whether AI applications, prompts, models, agents, and evaluation workflows can withstand realistic pipeline abuse, guardrail bypass, and deployment-control failure scenarios.
Risk made visible
Pipeline permissions, secret handling, untested prompts or models, weak guardrail validation, unsafe deployment approvals, and rollback gaps.
Source and supply chain
Repository access, branch protections, dependencies, artifact integrity, provenance, signing, and policy enforcement.
Offensive pipeline scenarios
Authorized simulation can test prompt changes, poisoned evaluation data, weak approval gates, exposed secrets, model configuration drift, and unsafe artifact promotion.
Client receives
AI DevSecOps maturity assessment, pipeline control analysis, offensive validation findings, improvement roadmap, framework mapping, and executive briefing.
Consultant-developed deliverables
- Pipeline discovery summary
- AI DevSecOps maturity assessment
- Attack simulation findings
- Control gap analysis
- Software supply chain findings
- Prompt and model lifecycle recommendations
- Prioritized improvement roadmap
Client participation
Engineering and security contacts should provide pipeline diagrams, repository policies, CI/CD configuration, deployment evidence, secrets handling documentation, and authorized access to review or simulation sessions.
Shipping AI changes without enough control evidence?
Review the delivery path before pipeline weaknesses become production AI risk.