DevSecOps for AI Agents: shipping agents safely to production
A complete Secure MLOps (MLSecOps) course covering the full agent deployment lifecycle: secrets and credential management, CI/CD pipeline security for prompts and agent configs, infrastructure-as-code scanning, guardrails-as-code, supply chain security, least-privilege enforcement, cost controls, and production monitoring and rollback. Ten modules, 29 lessons, 6 hands-on labs, and a real incident case study.
10modules · 29 lessons
6hands-on labs, free tools
1real incident case study
$0lab tooling cost
Why this course
The integration layer of this catalogue — ties OpenBao/Vault, Cloud Security, MCP, and AI Agent Security into one deployment pipeline discipline.
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Extends what you know
Not a ground-up reinvention — the same six-capability DevSecOps model, extended to prompts, agent configs, and models.
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Real incident grounding
A full case study built on a real, documented AI infrastructure compromise pattern — not a hypothetical.
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Genuinely hands-on
Write real Checkov and OPA policies, build a real guardrail test suite that fails a real pipeline on regression.
What You'll Actually Achieve
Concrete outcomes, not vague promises — visible here in the free preview before you decide.
📚 Learning Outcomes
Explain precisely why traditional DevSecOps assumptions (static artifacts, human-paced deploys) break down for autonomous agent deployments
Design a least-privilege permission model for an AI agent's tool access, not just its human operators
Distinguish supply-chain risk unique to AI systems (model provenance, MCP server trust) from traditional software supply-chain risk
Design cost and rate controls that catch runaway agent behavior before it becomes a budget incident
🔬 Lab Outcomes — What You'll Actually Build
A working dynamic-credential flow for agent tasks using OpenBao, tested against a real mid-task expiry edge case
A custom Checkov policy you wrote yourself, enforcing resource limits on agent execution containers
A pytest guardrail suite wired into GitHub Actions that actually fails the pipeline on a real regression
An AI-specific SBOM covering models and MCP servers — a format most teams haven't built yet
Industry Relevance — JobTkl's Assessment
Our own rubric based on tool currency, real-world grounding, and current hiring signal — not a third-party certification or independently verified score.
Tool/Framework CurrencyCheckov, OPA/Rego, syft — the exact tools named in current DevSecOps job postings
Real-World GroundingBuilt directly around a documented production AI infrastructure incident pattern, not a hypothetical
Emerging-Skill DemandAI-specific supply chain security is a newly-forming discipline — genuinely ahead of most existing training
Curriculum
Module 1 is free. The rest unlock with a subscription.
One course. One price.
Lifetime access to all 10 modules and future updates.