Secure AI-Assisted Development in Production: Practical Guardrails for Platform and DevOps Teams

Course details

What strategies do you use for securing AI-assisted development in production environments? This course covers how to map risk and establish baseline guardrails to catch failures before they escalate. Discover how to apply policy-as-code to block high-risk patterns and protect infrastructure, Kubernetes configurations, and the supply chain. Explore the essentials of making delivery reversible by default and learn how to govern exceptions without losing control. Review key topics like managing continuous feedback loops and creating a culture of continuous improvement. This course is ideal for platform engineers, DevOps engineers, and SREs aiming to enhance their operational reliability when working with AI tools. By the end of the course, you'll be more equipped to implement guardrails that reduce incidents and maintain developer velocity without compromising security. Whether you are responsible for platform workflows or interested in AI's impact on software delivery, you can benefit from the insights shared on safeguarding AI-driven development processes.

Concepts

Start Here - Why Guardrails Matter

Catch a plausible-but-wrong AI change before production
Why AI code increases risk faster than review can scale

Map Risk before Writing Policy

Where AI-generated code fails in real platforms

Baseline Guardrails for Every Change

Prevent pipeline bypass and fast paths

Policy as Code - Blocking High-Risk Patterns

Stop insecure defaults automatically
Make policy feedback actionable

Infrastructure Guardrails

Validate infrastructure changes in pull requests
Enforce ownership and approval for sensitive IaC

Kubernetes and Service Configuration Guardrails

Enforce safe deployment defaults
Prevent cascading failures from config changes

Supply Chain Guardrails

Scan dependencies and licenses automatically
Generate and store an SBOM

Make Delivery Reversible by Default

Gate releases with progressive delivery checks
Automated rollback using runtime signals

Govern Exceptions without Losing Control

Design auditable exception workflows
Ownership-based approvals and expiry

Feedback Loops and Continuous Improvement

Turn guardrail failures into guidance
Measure guardrail effectiveness

Wrap-Up and Takeaways

Implement your platform guardrails and 30 day adoption plan