Autopilot-Devsecops-Dashboard

Inspiration: I was inspired by the need to make software delivery faster, safer, and more intelligent. Modern software teams need automation, but critical production decisions should still have human oversight. This inspired me to build an AI-assisted DevSecOps control center that combines automation, security, governance, and human approval.

What it does: Autopilot DevSecOps Dashboard is an AI-assisted Release Guardian that manages and visualizes the software delivery lifecycle:

Plan → Create → Verify → Package → Secure → Release → Configure → Monitor → Govern → Deploy It provides release-readiness visibility, automated lifecycle stages, governance controls, an audit trail, and human approval for critical production releases. I deployed the application on Google Cloud Run through GitLab CI/CD

How we built it: I built the project using:

I built Autopilot DevSecOps Dashboard using the following technologies and tools:

• GitLab & GitLab CI/CD — Source control, pipeline automation, and continuous integration/deployment.
• GitLab Duo CI Expert Agent — AI-assisted, read-only review of my CI/CD pipeline, DevSecOps lifecycle stages, deployment configuration, and production approval controls.
• Google Cloud Build — Building application container images.
• Google Artifact Registry — Storing container images for deployment.
• Google Cloud Run — Hosting and running the production application.
• GitLab OIDC / Workload Identity Federation — Secure, keyless authentication between GitLab and Google Cloud.
• DevSecOps Lifecycle Stages — Organizing the software delivery workflow around verification, security, release, configuration, monitoring, and governance.
• Release Guardian Dashboard — Providing release-readiness visibility, governance decisions, an audit trail, and human approval requirements for critical production releases.

By combining GitLab automation, GitLab Duo AI-assisted analysis, and Google Cloud infrastructure, I developed a solution designed to improve software delivery visibility, strengthen release governance, and keep humans in control of critical deployment decisions.

Challenges we ran into: My biggest challenge was making the complete GitLab-to-Google Cloud deployment pipeline work securely and reliably.

I had to troubleshoot CI/CD configuration, Workload Identity Federation, IAM permissions, Google Cloud Build, Cloud Storage, Artifact Registry, Docker image publishing, Cloud Run configuration, environment variables, and deployment errors.

Another important part of the journey was integrating GitLab Duo CI Expert into my workflow. I used the agent to conduct a read-only AI review of my actual CI/CD pipeline, examine the DevSecOps lifecycle stages, and assess deployment configuration and production approval controls without modifying my project files.

Working through these challenges strengthened my practical understanding of DevSecOps automation, AI-assisted pipeline analysis, secure cloud authentication, and real-world deployment troubleshooting.

Accomplishments that we're proud of: I am proud that I took Autopilot DevSecOps Dashboard from an idea to a functional application deployed on Google Cloud Run.

My key accomplishments include:

• Built a functional Release Guardian dashboard for release readiness and governance visibility.
• Designed a DevSecOps workflow covering the software delivery lifecycle.
• Developed a GitLab CI/CD pipeline for automated software delivery.
• Used GitLab Duo CI Expert Agent to perform an AI-assisted, read-only review of my actual pipeline and production approval controls.
• Configured secure, keyless authentication between GitLab and Google Cloud using Workload Identity Federation.
• Built and published a Docker container image.
• Successfully deployed the application to Google Cloud Run.
• Implemented human-in-the-loop production release governance.
• Created an audit trail to record important release and governance events.
• Verified the application through its live deployment URL.

These accomplishments demonstrate my ability to combine CI/CD automation, AI-assisted analysis, cloud infrastructure, secure authentication, and human-centered governance in a practical DevSecOps solution.

What we learned: I learned that DevSecOps is much more than simply deploying an application. It brings development, security, operations, automation, cloud infrastructure, and governance together.

Through this project, I gained practical experience with CI/CD, cloud deployment, IAM, identity federation, containerization, Artifact Registry, Cloud Run, security gates, governance, monitoring, and production troubleshooting. Most importantly, I learned how to use automation to accelerate software delivery without removing human accountability.

What's next for Autopilot-Devsecops-Dashboard: My goal is to evolve Autopilot into a more complete AI-powered DevSecOps platform.

Future plans include:

• AI-powered release risk analysis
• Advanced vulnerability scanning
• Intelligent release-readiness scoring
• AI recommendations for pipeline failures
• Security and deployment notifications
• Advanced monitoring and observability
• Policy and compliance automation
• Kubernetes support
• Multi-cloud deployment
• Team and organization management
• Advanced audit and compliance reporting My vision is to help teams ship software faster and more securely while keeping humans in control of critical production decisions.

Built With

• ai
• artifact-register
• css
• dev-sec-ops
• docker
• gitlab
• google-cloud
• google-cloud-build
• google-cloud-run
• html
• javascript
• oidc