sec-do/automated-ci-cd-and-security-compliance-pipeline-for-cloud-applications — explained in plain English
Analysis updated 2026-05-18
Automatically block deployments that fail security scans or drop below a compliance score threshold
Scan source code, dependencies, containers, and infrastructure-as-code for vulnerabilities in one pipeline
Check a cloud application's setup against CIS, ISO 27001, NIST, and PCI DSS standards
Get AI-generated remediation suggestions for detected security and compliance issues
| sec-do/automated-ci-cd-and-security-compliance-pipeline-for-cloud-applications | 0xhossam/uncanny | 89171/web3-101 | |
|---|---|---|---|
| Stars | 12 | 12 | 12 |
| Language | — | C | — |
| Setup difficulty | — | hard | easy |
| Complexity | 4/5 | 5/5 | 1/5 |
| Audience | ops devops | researcher | general |
Figures from each repo's GitHub metadata at analysis time.
SecureDevOps is a project that automates the process of checking software for security problems before it goes live. When a developer pushes code, a series of automated checks run in sequence: the code is scanned for common programming mistakes that create security holes, the external libraries the code depends on are checked for known vulnerabilities, any container images are inspected for malware or exposed secrets, and the infrastructure configuration files are reviewed for settings that could leave resources open to the internet. If everything passes, the software is deployed automatically to a Kubernetes cluster. If something fails, deployment is blocked. The threshold is strict: no critical vulnerabilities can be present, and the compliance score must be at or above 90%. This gates releases on security results rather than leaving it to manual review. The compliance layer checks the setup against several established security standards, including CIS Benchmarks, ISO 27001, NIST, and PCI DSS. After each run it produces a score and lists which specific controls passed and which failed, along with what the actual problem was. The project documentation shows an example output: a 92% compliance score with four failed controls, including a public storage bucket and a weak password policy. There is also an AI assistant component that reads the list of detected issues and suggests how to fix them. The example in the README shows it responding to a SQL injection finding with a recommendation to use parameterized queries. Monitoring after deployment uses Prometheus for metrics, Grafana for dashboards, and Falco to watch for unusual container behavior at runtime. The tech stack is Python and FastAPI for the backend, React for the frontend, PostgreSQL for the database, Docker and Kubernetes for containers and orchestration, and various security scanning tools wired together. The project is licensed under MIT.
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