This project demonstrates a secure AI development pipeline that integrates automated security scanning into a CI/CD workflow. The goal is to identify potential vulnerabilities in Python-based AI/ML code during development using automated security tools.
The pipeline runs security checks every time code is pushed to the repository, helping detect insecure code patterns, vulnerable dependencies, and general code quality issues early in the development lifecycle.
The pipeline integrates several security and code quality tools:
- Bandit – Detects common Python security vulnerabilities in source code.
- Semgrep – Performs static analysis to identify insecure coding patterns.
- Pylint – Analyzes code quality and potential programming errors.
- Safety – Scans project dependencies for known security vulnerabilities.
These tools help enforce secure coding practices in AI/ML development environments.
A GitHub Actions workflow automatically runs the security scans on every push to the repository.
Workflow file location:
.github/workflows/security-scan.yml
Pipeline steps include:
- Checkout repository
- Set up Python environment
- Install security scanning tools
- Run static security scans
- Generate security reports
Scan results are stored in the following directory:
security-reports/
Generated reports include:
bandit-results.jsonsemgrep-results.jsonpylint-results.txtsafety-results.json
These reports provide detailed information about detected vulnerabilities and potential risks in the codebase.
This project demonstrates how DevSecOps practices can be applied to AI systems, ensuring that security testing is integrated directly into the development pipeline.
By automating security scans in CI/CD, teams can detect and address vulnerabilities earlier in the development lifecycle.