[100% Off] Devsecops For Ai-Assisted Development: Hands-On Security

Build secure AI-assisted workflows with threat modeling, SAST, secret scanning, CI/CD, and AI coding agent guardrails.

Added on October 31, 2026 Development 2 min read

What you’ll learn

• Implement DevSecOps security practices throughout the AI-assisted software development lifecycle.,Identify
• analyze
• and fix security vulnerabilities in AI-generated code.,Secure AI coding assistants
• autonomous coding agents
• and Model Context Protocol (MCP) integrations.,Build secure CI/CD pipelines using GitHub Actions with automated security checks.,Perform static application security testing (SAST)
• dependency scanning
• and secret detection using industry-standard tools.,Apply secure coding principles
• threat modeling
• and OWASP security best practices.,Secure Docker containers
• software dependencies
• and cloud deployment workflows.,Implement software supply chain security controls
• including Software Bills of Materials (SBOMs).,Protect sensitive data
• API keys
• credentials
• and enterprise source code when using AI development tools.,Apply NIST and OWASP frameworks to establish security governance and compliance practices.,Monitor security risks
• investigate vulnerabilities
• and implement continuous security improvements.,Build and secure an AI-assisted application through a practical end-to-end DevSecOps capstone project.

Requirements

• Basic understanding of software development concepts and programming fundamentals.,Familiarity with Git
• GitHub
• and command-line operations is helpful but not mandatory.,Basic knowledge of Python or another programming language is recommended.,A computer with internet access capable of running VS Code
• Python
• Git
• and Docker.,A free GitHub account for source control and CI/CD pipeline exercises.,Access to an AI coding assistant such as GitHub Copilot
• Cursor
• or Claude Code is helpful; paid subscriptions are not required for the core lessons.,No prior DevSecOps certification or advanced cybersecurity expertise is required.,A willingness to experiment with security tools
• complete hands-on exercises
• and learn modern secure development practices.

Description

This course contains the use of artificial intelligence.

AI coding assistants are changing how software is designed, written, tested, and deployed. While these tools can accelerate development, they also introduce security challenges involving generated code, sensitive data, dependencies, autonomous agents, and development infrastructure.

In this hands-on course, you will learn how to apply DevSecOps principles throughout the AI-assisted software development lifecycle.

You’ll begin with secure software development fundamentals, including threat modeling, shift-left security, OWASP guidance, and the NIST Secure Software Development Framework. You’ll then explore how to configure AI coding environments, review generated code, and establish security guardrails for developers and autonomous coding agents.

Through practical demonstrations and guided exercises, you’ll work with GitHub Actions, Semgrep, CodeQL, Gitleaks, Docker, Trivy, and other security tools to identify vulnerabilities and automate security verification.

You’ll also explore software supply chain security, Software Bills of Materials (SBOMs), secure container development, MCP integration risks, and enterprise AI development governance.

Throughout the course, you will build and secure a sample application using AI-assisted development techniques.

By completing the capstone project, you will have practiced implementing a development workflow that combines AI-assisted coding, automated security scanning, human review, and controlled software delivery.

This course is designed for software developers, DevOps engineers, security professionals, platform engineers, and technical architects who want practical experience securing modern AI-assisted development workflows.