AI in SDLC: Software Development Lifecycle with AI

This course provides a comprehensive understanding of how artificial intelligence risks in cybersecurity is transforming the Software Development Lifecycle (SDLC), enabling teams to build, test, and deliver software faster and more efficiently.

Modern development teams are increasingly adopting AI tools to improve productivity, reduce manual effort, and enhance code quality. In this course, you will learn how AI can be applied across every phase of the SDLC, from planning and requirement analysis to development, testing, and deployment.

You will begin by understanding the traditional SDLC phases and how AI integrates into each stage. The course then explores how AI tools can assist in requirement gathering, design decisions, code generation, debugging, testing automation, and documentation.

You will learn how to use tools such as GitHub Copilot, AI-based testing tools, and modern AI workflows to improve development efficiency. The course also covers how AI supports DevOps practices, CI/CD pipelines, and continuous delivery.

In addition, the course highlights real-world use cases, showing how organizations are adopting AI to streamline development workflows and improve collaboration across teams.

By the end of this course, you will have a clear understanding of how to apply AI across the software development lifecycle, making this course highly valuable for developers, DevOps engineers, and teams adopting AI-driven development practices.