Fuzz testing is a crucial method for identifying software vulnerabilities by providing unexpected or malformed inputs to programs, effectively uncovering edge cases and security flaws. In modern software development, integrating fuzz testing into Continuous Integration and Continuous Deployment (CI/CD) pipelines has become essential for maintaining robust security standards and ensuring high code quality. However, traditional fuzz testing approaches can be time-intensive, often consuming significant computational resources and potentially disrupting development workflows. This thesis introduces a Delta-Fuzzing methodology leveraging Atheris, a coverage-guided Python fuzzing tool, to address these challenges. Delta-Fuzzing narrows its focus to code changes between software versions, targeting only modified or newly added segments of the codebase. By concentrating on these changes, Delta Fuzzing significantly reduces testing time and computational overhead while maintaining comprehensive bug detection capabilities. The study outlines a detailed framework for integrating Delta Fuzzing into CI/CD pipelines, enabling efficient and timely feedback on newly written or updated code without the need to retest unchanged sections. This approach ensures that developers receive faster insights into potential vulnerabilities, facilitating rapid iteration and maintaining workflow continuity. Experimental results highlight the effectiveness of Delta-Fuzzing with Atheris in achieving high bug detection accuracy while minimizing testing time. The findings demonstrate that this methodology not only reduces resource utilization but also aligns with the dynamic demands of modern software development practices. Delta-Fuzzing emerges as a practical, scalable, and efficient solution for integrating fuzz testing seamlessly into CI/CD workflows, ultimately enhancing software reliability and security.