AI is becoming increasingly prevalent in the software development lifecycle (SDLC) due to its ability to improve efficiency, security, and innovation, but it also poses risks such as data leaks, skill atrophy, hallucinations, bad suggestions, license infringement, and the potential for malicious actors to exploit AI-powered tools. To effectively integrate AI into the SDLC, it is essential to be aware of these pitfalls and adopt best practices that prioritize responsible AI adoption, including ensuring secure data handling, preventing over-reliance on AI, and mitigating the risks associated with AI-generated code. By doing so, developers can harness the benefits of AI while minimizing its drawbacks.