AI coding assistants like Copilot, Cursor, and Gemini are transforming software development with speed and productivity gains, but they also introduce significant risks. Studies, including BaxBench, show that much AI-generated code is insecure, with vulnerabilities replicated from flawed training data. Surveys reveal developers overestimate security while neglecting policies, allowing buggy code into production. Secure Code Warrior’s research finds LLMs struggle with complex vulnerabilities, especially misconfigurations. The paper urges embedding secure coding skills into the SDLC, benchmarking developer security, enforcing repository governance, and adopting ongoing, role-specific training to balance AI’s benefits with safe software practices.

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