Imagine a world where software development teams aren’t constantly bombarded by security alerts, a world where potential vulnerabilities are clearly explained, their financial impact laid bare, and developers are actually *motivated* to fix them. Sounds utopian, right? New research suggests Large Language Models (LLMs) might be the key to achieving this security nirvana. Alert fatigue, that numbing deluge of warnings from security scanning tools, is a major problem in DevSecOps. It desensitizes teams, especially smaller ones with limited resources, to genuine threats, leaving vulnerabilities lurking in the code. This research paper explored using LLMs to generate more impactful security reports. Instead of just spitting out technical jargon, these LLM-generated reports emphasize the *financial consequences* of ignoring vulnerabilities—like the potential damage from a credential leak. Think real-world dollar amounts, not just abstract warnings. To test their effectiveness, a survey was conducted with software developers. The result? LLM-generated reports significantly increased the likelihood of developers taking immediate action. The clear, concise explanations, coupled with the stark financial impact assessments, provided the motivation often missing from traditional security alerts. While both OpenAI’s ChatGPT and Meta’s Llama 3 showed promise, there’s still work to be done. Developers, while motivated by the reports, remain somewhat skeptical of LLM-generated recommendations, highlighting the ongoing challenge of building trust in AI-driven security advice. Future research could explore more tailored solutions, ensuring the recommendations are not just generic best practices, but specifically relevant to the vulnerability at hand. This research paints a compelling picture of a future where LLMs transform DevSecOps, not just automating tasks, but making security a core driver of development, ensuring that critical vulnerabilities don’t go unnoticed and unaddressed.