AI development is advancing rapidly, but security often lags behind. Many organizations treat security as an afterthought, risking significant vulnerabilities. Damien Lim from Tenable highlights that the race to innovate can overshadow security concerns, leading to the same mistakes seen in past software development cycles. The unpredictable nature of AI introduces unique risks, such as prompt injection and rogue AI behavior, making it essential for security to be integrated throughout the AI lifecycle. AI models differ fundamentally from traditional software. While conventional software produces consistent results, AI can generate unpredictable outputs, which creates new challenges for security. For instance, generative AI can provide false information or inadvertently expose sensitive data. Lim points out that AI agents, which use large language models (LLMs) to make decisions, can unintentionally perform harmful actions if not properly configured. Security teams must approach AI as a distinct threat model, focusing on the risks posed by the AI's own outputs and behaviors. To address these challenges, organizations like OWASP are developing frameworks for testing AI security. OWASP has created a Top 10 vulnerabilities list for LLMs and established best practices for securing generative AI. Tenable offers tools like AI Exposure and AI Security Posture Management (AI-SPM) to help organizations monitor and secure their AI implementations. These tools provide visibility into AI agents' activities and allow for remediation if issues arise, moving away from the traditional "black box" approach to AI security.