Addressing the Challenges in Device Identification and Vulnerability Discovery for Behind-the-Meter [BTM] Energy Systems For Clean Energy Identifying devices and vulnerabilities in behind-the-meter [BTM] energy systems presents significant challenges that hinder effective energy asset management and security. The current approach to device identification and vulnerability discovery in BTM systems is fraught with obstacles that need to be addressed to ensure optimal performance, risk mitigation, and efficient energy operations. One of the primary pain points is the lack of comprehensive visibility into the multitude of devices present within BTM energy systems. These systems often consist of a diverse array of interconnected devices, each with its unique characteristics and functionalities. The absence of an efficient and systematic process for device identification makes it challenging for operators to gain a holistic view of their energy infrastructure, leading to blind spots and potential vulnerabilities that remain undetected. Furthermore, the dynamic nature of BTM systems adds complexity to the task of vulnerability discovery. As devices interact and evolve over time, the risk landscape continuously changes, necessitating continual monitoring and assessment. The current methods struggle to keep pace with emerging vulnerabilities, leaving BTM systems exposed to potential threats and security breaches. Additionally, the diverse range of protocols and communication interfaces used in BTM energy systems poses interoperability challenges for device identification and vulnerability assessment. The absence of standardized approaches hinders seamless integration and data correlation, making it difficult for operators to identify potential risks comprehensively. To address these challenges, there is a critical need for an advanced and data-driven approach to device identification and vulnerability discovery in BTM energy systems. By leveraging the power of AI, machine learning, and comprehensive data analytics, operators can gain a deeper understanding of their energy infrastructure, identify devices accurately, and proactively discover vulnerabilities. Such an approach would empower operators to optimize performance, enhance security measures, and ensure the resilience of their BTM energy systems in the face of ever-evolving threats.