Since the release of ChatGPT in 2022, the rapid adoption of generative AI (GenAI) has highlighted its dual potential for innovation and cybersecurity risks. Companies grapple with managing these risks as GenAI's unpredictable nature presents new challenges. The blog discusses various existing frameworks for managing GenAI risks, such as the NIST AI Risk Management Framework and the FAIR-AIR approach, each offering unique perspectives on risk management. These frameworks emphasize understanding the threats associated with generative AI, such as prompt injection, model poisoning, and data biases, while highlighting the importance of contextualizing these risks within broader security contexts. The blog explores how some GenAI risks are novel, like prompt injection, while others evolve from traditional cybersecurity concerns. Elastic InfoSec employs the FAIR quantitative risk analysis model to navigate these challenges, advocating for a comprehensive approach that integrates strategic oversight and technical detail. The discussion underscores the need for continuous learning and adaptation as GenAI becomes more embedded in organizational workflows, with a focus on ethical, legal, and privacy considerations alongside traditional security measures.