Generative AI Risks: Security & Mitigation Guide | Sentra

Executive Summary

Generative AI risks are no longer hypothetical. They’re shaping the way enterprises think about cloud security. As artificial intelligence (AI) becomes deeply integrated into enterprise workflows, organizations are increasingly leveraging cloud-based AI services to enhance efficiency and decision-making. In 2024, 56% of organizations adopted AI to develop custom applications, with 39% of Azure users leveraging Azure OpenAI services. However, with rapid AI adoption in cloud environments, security risks are escalating. As AI continues to shape business operations, the security and privacy risks associated with cloud-based AI services must not be overlooked. Understanding these risks (and how to mitigate them) is essential for organizations looking to protect their proprietary models and sensitive data. Many AI systems use Retrieval-Augmented Generation (RAG) to improve accuracy. Instead of solely relying on a model’s pre-trained knowledge, RAG allows the system to fetch relevant data from external sources, such as a vector database, using algorithms.