As Large Language Models (LLMs) become integral to enterprise systems, they pose significant risks of sensitive information disclosure, which traditional security frameworks are not equipped to handle. This occurs when LLMs memorize and reconstruct sensitive data, such as personal identifiers, proprietary knowledge, or customer information, during inference processes. The OWASP LLM Top 10 framework offers guidance on mitigating these vulnerabilities, emphasizing the importance of robust anonymization, alignment, and procedural controls during model training and fine-tuning. Risks are amplified by factors like data duplication, model size, and deployment context, particularly in multi-tenant environments. Defense strategies include conducting due diligence on third-party models, implementing data sanitization and access controls, and employing red team assessments to evaluate potential security breaches. Ultimately, securing LLMs requires a comprehensive approach that aligns with broader AI security practices to prevent unauthorized data exposure.