AI is still a workload: A practical guide to securing AI workloads

AI workloads, while often perceived as complex and magical, require the same security measures as traditional IT infrastructures, especially given their vulnerability to threats such as data leaks, model poisoning, and unauthorized access. The article outlines the security risks associated with different AI applications, such as large language models (LLMs) and company-specific models, and offers mitigation strategies including access control, model and data security, and threat management. It emphasizes the importance of educating users on best practices, securing credentials, filtering user inputs, and protecting against attacks like LLMJacking and denial of service. Furthermore, the text highlights the necessity of compliance with data protection laws such as GDPR and CCPA and recommends utilizing security benchmarks to identify and address vulnerabilities. Ultimately, the guide draws parallels between AI workload security and conventional security practices, encouraging a comprehensive approach to safeguard AI infrastructures.