LLM security at scale: How to manage keys, tokens, and config across AI pipelines

AI pipelines are complex systems that necessitate robust secret management to prevent the widespread propagation of sensitive credentials, such as keys and tokens, across various stages of data ingestion, training, and deployment. As AI workflows grow in size and complexity, secret sprawl becomes a significant risk, with credentials often leaking into logs, containers, and configuration files due to rapid development cycles and insufficient security practices. Effective management involves centralizing secrets in a dedicated manager, automating credential rotation, and enforcing runtime injection to ensure that secrets are only accessible when needed. Implementing least privilege access controls by assigning specific credentials to different pipeline stages helps to contain potential breaches. Additionally, continuous monitoring of logs and outputs is crucial for early detection of leaks, enabling quick mitigation. By embedding secret management into the pipeline architecture rather than treating it as an afterthought, organizations can safeguard their AI systems against security vulnerabilities as they scale.