Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Why Data Masking Breaks AI Workflows (And What Works Instead)

What if masking sensitive data actually broke your AI application? Most AI security strategies start with a simple rule: mask sensitive information before it reaches the LLM. But real AI workflows are more complex. An AI agent might need to search a CRM, retrieve a contract, check account details, call external tools, and generate a response. Each step may require a different level of access to sensitive data.

PDF Data Masking: The Missing Security Step in Document AI

PDF data masking is the security step most document AI stacks skip. Most companies building RAG pipelines, agentic workflows, or document intelligence systems have secured the model, the vector store, and the API. They haven’t secured the step before any of that: the data inside the document itself, before it enters the pipeline. That gap shows up everywhere documents meet AI.