Agentic data engineering is a workflow in which an AI agent is given a defined data objective, performs the necessary steps, checks the results, and adjusts its approach until the task is complete. Instead of a human manually chaining every transform or validation step, the agent can orchestrate data preparation, pipeline changes, and quality checks within set boundaries.
In cyber security, this matters because the agent often has access to valuable data and write privileges in important systems. If permissions are too broad, a compromised or misconfigured agent can expose sensitive records, alter datasets, or publish flawed outputs that poison analytics and downstream automation. Defenses center on least privilege, approval for destructive or external actions, strong logging, sandboxing, and independent validation of outputs. The safest deployments treat the agent as an assistant, not a trusted administrator.