Defenders are deploying AI faster than ever, which means we’re also creating new attack surfaces. Prompt injection, training data poisoning, and model theft are not hypothetical—they’re happening in production systems today.
When we red team AI workloads, we focus on the same fundamentals that work for classic applications:
- Identity and access: assume your model is an API. Enforce least privilege and harden tokens, keys, and data plane controls.
- Input control: normalize and constrain inputs where you can, and instrument the rest. Adversarial payloads should be observable, not invisible.
- Safety breaks: embed deterministic policy checks around generative systems. Treat the model output as untrusted input until proven otherwise.
Good telemetry is the differentiator. If you can’t see how the model was called, what it returned, and what downstream systems consumed, you can’t defend it. Start with a red team, keep shipping guardrails, and keep the loop tight between research and operations.