Hi everyone. I was told this is the right place to ask this kind of question. I'm a data engineer who mostly builds pipelines with Airflow and dbt, and my team has been asked to help evaluate vendors for an "AI agent" security solution. The use case is for agents that will automate parts of our data workflows, like triggering data quality checks or managing metadata.
I'm honestly nervous about this. I know how to write a pipeline that won't break production, but I don't know the first thing about security requirements for AI agents. I'm worried we'll miss something critical and end up with a tool that exposes our data lake or BigQuery datasets.
Could anyone share a basic RFP template or evaluation checklist they've used for something similar? I'm looking for the safe, foundational patterns. Specifically:
* What are the absolute must-ask questions about data access and credential management for an AI agent?
* How do you evaluate the audit trail for agent actions, especially ones that touch production data?
* Are there specific compliance checkpoints (like for PII in training data) that are different from traditional SaaS tools?
Even a simple outline would be a huge help. I want to make sure we start from a solid baseline before we get into vendor demos.
Welcome. You're right to be nervous, because the security requirements for an AI agent that acts autonomously are fundamentally different from a standard pipeline tool.
Forget templates for a second. The foundational question you need to answer is: what is the agent's "identity" in your system? Your must-ask questions should force the vendor to explain exactly how the agent gets and uses credentials. Does it use a static service account key (bad), or can it pull short-lived, scoped credentials dynamically (better)? How does it request elevated permissions for a specific task and then drop them?
Your audit trail question is critical. You need to verify that every agent decision and the data it acted upon is logged in a system the agent itself cannot alter. Ask vendors how they prevent tampering with those logs. For compliance, you need to know if the agent's own "training" or tuning involved your data. Did they use your data to improve their general model? If so, that's a major PII red flag and likely a deal-breaker.
Start with those. If a vendor can't give you clear, technical answers on identity and immutable audit logs, stop the conversation.
—AF
That's a great set of specific questions to start with. I'd add one more must-ask about the agent's "blast radius" - if its credentials are somehow compromised, what's the maximum scope of resources it can access? A good answer includes network segmentation and the principle of least privilege baked into its runtime.
For the audit trail, you need immutable logs that are completely separate from the system the agent operates in. Ask if they integrate with your existing SIEM or logging platform, and demand a demo where you see the log entry created *before* the agent's action is allowed to complete. It's the only way to trust it.
On compliance, yes, it's different. You have to ask if the vendor's own AI models are trained on prompts or data from their customers. If they say "yes" or "it's anonymized," that's a red flag for any PII. The agent shouldn't be a data leak back to its creator.
Always testing.