A county detection team asks an AI agent to propose Athena queries over VPC Flow Logs, then requires analyst review before execution. What AI-assisted investigation pattern is that?

AI-assisted investigation querying on AWS with human review before executing proposed Athena queries lets a county detection team accelerate query authoring over VPC Flow Logs while retaining control of what runs against production analytics. The agent proposes; the analyst validates scope, cost, and safety, then executes. That pattern improves investigation speed without granting unsupervised agents free rein over Athena in municipal environments.

AI-assisted investigation querying on AWS with human review after executing proposed Athena queries fails by concept because auditing results after the fact does not stop expensive, overly broad, or unsafe queries from running against production VPC Flow Logs in the first place. AI-assisted investigation building optimized Athena tables over VPC Flow Logs with human review before use fails because it addresses table design and reuse rather than the decision to run a proposed query, so it places the analyst review gate on table creation instead of query execution. AI-assisted investigation querying CloudTrail logs with human review before executing proposed Athena queries fails because it shifts the review gate to CloudTrail logs instead of VPC Flow Logs, so it does not govern the flow-log queries the team actually needs to run.

Exam caveat: Propose-then-review is the safe AI-assisted Athena investigation pattern. Review after execution, table building with review before use, and reviewing CloudTrail logs instead of flow logs are unsafe or off-target distractors.

Operational check for county detection: configure the agent to emit proposed Athena SQL only, require analyst approval in the investigation UI, deny DROP/DELETE privileges to the investigation role, and test that a proposed flow-log query cannot run until a human approves it in the municipal workflow.