Map GenAI Failure Modes Using NIST AI 600-1
Explore how to map generative AI failure modes using NIST AI 600-1. Understand risks like hallucinations, fabricated citations, over-reliance, and data leaks. Learn to apply consistent labels and document risks to support safe AI use in secure Department of Defense workflows.
We'll cover the following...
- A NIST AI 600-1 risk scan you can apply to one workflow step
- Tell similar-looking outputs apart by what failed
- Insecure or manipulated outputs are still just outputs until someone forwards them
- Scope the risk focus set to intended use, not tool branding
- What to record so someone else can review the scan
The incident synopsis looks solid. It has crisp headings, confident language, a few citations, and a recommended priority label. That polish is the trap, because a fluent draft can fail in predictable ways while still reading like an experienced analyst wrote it. The common mistake is to treat the output as a single unknown risk called AI, then argue about whether the tool is trustworthy. The more useful move is to treat the synopsis as a work product with failure modes a reviewer can name and check.
The same synopsis can be wrong in ways that land in different places. A single invented hostname might be harmless alone, but it becomes sensitive beside a real ticket number and a location. A plausible citation can be the most dangerous part of the draft, because it invites reliance without verification. Once the draft is forwarded, the team ...