The Evaluation Framework

When a new AI-related security story emerges — a new attack vector, a newly discovered vulnerability, a tool being misused at scale — ask five questions.

First: Is this from a credible source? Security researchers at known institutions carry more weight than a blog post from an unknown source. Major news outlets with technical journalists have editorial standards. CISA advisories are officially verified. A vendor with a product to sell has incentive to exaggerate threats. Learning to distinguish credible sources from noise is the first filter.

Second: How severe is the potential impact? There's a difference between a vulnerability that affects a niche feature and one that compromises user data. Distinguish between "interesting to security researchers" and "actually dangerous to users." Most security stories fall somewhere in the middle.

Third: How likely is this to affect me specifically, given my actual usage patterns? A vulnerability in a feature you don't use is relevant to your security posture, but it doesn't warrant immediate action. A vulnerability in something you use daily does. Knowing your own exposure is as important as understanding the threat.

Fourth: Has anyone else confirmed it? Single-source security claims warrant skepticism until corroborated. This doesn't mean waiting forever. But if only one researcher is talking about something, and major security organizations are silent, that's a signal to monitor rather than to panic.

Fifth: What specific action, if any, is proportionate? This is the most important question. Some threats warrant immediate action: updating a tool you use, changing a password, revoking access. Some warrant monitoring and nothing else right now. Some warrant research before deciding. Most warrant acknowledging the threat and assessing whether it changes your behavior.