The Discoverability Problem: How AI Collapsed Practical Obscurity
Investigators found a spiral notebook in a killer's SUV containing the addresses of 45 officials and a handwritten list of data broker websites with pricing notes. No tradecraft, no expertise, just websites anyone can access. He found and correlated every target by hand. AI does it in a prompt.
In 1989, the Supreme Court recognized that even public information carries a privacy interest when it’s difficult to find and compile. They called this principle practical obscurity, and for decades it held. The effort required to locate and correlate personal information was itself a form of protection.
A $280 billion data broker industry eroded the first barrier by making personal information findable at scale. AI is eroding the second by automating the expertise once required to correlate that information into something actionable. The result is democratized targeting, where the distance between digital exposure and physical threat no longer depends on skill or resources, only intent.
The Discoverability Problem traces a single argument through legal doctrine, intelligence frameworks, and documented cases: findability precedes targetability. When the cost of discovery approaches zero, discoverability itself becomes the critical variable in every risk calculation.
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