AI Privacy Is a Plumbing Problem, Not a Policy Problem
Privacy in AI systems fails in the implementation details -- what gets logged, who can replay prompts, how long artifacts linger. Treat it as infrastructure, not a compliance checkbox.
Privacy coverage in this archive spans 7 posts from Feb 2017 to Apr 2026 and frames privacy as continuous risk reduction instead of one-time policy work. The strongest adjacent threads are compliance, security, and gdpr. Recurring title motifs include gdpr, privacy, ai, and sovereign.
Privacy in AI systems fails in the implementation details -- what gets logged, who can replay prompts, how long artifacts linger. Treat it as infrastructure, not a compliance checkbox.
Local AI is no longer a hobby project. Here's how to set it up properly: provider abstraction, versioned models, evaluation harnesses, and cloud fallback for when local isn't enough.
A personal look back at 2018 -- from GDPR scrambles at the fintech startup to Google for Startups Seoul, Spectre/Meltdown fallout, and the infrastructure shifts that defined the year.
GDPR went live on May 25th. Here's what the first week looked like from inside a fintech company -- the scrambles, the surprises, and the things we got right.
Eleven days before the GDPR deadline, here's the technical implementation work we did at the fintech startup — data mapping, consent storage, erasure pipelines, and the backup problem nobody warns you about.
We're 15 months from GDPR enforcement. Here's the technical checklist I'm working through at the fintech startup — data inventory, consent, deletion, and everything else engineering actually has to build.