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Discovering PII Inside InterSystems IRIS

Data privacy regulations such as GDPR, LGPD, and HIPAA demand that organizations know exactly where Personally Identifiable Information (PII) lives inside their databases. Yet in practice, most teams rely on manual inventories, tribal knowledge, or external scanning tools that require data to leave the database engine — a process that itself creates privacy and security risks. This article presents an MVP that takes a different approach: it runs PII detection inside InterSystems IRIS using Embedded Python, analyzing data where it lives and never exporting it to an external process. The result is a lightweight, non-intrusive utility that scans your tables, identifies PII using AI, and produces a structured CSV report — all without data ever leaving the IRIS process. The Problem: PII You Don't Know You Have Organizations today face a painful blind spot. A typical IRIS instance may contain hundreds of tables across dozens of schemas, some holding decades of accumulated data. Columns named ContactInfo , Notes , or Description might silently contain social security numbers, email addresses, or government IDs — sometimes intentionally, sometimes as a side effect of free-text fields that capture whatever users type in. Traditional approaches to PII discovery share a common flaw: they require data extraction. You export samples, send them to an external service, or pipe them through a standalone tool. Every step in that pipeline is an additional attack surface and a potential compliance violation. The principle of data sovereignty — keeping data within its jurisdiction and under controlled access — suggests a better path: bring the analysis to the data, not the data to the analysis. This is not just a technical preference; it is a governance requirement: GDPR (EU) — Article 28 requires that any processing of personal data by a third-party processor be governed by a binding contract covering subject-matter, duration, purpose, data types, and obligations [ Art. 28 GDPR ]. Art

2026-06-16 原文 →
AI 资讯

The Cybercab is the lightest, most efficient Tesla ever made

Against all odds, the Tesla Cybercab is in production. And while Elon Musk's company may not have a very coherent plan for the tiny, autonomous two-seater, it's still taking the necessary steps to certify the EV's legitimacy. As such, Tesla recently filed paperwork with the Environmental Protection Agency that reveal many of the Cybercab's specs, […]

2026-06-16 原文 →
AI 资讯

Pour one out for Roku City

By this time next year, Fox Corporation CEO Lachlan Murdoch intends to have added Roku to his already expansive media empire. Should the acquisition go through, Fox will gain control of Roku's modest library of original programming, and the newly combined company will become "the third-largest player in U.S. television" in terms of viewing share. […]

2026-06-16 原文 →
AI 资讯

I built a storage engine in Go (from scratch without any AI), here's the entire process documented.

I spent the last 2 months building a storage engine from scratch understand how storage engines actually work. To respect the rules of this subreddit, we will not discuss the features ør benchmarks, for that you should read the repo yourself. To understand how I came up with the bits and pieces to put together this project, follow along. So here's my entire journey. Two things: - I built this project because of a recent interest in low-level and systems programming, that combined with my general affinity towards stateful systems made this project an obvious choice. - I see a lot of cool projects (on various social platforms) and when I attempt to read their code, it's obvious that it's written by AI. I intend to share my thought process here because I want to spread awareness that it's very much possible to build something like a storage engine using your first principles intuition. ---------------- Let's begin: 1 . Given all the knowledge I had and using first principles thinking, I setup an in-memory KV store but then it's obvious I had to make it persistent. I added persistence by writing a single line to the file every time someone made a PUT request. The file now had a bunch of {key: "hello", value: "world"}\n. So during startup, I would read all these lines and recover everything into in-memory. 2 . For me, at this point that's all the upfront knowledge I had. So I asked some very basic questions: > How would I recover the entire file into memory on startup? At some point it just wouldn't be possible because the file is growing unbounded. This means that I must not load everything in-memory and instead access the file directly > But then if I read every line top to bottom on every GET then my latency would be literally obliterated? This means I must somehow efficiently query the file. I came up with a solution, I created files based on alphabets, all keys with prefix A will end up in file A, all keys with prefix B will end up in file B and so on. By first prin

2026-06-16 原文 →