<?xml version="1.0" encoding="UTF-8" ?> <rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/"> <channel> <title>credgate</title><description>Keep Aadhaar, PAN and other Indian personal data out of LLM prompts - detection, a policy proxy for Claude Code, Copilot and Ollama, and an audit log. R and Python.</description><link>https://dwarpal.github.io/credgate/</link><atom:link href="https://dwarpal.github.io/credgate/feed_rss_updated.xml" rel="self" type="application/rss+xml" /> <docs>https://github.com/dwarpal/credgate</docs><language>en</language> <pubDate>Tue, 01 Jan 1980 00:00:00 -0000</pubDate> <lastBuildDate>Tue, 01 Jan 1980 00:00:00 -0000</lastBuildDate> <ttl>1440</ttl> <generator>MkDocs RSS plugin - v1.17.9</generator> <image> <url>None</url> <title>credgate</title> <link>https://dwarpal.github.io/credgate/</link> </image> <item> <title>Using Claude Code on Indian bank data without sending a single PAN</title> <author>Bharat Sikka</author> <category>Claude Code</category> <category>Proxy</category> <description>Running Claude Code on Indian customer data, with every PAN, Aadhaar and mobile number tokenised or redacted before it leaves the machine, and an audit trail to prove it.</description> <link>https://dwarpal.github.io/credgate/blog/claude-code-without-a-pan/</link> <pubDate>Tue, 01 Jan 1980 00:00:00 +0000</pubDate> <source url="https://dwarpal.github.io/credgate/feed_rss_updated.xml">credgate</source><guid isPermaLink="true">https://dwarpal.github.io/credgate/blog/claude-code-without-a-pan/</guid> </item> <item> <title>Four Indian PII detectors on three datasets, including ones we did not write</title> <author>Bharat Sikka</author> <author>Aishwarya Bais</author> <category>Benchmarks</category> <category>Detection</category> <description>credgate, MaskFlow, Microsoft Presidio and Kavach PII on two public Indian PII datasets and credgate&#39;s own corpus - where each one is strong, where credgate trails, and what running other people&#39;s data taught us.</description> <link>https://dwarpal.github.io/credgate/blog/indian-pii-benchmarks/</link> <pubDate>Tue, 01 Jan 1980 00:00:00 +0000</pubDate> <source url="https://dwarpal.github.io/credgate/feed_rss_updated.xml">credgate</source><guid isPermaLink="true">https://dwarpal.github.io/credgate/blog/indian-pii-benchmarks/</guid> </item> <item> <title>Redacting Indian personal data in R before it reaches an LLM</title> <author>Bharat Sikka</author> <author>Aishwarya Bais</author> <category>Detection</category> <category>R</category> <description>Find Aadhaar, PAN, mobile numbers and other Indian personal data in R text and data frames, and redact or tokenise it before a prompt reaches a language model - with ellmer, Ollama or any provider.</description> <link>https://dwarpal.github.io/credgate/blog/redacting-indian-pii-in-r/</link> <pubDate>Tue, 01 Jan 1980 00:00:00 +0000</pubDate> <source url="https://dwarpal.github.io/credgate/feed_rss_updated.xml">credgate</source><guid isPermaLink="true">https://dwarpal.github.io/credgate/blog/redacting-indian-pii-in-r/</guid> </item> <item> <title>A Shiny app for InfoSec: which models may see which data</title> <author>Bharat Sikka</author> <author>Aishwarya Bais</author> <category>Policy editor</category> <category>R</category> <description>A Shiny app that lets InfoSec decide which language models may see which personal data, test the decision before saving it, and keep a tamper-evident record of every change - without editing YAML.</description> <link>https://dwarpal.github.io/credgate/blog/shiny-policy-editor/</link> <pubDate>Tue, 01 Jan 1980 00:00:00 +0000</pubDate> <source url="https://dwarpal.github.io/credgate/feed_rss_updated.xml">credgate</source><guid isPermaLink="true">https://dwarpal.github.io/credgate/blog/shiny-policy-editor/</guid> </item> </channel> </rss>