Technical insights and practical guidance on privacy engineering, AI governance, and building secure AI workflows.
Traditional DLP was built to stop data from leaving the network. But when your team is actively sending documents to AI models, the problem is fundamentally different. You need a control layer that enables safe use, not one that blocks everything.
Read articleBanning AI tools doesn't work. Teams find workarounds within days. The real answer is a control layer that lets people use AI while keeping sensitive data out of model inputs.
Read articleYour AI provider's enterprise plan helps, but it doesn't solve your internal control problem. You still need to manage what gets sent, reduce unnecessary data exposure, and maintain audit visibility.
Read articleA practical guide for teams that want to use multiple AI models on real work without exposing client names, financial data, or personal identifiers to external providers.
Read articleBuilding PII detection that works across Turkish, German, French, and 50+ languages. Lessons from training models, testing edge cases, and handling mixed-language documents in production.
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