The Blog
Thoughts on privacy, AI, and building tools that put people first.
Grounding AI answers in the real law
A general chatbot guesses plausible text, which is how it invents cases. The fix is to connect the model to the actual law so every answer carries a real citation.
Tokenized vs pseudonymized masking, explained
Adimen Velum masks data two ways before it reaches an AI model. Learn how tokenized and pseudonymized masking differ and which one fits the job you have.
Your prompts can become training data
People assume a chatbot chat is private. Many consumer AI tools may keep what you type, let humans review it, and use it to train. Here is the safe rule.
The hidden risk of cloud PII scrubbers
Many tools promise to strip personal data before it reaches the AI, but they do the work in their own cloud, which means they see your raw data first.
Why one-way redaction breaks your AI answers
Blacking out data keeps it safe but ruins the reply. Reversible masking swaps private details for placeholders, then restores them, so the answer stays useful.
The GDPR fines that should worry every firm using AI
Regulators are issuing huge GDPR fines for mishandling personal data, and AI tools open new ways to leak it. Here is the scale, and the simple fix.
GDPR and AI chatbots: where prompts cross the line
Pasting another person's data into a public chatbot can be unlawful processing or an unlawful transfer under GDPR. Here is where a prompt crosses the line.
What counts as personal data in an AI prompt
A plain guide to spotting personal data hiding in everyday AI prompts, from names and emails to small facts that together identify a real person.
Lawyers are getting sanctioned for using AI
Public chatbots invent fake case law and absorb privileged client facts. Here is how two real risks lead to sanctions, and how grounded AI plus masking prevents both.
The day Samsung banned ChatGPT
How confidential code and meeting notes leaked into ChatGPT at Samsung, why a single prompt cannot be taken back, and the simple fix that prevents it.
Introducing Velum: Privacy-first AI masking
How we built a tool that lets you use any AI without exposing sensitive information, and why privacy by architecture matters.
How we detect PII with zero cloud dependency
A technical look at local PII detection, regex patterns, and why keeping data on-device matters.
Why privacy-first architecture wins
The case for building software that respects user data by design. Lessons from building the Adimen Suite.
The future of local AI processing
Why on-device AI is the next frontier and how we are building for a world where privacy is the default.