Mithril x Tramscribe: Confidential LLMs for medical voice notes analysis
How we partnered with Tramscribe to leverage LLMs deal with Medical voice notes analysis
Mithril Security Raised €1.2 Million to Protect LLM Users' Data
With BlindBox, you can use Large Language Models without any intermediary or model owner seeing the data sent to the models. This type of solution is critical today, as the newfound ease-of-use of generative AI (GPT4, MidJourney, GitHub Copilot…) is already revolutionizing the tech industry.
Announcing BlindBox, a secure infrastructure tooling to deploy LLMs, available on Confidential containers on Azure Container Instances
We are excited to introduce BlindBox, our latest open-source solution designed to enhance SaaS deployment security. Our tooling enables developers to wrap any Docker image with isolation layers and deploy them inside Confidential Containers.
BlindAI passes an independent security audit by Quarkslab
We take security and open-source data privacy seriously at Mithril Security. So we're very proud that our historical confidential computing solution, BlindAI, was successfully audited by Quarkslab!
Identifying a critical attestation bypass vulnerability in Apache Teaclave
This vulnerability can be used to mount a Man in the Middle attack. We found a fix, that Teaclave implemented following the release of this article.
Mithril x Avian: Zero Trust Digital Forensics and eDiscovery
How we partnered with Avian to deploy sensitive Forensic services thanks to Zero Trust Elastic search.
Rust: How We Built a Privacy Framework for Data Science
We could have built our privacy framework BastionLab in any language - Python for example which is data science’s beloved. But we chose Rust because of its efficiency and security features. Here are the reasons why we loved doing so, but also some challenges we encountered along the way.
Data Science: The Short Guide to Privacy Technologies
If you’re wondering what the benefits and weaknesses of differential privacy, confidential computing, federated learning, etc are, and how they can be combined to improve artificial intelligence and data privacy, you’ve come to the right place.
How Python Data Science Libraries Can Be Hijacked (and What You Can Do About It)
Hackers can easily hijack the data science libraries you use everyday and get full access to the datasets you are working with. Data owners need tools to prevent it from happening.
Jupyter Notebooks Are Not Made for Sensitive Data Science Collaboration
When collaborating remotely on sensitive data, their usually amazing interactivity and flexibility need safeguards, or whole datasets can be extracted in a few lines of code.
Introducing BastionLab - A simple privacy framework for data science collaboration
BastionLab is a simple privacy framework for data science collaboration. It lets data owners protect the privacy of their datasets and enforces that only privacy-friendly operations are allowed on the data and anonymized outputs are shown to the data scientist.
Our roadmap to build a simple privacy toolkit for data science collaboration
One year and a half later, Mithril Security’s roadmap has transformed significantly, but our initial goal stayed the same: democratizing privacy in data science.