Working with SOC 2 certified vendors, such as MOSTLY AI, assures the customers that the vendor follows consistent security practices and are able to keep customers’ valuable data always safe and protected through the implementation of standardized controls as defined in the AICPA Trust Service Principles framework.

Having an environmentally responsible business in 2021 leads to healthy, productive, and forward-thinking employees without jeopardizing growth and profitability. Companies like ours are the future, and we believe that by implementing environmentally conscious efforts early on, we can create long-term sustainability.

AI governance requires access to models just as well as to data. However, sensitive training data cannot be shared further. MOSTLY AI’s highly representative synthetic data serves as a drop-in replacement to enable model documentation, model validation, as well as model certification.

Our featured guest, Belinda Enoma, talks about areas for algorithmic compliance and challenges that need to be continuously addressed as models are created.

Kerem Erdem, a seasoned engineer, and sailor joins MOSTLY AI as CTO. His insights from the sea will be valuable assets.

In this webinar, you will learn about the options for cross-border data sharing in 2021 and how synthetic data makes the process easy and privacy-compliant.

The world is changing, and anonymous data is not anonymous anymore. Classic anonymization methods are no longer the fail-safe tools they once were. It's time to upgrade to synthetic data!

This is the second part of our blog post series on truly anonymous synthetic data. While Part I introduced the fundamental challenge of true anonymization, this part will detail the technical possibilities of establishing the privacy and thus safety of synthetic data.

In this two-part series, we are taking a look at how data privacy legislation evolved and explore how technology can keep up with the demands of regulation.

Not all synthetic data is created equal and in particular, synthetic data generation today is very different from what it was 5 years ago. Let’s take a look at different methods of synthetic data generation from the most rudimental forms to the state-of-the-art methods to see how far the technology has advanced!