How to Redefine AI Security with the PetalGuard Approach

In 2023, genetic testing giant 23andMe suffered a massive breach, exposing sensitive
ancestral and health data of millions. Fast forward to today, and the company is filing for
bankruptcy.

That's not just a cautionary tale, it's a loud wake-up call for industries that rely on
sensitive data.

When data breaches happen in high-stakes sectors like healthcare, finance, or
government, the impact goes far beyond fines and downtime. Trust crumbles.
Reputations erode. Entire businesses collapse. It's no wonder that many organizations
approach artificial intelligence (AI) projects with hesitation. After all, AI models are only
as good as the data they learn from, and the most valuable data often comes with the
highest privacy risks. 

Federated learning is a new promising approach that minimizes data breach leaks and
improves compliance with privacy laws. Federated learning lets multiple parties
collaboratively train a shared AI model without pooling all their raw data in one place.
Each participant compute updates locally, and only the model updates are shared with a
central server.

However, even though it sounds private, there's a catch. That central server still gets to
see everyone's updates, and if it is compromised, misconfigured, or simply curious, it
can reconstruct sensitive insights. It's a single point of failure, and in high-risk industries.
It's just not good enough.

At the Technology Innovation Institute (TII), we have gone a step further to provide real
privacy and data protection by launching PetalGuard. PetalGuard is a federated
learning framework built with privacy at its core. Unlike traditional systems that rely on
one central server to aggregate updates, PetalGuard uses Multi-Party Computation
(MPC), which enables secure aggregation across multiple independent servers. 

Here's why that matters. First, there’s no single point of trust. Model updates are
encrypted and split across several servers, so no one server sees the whole picture.
Second, there’s built-in robustness. Even if one server is compromised, the system
stays secure. Third, true privacy is guaranteed since updates are aggregated without
any individual contribution being revealed. Not to other participants, not to the
aggregation servers, not to anyone. 

With PetalGuard, companies can collaborate on AI models using sensitive data without
ever exposing that data or the updates derived from it. It's a game-changer for
industries that want the power of AI without the privacy risks such as financial services,
legal and healthcare. 

Privacy shouldn't be a trade-off. As the AI era accelerates, secure infrastructure isn't just
a nice-to-have, it's essential. PetalGuard shows that we can innovate responsibly by
removing unnecessary points of failure and keeping privacy front and center.

If your organization works with sensitive data, the question isn't whether you can use AI.
It's whether you can do it securely.

With PetalGuard, the answer is yes.