2 years of lessons part 1
First, the bad news. It’s worse than we imagined. Since our inception, we’ve identified dozens of hacked devices affecting a broad range of our users. We expected to find that the less prominent brands are most vulnerable. And they certainly are. But we were surprised to see how many top-of-the-line brands were hacked, sending users’ data to places it shouldn’t have gone.
Now, the good news. We are more convinced than ever that our solution works. Our approach, which uses pattern-recognizing AI on crowdsourced data, covers the gaps left open by porous firewalls, sloppily engineered devices, and well-intentioned family members and guests who unknowingly expose our privacy and security to cyber criminals.
We’re running our beta in hundreds of homes. It’s a large enough sample to draw some solid inferences. Here’s a recap of some of the things we’re noticing:
The number of devices in people’s homes keeps increasing month after month after month. In 2023, our median user has 32 connected devices in their homes, an increase of 33% over 2021.
People underestimate the number of devices they have by 50%. For example, they forget about that wireless printer, smart vacuum cleaner, or stationary bike.
Users usually can’t tell if their devices have been hacked. That’s because those devices also continue to do the functions they were designed for.
Approximately 10% of our users have hacked devices each year. One in ten may not sound like a large number, until it happens to you. (The good news is we were able help users in time to fix, block, or rectify their situation.)
Hacked devices include those built and distributed by major manufacturers, including computers, electric cars, entertainment devices, security doorbells, and video cameras.
How were these devices hacked? Some were missing key updates to their operating systems, some were poorly engineered, and some allowed users’ data to flow to unexpected and dangerous places. By quickly notifying our users in a timely fashion, we empowered them to take back control of their data security.
These are dangerous times, and the threats are coming from multiple directions. Conventional (in)security solutions aren’t cutting it. Bigger walls and deeper moats work for a while, until hackers figure out how to climb over or burrow under.
Our approach is superior. We don’t have to issue new releases of software or engage in futile cat and mouse games. Instead, we use the power of crowdsourcing and the brilliance of AI, machine learning, and statistical techniques to recognize aberrations in device behaviors and their data transmission patterns. And the power of our solution expands automatically: as new smart device products come on the market, we very quickly learn their behaviors and keep them protected too.