Our Story
The Gap We Set Out to Close
The real challenge with AI wasn’t adoption. It was losing sight of how it was being used.
As AI adoption picked up, we started seeing the same pattern across organizations. Teams were rolling out internal tools and setting guidelines, but employees weren’t sticking to just sanctioned systems. They were using ChatGPT, Claude, Gemini, and whatever else helped them move faster. Shadow AI wasn’t an edge case, it became the default behavior. Security teams came to us with a simple problem. They didn’t want to block AI, but were running blind to the data leakage risk. Existing controls weren’t built for this. They relied on structured patterns, rules, and network signals, while AI usage was happening in unstructured prompt conversations where context mattered more than patterns.
Wald was built to give teams that visibility and control back, without slowing anyone down. By operating at the endpoint, we allow organizations to understand AI usage in real time, reduce false positives and negatives through contextual classification, and apply policy where it actually matters. At the same time, giving employees secure access to multiple leading models removes the need to constantly chase the next tool or workaround. The result is simple. A secure AI stack with full observability, where governance becomes practical instead of reactive, and security teams finally have a system that keeps up with how AI is actually used.
