Interesting tool! Generating synthetic encrypted data is a smart approach to avoid exposing real data during security testing. For me, the biggest challenge with bypassing detection systems is making the fake data both realistic enough to evade detection while still being entirely synthetic. Ensuring that the data behaves like real-world data (in terms of structure and randomness) without being too predictable is key. How does Veilgen manage the balance between randomness and structure to avoid triggering detection systems? Also, curious if you've considered integrating machine learning models to make the generated data evolve based on specific detection mechanisms over time?