Data Breach Risk
AI-related breaches caused billions in losses in 2023. Protecting sensitive data isn't optional — it's foundational to trust.

Regulatory Pressure
Global regulations like GDPR and Japan's APPI demand secure, auditable data use. Compliance must be built-in, not added later.

Erosion of Trust
When data leaks or is misused, so is public confidence. Rebuilding trust requires transparency and accountability from the start.

Industry-wide Losses
Over $1.7 trillion lost annually to preventable fraud because data stays locked away in silos.

Innovation Stagnation
Drug development delays by 5–10 years due to the lack of shared, high-quality datasets.

Duplicated Investment
Companies and governments spend billions re-solving identical problems independently.

The 2026 Problem
Global supply of open training data is expected to plateau by 2026 — slowing innovation.

Synthetic Data Limitations
Artificial datasets can't fully replace the richness or reliability of real-world signals.

Widening Gap
Data-rich organizations accelerate ahead, while data-poor ones risk being left behind.