How do we preserve customer trust while enabling faster engineering and AI innovation?

Refined Question

Customers are asking harder questions about how their data is used — especially by AI features — at exactly the moment we want to ship those features faster. How do we earn a credible "your data is safe" answer without throttling innovation to get it?

Why This Matters

Trust is now evaluated technically: security questionnaires, DPAs, and enterprise procurement probe how data is actually protected, not how it is described. Vague answers lose deals, and a single misstep with customer data in an AI feature can undo years of trust.

Why CipherStash

CipherStash gives you a concrete, verifiable trust story: field-level encryption with per-value keys, identity-bound decryption, and a cryptographic audit trail — running over your existing stack so the engineering and AI roadmap keeps moving.

This allows:

• Security questionnaires to be answered with specific, verifiable controls
• AI features to ship with sensitive data encrypted by default
• Customers to be offered audit evidence of how their data was accessed
• Engineering velocity and the trust posture to improve together

Key Differentiators

• Cryptographic auditability — a verifiable record of who decrypted what, and when
• Identity-aware decryption — every decryption is bound to the identity behind the request
• Per-value keys via ZeroKMS — keys are derived on demand, never stored
• Searchable encryption — equality, range, and free-text queries over encrypted Postgres fields, with standard indexes
• No re-platforming — works over the Postgres you already run