Bring AI to the data you can't move
Your most regulated records sit on the wrong side of the AI line because every model has to decrypt to compute.
For financial services
AI on the data your regulators write rules about.
Trading IP, customer PII, transaction history. Your most valuable records are also the ones every regulator audits. Most AI workflows still ask you to choose between using them and complying.
Encryption, identity, policy, and proof on every model your bank touches.
Use cases for this audience
8 scenarios
Encrypted context, memory, and vector DB for every model and agent
Knowledge bases, vector stores, conversation history, agent memory. Every piece of context an AI reaches for is plaintext the moment it's loaded.
Joint analysis with partners who can't share their data
Reinsurers, banks, hospitals, and governments want shared intelligence but can't legally pool the underlying records.
Fine-tune on customer data without ever seeing it
Domain models need real customer data to be useful. Training in plaintext means you hold what you swore you wouldn't.
Show the auditor what your AI did, and why
"Trust us, we logged it" doesn't pass a regulator anymore. You need signed, tamper-evident proof for every decision.
Answer security questionnaires with evidence, not adjectives
Sales cycles stall on 400-row spreadsheets your team rewrites by hand every quarter.
Let AI review code that never leaves your machine
To use an AI code tool you usually hand it your source, secrets, and proprietary logic. And trust a retention promise.
Stop your model from being cloned out the back door
Distillation attacks turn your API into someone else's competing model in weeks.
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See encrypted AI security in action.
FHE-native inference. Runtime agent guardrails. Continuous red teaming. One platform. Book a working session with the team.