Encrypt data in motion across AI pipelines - including research into computation-friendly encryption for sensitive estates.
SecureDataBridge is the privacy layer between orchestration, retrieval, and model components. Encrypter/decrypter stages sit on those hops, with research into end-to-end and homomorphic-style patterns so sensitive payloads can move through AI workflows without becoming plaintext sprawl. It is how Fusion and CRAFT stay discussable with security teams.
AI pipelines multiply copies of sensitive text across logs, caches, and vendor APIs.
Compliance reviews stall when 'the model needs the data' has no encryption story.
Multi-component systems (orchestrator ↔ garden ↔ tools) need a consistent trust boundary.
→Encrypt on egress / decrypt on ingress between research pipeline stages.
→Pair with Guardrail Engine so policy and cryptography reinforce each other.
→Explore encryption schemes that allow limited computation without full exposure.
→Align with Trust page practices: least privilege, residency, and auditability.
How the pieces fit
BRIDGE ENDPOINTS
Typed channels between Fusion/CRAFT components with key custody separated from app logic.
ENCRYPTER / DECRYPTER
Symmetric stages for data in motion inside the Symbiosis-controlled path.
POLICY HOOKS
Blocks bridge transit when guardrails fail classification or destination policy.
EVIDENCE LOG
Tamper-evident records of transit events for security review - without logging plaintext payloads.
- Which workloads truly need homomorphic patterns vs. strong transport + enclave-style isolation?
- How do we make encrypted pipelines debuggable for operators without undoing the privacy win?
→Makes multi-model orchestration viable for regulated industries.
→Complements Symbiosis Secure and Agent Cloud credential brokering.
LLM FUSION
Route work across models like a specialist team - pick by task, merge outputs, keep guardrails in the path.
CRAFT
Fine-tune with retrieval in the loop - models that learn from your documents, not just generic next-token habit.
SYMBIOSIS VECTORSTORE
Vector storage and search built for RAG-scale retrieval - fast, filterable, and deployable where your data has to live.