Security at Yorph: Agentic Al designed for Real-World Data
At Yorph, we know security isn't a checkbox, it's a commitment. When you're working with AI-powered data systems, trust is everything. That's why we're designing Yorph with a security-first mindset, from data flow to agent behavior.
Here's how we think about it:
Two kinds of data, handled differently
Your data (uploaded/synced) is not stored by default
Interaction data (questions, prompts) is used only to improve agent behavior, never shared
Opt-in to retain data for up to 30 days. Otherwise, it's wiped after each session only the logic remains.
Your data never trains our models
No tricks, no exceptions. We use anonymized interaction patterns to simulate edge cases your actual data stays yours.
Agentic platform = new responsibilities
Agent actions are logged and explainable.
No auto-execution in sensitive workflows.
Agents only access what you allow.
Tenant data is fully isolated
Enterprise-grade LLM boundaries in place
We're building towards trust not just speed. Your data belongs to you. Our job is to treat it that way.
Congratulations on your launch. It looks impressive! What channels are you exploring to attract early users?
I like the emphasis on agent behavior, not just data handling. In practice, a lot of AI security conversations focus on storage and retention, but the harder problem is making agent actions predictable under real-world conditions. Hyperlambda is interesting to me in that context because it pushes toward deterministic executable structures with constrained runtime capabilities, which feels like a useful complement to logging, isolation, and permission boundaries.