JLINC Your Data
Deriving trusted business intelligence from agentic workflows is inherently challenging. Relying on AI agents to deliver quality results requires extensive testing, training, re-training, constant monitoring and ultimately, trust.
This is even more cumbersome for organizations or enterprises that want to activate AI with sensitive or regulated data requiring compliance guardrails to operate, such as financial, PHI, or PII. This is where the traceabilty and auditability of inputs and outputs between AI Agents becomes of paramount importance to scale the use of sensitive data safely.
Most workflows focus only on the initial inputs and outputs, or pure observability tools to monitor data moving across agentic systems. However JLINC adds a provable cryptographically signed record of all input and output exchanges across agentic systems to properly trace, and audit critical issues, changes, or misuse.
Using sensitive or regulated data to derive trusted business intelligence requires a reliable enterprise grade compliance framework to satisfy the strictest audits that report on actions between users, LLM’s and MCP tooling within agentic workflows. JLINC allows your company to provide third-party auditors zero-knowledge records that can be leveraged for external validation of data while keeping sensitive data out of the hands of vendors.
In addition to cryptographic signing of data, signatures are made with a key assigned to each system in your AI workflow chain. This means the origin of every set of inputs and outputs can be validated in addition to the integrity of the data exchanged.
We provide many ways to incorporate JLINC into your system. From standardized Docker and Kubernetes deployments to a feature-rich SaaS service, integrating JLINC has never been easier.
