How Relyance AI Works

Relyance AI runs on one graph. Data Journeys models the paths to your data: what your code does with data, where that data lives, which identities can reach it, and which AI systems sit in the path. From that graph, Relyance detects risky paths, enforces rules, computes blast radius, and produces audit evidence.

Every output reads from the same graph, so evals, merge checks, runtime guardrails, and privacy records all work from one current picture of your data.

#Connect: what you already run

Each source adds edges the others lack: repos show what your code does with data, cloud and data platforms show where it lives, identity providers show who and what can reach it, AI provider integrations show the models and agents in the path, and SaaS integrations show where data leaves your boundary. Relyance joins all of it into one model. Connector-level detail lives in Integration Features.

#Detect and assess: what the graph computes

#End-to-end lineage

Relyance derives lineage from the code that produces data, through the stores it lands in, to the third parties it leaves through.

#Effective permissions

For every identity, Relyance resolves roles, scopes, and grants down to what that identity can reach. Service accounts and AI agents get the same treatment as people.

#The AI surface

Relyance inventories every agent, model, RAG pipeline, and MCP server with its permission reach and its path to data.

#Context-aware classification

The graph holds the code and business process around a field, so Relyance classifies with that context rather than string patterns in isolation. Classification covers structured and unstructured data and runs at petabyte scale. Relyance watches classified data in motion and blocks misuse and leaks at the enforcement points below.

The graph updates continuously as code merges and systems change. Findings carry the business process they belong to rather than a bare resource ID.

#Govern and enforce: where guardrails run

Before release. Relyance runs evals tailored to your business and each AI use case against every release.

At merge. Relyance runs checks in CI/CD that catch an over-scoped agent or a risky data flow before it ships.

At runtime. Guardrails enforce the same rules the evals tested: they enforce alignment, stop data exfiltration, prevent prompt injection, and block unauthorized actions.

Enforcement reads the same graph as detection, so every finding carries its blast radius: the data, identities, and paths on the affected route. Impact analysis and the fix are one click from the finding.

#One graph, many consumers

The privacy modules run on Data Journeys, so ROPAs and data maps update from live flows instead of point-in-time surveys. When an auditor asks how you know, the answer is the same graph that did the blocking.

#Deployment options

The same product ships three ways. Choose on architecture and data residency, not on features.

Full SaaS. Relyance-hosted and fully managed. Fastest to deploy — nothing to run or maintain — with automatic updates and managed scaling. Best when you are standardizing on SaaS.

Outpost. Runs inside your own cloud, so data and processing stay in your perimeter. For strict data-residency and isolation needs. You own the environment; Relyance operates it.

DirectConnect. Private connectivity to your systems: managed control plane, your data plane. The network path stays private with no public exposure — a balance of fast setup and tight control.