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Introduction to Relyance AI

What is Relyance AI?

Updated October 20, 2026View as Markdown ↗

Relyance AI builds tools for AI safety and security. Customers use Relyance to secure, govern, and create alignment for every AI feature they build and ship.

The problem

AI writes a growing share of your code, and the code you ship now embeds AI: agents, model calls, RAG pipelines, MCP servers. Each of these can reach data along paths nobody drew in a design review. The paths are non-deterministic: an agent decides at runtime what it calls and what it reads.

The exposure multiplies across three dimensions: AI, data (structured and unstructured), and identities (human and non-human). AI-generated code, agents, and prompts have grown roughly 10x, and so have identities, scopes, and permissions.

What Relyance AI does

AI Security and Governance, which has two components:

  • AI Governance discovers your AI, classifies data, maps access with data flows, closes risky exposure by enforcing policy, and automates governance operations.
  • AI Evals and Runtime Guardrails builds custom evals for every AI feature tailored to your business context, goals, and policies, runs them before every release, and enforces the same rules in production. It stops data exfiltration, prevents prompt injection, and blocks unauthorized actions.

Privacy Automation. Relyance generates ROPAs and data maps from live flows and automates DSRs, assessments, and consent. Your records match production on the day a regulator or customer asks.

Both capabilities are powered by the Data Journeys graph: a live map of where your data lives, how it moves, and which people, services, identities, and AI can reach it. You connect the systems you already run: code, cloud, data platforms, identity, AI providers, SaaS. The graph stays current as they change and your business evolves.

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