# Secret Name: reference credentials by name, never by value

> Secret Name lets flows reference credentials by name, keeping raw values out of definitions, logs, and exports.

**Category:** Secrets & Credentials
**Author:** NeuralSeek Team · **Published:** June 9, 2026
**Canonical:** https://neuralseek.ai/ai-grounded/secret-name
**Section index:** https://neuralseek.ai/ai-grounded

Secret Name is one of NeuralSeek's Secrets & Credentials guardrails — part of the platform's 118 individually configurable, fully auditable controls. In regulated, high-volume AI, the difference between a system you can trust and one you merely hope works comes down to specific, tunable controls exactly like this one. Here is what Secret Name does, why it matters to the business, and how to set it for your own environment.

## What it actually does

This is a named secret slot that flows reference instead of embedding a credential directly. Workflows point at the name; the value lives in the vault.

## Why business teams care

Referencing secrets by name keeps credentials out of flow definitions, logs, and exports entirely. It's the structural habit that prevents accidental leaks.

## How to tune it in practice

Name secrets clearly and reference them everywhere a credential is needed. Never inline a raw value where a name will do.

## Common failure modes it prevents

Hard-coded credentials and secrets stored in plain sight are a breach waiting to happen. Secret Name closes that gap directly. By making the behavior an explicit, enforced control rather than something left to chance, it converts a latent risk into a managed, observable event — one that surfaces in the audit trail instead of in a customer complaint or a compliance finding.

## Where it fits in the stack

It governs how flows reference and resolve secrets, with values held in vault back-ends rather than on the platform. Because it lives in NeuralSeek's governance layer rather than inside any single model, the control holds identically whether a request routes to OpenAI, Anthropic, Gemini, Llama, Mistral, IBM watsonx, or an in-house model.

## Zero secrets stored on the platform

With six vault back-ends, BYOK/HYOK, automated rotation, and cryptographic erasure on offboarding, credentials stay where they belong — under your control, never the platform's.

> A credential in a config file is a credential already half-leaked.

## The takeaway

Secret Name lets flows reference credentials by name, keeping raw values out of definitions, logs, and exports.

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From NeuralSeek's AI Grounded — practical, web-verified guidance on building governed, grounded enterprise AI. NeuralSeek is the model-agnostic, governed AI platform you own: any LLM (swap with no rebuild), your data in your own tenant (cloud or on-prem), 118 guardrails enforced before any action, one container that runs anywhere.