AI Governance

A guide to the five principles of an enterprise AI governance framework and the operational controls that make each one real in production.

Five approaches to governing coding agents compared, from a dedicated AI gateway and open-source proxies to agent-native admin controls and endpoint governance.

A guide to mapping an enterprise AI governance framework onto gateway controls, from virtual keys and budgets to guardrails and access profiles.

TL;DR * AI guardrails are policy checks that validate model inputs and outputs in real time, blocking or redacting prompt injection, PII leakage, credential exposure, and unsafe content before it reaches a model or a user. * Implementing guardrails at the gateway layer applies one policy set to every model and

AI guardrails are runtime policy checks on LLM inputs and outputs. Bifrost enforces them at the gateway layer, across every model call and every tool execution. AI guardrails are runtime policy controls that inspect what an application sends to a language model and what the model sends back, then allow,

A complete guide to AI governance for enterprise LLM deployments, covering access control, cost management, and compliance enforced at the gateway layer. TL;DR * AI governance for enterprise LLM deployments has moved from voluntary best practice to an operational requirement, driven by shadow AI exposure, runaway token spend, and regulations