AI Governance

How enterprise AI guardrails at the gateway validate every prompt for injection, PII and credential leakage before it reaches a provider.

TL;DR * An enterprise AI governance framework only counts if its controls run on live traffic, which makes the AI gateway the practical enforcement point. * Five practices cover the operational layer: identity-bound access, pre-spend cost limits, guardrails on prompts and responses, tool authorization, and a tamper-evident audit

TL;DR * AI governance architecture in Bifrost rests on three Enterprise layers: role-based access control (RBAC), data access control (DAC), and access profiles, all centered on virtual keys. * RBAC decides which operations a role can perform on resources such as virtual keys, logs, and guardrails; Bifrost ships Admin (42

TL;DR * Enterprise AI governance controls which teams and applications reach which models, tools, and budgets, and records that access for audit. * In Bifrost, the virtual key is the single control point: it carries provider and model access, MCP tool permissions, budgets, and rate limits for every request made with

An explanation of AI governance covering NIST AI RMF, ISO 42001, the EU AI Act, and where policy becomes an enforced control.

TL;DR * Enterprise AI governance platforms control which teams reach which models, cap spend per consumer, filter sensitive data in prompts and responses, and record audit evidence for compliance reviews. * AWS, Azure, and Google Cloud now offer token quotas, guardrails, and MCP tool governance, but each assembles them from several