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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

TL;DR * Gateway-enforced AI policy turns written rules for access, spend, routing, and content safety into checks that run on every model and tool request. * A Cloud Security Alliance survey published in April 2026 found that 82% of organizations had discovered previously unknown AI agents in their environments in

TL;DR * AI guardrails at the gateway validate every model response before it is returned, so one policy covers every application and provider. * Bifrost guardrail rules use CEL expressions on request metadata (model, provider, headers, team, virtual key) and an apply_to of input, output, or both; linked profiles inspect