What Is AI Governance? Frameworks and Enforcement
An explanation of AI governance covering NIST AI RMF, ISO 42001, the EU AI Act, and where policy becomes an enforced control.
Latest
Governing Enterprise AI and Data: The Leading Platforms
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
Policy-Based Governance at the Gateway: One Control Plane for Every AI Call
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
AI Guardrails at the Gateway: Catching Hallucinations on Every Model Response
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
What Is an AI Gateway? The Control Plane for Enterprise LLM Traffic
An explanation of what an AI gateway is, the seven capabilities that define one, and how it differs from a traditional API gateway.
How to Track LLM Usage and Spend by Team
TL;DR
* Provider invoices group spend by API key or project, so tracking LLM usage by team requires attribution upstream of the provider.
* Bifrost virtual keys attach to one team or one customer, and every request is priced and logged with that team identity.
* Bifrost Prometheus metrics carry team_id,
Understanding LLM Access Control
TL;DR
* LLM access control decides which users and applications can reach which models, providers, tools, and data, and how much of each they may consume.
* Access control for LLMs differs from the traditional kind because every call carries a variable cost, most calls leave the network boundary, and models