AI Governance

TL;DR * AI guardrails are policy controls that inspect, filter, and block LLM inputs and outputs; implementing them at the gateway layer applies one policy to every request instead of scattering checks across application code. * Prompt injection ranks as the number one risk in the OWASP Top 10 for LLM

TL;DR * Version-controlled configuration, least-privilege access, per-team budgets, multi-provider fallback, and audit logging are the core claude code best practices for enterprise teams in 2026. * Routing Anthropic Claude Code through an AI gateway like Bifrost adds only 11 microseconds of overhead per request at 5,000

Bifrost enforces enterprise AI guardrails at the gateway: policy rules, PII and secrets redaction, and audit evidence across every model and agent tool call. Enterprise AI guardrails are runtime controls that validate LLM inputs and outputs against company policy before a request reaches a model provider and before a response

Bifrost leads this roundup of enterprise AI governance platforms for LLM traffic and access control. Bifrost is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. Enterprise teams now route production traffic to multiple LLM providers, and most have no

Bifrost applies LLM guardrails at the gateway layer, enforcing PII redaction, prompt-injection defense, and policy checks centrally for enterprise AI security. Large language model applications now process prompts and responses that can contain personal data, API credentials, and adversarial instructions, and most of that traffic reaches providers through application

Enterprise AI governance is enforced on the request path, not in a policy doc. How Bifrost uses virtual keys, budgets, and RBAC to govern LLM and MCP traffic. A shared provider API key pasted into three services produces one invoice, no attribution, and no way to cut off a single