AI Governance

Bifrost implements a four-tier hierarchical budget and rate-limit system that gives platform teams precise cost isolation and traffic governance across every tenant, team, and provider. Multi-tenant LLM platforms share infrastructure across many consumers, which means a single design decision about budget scoping or rate limit granularity propagates

LLM governance is the set of technical controls that determine who can call which models, what they can spend, what content is permitted, and what audit evidence the organization can produce. Bifrost enforces all four dimensions at the gateway layer, so platform teams don't need to rebuild these

Bifrost centralizes PII redaction at the gateway layer, giving regulated industries consistent data protection across every LLM provider without per-application enforcement logic. Sensitive Information Disclosure is ranked LLM02:2025 in the OWASP Top 10 for LLM Applications, and the risk surface keeps expanding as AI agents access more organizational

Model allowlists, per-developer budgets, and tool restrictions for AI coding agents are infrastructure problems. Bifrost enforces all three at the gateway layer across every Claude Code and Cursor session. Anthropic's own enterprise deployment data puts the average Claude Code cost at $13 per developer per active day,

Bifrost enforces prompt injection defense at the gateway layer across every LLM provider and MCP tool, with dual-stage input/output guardrails, CEL-based rule targeting, and MCP tool allow-lists that prevent injection-driven tool abuse with no application code changes. OWASP ranks prompt injection as LLM01:2025, the

Bifrost integrates GraySwan Cygnal so teams can define natural-language safety rules in plain English, with violation scoring and prompt-injection detection. Prompt injection ranks as the number one security risk for large language model applications in the OWASP Top 10 for LLM Applications 2025. Most teams respond by writing