The fastest LLM gateway in the world

Kamya Shah

Redacting Sensitive Data in LLM Traffic with Google Model Armor and Bifrost

Redacting Sensitive Data in LLM Traffic with Google Model Armor and Bifrost

Most AI security tooling is built around a binary: a prompt is either allowed through or rejected. That works for an obvious attack, but it is a blunt instrument for the situation enterprises actually hit most often, which is legitimate work that happens to carry sensitive data. A support agent

MCP Audit Logging: Requirements for Enterprise Governance and Compliance

MCP Audit Logging: Requirements for Enterprise Governance and Compliance

MCP adoption inside enterprise AI stacks moved from a single experimental server to fleets of tools wired into production agents in a remarkably short window. As that surface area expanded, three questions stopped being theoretical for platform teams: which tools is an agent allowed to call, who triggered any given

Evaluating LLM Output for Quality, Safety, and Brand Voice with Patronus AI and Bifrost

LLM features in production fail in ways that classic guardrails are not looking for. A coding assistant returns JSON that looks valid and silently breaks the downstream pipeline that parses it. A customer support reply apologizes three times in five sentences and reads as defensive. A marketing draft names a

Bringing Enterprise AI Security to LLMs with CrowdStrike AIDR Guardrails and Bifrost

Secure your AI workflows with CrowdStrike AIDR Guardrails and Bifrost

See how Bifrost and CrowdStrike AIDR secure enterprise LLM traffic: AIDR handles detection, Bifrost enforces policy inline before any prompt reaches a model. Enterprise AI deployments run into the same set of security issues within a few months of going live. A support rep pastes a full customer record into

Enterprise MCP Governance: Access, Audit, and Cost Controls for AI Agents

Enterprise MCP Governance: Access, Audit, and Cost Controls for AI Agents

Enterprise MCP governance comes down to three controls every production AI agent deployment needs: access, audit, and cost. Here is what each one looks like in practice. When a new engineer joins a team, they are not handed unrestricted access to every system the company runs. Their access is scoped