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The realistic build path is two to three engineering quarters of focused work plus an ongoing detection-engineering tax forever. The buy path is a one-line application change. This guide is the cost-of-ownership comparison: what an in-house build actually has to cover, what an AI runtime control product covers out of the box, and the verdict line a buyer can quote.

This is a reference architecture for secure AI development: an LLM application talks to its existing SDK, which routes through an inline AI gateway (Vaikora), which forwards to one of 12 supported LLM providers, while audit and detection events flow into a SIEM and identity is centralized via SAML/SCIM.

AI gateway, AI firewall, and AI proxy are three terms vendors use almost interchangeably for products in the AI security space — but they emphasize different jobs. An AI gateway is a routing and integration layer for LLM traffic; an AI firewall is a deny / block control plane for prompts and responses; an AI proxy is the inline transport that carries either of those jobs.

Can You Enforce AI Security in Real Time Without Breaking Latency? Yes — Vaikora adds about 8 ms at the median and stays under 50 ms at P99, which is well under 1% of a typical LLM round-trip time. This guide breaks down where the 8 ms goes, shows the latency histogram in text, explains the methodology behind the measurements, and addresses the three latency objections platform engineers actually raise.

This guide walks through how the three redaction modes work, shows a before / after redacted-then-restored payload, presents the architecture diagram for the egress block, and explains the metadata-only audit pattern that keeps your audit log out of HIPAA / GDPR / PCI scope.

You can put an OpenAI-compatible gateway in front of an existing application by changing one line. No SDK swap, no client rewrite, no application redeploy beyond the config change. This guide shows how Vaikora's drop-in proxy applies the same security policy across 12 LLM providers with provider fallback routing.

An AI agent control plane is a single inline enforcement layer that applies the same deterministic policy engine, probabilistic risk scoring, and tamper-evident audit log across every AI agent protocol. This guide explains why each protocol's native controls are insufficient on their own, presents Vaikora's protocol-agnostic enforcement architecture.

This is a reference architecture for securing AI agents with an inline proxy layer. The design has five layers — Middleware, AuthN/Z, Interceptor Proxy, Threat Detection and Enforcement, and Audit and Compliance — arranged in fixed order between the agent and the upstream LLM or partner agent.

ACP (Agent Communication Protocol) and ANP (Agent Network Protocol) are the two AI agent protocols most teams encounter after MCP and A2A. This guide defines each acronym on first use, covers their architectures with example payloads, ends with a decision matrix mapping project types to protocol fit, and shows where Vaikora applies a single enforcement layer across both.