NEW! Data443 Acquires Vaikora – Real-Time AI Runtime Control & Enforcement for AI Agent
AI runtime control is the practice of intercepting AI agent and LLM actions before they execute, evaluating each action against deterministic policy in real time, and returning an allow, allow-with-modification, log, or block decision. It is the action-side equivalent of an application firewall, applied to AI rather than network traffic.
Agentic AI systems are no longer experimental. AI agents send emails, write to databases, call APIs, post to Slack, and execute shell commands inside production workflows. Detection-only tooling produces a record after the agent has already acted, which is helpful for audit but not for prevention. Runtime control closes the gap between intent and execution.
The control point also changes what regulators and auditors can ask for. A SOC 2 or HIPAA auditor reviewing AI-assisted workflows wants evidence that risky actions were prevented, not only that they were logged. A runtime control layer produces that evidence by signing each decision into an append-only chain.
AI runtime control is different from AI observability, which records what AI systems did after the fact. It overlaps with AI gateways like LiteLLM or Portkey, which focus on routing, fallback, and caching across LLM providers. Runtime control sits in front of those gateways or replaces the gateway role where security policy is the priority.
An AI customer support agent attempts to email a refund confirmation containing a customer credit card number. The runtime control layer detects the PII in the proposed message, redacts it, and lets the agent send the modified version. A second example: an autonomous coding agent proposes a database migration that would drop a production table. The runtime control layer holds the action in a wait state, sends an approval request to the SOC channel in Slack, and only proceeds after a human signs off.
Observability records what an AI system did. Runtime control evaluates what the agent is about to do and decides whether the action proceeds. Observability produces logs. Runtime control produces decisions.
The action the agent is proposing (an API call, an email, a tool invocation, a database write, an LLM message), plus the context the agent is operating in (user identity, data classification, system prompt, prior actions). The engine returns a verdict in milliseconds.
Yes. The Vaikora gateway ships adapters for OpenAI, Anthropic, Google Gemini, and OpenRouter, and supports MCP and A2A at the protocol layer. The policy engine is provider-agnostic.
Sub-500ms p95 enforcement at the policy engine. Most enterprise deployments measure single-digit milliseconds. The latency floor is set by the network hop, not the policy evaluation itself.
Last updated: 2026-05-20.
