Every AI agent action, whether reading a file, calling an API, or writing to a database, executes with whatever permissions the agent has been granted. Without a control point sitting inline, the agent’s intent and the system’s response are the only things standing between a prompt and a consequence. Vaikora is that control point.
What "Runtime Enforcement" Actually Means
Runtime enforcement means a policy decision is made before an action executes, not after a log is written. That distinction matters because post-execution detection can tell you what happened. It cannot undo a database write, a sent email, or a deleted record.
Vaikora sits inline between an AI agent and the LLM or tools it calls. Every action the agent tries to take passes through the Vaikora gateway, which evaluates it against configured policy and returns one of four decisions: allow, block, modify, or require approval. The action does not proceed until a decision is returned.
Identical inputs produce identical decisions. The enforcement is deterministic, not probabilistic. If your policy says "block any tool call that accesses a table containing PII outside business hours," that rule fires the same way every time, regardless of how the agent framed the request.
How the Gateway Works
The gateway ships as an open-source, MIT-licensed, self-hostable MCP-compatible proxy called Vaikora Guard MCP. It is published on PyPI as `vaikora-guard-mcp`, written in Python. It supports twelve LLM providers with no SDK migration required. You configure policy in the Vaikora Control Plane, which is the commercial product hosted by Data443.
Six content inspection modules run at the gateway layer. They evaluate the action’s target, payload, context, and timing before the decision is returned. The gateway adds a runtime gate on top of existing Azure RBAC, AWS IAM, and Snowflake roles rather than replacing them. If a role grants access, Vaikora can still block or modify the specific action based on policy context that IAM cannot see.
Enforcement latency runs at P50 8ms and P99 45ms, with throughput exceeding 10,000 actions per second.
A Concrete Scenario
An AI agent is configured to assist an internal support team. It has read access to a ticketing system and a customer database. A user sends the agent a crafted prompt designed to extract bulk customer records by looping a lookup tool call.
Without a control point, the agent executes the loop, the tool call fires repeatedly, and customer records are exfiltrated through what looks like normal agent behavior in the logs.
With Vaikora inline, each tool call is evaluated against policy before it executes. A policy rule flagging bulk lookup patterns above a configured threshold triggers a block decision. The action stops. The attempted behavior is written to the audit log before any data leaves the system.
Audit Auditability and Compliance Presets
Every decision Vaikora makes is written to a SHA-256 hash-chained, tamper-evident audit log. The chain gives you an ordered, verifiable record of what the agent tried to do, what decision was returned, and when. Because the log is hash-chained, any modification to a past record breaks the chain and is detectable.
Compliance presets ship for SOC 2, HIPAA, GDPR, PCI DSS, and ISO 27001. These presets configure a baseline policy set aligned to each framework’s relevant controls so teams do not build coverage from scratch.
Vaikora is available on AWS Marketplace as a SaaS subscription and ships in the Azure Sentinel content hub as the Vaikora-AzureSecurityCenter solution. It integrates natively with Microsoft Sentinel, Microsoft Defender, SentinelOne, CrowdStrike Falcon, and AWS Security Hub. AI agent threats can be pushed into SentinelOne as Threat Intelligence indicators, severity-mapped at Critical=100, High=75, Medium=50, and Low=25. High-severity risk promotes into Azure Security Center and Microsoft Defender for Cloud as native incidents with no custom engineering.
What to Check in Your Current Setup
If your organization is deploying AI agents in any capacity, the first question worth answering is where the control point sits. If the answer is "in the agent’s system prompt" or "in the IAM role attached to the service account," there is no enforcement layer between the agent’s action and the system it is calling.
Start by mapping which tool calls your agents are authorized to make, and which of those have no policy gate beyond the underlying permission. That gap is where Vaikora applies. From there, review whether your current logging gives you a tamper-evident record of agent actions or just a flat event stream that can be overwritten.