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Your AI agent can read a file, call an API, and exfiltrate data before any alert fires. Vaikora stops it at the gate.

Product Updates · Nov 5, 2026 · 4 min read

An AI agent operating inside your environment does not need elevated privileges to cause a serious incident. It needs only the permissions your existing IAM and RBAC roles already grant, and the absence of a control that evaluates each action before it runs.

That gap is where Vaikora sits.

What "runtime enforcement" actually means here

Runtime enforcement means the policy decision happens before execution, not after. Vaikora places a gateway inline between an AI agent and every LLM call or tool invocation it makes. Before the action executes, Vaikora evaluates it against deterministic policy and returns one of four decisions: allow, block, modify, or require approval.

Deterministic means identical input always produces identical output. There is no probabilistic scoring that lets the same prompt through on Monday and blocks it on Tuesday. The policy is a rule, not a suggestion.

The gateway adds a P50 latency of 8ms and P99 of 45ms. At that level, enforcement is invisible to the agent and to users. The gateway sustains 10,000 or more enforcement decisions per second, so throughput is not a constraint in any realistic deployment.

Where your existing controls fall short

Azure RBAC, AWS IAM, and Snowflake roles control what an identity can access. They do not evaluate what an agent is doing with that access at the moment it acts. An agent with read access to a data store can use that access legitimately or can exfiltrate records to an external endpoint. IAM sees the same permission either way.

Vaikora adds a gate on top of those existing controls rather than replacing them. If a policy rule says "block any tool call that sends customer PII to an external URL," that rule fires before the HTTP call leaves the agent, regardless of what IAM says about the identity.

A worked example: an agent has permission to query a customer database and to call an external enrichment API. Without a runtime gate, those two permissions compose freely. The agent can query the database, include the results in the enrichment API call, and nothing in IAM or RBAC raises a flag. With Vaikora inline, the tool call to the enrichment API is inspected before it runs. If the payload contains PII that the policy marks as restricted for external transmission, Vaikora blocks the call and writes the decision to the audit log.

Policy, compliance presets, and audit

Vaikora ships compliance presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001. Each preset is a bundle of policy rules tuned to that framework's control requirements. You can apply a preset as a starting point and extend it with organization-specific rules.

Every enforcement decision, allow or block, is written to a SHA-256 hash-chained audit log. The chain is tamper-evident: any modification to a past record breaks the chain and is detectable. That log is what gives an audit or investigation a reliable, ordered record of exactly what each agent did and what the enforcement layer decided.

Detection accuracy in testing is 99.9%. That matters because a high false-positive rate on a blocking control will cause teams to disable or bypass the control. A gate that fires accurately is one that stays in the path.

How Vaikora connects to the rest of your security stack

Vaikora integrates natively with Microsoft Sentinel, Microsoft Defender, SentinelOne, CrowdStrike Falcon, and AWS Security Hub. High-severity AI agent threats are promoted into Azure Security Center and Microsoft Defender for Cloud as native incidents, with no custom engineering. Threats are pushed into SentinelOne as Threat Intelligence indicators with severity mapped numerically: Critical at 100, High at 75, Medium at 50, Low at 25.

The gateway supports 12 LLM providers with no SDK migration. If your agents call OpenAI today and you switch providers next quarter, the enforcement layer does not change.

Vaikora Guard MCP is the open-source, MIT-licensed, self-hostable component, published on PyPI as `vaikora-guard-mcp`. It is MCP-compatible and written in Python. The commercial control plane, hosted by Data443, adds centralized policy management, the audit chain, compliance presets, and the SIEM integrations. Vaikora is also available on AWS Marketplace and in the Azure Sentinel content hub as the Vaikora-AzureSecurityCenter solution.

What to check next

Start with one agent that has access to sensitive data or external tool calls. Map the tool calls it makes today and ask which of them are evaluated before execution rather than logged after. If the answer is none, that is where to place the enforcement gate first.

Review Vaikora Guard MCP on PyPI (`pip install vaikora-guard-mcp`) to see the configuration surface before committing to a deployment model. The compliance preset list will tell you quickly whether the frameworks you need are covered out of the box.

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