Policy decisions for AI agents are almost always applied after the fact, through logs, alerts, or incident reviews. By that point, the action has already executed. Vaikora is built on the opposite premise: the enforcement decision happens before the agent is allowed to act, every time, with no exceptions based on load or latency.
What "Runtime Control" Actually Means
Runtime control means the enforcement layer sits inline. The agent sends a request to call a tool, write to a database, send an API call, or prompt an LLM. That request hits Vaikora first. Vaikora evaluates it against policy, then returns one of four decisions: allow, block, modify, or require-approval. Only after that decision does execution proceed.
This is different from observability or monitoring, which record what happened. Those approaches are useful for investigation. They do not prevent the action.
The practical difference matters most for irreversible operations: deleting records, sending emails to external addresses, writing to production databases, or calling third-party APIs with sensitive payloads. If the decision comes after execution, the damage is already done.
How Vaikora Fits Into an Existing Stack
Vaikora sits between the AI agent and whatever it calls: an LLM, an MCP tool, an API endpoint. It supports 12 LLM providers with no SDK migration required. You do not rewrite the agent. You route traffic through the enforcement layer.
The open-source component, Vaikora Guard MCP, ships as an MIT-licensed, self-hostable MCP-compatible proxy. That handles tool-call interception at the protocol level. The enterprise control plane adds centralized policy management, compliance presets, and SIEM integration.
Enforcement latency at P50 is 8ms, and at P99 is 45ms. At 10,000 or more actions per second, the throughput does not degrade policy evaluation. Detection accuracy in testing is 99.9%.
Policy Configuration and Compliance Presets
Policy in Vaikora is declarative. You define what agent identities are allowed to do, under what conditions, with what data classifications. Rules can reference the content of a request, the calling agent’s identity, the target tool, and the sensitivity of data in scope.
Vaikora ships with presets mapped to HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001. These presets give a compliance team a starting configuration that reflects what each framework actually requires about data handling and access control. They are starting points, not a substitute for reviewing your specific obligations.
A concrete example: an agent authorized to read customer records in a CRM should not be allowed to export those records to an external endpoint. A GDPR preset can enforce that boundary by blocking export operations that include personal data fields, without modifying the agent code itself.
SIEM Integration and Threat Signal Routing
Enforcement decisions generate telemetry. Vaikora routes that telemetry into the platforms your security operations team already works in, with no custom engineering.
- Microsoft Sentinel, Microsoft Defender for Cloud, and Azure Security Center receive high-severity AI agent risk as native incidents. The signal appears in the same queue as other incidents.
- CrowdStrike Falcon and SentinelOne receive AI agent threats as Threat Intelligence indicators. Severity mapping is fixed: Critical at 100, High at 75, Medium at 50, Low at 25.
- AWS Security Hub receives findings through the same integration layer.
This matters because a blocked agent action that looks like a policy test in isolation may look like a coordinated exfiltration attempt in context. Routing AI enforcement telemetry into the SIEM allows analysts to correlate it with other signals.
Auditability and the Audit Log
Every enforcement decision Vaikora makes is written to a SHA-256 hash-chained, tamper-evident audit log. Hash chaining means you can verify that no entry has been modified or deleted after the fact.
This is the property that makes Vaikora useful to compliance and legal teams, not only security teams. When a regulator or auditor asks what the AI agent did, when, and under what policy, the audit log provides a verifiable record. The record covers allowed actions, blocked actions, modified payloads, and approval-gated actions that were escalated to a human reviewer.
What to Check Next
If you are running AI agents against production tools today, the first question to answer is whether any enforcement layer sits inline before those tool calls execute. If the answer is no, or if the answer is "we have logging," that is the gap to close first.
Review the Vaikora documentation to see how the Guard MCP proxy intercepts MCP tool calls and how the policy rule schema maps to your existing access control model. If you have HIPAA or PCI-DSS obligations, compare the relevant preset against your current agent permissions before your next compliance review.