The hardest part of securing an AI agent pipeline is that most controls arrive too late. The agent has already called the tool, written the record, or sent the request by the time a log entry appears. Vaikora Guard MCP is now MIT-licensed and self-hostable, which means any team can place a deterministic policy check inline, before every tool call, without waiting for a vendor deployment cycle.
What Vaikora Guard MCP Actually Does
Vaikora Guard MCP is an MCP-compatible proxy. It sits between an AI agent and the LLM or tools it calls. Every action the agent attempts passes through the proxy before execution. The proxy applies a policy decision, either allow, block, modify, or require-approval, and then either permits the call or stops it. Nothing executes first and gets reviewed second.
That ordering matters. A "require-approval" decision means a human sees the proposed action and approves or rejects it before the tool call fires. A "block" decision means the action never reaches the target system. Detection after the fact handles neither of these cases.
The proxy writes every decision to a SHA-256 hash-chained, tamper-evident audit log. That log is the record of what the agent was allowed to do, what it was stopped from doing, and when. Regulators and internal auditors can read it without additional tooling.
Why Open Source Changes the Deployment Calculus
Security teams evaluating AI agent controls have historically faced a choice between SaaS platforms with opaque internals and custom middleware that requires ongoing engineering investment. An MIT-licensed, self-hostable proxy is a third option.
Self-hosting means the proxy runs inside your own infrastructure. Sensitive prompts, tool parameters, and policy decisions do not transit a third-party network. For teams operating under HIPAA, PCI-DSS, or GDPR constraints, that residency question is often the first one a compliance officer asks.
MIT licensing means the code is inspectable. A security engineer can read the policy evaluation logic, verify how the audit log is constructed, and confirm that hash chaining works as documented. Trust is grounded in the code, not in a vendor’s word.
What You Can Enforce From Day One
The proxy ships with compliance presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001. A preset is a starting policy configuration aligned to the data handling and access control requirements of that framework. Teams can extend these configurations or write their own policies from scratch.
The policy model is deterministic. Given the same agent action and the same policy configuration, the proxy produces the same decision every time. There is no probabilistic classification layer deciding whether a tool call looks risky. The rule either matches or it does not.
A concrete example: an agent with access to a patient record database attempts a bulk read that would return more than a threshold number of records. The policy rule matches on the tool name, the operation type, and the row count parameter. The proxy blocks the call and writes the decision to the audit log before a single record leaves the database. The agent receives a rejection. The database receives nothing.
Performance Considerations for Production
One objection teams raise about inline enforcement is latency. Adding a synchronous check to every tool call introduces a round trip. Vaikora Guard MCP runs at P50 8ms and P99 45ms enforcement latency, and the proxy handles more than 10,000 actions per second at throughput. For most agent workloads, those numbers place the enforcement overhead below the threshold where it affects agent behavior in any observable way.
Detection accuracy in testing is 99.9%. That figure applies to the proxy’s ability to match actions against defined policies, not to a probabilistic detection model. The policy either fires or it does not, and the accuracy metric reflects how reliably that evaluation runs at scale.
Integration With the Enterprise Control Plane
The open-source proxy pairs with an enterprise control plane for teams that need centralized policy management, SentinelOne integration, CrowdStrike Falcon integration, or direct promotion of high-severity AI agent risk into Microsoft Defender for Cloud as native incidents. The proxy can also push AI agent threats into SentinelOne as Threat Intelligence indicators, with severity mapped to numeric values (Critical=100, High=75, Medium=50, Low=25) so existing workflows consume them without modification.
Vaikora supports 12 LLM providers with no SDK migration required. The proxy intercepts at the transport layer, which means switching the enforcement configuration does not require changing the agent’s own code.
Where to Start
The Vaikora Guard MCP source is available under the MIT license and can be self-hosted in your own environment. If you are evaluating it for a production agent deployment, the first thing to check is whether your current pipeline has any synchronous policy check between the agent’s intent and the tool call execution. If the only record of what an agent did is a log entry that appears after the fact, that is the gap the proxy addresses.
Start with the compliance preset closest to your regulatory obligations, review the default policy rules against your actual tool inventory, and confirm that the audit log output meets your retention and tamper-evidence requirements before you move to production.