Most AI agent frameworks decide what to do and then do it. Policy, logging, and access checks are typically bolted on afterward, if they exist at all. Vaikora Guard MCP reverses that order: it sits inline between an agent and whatever tool or LLM it is about to call, enforces a policy decision first, and only lets the action proceed if it passes.
The proxy is MIT-licensed, self-hostable, and compatible with the Model Context Protocol (MCP). That means you can run it in your own environment, inspect the source, and adapt the policy layer without waiting on a vendor.
What Inline Enforcement Actually Means
When an AI agent generates a tool call, that call passes through Vaikora Guard MCP before it reaches its target. The proxy evaluates the call against your configured policies and returns one of four decisions: allow, block, modify, or require-approval.
- Allow: the call proceeds unchanged.
- Block: the call is stopped and the agent receives a structured refusal.
- Modify: the proxy rewrites the call (for example, stripping a sensitive field before it reaches an external API) and then forwards the sanitized version.
- Require-approval: the call is held until a human explicitly approves or rejects it.
This happens before execution. If a block is warranted, the tool never receives the request. There is no "detect and remediate after the fact" path for blocked actions.
At P50 the enforcement decision takes 8ms. At P99 it takes 45ms. At scale the proxy handles more than 10,000 actions per second.
What Policies Cover
Policies are evaluated against the full context of each tool call: the calling agent identity, the tool being invoked, the parameters included, and the data classification of any payload. You can write rules around any of those axes.
Practical examples:
- Block any agent from calling a file-write tool on paths outside a defined working directory.
- Require human approval before an agent sends an outbound HTTP request to a domain outside an allowlist.
- Modify any tool call that includes a field matching a PII pattern before forwarding it to an external service.
- Block any prompt that contains content classified as confidential from reaching an LLM endpoint not on the approved list.
Vaikora ships compliance presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001. Those presets map regulatory requirements to concrete policy rules, so you are not building from zero if you have a compliance obligation to satisfy.
Auditability
Every decision the proxy makes is written to a tamper-evident audit log. Each entry is SHA-256 hash-chained to the previous one, so any modification to the log breaks the chain. That matters when you need to demonstrate to an auditor that a specific action was blocked at a specific time, or when you are investigating an incident and need to reconstruct exactly what an agent attempted.
Detection accuracy in testing is 99.9%. That is a testing figure, not a production guarantee, but it reflects the fidelity of the classification layer that feeds policy decisions.
Integration With Your Existing Security Stack
The proxy is not an island. Vaikora integrates natively with Microsoft Sentinel, Microsoft Defender, SentinelOne, CrowdStrike Falcon, and AWS Security Hub.
When the proxy flags a high-severity AI agent action, it promotes that event into Azure Security Center and Microsoft Defender for Cloud as a native incident, with no custom engineering required. It also pushes AI agent threats into SentinelOne as Threat Intelligence indicators, with severity mapped as: Critical=100, High=75, Medium=50, Low=25.
That means a blocked or flagged agent action can trigger the same SOC workflows as any other high-severity alert. Your analysts do not need a separate console to track what AI agents are doing.
The Open-Source Layer and the Enterprise Control Plane
Vaikora Guard MCP (the proxy itself) is open source under the MIT license and supports 12 LLM providers without requiring an SDK migration. You can point it at whatever LLM your agent is already calling.
Data443 also ships an enterprise control plane on top of the proxy. The control plane adds centralized policy management, fleet-wide visibility across multiple agents and environments, and the integrations with commercial security platforms described above.
The split is intentional. Teams that want to evaluate the enforcement mechanism without a commercial commitment can run the open-source proxy and inspect how it works. Teams that need governance across a large agent deployment, regulatory compliance presets, and integration with their SOC tooling have a path to the enterprise layer.
What to Check Next
If you are running AI agents in production or planning to, the practical starting point is to map every tool call your agents can make and ask whether any of those calls have access to data or systems that your existing access controls would restrict for a human user. If the answer is yes, the enforcement gap is there regardless of whether you use this proxy or something else.
To evaluate Vaikora Guard MCP specifically: review the source on GitHub, stand it up in front of a non-production agent, and run a set of test calls against a policy that reflects one real control you would want enforced. Watch what ends up in the audit log and whether the latency is acceptable for your agent’s response-time requirements.