An AI agent that can call tools, query databases, and execute code operates with the same trust level as the credentials it runs under. If nothing checks what the agent is about to do before it does it, your access controls are only as good as the agent’s judgment. That is a problem, because agents do not have judgment.
Vaikora is Data443’s AI runtime control platform. It sits inline between an AI agent and every LLM call or tool invocation the agent makes. Before any action executes, Vaikora evaluates it against policy and returns one of four decisions: allow, block, modify, or require-approval. The action does not proceed until a decision is made.
What "Inline" Actually Means
Most security controls for AI systems work after the fact. They log what happened, alert on anomalies, or flag outputs for review. That is fine for visibility, but it does not stop the action.
Vaikora intercepts the call before execution. If an agent tries to read a file it should not access, exfiltrate data to an external endpoint, or call a tool outside its permitted scope, the block happens before the underlying system ever receives the request. The same input will always produce the same decision, because the enforcement logic is deterministic, not probabilistic.
Enforcement latency is P50 8ms and P99 45ms. At 10,000-plus actions per second, the gateway stays in the critical path without becoming the bottleneck.
How Policy Gets Applied
Vaikora ships six content inspection modules and supports 12 LLM providers with no SDK migration. You connect the gateway, define or import a policy, and enforcement begins.
Policy can be written from scratch or imported from compliance presets that cover HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001. The presets give you a working baseline immediately rather than starting from a blank policy file.
A concrete example: an agent integrated with a CRM has a policy that permits read access to customer records but blocks any write or delete operation during business hours unless a human approves it. A developer tests a prompt that causes the agent to attempt bulk deletion. Vaikora intercepts that tool call, applies the write-block rule, and returns a block decision. The CRM never receives the request. The decision is written to the audit log with the full context of what was attempted.
The require-approval decision is worth calling out separately. Not every edge case should be a hard block. Some actions need a human in the loop before they proceed. Vaikora can hold an action pending approval, route the request to a reviewer, and only release it when approved. The approval state, the reviewer identity, and the final outcome all appear in the audit log.
The Audit Chain
Every policy decision Vaikora makes is signed into a SHA-256 hash-chained, tamper-evident audit log. Each entry references the previous one. If a record is altered or deleted after the fact, the chain breaks and the tampering is detectable.
This matters for compliance investigations. When an auditor asks what an AI agent did at a specific time and why, you have an ordered, verifiable record rather than application logs that could have been modified.
SIEM and EDR Integration
Vaikora integrates natively with Microsoft Sentinel, Microsoft Defender, SentinelOne, CrowdStrike Falcon, and AWS Security Hub.
For SentinelOne specifically, Vaikora pushes AI agent threats as Threat Intelligence indicators with severity mapped numerically: Critical at 100, High at 75, Medium at 50, Low at 25. That means an AI agent attempting a high-severity policy violation shows up in your EDR the same way a known-malicious file hash would.
For Microsoft environments, high-severity AI agent risk promotes into Azure Security Center and Microsoft Defender for Cloud as native incidents. No custom engineering is required.
This closes the gap between AI-specific enforcement and the security operations workflow your team already uses.
Deployment Options
Vaikora Guard MCP ships as open-source, MIT-licensed software published on PyPI. It is self-hostable and MCP-compatible, which means it works with the growing ecosystem of tools built on the Model Context Protocol. The enterprise control plane is the commercial product, hosted by Data443, and is available on AWS Marketplace as a SaaS subscription with support for EDP draw-down and private offers. It also ships in the Azure Sentinel content hub.
For teams using Microsoft 365, Vaikora includes email security analysis that runs on a virtual machine in your own Azure subscription, connects through Microsoft Graph with no MX record change required, and reports into your own Sentinel workspace.
What to Check Next
If your organization runs AI agents in production, start with two questions. First, what tools and APIs can those agents call, and is there a policy controlling which calls are permitted? Second, can you produce a complete, tamper-evident record of every action an agent took in the last 30 days?
If the answer to either is "not precisely," review how your current setup handles enforcement before execution. The Vaikora gateway installation and policy configuration documentation is the right starting point.