An AI agent with write access to your CRM, code repository, or cloud storage can act on a misunderstood instruction and cause real damage before any log entry reaches your SIEM. The problem is not that agents are malicious. The problem is that most deployment architectures give agents no enforcement boundary at all: the model decides, the tool call fires, and the audit happens afterward.
Vaikora is Data443’s AI Runtime Control & Enforcement Platform. It sits inline between an agent and the LLM or tools it calls, and it applies a deterministic policy decision before each action executes. The decision is one of four: allow, block, modify, or require-approval. Nothing executes until that decision is made.
What "Inline" Actually Means
Most runtime AI controls work by observing traffic after the fact or reading logs. Vaikora is placed in the call path itself. When an agent issues a tool call or sends a prompt to an LLM, the request hits Vaikora first. Policy evaluation runs there. The result comes back as an enforcement decision, and only then does the underlying call proceed or not.
This matters because a block that happens after execution is not a block. It is a notification.
Vaikora ships as an open-source, MIT-licensed, self-hostable MCP-compatible proxy called Vaikora Guard MCP. It requires no SDK migration and supports 12 LLM providers. The enterprise control plane adds centralized policy management, compliance presets, and native SIEM integration.
What Policies Look Like in Practice
Consider a realistic agent workflow: a sales automation agent has read access to a customer database and write access to an outbound email tool. A misconfigured prompt causes the agent to attempt exfiltrating the full customer list as a CSV attachment.
Without an enforcement layer, that tool call runs. With Vaikora in the call path, the outbound payload is evaluated against policy before the email tool fires. A policy rule scoped to that agent’s identity and the email tool can block the call, modify the payload to strip the attachment, or route it to a human approval queue. The agent receives a response, but the data does not leave.
Policy can be scoped by:
- Agent identity
- Tool type and target
- Payload content (for example, PII patterns or credential formats)
- Time of day or session context
- Sensitivity classification of data referenced in the call
Vaikora ships compliance presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001. These are not documentation templates. They are enforcement configurations applied at the proxy layer.
Detection Accuracy and Latency
Policy enforcement that slows down agent workflows gets disabled. Vaikora’s detection accuracy is 99.9% in testing, with enforcement latency at P50 of 8ms and P99 of 45ms. At 10,000+ actions per second, it can handle high-frequency agentic pipelines without becoming the bottleneck.
Those numbers matter operationally. If your agents are running automated workflows against APIs or internal tools, you cannot afford an enforcement layer that adds hundreds of milliseconds per call.
What Happens to Every Decision
Every enforcement decision Vaikora makes is written to a SHA-256 hash-chained, tamper-evident audit log. That means you can produce a complete, verifiable record of what every agent attempted, what policy applied, and what the outcome was. For compliance investigations or incident response, that log is the evidence chain.
Audit logs that can be altered after the fact are not audit logs. The hash-chaining means each entry is bound to the one before it. Any modification to a past record breaks the chain and is immediately visible.
Integrating with the Security Stack
Vaikora pushes enforcement signals into the platforms your team already operates:
- Microsoft Sentinel, Defender for Cloud, and Azure Security Center (native incidents, no custom engineering)
- SentinelOne (Threat Intelligence indicators, severity-mapped: Critical=100, High=75, Medium=50, Low=25)
- CrowdStrike Falcon
- AWS Security Hub
High-severity AI agent risk surfaces in your existing SIEM and EDR workflows without a separate console. Your SOC sees AI agent threats alongside every other threat type, in the same interface, with the same triage process.
Where to Start
If you are running agents against internal tools today, start by mapping every tool call surface each agent can reach and what data those tools can access or modify. Then ask whether any of those actions can execute without a deterministic policy check in the path.
If the answer is yes, you have an enforcement gap. The practical next step is deploying Vaikora Guard MCP in front of the highest-risk tool surface, setting a policy for that agent identity and tool type, and reviewing the first week of audit log output. What agents actually attempt is often different from what they were designed to do.