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Vaikora enforces AI agent policy before execution: how the runtime gate works

Product Updates · Nov 12, 2026 · 4 min read

Approval workflows and audit logs written after an AI agent acts do not undo file deletions, credential exfiltration, or mass API calls. The enforcement problem is ordering: most control points see what an agent did, not what it is about to do.

Vaikora sits inline between an AI agent and every LLM call or tool invocation it makes. Before the action executes, Vaikora evaluates it against policy and returns one of four decisions: allow, block, modify, or require-approval. The action either proceeds, is rewritten, waits for a human, or is stopped. Nothing executes before that decision is returned.

What "inline" means in practice

When an AI agent sends a request through the Vaikora gateway, the request does not go to the LLM or tool directly. It goes to Vaikora first. Vaikora runs six content inspection modules against the request payload, checks it against the active policy set, and signs the decision into a SHA-256 hash-chained audit log before anything downstream receives the call.

At P50, that inspection takes 8ms. At P99, 45ms. The gateway sustains over 10,000 actions per second. For most agent workflows, the latency is invisible. For high-throughput pipelines, the throughput ceiling is high enough that enforcement does not become the bottleneck.

This is deterministic policy enforcement. Identical input always produces an identical decision. There is no model that learns to approve something it previously blocked. Policy is policy.

How the gateway connects

Vaikora supports 12 LLM providers with no SDK migration. You point your agent's API calls at the Vaikora gateway URL instead of directly at the model endpoint. The gateway adapts to each provider's format. If you are using the Model Context Protocol, vaikora-guard-mcp is the open-source, MIT-licensed Python package published on PyPI that wraps every MCP tool call in that same enforcement gate.

For teams that want to self-host the inspection layer, the gateway is open source. The commercial product is the Vaikora Control Plane, hosted by Data443, which provides the policy management interface, compliance presets, and the enterprise audit chain.

Vaikora does not replace your existing identity controls. It adds a runtime gate on top of Azure RBAC, AWS IAM, and Snowflake roles. An agent might have IAM permissions to call an S3 deletion API, and Vaikora can still block that call if it falls outside active policy. IAM says what the agent can do in principle. Vaikora says what it may do right now, given the request content and context.

What the audit chain gives you

Every decision Vaikora writes is signed into a SHA-256 hash-chained, tamper-evident log. The chain gives you an ordered, verifiable record of what every agent requested, what policy matched, and what outcome was enforced. If a decision record is altered or deleted, the chain breaks and the tampering is detectable.

For compliance purposes, Vaikora ships presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001. A team working under HIPAA can activate that preset and have policy rules covering protected health information handling applied to every agent action without writing the rules from scratch.

Vaikora's detection accuracy in testing is 99.9%. That number matters most when the block decision is the last line of defense before a consequential action executes.

Where threats surface in your existing tooling

When Vaikora flags a high-severity AI agent risk, it does not stop at logging it internally. It pushes threats into SentinelOne as Threat Intelligence indicators, severity-mapped at Critical=100, High=75, Medium=50, and Low=25. High-severity risk promotes into Azure Security Center and Microsoft Defender for Cloud as native incidents with no custom engineering. Vaikora also integrates natively with Microsoft Sentinel, CrowdStrike Falcon, and AWS Security Hub.

For a SOC analyst, that means an AI agent attempting to exfiltrate data appears as an incident in the same console where network and endpoint alerts land. It does not require a separate dashboard or manual export.

Where to start

If you want to see the enforcement layer without a commercial commitment, pull vaikora-guard-mcp from PyPI and point an MCP-compatible agent at it. Review the policy configuration, run a few test tool calls, and check the audit log output. The policy logic and decision chain are the same ones the enterprise control plane runs.

If you are already using Microsoft Sentinel, Vaikora is available in the Azure Sentinel content hub as the Vaikora-AzureSecurityCenter solution. That is the fastest path to getting AI agent enforcement signals into an environment where your analysts already work.

The specific question to answer before deployment: which agent actions in your environment are consequential enough that a wrong decision cannot be corrected after the fact. Those are the calls that need a gate in front of them, not a log behind them.

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