At P50, Vaikora makes an allow, block, modify, or require-approval decision in 8 milliseconds. That number matters because the enforcement happens before the action executes, not after a log entry is written and a human reviews it the next morning.
Most AI agent deployments have a control gap at the tool call layer. The agent decides to do something, the framework calls the tool, and the action completes before any policy can intervene. Vaikora sits inline between the agent and whatever it calls, whether that is an LLM, an API, a database, or a file system, and makes a deterministic policy decision at that junction.
What "inline" actually means in practice
Vaikora runs as an MCP-compatible proxy. The agent routes its calls through Vaikora instead of directly to the target. Vaikora evaluates each call against your policy set, issues a decision, and either passes the call through, blocks it, rewrites it, or holds it for human approval before anything downstream sees it.
There is no post-hoc analysis. The call either completes with a policy decision attached or it does not complete. That distinction matters for compliance: a blocked action that never executed is a fundamentally different audit event than a harmful action that was detected after the fact.
Consider a concrete scenario. An AI agent is tasked with summarizing patient records and has a tool call that would write output to an external storage endpoint. Without inline enforcement, the write happens and data leaves your environment. With Vaikora in the path, that tool call hits a HIPAA-preset policy rule, the decision is block, and the write never executes. The decision is written immediately to a SHA-256 hash-chained audit log.
How policy decisions are structured
Every decision Vaikora makes falls into one of four categories:
- Allow: the call proceeds as submitted
- Block: the call is rejected before reaching the target
- Modify: the call is rewritten, for example, stripping a field containing PII before it reaches the LLM
- Require approval: the call is held in queue until a human approves or denies it
The require-approval path is underused by most teams but covers the cases that matter most: high-risk tool calls where automation alone should not make the final call, actions that cross a data classification boundary, or anything that touches regulated data in a context the policy author did not fully anticipate.
Policies are built against compliance presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001. You can layer custom rules on top of those presets.
Throughput and auditability at scale
At 10,000 or more actions per second with P99 latency at 45ms, Vaikora runs inline without becoming a bottleneck even in high-volume agent deployments. The 99.9% detection accuracy in testing means the policy engine is not generating noise that causes teams to tune out alerts.
Every enforcement decision, allow and block alike, goes into the audit log. The hash-chain structure means you can prove in an investigation that no log entries were modified or deleted after the fact. That auditability property is what turns enforcement records into evidence usable for regulatory response, SOC investigations, or internal governance reviews.
Integration with the security stack you already run
Vaikora connects natively to Microsoft Sentinel, Microsoft Defender, SentinelOne, CrowdStrike Falcon, and AWS Security Hub. That means AI agent enforcement events flow into the same workflows your SOC already uses for other threat classes.
High-severity AI agent risk surfaces in Azure Security Center and Microsoft Defender for Cloud as native incidents, with no custom engineering required. Threat indicators push into SentinelOne with severity mapping: Critical at 100, High at 75, Medium at 50, Low at 25. Your existing triage and response playbooks apply.
For teams not ready to deploy the enterprise control plane, Vaikora Guard MCP ships as an open-source, MIT-licensed, self-hostable proxy. It supports 12 LLM providers with no SDK migration, which means you can place it in front of whatever model your agent already calls.
What to check before your next AI agent deployment
Before you ship an agent that has tool call access, confirm three things. First, is there an inline enforcement point between the agent and every tool it can call, or does your current setup only log after execution? Second, do your policies map to your actual compliance obligations, and are they enforced deterministically rather than flagged for later review? Third, do enforcement events flow into the security tools your SOC monitors, so AI agent risk sits in the same triage queue as every other signal?
If any of those three are not confirmed, start there. The enforcement model is the first thing to get right; the policy tuning comes after.