Every AI agent action that executes without a policy check is a control gap. Vaikora closes that gap by sitting inline between the agent and whatever it calls, making an allow, block, modify, or require-approval decision before the call reaches the LLM or tool.
This article explains the mechanics: how enforcement works, what the audit trail looks like, and how Vaikora connects to the security stack most teams already operate.
The Inline Position and Why It Matters
Most agent observability tools watch what happens and record it. Vaikora operates differently. It intercepts each action request before execution and applies a deterministic policy decision. If the action violates policy, it never reaches the target. There is no after-the-fact remediation because there is nothing to remediate.
The enforcement position is between the agent and the LLM or tool it calls. This means the agent’s intent, the parameters it is passing, and the tool it is targeting are all visible at decision time. A policy can inspect any of those attributes before returning an allow, block, modify, or require-approval verdict.
Enforcement latency at P50 is 8ms and at P99 is 45ms. The platform handles 10,000 or more actions per second. For most agent workflows, that throughput means enforcement adds no perceptible delay to the task.
How Policy Decisions Are Structured
Policies in Vaikora are written declaratively. A rule can target a specific tool, a class of parameters, a data pattern in the payload, or a risk threshold. When an agent calls a tool, Vaikora evaluates the call against the active ruleset and returns one of four outcomes.
- Allow: the call proceeds as submitted
- Block: the call is rejected and the agent receives an error or a defined fallback
- Modify: the call is altered before it reaches the target (for example, stripping a sensitive field from a payload)
- Require approval: the call is held pending a human decision before it continues
That last outcome is where governance requirements intersect with agent autonomy. High-risk operations such as deleting records, sending external communications, or modifying access control lists can be gated on human approval without re-architecting the agent.
Detection accuracy in testing is 99.9%. Compliance presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001 are included, so teams operating under regulatory frameworks can start with a validated baseline rather than building policy from scratch.
The Audit Chain
Every decision Vaikora makes is written to a SHA-256 hash-chained, tamper-evident audit log. Each entry is linked to the previous one, which means any modification to a past record breaks the chain and is detectable.
For compliance investigations, this matters in a specific way. An auditor or incident responder can present the log and demonstrate that the record of what an agent did, and what was blocked, has not been altered after the fact. That property is difficult to produce from an observability tool that writes to a mutable data store.
The log captures the agent identity, the action requested, the parameters passed, the policy that evaluated the call, the verdict returned, and the timestamp. That is enough to reconstruct exactly what an agent attempted during any window of time.
Connecting to the Security Stack
Vaikora integrates natively with Microsoft Sentinel, Microsoft Defender, SentinelOne, CrowdStrike Falcon, and AWS Security Hub. These are not webhook pushes that require custom engineering. They are packaged integrations.
When Vaikora detects a high-severity AI agent threat, it promotes that event into Azure Security Center and Microsoft Defender for Cloud as a native incident. SOC analysts working those queues see AI agent risk alongside endpoint and network alerts without any additional pipeline.
Vaikora also pushes AI agent threats into SentinelOne as Threat Intelligence indicators with severity mapped numerically: Critical at 100, High at 75, Medium at 50, Low at 25. That mapping lets SentinelOne’s automated response rules act on AI agent risk using the same logic already configured for other threat types.
For teams running Microsoft Sentinel, AI agent decisions and blocked actions appear as events that can be correlated against identity, endpoint, and network telemetry. An agent attempting lateral movement through a tool call shows up in the same investigation surface as a compromised credential or a suspicious login.
Deployment and LLM Coverage
Vaikora ships as an open-source, MIT-licensed, self-hostable MCP-compatible proxy called Vaikora Guard MCP, plus an enterprise control plane. Teams that want to start with the open-source path can run it without a commercial commitment and inspect the enforcement logic directly.
It supports 12 LLM providers with no SDK migration required. An agent calling GPT-4, Claude, Gemini, or another supported model connects through Vaikora without changing the application code that manages the LLM call.
What to Check Next
If you are evaluating Vaikora against your current architecture, the questions worth answering first are:
- Which tools do your agents call today, and which of those calls have no policy check before execution?
- Do you have a tamper-evident record of what each agent has attempted, with enough detail to satisfy an audit or an incident investigation?
- Are AI agent events currently visible in your SIEM alongside other threat types, or are they siloed in a separate observability tool?
Start with the open-source Vaikora Guard MCP server on GitHub if you want to test enforcement behavior against a real agent workflow before committing to the enterprise platform. The codebase is MIT-licensed and the proxy can be running locally in under an hour.