When an AI agent is told to read a file, send a message, or call an external API, the question of whether that action is allowed should be answered before the action runs, not after. Vaikora sits inline between the agent and whatever it calls, applies a deterministic policy decision, and either allows, blocks, modifies, or holds the action for approval. The sequence is enforce-then-execute, not execute-then-audit.
The Enforcement Gap That Runtime Control Closes
Most organizations deploying AI agents have some form of logging in place. What they often lack is a control point that can actually stop an action from happening.
Logging tells you what an agent did. It does not prevent the agent from doing it. If an agent exfiltrates sensitive data to an unauthorized endpoint, a log entry confirms the event. The data is still gone.
Runtime control means the policy check happens at the action layer, before the LLM or tool receives the call. The agent submits an action. Vaikora evaluates it against policy. If the action is blocked, the tool never receives the request. If it requires approval, execution pauses until a human responds. The log entry records a decision, not a consequence.
This matters most in agentic workflows where one agent can trigger dozens of downstream tool calls. A single misconfigured or manipulated agent can chain actions across filesystems, APIs, and databases. Without an inline control point, each of those calls executes before any signal reaches a SOC analyst.
What Vaikora Actually Evaluates
Vaikora evaluates every action that passes through it against policies you define. The decision categories are concrete:
- Allow: the action meets policy and executes normally.
- Block: the action violates policy and is stopped before execution. The agent receives a rejection.
- Modify: the action is altered in transit, for example, sensitive fields are redacted from a prompt before it reaches the LLM.
- Require approval: the action is queued for human review. Execution does not proceed until a reviewer responds.
Every decision is written to a SHA-256 hash-chained, tamper-evident audit log. Each entry records what the action was, what policy matched, what decision was made, and when. The chain integrity means no entry can be altered or deleted without detection, which matters for compliance investigations and for satisfying auditors under frameworks like SOC 2, HIPAA, PCI-DSS, GDPR, and ISO 27001.
Enforcement latency runs at P50 8ms and P99 45ms, and the platform processes over 10,000 actions per second. For most agentic workflows, the overhead is not observable at the application level.
A Concrete Example: An Agent with Filesystem and API Access
Consider an AI agent that has access to a document store and an outbound email API. The intended workflow is: read customer records, summarize them, send the summary to an approved internal address.
Without runtime control, if the agent is manipulated through prompt injection to send records to an external address, the email API accepts the call and delivers the message. The log records it after the fact.
With Vaikora inline, the outbound email action is evaluated before it reaches the API. If the destination address is not on the approved list, the action is blocked. The policy is defined once and enforced on every call, regardless of how the agent arrived at the instruction.
The same policy layer covers the read action on the document store. If the agent attempts to read a directory it has no business accessing, that call is blocked before the filesystem returns any data.
Integration with Existing Security Infrastructure
Vaikora does not require replacing anything in your current stack. It supports 12 LLM providers with no SDK migration required, and ships as an open-source, MIT-licensed, self-hostable MCP-compatible proxy (Vaikora Guard MCP) alongside an enterprise control plane.
For teams already running a SIEM or EDR, Vaikora integrates natively with Microsoft Sentinel, Microsoft Defender, SentinelOne, CrowdStrike Falcon, and AWS Security Hub. High-severity AI agent risk is promoted into Azure Security Center and Microsoft Defender for Cloud as native incidents, with no custom engineering. Threats are pushed into SentinelOne as Threat Intelligence indicators, severity-mapped at Critical=100, High=75, Medium=50, and Low=25.
This means an AI agent action that Vaikora flags at critical severity surfaces in the same incident queue your SOC already works, without a separate tool or a custom pipeline.
What to Check in Your Current Agent Deployment
If you are running AI agents today, the following questions determine whether your current controls are sufficient:
- Can any agent call a tool or API without a policy check occurring first?
- If an agent is manipulated through prompt injection, does your current setup block the resulting action or log it after it executes?
- Do you have an audit trail that records every action decision in a tamper-evident format you can present to an auditor?
- Are high-severity agent events surfacing in your SIEM automatically, or only if someone writes a custom integration?
If any of those answers is "no" or "I don’t know," the place to start is the Vaikora Guard MCP repository, which you can deploy and test against your own agent traffic before evaluating the enterprise control plane.