Detection after the fact works for many threat categories. For AI agents with tool access, it often works too late. By the time a detection fires, the agent may have already exfiltrated a file, called an external API with sensitive payload data, or written a record to a production database. The action is done. The detection is an incident report.
The architectural question is not whether to monitor AI agents. It is where in the execution path you apply policy decisions.
The Problem With Post-Execution Detection
An AI agent operating with tool access runs a tight loop: receive instruction, decide which tool to call, construct the call, execute, observe the result, repeat. Each iteration can take seconds. A single session might generate dozens of tool calls.
A monitoring system that sits outside this loop receives telemetry after each action completes. That is fine for investigation and audit. It is insufficient for prevention.
Consider a concrete scenario. An agent handling customer support is given access to a CRM read tool and an email-send tool. A prompt injection buried in a customer message instructs the agent to pull all records for accounts with balances over a threshold and email a summary to an external address. The agent constructs both calls. A post-execution monitor sees the email go out, logs an alert, and pages someone. The data has already left.
The only point where that action is preventable is before the email-send call executes.
How Inline Enforcement Works
Vaikora sits inline between the agent and every tool or LLM call it makes. Before each call executes, Vaikora evaluates the action against policy and returns one of four decisions: allow, block, modify, or require-approval. The tool call does not proceed until that decision is returned.
At P50, that decision takes 8ms. At P99, it takes 45ms. At 10,000+ actions per second, it is not a bottleneck for any realistic agent deployment.
For the scenario above, the policy would be written against the email-send tool’s destination parameter. Any destination address outside the organization’s domain triggers a block or routes to a human approval queue. The agent receives a denial response and can surface it to the user. The data never leaves.
This is deterministic policy enforcement. The outcome is not probabilistic. A rule that blocks external email destinations blocks external email destinations, every time, with a 99.9% detection accuracy in testing.
What Policies Actually Cover
Vaikora policy governs at the action level, not the session level. Practical categories include:
- Tool allowlisting and denylisting: an agent role that should never call a file-write tool cannot call it, regardless of what the LLM decides
- Payload inspection: outbound content is checked against data classification rules before a call to an external API proceeds
- Destination control: API endpoints, file paths, email addresses, and database targets are checked against an allowlist or pattern rules
- Approval routing: high-risk actions, such as any call that touches production infrastructure or PII above a record threshold, go to a human queue before execution
- Prompt injection detection: inputs that contain known injection patterns are blocked before they reach the LLM
Compliance presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001 ship with the platform, so teams do not have to write baseline rules from scratch.
Audit and Integration
Every enforcement decision Vaikora makes is written to a SHA-256 hash-chained, tamper-evident audit log. That log records what action was attempted, what policy evaluated it, what decision was returned, and what payload was involved. For regulated environments, this is the audit trail that demonstrates controls were active and enforcement was applied.
Vaikora integrates natively with Microsoft Sentinel, Microsoft Defender, SentinelOne, CrowdStrike Falcon, and AWS Security Hub. High-severity AI agent risk surfaces in Azure Security Center and Microsoft Defender for Cloud as native incidents. Threats push into SentinelOne as Threat Intelligence indicators with severity scores mapped at Critical=100, High=75, Medium=50, Low=25. No custom engineering is required for any of these integrations.
For teams that want to start without a full enterprise deployment, Vaikora Guard MCP is the open-source, MIT-licensed, self-hostable proxy that covers the enforcement layer. It supports 12 LLM providers with no SDK migration, so it drops into existing agent infrastructure without requiring a rewrite.
Where to Start
If your organization is deploying AI agents with tool access and the current control layer is monitoring only, the first thing to check is whether any of those tools can write data externally. Email, file storage, external APIs, databases. Map those tools, identify which agent roles have access to them, and ask whether a policy block exists at the call level before execution.
That mapping is the gap analysis. Enforcement configuration follows from it.