Putting a guardrail on an AI agent after a tool call has already run is not security, it is forensics. Vaikora sits inline between the agent and whatever it is calling, applies a deterministic policy decision, and either allows, blocks, modifies, or holds the action for human approval before a single byte is written or read.
That ordering matters more than it might appear. Most AI agent incidents do not look like intrusions. They look like normal agent behavior that crossed a line no one drew clearly in advance.
What "Inline" Actually Means
Vaikora is not a log aggregator or an alerting layer. It is a proxy. Every action an AI agent wants to take passes through Vaikora first. The agent calls a tool, Vaikora receives the request, evaluates it against your policy, and either lets it through or stops it. The downstream system, whether that is an LLM, a database API, a file store, or any other tool, never sees the request until Vaikora has made its decision.
That decision carries four possible outcomes:
- Allow: the action matches policy and executes normally
- Block: the action violates policy and is rejected before execution
- Modify: Vaikora rewrites the request to a compliant form, then forwards it
- Require-approval: the action is held in a queue until a human explicitly approves or rejects it
Enforcement latency at P50 is 8ms. At P99 it is 45ms. The platform sustains more than 10,000 actions per second. In a production agent pipeline, that overhead is not perceptible to end users, but the policy boundary is real.
How Policy Gets Defined
Policy in Vaikora is written once and applied to every agent action in scope. You define what agents are permitted to do, what they are prohibited from doing, and what requires a human in the loop. The policy engine is deterministic: given the same input and the same policy, it always produces the same decision. There is no probabilistic scoring that passes one day and blocks the next.
A concrete example: an AI agent is authorized to query a customer database for support purposes. The agent constructs a query that would return records for all customers in a given region. That is not the query it was authorized to run. Vaikora’s policy, which defines the allowed query scope, blocks the request before it reaches the database. The agent receives a rejection. The database never sees the query. The rejection is written to the audit log with full context.
That is different from detecting the over-broad query after it ran and the data was returned.
Compliance Presets and Audit Integrity
For organizations working under HIPAA, PCI-DSS, GDPR, SOC 2, or ISO 27001, Vaikora ships presets that map regulatory requirements to enforcement rules. You do not start from a blank policy. You load the preset, review it against your environment, and adjust where your specific controls differ from the default.
Every decision Vaikora makes is written to a SHA-256 hash-chained, tamper-evident audit log. Each entry chains to the one before it. If an entry is altered or deleted after the fact, the chain breaks and the tampering is detectable. For compliance investigations or incident response, that log is the record of what was allowed, blocked, modified, or escalated, in the exact sequence it happened.
Integration with the Security Stack
Vaikora does not require your team to build a parallel monitoring stack around it. It integrates natively with Microsoft Sentinel, Microsoft Defender, SentinelOne, CrowdStrike Falcon, and AWS Security Hub.
High-severity AI agent risk promotes into Azure Security Center and Microsoft Defender for Cloud as native incidents, with no custom engineering required. Threats push into SentinelOne as Threat Intelligence indicators with severity mapping: Critical at 100, High at 75, Medium at 50, Low at 25.
Vaikora supports 12 LLM providers with no SDK migration required. If you are already running agents against multiple models, you do not re-instrument to add enforcement.
For teams evaluating before committing to a full deployment, Vaikora Guard MCP ships as an open-source, MIT-licensed, self-hostable MCP-compatible proxy. You can run it in your own environment, inspect the code, and validate enforcement behavior against your own agent workloads before touching the enterprise control plane.
Where to Start
If you are responsible for an AI agent deployment today, three checks are worth running before anything else.
First, identify every tool your agents can call and confirm whether each call passes through any policy gate before it executes. If the answer is no, you have enforcement gaps regardless of what your logging stack captures.
Second, review whether your current audit trail can prove, for any given agent action, exactly what policy was evaluated and what decision was made. A log of what happened is not the same as a tamper-evident record of what was permitted and why.
Third, check whether your existing SIEM or EDR receives AI agent risk as structured, severity-mapped signals. If AI agent activity is invisible to your SOC tooling, it cannot be part of your response workflow.
Those three gaps are what an inline enforcement layer is designed to close.