Most AI agent security problems share a common structure: the agent acts, something goes wrong, and the team investigates afterward. Vaikora is built to interrupt that sequence before the action runs, not to analyze it once it has.
What Vaikora Is and Where It Sits
Vaikora is Data443’s AI runtime control platform. It sits inline between an AI agent and whatever the agent is trying to call, whether that is an LLM API, a database, a file system, or a downstream tool exposed through the Model Context Protocol (MCP). Every action the agent attempts passes through Vaikora before it executes.
The platform applies a deterministic policy decision to each action: allow, block, modify, or require approval. Identical input always produces an identical decision. That is not a minor implementation detail. It means you can reason about Vaikora’s behavior in advance, write tests against it, and expect those tests to hold in production.
Vaikora supports 12 LLM providers with no SDK migration. You route traffic through the gateway, configure your policies, and the enforcement layer works regardless of which model the agent calls.
How Enforcement Actually Happens
Vaikora ships as an open-source, MIT-licensed, self-hostable MCP-compatible proxy called Vaikora Guard MCP, available on PyPI as `vaikora-guard-mcp`. The commercial product is the Vaikora Control Plane, hosted by Data443, which manages policy configuration, audit records, and integrations.
When an agent submits a tool call, the gateway runs it through six content inspection modules before the call reaches the tool. At P50, that inspection completes in 8ms. At P99 it takes 45ms. The gateway can process more than 10,000 actions per second. Those numbers matter for production AI systems where latency compounds across multi-step agent workflows.
Consider a concrete case: an AI agent is given credentials to query a customer database and generate a report. Without a policy layer, the agent can query any table, export any field, and pass that output to the next tool in the chain. With Vaikora inline, a policy rule can restrict the agent to specific tables, block queries that return fields tagged as PII, and require human approval before any data leaves the workspace. If the agent attempts a query outside that boundary, the block happens before the database call, not after the rows have already been fetched.
Audit Records That Can’t Be Altered
Every Vaikora policy decision is written to a SHA-256 hash-chained, tamper-evident audit log. Each record is signed into an ordered chain, so any attempt to delete or modify a past decision breaks the chain and is detectable.
That design matters for two reasons. First, compliance audits require evidence that controls were actually applied, not just configured. A tamper-evident log provides that evidence in a form that can be verified independently. Second, incident investigations depend on knowing exactly what an agent did and when. Because every action passes through enforcement, the log is complete by construction.
Vaikora ships compliance presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001. The presets are starting points. Teams configure additional rules on top of them through the Control Plane.
Integration With Existing Security Infrastructure
Vaikora does not replace existing identity and access controls. It adds a runtime gate on top of Azure RBAC, AWS IAM, and Snowflake roles. An agent might have IAM permissions to call an S3 API, but Vaikora can block or modify that call based on content inspection, context, or policy rules that IAM cannot express.
For security operations teams, Vaikora integrates natively with Microsoft Sentinel, Microsoft Defender, SentinelOne, CrowdStrike Falcon, and AWS Security Hub. High-severity AI agent threats are pushed into SentinelOne as Threat Intelligence indicators with severity mapped numerically: Critical at 100, High at 75, Medium at 50, Low at 25. High-severity risk promotes into Azure Security Center and Microsoft Defender for Cloud as native incidents, with no custom engineering required.
Vaikora is available on AWS Marketplace as a SaaS subscription and ships in the Azure Sentinel content hub as the Vaikora-AzureSecurityCenter solution.
What to Check Before You Deploy
Before deploying Vaikora, work through these questions:
- Which agents in your environment currently have credentials to call external APIs, write to storage, or query databases? Those are the highest-priority enforcement points.
- What actions should require human approval rather than automated allow or block decisions? The require-approval mode is specifically for cases where the risk is real but the action is sometimes legitimate.
- What compliance preset matches your primary regulatory obligation? Configure that first, then layer additional rules on top.
- Do you need the audit log accessible to your SIEM? If you are running Microsoft Sentinel, the native integration handles that without custom connectors.
The open-source `vaikora-guard-mcp` package is the fastest way to test enforcement behavior against your own agent traffic before committing to the full Control Plane deployment.