An AI agent that can call tools, read files, query APIs, and send messages is also an agent that can exfiltrate data, execute unauthorized code, or chain together a sequence of actions no one intended. The problem is not the model. The problem is that most AI deployments have no control point between the agent deciding to act and the action actually running.
Detection after the fact is not a substitute for enforcement before execution. Once a tool call completes, the damage is already done.
Why Agents Create a Different Risk Surface
Traditional application security assumes a human is in the loop at the decision point. An AI agent removes that assumption. A single agent session can fan out into dozens of tool calls in seconds, each one carrying implicit trust because the agent was authorized to act in the first place.
The attack paths this opens include:
- Prompt injection that redirects an agent toward a tool call it was never meant to make
- Data exfiltration through a legitimate-looking API call with sensitive content in the payload
- Privilege escalation by chaining tool calls in a sequence that individually look acceptable
- Policy drift, where an agent begins acting outside its original scope because no runtime guardrail checks the action against current policy
None of these are hypothetical. They are documented behavior patterns in deployed agent systems.
What Inline Enforcement Actually Means
Vaikora sits inline between an AI agent and every LLM or tool it calls. Before any action executes, Vaikora applies a deterministic policy decision: allow, block, modify, or require approval. The action does not proceed until that decision resolves.
Consider a concrete scenario. An HR agent is authorized to query an employee database and draft responses. A prompt injection in a document it processes tells the agent to forward the query results to an external endpoint. Without inline enforcement, that tool call runs. With Vaikora, the outbound call to an unrecognized endpoint hits a policy rule, gets blocked, and the block is written to the audit log before the exfiltration completes.
Vaikora processes enforcement decisions at a P50 latency of 8ms and P99 of 45ms, at throughput above 10,000 actions per second. At those numbers, enforcement does not create a bottleneck in production agent workflows.
How Policy Is Structured
Vaikora ships with compliance presets for HIPAA, PCI-DSS, GDPR, SOC 2, and ISO 27001. Each preset maps to a set of policy rules that apply at runtime without custom configuration for each one.
Beyond presets, policy rules can target:
- Specific tool categories (file read, code execution, external HTTP, database query)
- Content patterns in the action payload (credit card numbers, PHI, secrets)
- Destination characteristics (internal vs. external, known-bad infrastructure)
- Sequence patterns (agent has already called tool X, so calling tool Y requires approval)
The require-approval decision is worth calling out separately. For high-risk actions where a hard block would break a legitimate workflow, Vaikora routes the action to a human approver before it runs. The agent waits. This gives security and compliance teams a practical way to gate sensitive operations without disabling the agent entirely.
Audit That Holds Up Under Scrutiny
Every enforcement decision Vaikora makes, including allows, is written to a SHA-256 hash-chained, tamper-evident audit log. The chain means any modification to a past record is detectable. In an investigation or a compliance audit, you can produce a complete record of what the agent did, what policy evaluated it, and what the outcome was.
This matters specifically for regulated industries. Auditors asking whether AI-assisted workflows comply with HIPAA or PCI-DSS need more than a policy document. They need a record that enforcement actually ran, per action, per session.
Security Stack Integration
Vaikora does not require a separate investigation workflow to surface AI agent threats to your SOC. 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. Threat indicators push into SentinelOne with severity mapping: Critical at 100, High at 75, Medium at 50, Low at 25. SOC analysts see AI agent threats in the same queues as endpoint and network threats, which is where they belong.
Vaikora supports 12 LLM providers with no SDK migration and ships as an open-source, MIT-licensed, self-hostable MCP-compatible proxy alongside an enterprise control plane. Teams that want to start with the open-source layer and expand to the enterprise control plane do not have to re-architect anything.
What to Check Before Your Next Agent Deployment
Before any AI agent goes to production, verify three things. First, can you list every tool the agent is authorized to call? If that list lives only in the prompt, it is not enforced. Second, do you have an audit record of every action the agent took in testing? If not, you are making a production decision without evidence. Third, what happens when the agent receives a prompt injection that redirects its behavior? Test for it explicitly.
If the answers to any of those questions are unclear, the gap is a control gap, not a model gap. That is the category of problem Vaikora is built to close.