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Sensitive data protection

Find secrets and sensitive data in AI agent traffic, and stop it in MCP calls.

On-device inspection classifies prompts, tool inputs and MCP tool arguments into sensitive-data categories, on the surfaces an administrator enables. Only category names and counts leave the device. On the local MCP gateway, usage policies act before the call runs.

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The problem

Why it matters.

A credential pasted into a prompt or passed to an MCP tool leaves your control the moment the call is made. Most data controls see it afterwards, or need the content shipped to a cloud service to decide.
  1. 01

    Inspect on the device

    Built-in detectors, patterns, thresholds and context rules run on the workstation. Content inspection is off until you enable a surface.

  2. 02

    Send categories, not content

    Vekri receives the category, confidence and count. Samples are never stored; exact values you define are kept only as salted hashes.

  3. 03

    Act before the call

    On the local MCP gateway, a usage policy can warn, require a justification or approval, deny, or refuse the call until the agent removes the data and repeats it.

  4. 04

    Record a finding

    Observe records the category and what policy did, so the alert carries evidence without carrying the data.

See it work

Step by step.

Sensitive data guardrail
Illustrative data

Claude Code

mcp: tracker.create_issue

body: "deploy failed … aws_secret_access_key=wJalr…EXAMPLEKEY"

body: "deploy failed … [removed]"

On-device inspection

Vekri Endpoint

Found: cloud credentials

Sent to Vekri: category · confidence · count

AI usage policy

Credentials in MCP calls

Repeated without the key: allow

REDACTION_REQUIRED

Issue tracker MCP server

Through the local MCP gateway

First call never reached it

Issue created

Vekri Observe · finding

HighCloud credentials in MCP argumentsClaude Code · tracker.create_issue · policy: remove and repeatNo content stored

What happens

Capabilities

What Vekri does today.

Each item is in the current release and verified by tests in the product repository, with its limits beside it.

  • Vekri Endpoint

    On-device sensitive-data inspection

    Classifies prompts, tool inputs and MCP tool arguments on the workstation, only on the surfaces an administrator enables. Only category names and counts leave the device.

    Claude Code hooks · local MCP gateway · macOS, Linux

    Limit: Off by default. Hooks report after the fact; only the local MCP gateway can act before a call runs.

  • Vekri Protect

    AI usage policies on MCP calls

    Rules on applications, tools and detected data, compiled to Cedar and applied by the local MCP gateway: monitor, warn, require a justification or approval, deny, ask the agent to remove detected data and repeat the call, or suggest an approved alternative.

    local MCP gateway · macOS, Linux

    Limit: Applied only on MCP calls through the local MCP gateway; elsewhere a policy outcome is recorded, not enforced.

  • Vekri Observe

    Sensitive-data findings

    When on-device inspection reports a category, Observe records a finding with the category and confidence, never the content.

    Claude Code hooks · local MCP gateway

    Limit: A match is a classification, not proof that data left.

  • Vekri Control

    Sensitive-data detection management

    Twenty-one categories with an honest detector status, enable and disable with a reason, custom categories (exact values stored only as salted hashes), sample testing that stores nothing, revisions and rollback.

    Vekri Control

  • Vekri Observe

    AI usage alerts

    Sensitive data in an interaction, AI usage policy outcomes and use of blocked applications, from recorded evidence, with CSV export.

    Vekri Control

In detail

Twenty-one categories, each with an honest detector status.

Each category says how it is detected and how confident a match is. Context-rule categories mean the words appear near each other, not that the content is what the category names.

Built-in detectors

  • API keys and secrets
  • Cloud credentials
  • Private keys
  • Payment transactions

Patterns and thresholds

  • Infrastructure configuration details
  • General PII
  • Bulk PII

Context rules (lower confidence)

  • Customer and employee PII
  • Customer authentication data
  • Employee financial information and employment records
  • Financial projections and investment portfolio data
  • Legal documents, mergers and acquisitions
  • Clinical trial data, electronic health records, insurance claims
  • Proprietary source code

Your own

  • Customer-defined categories with keywords, patterns or exact values (stored as salted hashes)

Products

Where it comes from.

Related capabilities

Contact Sales

See Vekri with your own AI applications, agents and systems.

Talk to us about the AI your organization uses, the systems agents reach and how you deploy: in Vekri's cloud, or inside your own Kubernetes cluster.

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