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AI Agent Security

Your agents have access. Pavri gives you control.

AI agents read your data, call your tools, run commands, and change production systems — faster than any security team can review. Pavri discovers every agent, enforces what each one is allowed to do, and contains them when behavior changes.

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Agent discovery · control planeLive
Claude Code · mcp.deploy → productionPROD-DEPLOY-04 · HOLD

The problem

Agents act. Security sees fragments.

Your prompt filter sees text. Your gateway sees API calls. Your EDR sees processes. None of them can tell you which agent did what, on whose behalf, and under which policy.

Three questions security teams cannot answer today:

  1. Which agent acted, and for which user?

  2. What did it touch — which tool, credential, repository, endpoint, or destination?

  3. Which policy applied, what was decided, and what can we do about it now?

What each system sees

Prompt filter
“deploy the fix to prod…”
Gateway
POST /v1/tools/call 200
EDR
node spawned /bin/sh
Identity
session refreshed · MFA ok
Cloud logs
iam:PassRole us-east-1

Pavri correlates

agent
Claude Code · claude-code-2841
user
developer@company.com
context
payments-api · production
action
mcp.deploy → prod-cluster
policy
PROD-DEPLOY-04 · HOLD

One record: the agent, the user, the context, the action, and the policy that decided.

The platform

One control plane for the agent workforce.

01 · See

See every agent

Framework agents, coding assistants, MCP servers, and the tools they reach — with owner, model, configuration, endpoint, workspace, and last activity.

Agent
cursor-agent-2841
Owner
developer@company.com
Last active
12s ago

02 · Control

Control every action

Policy on tool calls, destinations, credentials, commands, files, and budgets, evaluated before the action runs.

Tool
mcp.deploy
Policy
PROD-DEPLOY-04
Decision
HOLD

03 · Catch

Catch AI-native attacks

Prompt injection, memory poisoning, capability escalation, credential exfiltration, and behavioral drift.

Detection
Credential exfiltration
Confidence
High

04 · Contain

Contain and investigate

Hold, deny, quarantine, revoke — then walk the trajectory from alert to agent to action to evidence.

Response
Session quarantined
Evidence
trajectory #4287

The wedge — coding agents

Secure coding agents where they actually run.

Cursor, Claude Code, Copilot, Windsurf, and Gemini CLI sit inside your developer trust boundary. They read source, hold credentials, execute shell commands, and call MCP tools — on endpoints your EDR watches but does not understand.

Pavri routes configured MCP tool calls through a local broker, so a production deployment or a credential read can be held for human approval before it executes.

  • Cursor
  • Claude Code
  • Copilot
  • Windsurf
  • Gemini CLI

macOS · Windows · Linux — managed endpoints

Inside the developer trust boundary

Developerdeveloper@company.com
Coding agentClaude Code · Cursor · Copilot
Pavri local controlMCP broker
mcp.deploy → productionPROD-DEPLOY-04HOLD

Policy evaluated before the action executes.

  • MCP
  • Shell
  • Credentials
  • Repository
  • Production

Coverage

Two agent environments. One control plane.

Available now

Framework agents

One-line integration for LangGraph / LangChain, CrewAI, OpenAI Agents SDK, Anthropic Claude Agent SDK, Google ADK, Microsoft AutoGen, and a generic adapter.

Decisions: allow · alert · deny

Available now

Coding agents

Assistant discovery, MCP visibility, workspace and process context, and broker-routed approval across macOS, Windows, and Linux.

Control point: local MCP broker

Coming soon

No-code / low-code

No-code and low-code automations. Zapier, Make, n8n, ServiceNow, Power Automate, Workato, and more.

Not shipped today

Attack

From file read to incident, correlated in seconds.

A coding agent reads an SSH key and tries to send it through an unapproved MCP server. Pavri correlates the file read with the outbound call, blocks the tool call, quarantines the session, and opens an incident with the full trajectory attached.

#4287 CreatedMonitoring
  1. 1. File read
    Claude Code reads~/.ssh/id_rsa

    agent reads a local credential file

  2. 2. MCP call
    Outbound tool call tounknown-mcp-server

    outbound to an unapproved MCP server

  3. 3. Correl
    credential read + outbound action

    credential read correlated with outbound action

  4. 4. Policy
    CRED-EXFIL-07

    matched before the action executes

  5. 5. Decide
    DENY— tool call blocked before execution

    tool call blocked before execution

  6. 6. Respond
    Session quarantined

    agent session isolated

  7. 7. Incident
    #4287 Created

    full trajectory attached

Operating model

Every surface maps to one chain.

Pavri sits in the execution path, not beside it. Every agent action is resolved through the same six stages.

  1. Who acted

    Identity

    Claude Code
    developer@company.com

  2. Under what context

    Context

    payments-api
    production

  3. What it tried

    Action

    mcp.deploy

  4. What policy decided

    Decision

    HOLD

  5. What was retained

    Evidence

    trajectory #4287

  6. What happened next

    Response

    Approval required

Bring agent actions into your security operating model.

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