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Multi-surface environment

July 14, 2026

The Limits of DNS Blocking: How to Map Your True AI Attack Surface

TLDR; Domain level filters fail to stop AI sidebars, developer IDE integrations, or local models. Establishing true network security…

By Stack Breach

5 min read

TLDR; Domain level filters fail to stop AI sidebars, developer IDE integrations, or local models. Establishing true network security requires deploying managed browser configurations and background socket monitoring agents.

Enterprise security departments often rely on domain-level blocking to manage the risks of generative AI. They add known AI domains to their web filters and assume the threat of data leakage is addressed. This approach does not protect the network. Modern AI adoption bypasses simple firewall blocklists through browser extensions, local developer environments, API integrations, and unmonitored software dependencies. This article provides the technical architecture and configuration details required to map your organization's true AI attack surface.

Why DNS Blocklists Fail

DNS-level blocking only restricts standard web interfaces. It is blind to the programmatic and integrated ways employees and developers interact with language models.

Modern AI usage is highly decentralized. Employees bypass web interface blocks by using:

  1. Browser Extensions: Chrome and Edge extensions with integrated AI sidebars. These inject scripts directly into pages and communicate with external servers using native API requests.
  2. Local Model Executions: Developers running open-source models (such as Llama 3 or Mistral) locally on their workstations using tools like Ollama. These do not generate outbound network traffic to known AI provider domains.
  3. Third-Party Integrations: AI applications integrated directly into corporate collaboration platforms like Slack, Microsoft Teams, or Jira.
  4. Programmatic SDK Calls: Developers using API wrappers and IDE extensions (such as Cursor, Github Copilot, or Tabnine) that query model endpoints directly from the development environment.

For example, a developer refactoring a private proprietary codebase using an IDE extension does not access a web portal. The query travels directly to API endpoints (such as api.githubcopilot.com or api.openai.com). Your firewall sees standard HTTPS egress to cloud hosting networks, but it cannot inspect the payload containing the code.

The Risk of Silent Data Leaks

Unmonitored outbound AI traffic introduces operational and regulatory risks.

According to the IBM Cost of a Data Breach Report, the average time to identify a data breach is 247 days. You can review the details of the breach timeline data here: https://www.ibm.com/reports/data-breach

If sensitive files, customer details, or source code are pasted into unapproved AI tools, the data is transmitted to external servers. Many public services use prompt data to optimize future models. By the time your security team detects the exposure, the information has already been integrated into external model weights.

In Australia, the regulatory consequences of information security failures are significant. The Federal Court ordered Australian Clinical Labs to pay a 5.8 million dollar penalty for data governance and security failures. The details of the judgment are hosted by the Office of the Australian Information Commissioner: https://www.oaic.gov.au/

The Multi-Vector Telemetry Pattern

To secure your network, you must establish visibility across all six capture vectors.

1. Browser Extensions

MDM-deployed extensions log text transfers and file uploads directly at the application boundary before encryption occurs. The managed config JSON schema pushed to Google Admin Console or Microsoft Intune looks like this:

{
  "ingestUrl": {
    "Value": "https://api.stackbreach.io/ingest"
  },
  "apiKey": {
    "Value": "sb_live_your_org_key"
  },
  "userId": {
    "Value": "${user.email}"
  },
  "enabled": {
    "Value": true
  }
}

2. Network Proxies

Instead of decrypting TLS traffic at the gateway, which breaks developer tools and increases latency, a Proxy Auto-Configuration (PAC) file routes matching AI hostname traffic to a dedicated telemetry proxy:

function FindProxyForURL(url, host) {
  var aiHosts = [
    "api.openai.com", "api.anthropic.com", "api.mistral.ai",
    "generativelanguage.googleapis.com", "api.cohere.ai",
    "api.perplexity.ai", "api.groq.com", "api.together.xyz"
  ];
  for (var i = 0; i < aiHosts.length; i++) {
    if (dnsDomainIs(host, aiHosts[i])) {
      return "PROXY telemetry-proxy.corp.com:8877";
    }
  }
  return "DIRECT";
}

3. SDK Wrappers

Library hooks track model dependencies inside developer codebases, logging token counts, model names, and latency.

4. SaaS Webhooks

Integrations monitor data flows within collaborative environments.

5. Host Agents

Daemons run on local machines to track local model executions.

6. API Gateway Plugins

Traffic monitors audit backend infrastructure requests.

The Standardized Telemetry Payload

A normalized telemetry schema ensures that every monitored event is recorded consistently, regardless of the capture method. This schema is transmitted via POST /api/v1/ingest:

{
  "deviceId": "uuid-v4-identifier",
  "userId": "user@company.com",
  "timestamp": "2026-05-06T10:00:00Z",
  "destination": {
    "domain": "api.openai.com",
    "path": "/v1/chat/completions"
  },
  "request": {
    "method": "POST",
    "sizeBytes": 2048
  },
  "device": {
    "os": "darwin",
    "hostname": "macbook-user"
  },
  "agentVersion": "1.0.0",
  "captureMethod": "extension"
}

Detailed Enterprise Deployment Playbook

For security operations teams, deploying these monitors at scale requires zero-friction automation. Below are the precise configurations for each target operating system and browser environment.

Pushing the Browser Agent via Google Workspace Admin Console

  1. Navigate to Devices > Chrome > Apps & extensions > Users & browsers.
  2. Select the target Organizational Unit (OU) on the left panel.
  3. Click the yellow + button and choose Add Chrome app or extension by ID.
  4. In the dialog, set the source to From the Chrome Web Store, input the Extension ID of the Stack Breach Agent, and click Save.
  5. Change the installation policy from Force-installed to Force-installed + pin to browser toolbar.
  6. In the right panel, locate Policy for extensions and upload the following managed policy schema:
{
  "ingestUrl": {
    "Value": "https://api.stackbreach.io/v1/ingest"
  },
  "apiKey": {
    "Value": "sb_live_0a49f8c872d8a4f91e92c"
  },
  "userId": {
    "Value": "${user.email}"
  },
  "capturePayloadMetadata": {
    "Value": true
  },
  "allowedDomainsList": {
    "Value": [
      "api.openai.com",
      "api.anthropic.com",
      "api.cohere.ai",
      "api.groq.com",
      "generativelanguage.googleapis.com"
    ]
  }
}

This configuration ensures that the browser agent automatically resolves the current logged-in corporate user's email via the ${user.email} macro, providing immediate attribution for any policy violations.

Edge Browser Deployment via Microsoft Intune (Settings Catalog)

To deploy the extension to Microsoft Edge across managed Windows and macOS endpoints, you must build a Configuration Profile using the Settings Catalog.

  1. In the Microsoft Endpoint Manager admin center, navigate to Devices > Configuration profiles > Create profile.
  2. Select Platform: Windows 10 and later, Profile type: Settings catalog.
  3. In the Settings picker, search for Control which extensions are installed silently under the Microsoft Edge category.
  4. Check the setting and input the extension details:
Extension ID: hpfmgdknfklmeapgdmkplp...;https://edge.microsoft.com/extensionapi/chrome/update2
  1. Next, search for Configure extension management settings (ExtensionSettings policy) to push the managed configuration. Enable the setting and paste the following XML-escaped JSON string containing the runtime config parameters:
{
  "*": {
    "installation_mode": "blocked"
  },
  "hpfmgdknfklmeapgdmkplp...": {
    "installation_mode": "force_installed",
    "update_url": "https://edge.microsoft.com/extensionapi/chrome/update2",
    "runtime_allowed_hosts": ["https://api.stackbreach.io"],
    "managed_configuration": {
      "ingestUrl": "https://api.stackbreach.io/v1/ingest",
      "apiKey": "sb_live_0a49f8c872d8a4f91e92c",
      "enabled": true
    }
  }
}

This Edge configuration blocks all unauthorized browser add-ons by default (using the * wildcard with installation_mode: blocked) while force-installing the Stack Breach agent and injecting the API parameters directly into the browser's registry keys.

Technical Auditing of Host Workstations

Developer workstations are a primary source of data exfiltration due to the local execution of large language models and command-line scripts. You must implement host-level socket monitoring to verify that these local instances do not bypass your compliance boundaries.

Auditing Linux Workstations with systemd

On Linux hosts, you must deploy a background daemon that periodically polls TCP connection states using the kernel's socket statistics system. The daemon parses output from ss -tnp and checks it against a locally cached list of resolved AI provider IP addresses.

Here is the systemd service file deployed via Ansible to ensure the daemon runs continuously:

[Unit]
Description=Stack Breach Host Connection Telemetry Daemon
After=network.target
[Service]
Type=simple
User=root
WorkingDirectory=/opt/stackbreach
EnvironmentFile=/etc/stackbreach.env
ExecStart=/usr/bin/node /opt/stackbreach/agent.js
Restart=always
RestartSec=10
StandardOutput=syslog
StandardError=syslog
SyslogIdentifier=stackbreach-agent
[Install]
WantedBy=multi-user.target

The accompanying environment configuration file /etc/stackbreach.env contains the required ingest variables:

SB_API_KEY=sb_live_0a49f8c872d8a4f91e92c
SB_INGEST_URL=https://api.stackbreach.io/v1/ingest
SB_POLL_INTERVAL_MS=5000
SB_DNS_CACHE_TTL_SEC=3600

Auditing macOS Workstations with launchd

For Apple macOS devices, the daemon is deployed as a launchd launch daemon running in the system context. The configuration file /Library/LaunchDaemons/io.stackbreach.hostagent.plist is pushed via MDM:

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>io.stackbreach.hostagent</string>
    <key>ProgramArguments</key>
    <array>
        <string>/usr/local/bin/node</string>
        <string>/opt/stackbreach/agent.js</string>
    </array>
    <key>RunAtLoad</key>
    <true/>
    <key>KeepAlive</key>
    <true/>
    <key>EnvironmentVariables</key>
    <dict>
        <key>SB_API_KEY</key>
        <string>sb_live_0a49f8c872d8a4f91e92c</string>
        <key>SB_INGEST_URL</key>
        <string>https://api.stackbreach.io/v1/ingest</string>
    </dict>
    <key>StandardOutPath</key>
    <string>/var/log/stackbreach-agent.log</string>
    <key>StandardErrorPath</key>
    <string>/var/log/stackbreach-agent.err</string>
</dict>
</plist>

Developing a Verification Plan

Organizations should implement a systematic audit to establish their AI usage baseline.

Step 1: Model Inventory

Identify all external AI APIs receiving queries from your corporate network.

Step 2: Data Classification Mapping

Log whether transactions involve public, internal, or confidential data.

Step 3: Integration Audit

Review collaboration suites for active AI add-ons.

Establish Governance

Continuous auditing is necessary to maintain compliance with information security standards. Proactive monitoring provides the evidence needed to satisfy regulatory reviews.

You can join the waitlist for a scoped Shadow AI Diagnostic to map your organization's risk profile: https://stackbreach.io/#staging-waitlist