Shadow AI: The Security Gap Your DLP Can't See

Your marketing team is drafting campaigns in ChatGPT. Your engineers are debugging code in Claude. Your sales team is summarizing calls in Gemini. Your support team is generating responses in Copilot.

You have zero visibility into any of it.

This is the shadow AI problem, and it's happening at every company right now. A majority of developers already use AI tools daily (Stack Overflow Developer Survey 2025). Every unmonitored prompt is a potential data leak — customer names, internal strategies, proprietary code, financial data, all flowing into AI models with no governance layer in between.

Why Your DLP Can't See It

Traditional Data Loss Prevention (DLP) tools were designed for a different era. They monitor email attachments, USB drives, cloud storage uploads, and network traffic for known data patterns. They're excellent at catching someone emailing a spreadsheet of customer records.

But when an employee types customer data directly into ChatGPT's browser interface, DLP sees nothing. The data travels over HTTPS to an AI provider — it looks like normal web traffic. There's no file attachment to scan, no email to intercept, no cloud storage upload to flag.

The same gap exists for CASB (Cloud Access Security Broker) tools. They can block access to ChatGPT entirely, but that's not governance — that's a ban. And bans don't work when AI tools make your employees significantly more productive. Block ChatGPT, and they'll use Claude. Block Claude, and they'll use a dozen alternatives you've never heard of.

The Enablement Approach

The right approach isn't blocking AI tools. It's governing how they're used. Your team is already using AI — Shrike makes sure they can do it safely.

Here's what enablement-first governance looks like in practice:

• Visibility — See every AI interaction across the organization. Which tools, which teams, what types of data.
• Protection — PII, credentials, and proprietary data detected and redacted before it reaches the model. The employee continues working. The data stays protected.
• Policy — Define what's allowed per team, per tool, per data classification. Marketing can use ChatGPT for campaign drafts, but customer PII gets redacted automatically.
• Audit — Every AI interaction logged with full context. When the auditor asks "what AI tools are in use and what data are employees sharing?" you have the answer.

Five Minutes to First Scan

The fastest path to shadow AI visibility is the LLM Proxy Gateway. Route your organization's outbound AI calls through Shrike, and every prompt gets governed before it reaches OpenAI, Anthropic, or Gemini. Here's what happens once it's running:

1. An internal AI assistant, dev tool, or workflow sends a prompt to the model provider.
2. The request routes through Shrike's LLM Proxy Gateway. The detection pipeline scans it in-flight.
3. PII is detected and redacted. Credit card numbers become [CARD_1]. Email addresses become [EMAIL_1]. SSNs become [SSN_1].
4. The redacted prompt reaches the AI model. The application gets its response. The sensitive data never left the organization.
5. The interaction is logged — timestamp, model, data types detected, action taken. Full audit trail.

No agent installation on workstations. The Proxy Gateway sits at the network egress or as a drop-in provider endpoint. It works with any AI tool your teams reach through the org's provider account — that's where shadow AI's data actually flows.

From Shadow AI to Governed AI

The goal isn't to eliminate shadow AI. It's to bring it into the light. When you have visibility into every AI interaction, shadow AI becomes governed AI. You can make informed decisions about which tools to sanction, which data classifications to protect, and which teams need additional training.

The companies that figure this out first won't just avoid data breaches — they'll move faster. Their employees will use AI tools confidently, knowing that sensitive data is protected automatically. Their CISOs will sleep at night, knowing they can answer "what AI tools are in use?" with data, not guesses.

Your team is already using AI. The only question is whether you can see it.