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Risk Calculator

Shadow AI Risk Estimator

Estimate your organization's exposure to unsanctioned AI tools in 2 minutes. See the cost, compliance risk, and data leak probability — and which Shield tier closes every gap.

Step 1 of 520%

Company Size

Larger organizations have more shadow AI surface area — more teams, more tools, more data flowing through unsanctioned channels.

The Problem

What Is Shadow AI — and Why It Matters

Shadow AI is the gap between the AI tools your team actually uses and the tools your IT department knows about. A 2024 survey found that 56% of knowledge workers use AI tools at work — but only 26% of those tools are sanctioned by IT. Every unsanctioned tool is an invisible data egress point: your security team can't log it, your compliance framework doesn't cover it, and your auditors will eventually ask about it.

56%
Workers use AI tools
Only 26% sanctioned by IT
3.4×
More tools than IT knows
Average per 100-employee org
$4.8M
Avg breach cost w/ AI
IBM 2024: $1.8M higher than avg

The Three Shadow AI Risks

Data Leak

Every unsanctioned AI tool is a data egress point your DLP doesn't cover. Source code, API keys, customer PII, and financial data flow through prompts with zero inspection, zero logging, and zero audit trail. Traditional DLP tools scan email and file attachments — they're blind to JSON API payloads.

Compliance Gap

SOC 2, HIPAA, and GDPR auditors now include AI workloads in their scope. But your existing controls — network firewalls, endpoint monitoring, access management — don't extend to LLM API calls. When an auditor asks 'How do you ensure PHI doesn't reach OpenAI?', you need evidence, not reassurances.

Cost Surprise

Shadow AI isn't free — API costs, data breach exposure, and compliance remediation add up fast. Unapproved OpenAI accounts on personal credit cards create procurement blind spots. A single API key leaked through a prompt context can run up thousands in unauthorized usage before anyone notices.

How Shield Closes the Shadow AI Gap

01

Deploy One Local Proxy

Shield installs as a local desktop application — one binary, one config file. Point your LLM clients to Shield instead of the provider directly. No per-tool approval workflow needed. No network changes required. Install it once and every AI API call flows through inspection automatically, regardless of which front-end tool initiates it.

02

Inventory Every AI Call Automatically

Instead of surveying employees about which AI tools they use (and hoping they're honest), Shield's audit log shows you exactly which providers are being called, by which users, and with what data classifications. You discover shadow AI you didn't know existed — and get the evidence to act on it.

03

Redact Sensitive Data Before It Leaves

Shield's detection engine inspects every request body before it reaches the provider. PII, PHI, secrets, credentials, and proprietary code are identified and redacted or blocked based on your policy. The provider never sees the raw data — and because redaction happens locally, data residency and sovereignty concerns are eliminated.

04

Prove Compliance with Tamper-Evident Logs

Every request generates a cryptographically chained audit entry. Each log entry includes metadata, detected data classes, redaction actions, and a hash linking to the previous entry. Export evidence packages mapped to SOC 2 CC5.1–CC5.3, HIPAA §164.312, GDPR Art. 30, and ISO 27001 A.12.4.

Close Your Shadow AI Gap Today

Shield deploys in minutes — one binary, one env var — and gives you visibility into every AI API call across your organization. Redact sensitive data before it leaves your device, prove compliance to auditors, and stop wondering what tools your team actually uses.

See Pricing Book a Demo

Frequently Asked Questions

Shadow AI refers to AI tools and services that employees use without IT department knowledge or approval — ChatGPT for debugging, Claude for report drafts, Gemini for data analysis, Notion AI for internal docs. A 2024 survey found that 56% of knowledge workers use AI tools at work, but only 26% of those tools are sanctioned by IT. Every unsanctioned tool is a data egress point your security team can't see, log, or control.
This estimator uses conservative multipliers derived from industry breach data and known AI data exposure patterns. It's designed to give a directional risk assessment — not an actuarial prediction. The primary value is surfacing the dimensions of shadow AI risk (tool count, data types, approval gaps, industry regulation) and mapping them to concrete Shield tier recommendations. For a formal assessment, the Shield compliance package includes a detailed control mapping auditors can review directly.
Shield sits as a local proxy between your apps and every LLM provider. Instead of trying to inventory and approve every individual AI tool your team discovers, you deploy Shield once and it inspects all LLM traffic regardless of which front-end tool initiates it. The audit log shows exactly which providers are being called, by whom, with what data classes detected — giving you visibility into shadow AI you didn't know existed, with redaction protecting data before it leaves your machine.
You can try, but LLM APIs are served over standard HTTPS on port 443 — the same port as every SaaS app your team already uses. Blocking by IP range is a whack-a-mole game across dozens of providers with constantly rotating endpoints. DNS filtering catches web interfaces but misses API calls from local tools, IDE plugins, and CLI wrappers. Shield takes the opposite approach: don't block AI (your team needs it to ship), but ensure every call is inspected, classified, and secured before data leaves the device.
Small teams have the highest shadow AI risk per capita. A 15-person startup might have every engineer using Cursor, every PM using ChatGPT, and every salesperson using Claude — all with zero IT oversight, because no one has 'IT' in their title. These teams send proprietary code, customer data, and internal strategy through AI tools daily. The 25% deposit tier on Shield's Foundation plan ($2,500 upfront) makes it accessible for startups — and the compliance documentation helps pass enterprise security questionnaires when you land your first big customer.
Source code and API keys are the top two by volume — developers paste entire files and .env snippets into prompts routinely. Customer PII (names, emails, addresses) comes next from support and sales teams. Financial data and internal strategy documents round out the top five. The dangerous pattern: proprietary business logic embedded in a prompt context is invisible to traditional DLP tools (which scan file attachments and email subject lines, not JSON API payloads). Shield's detection engine inspects the request body itself — the only layer where these patterns are visible.