The Hidden Risk

Shadow AI Is Already
Inside Your Organization

Your employees are using AI every day — in browsers, in code editors, in SaaS tools, and through APIs. Most of it is invisible to your security team. That's Shadow AI.

AI Usage Is Everywhere — and Growing Fast

AI is no longer limited to a data science team running experiments. Today, developers embed AI SDKs directly in production code. Marketing teams use AI writing assistants. Sales reps rely on AI-powered CRM features. Support agents interact with AI copilots. And employees across every department access AI web apps on their own.

The result is a sprawling, unmonitored AI footprint — spanning developer tools, browser extensions, SaaS platforms, API integrations, and consumer apps — that your existing security stack was never designed to see.

78%
of employees use AI tools not provisioned by IT
4x
growth in AI API calls year over year
0%
visibility in most organizations

The AI usage you're not seeing

AI Provider Access
456
AI Web App Usage
325
Dev / Embedded AI
278
AI-Enabled SaaS
188
OpenAI SDK
Anthropic SDK
Grammarly AI
Huggingface
LangChain
Chrome AI Extensions
LlamaIndex
CRM AI Features

How Organizations Try to Detect Shadow AI

There are several approaches to uncovering unauthorized AI usage — each with significant trade-offs.

1

Network Traffic Inspection

Monitor outbound traffic for connections to known AI provider domains and APIs. Firewalls and proxies can flag requests to OpenAI, Anthropic, Hugging Face, and similar endpoints.

Limited to known domains. Misses embedded AI in SaaS tools, browser extensions, and SDK calls routed through intermediary services.
2

Endpoint / DLP Agents

Deploy agents on employee devices to monitor installed applications, browser activity, and clipboard data for AI tool usage and data leakage to AI services.

High friction, privacy concerns, and blind to server-side AI usage in developer pipelines or API-to-API integrations.
3

Manual Surveys & Audits

Ask teams to self-report which AI tools they use. Conduct periodic audits of SaaS subscriptions and procurement records to identify AI-related purchases.

Relies on honesty. Massively underreports actual usage. Already outdated by the time the audit is complete.

The Aiceberg Approach

Instead of bolting on another point solution, Aiceberg connects to the data your security stack already collects — and turns it into complete AI visibility.

Live

SIEM Alert Forwarding

Aiceberg integrates with every major SIEM in three clicks. Our Guardian can forward every AI safety and security event — blocked prompts, policy violations or language based attacks as a structured alert to your SOC team so it can act immediately.

Three-click setup with any SIEM
Structured alerts with full context
Fits existing SOC workflows
Live

SIEM Log Analysis

The same integration works in reverse. Aiceberg reads your existing SIEM logs — firewall events, proxy logs, DNS queries, SaaS access records — and uses them to discover, classify, and catalog all AI usage across your organization.

Discovers AI usage from existing log data
Classifies by category, service, and risk
Continuous monitoring — not a one-time scan
Roadmap

Code Repository Scanning

Aiceberg will connect to your code repository management solution — GitHub, GitLab, Bitbucket — to scan for AI SDK imports, API keys, model calls, and embedded AI usage as part of ongoing development projects.

Detects AI SDKs and API integrations in code
Flags unapproved models in CI/CD pipelines
Shift-left visibility into developer AI adoption
1

Select Your SIEM

Choose from Splunk, Sentinel, QRadar, Chronicle, and more

→
2

Authorize Access

Grant read/write via API key or OAuth — no agents to deploy

→
3

Full AI Visibility

AI usage is discovered, classified, and alerts start flowing

See what AI your organization is really using

Get complete AI visibility in minutes — not months. No agents, no network taps, no disruption.

Request a Demo If you can't see it, you can't secure it.