Shadow AI Inventory
Shadow AI Inventory is a comprehensive, centralized record of all unauthorized or unmanaged artificial intelligence tools, large language models (LLMs), machine learning datasets, and AI-integrated applications currently in use within an organization.
This inventory serves as the primary artifact for quantifying "Shadow AI"—the unsanctioned use of AI by employees to bypass corporate governance. Unlike a general software inventory, a Shadow AI Inventory specifically tracks AI-unique attributes, such as data training policies, model hallucination risks, and the sensitivity of the data being fed into these systems.
Why a Shadow AI Inventory is Critical
Creating a Shadow AI Inventory is the first step in regaining control over an organization's data privacy and intellectual property. Without visibility, organizations face specific risks that general "Shadow IT" management does not address.
Data Training Leaks: Many public AI tools grant themselves the license to train their models on user data. An inventory identifies which tools are ingesting corporate secrets to retrain public models.
Regulatory Non-Compliance: Frameworks such as the EU AI Act, GDPR, and HIPAA require strict governance of data processing. Unmanaged AI usage creates immediate compliance violations.
Intellectual Property Exposure: Employees often paste proprietary code or sensitive strategy documents into chatbots for summarization. An inventory helps security teams identify high-risk departments (e.g., Engineering or Legal) where this behavior is prevalent.
Supply Chain Vulnerability: AI libraries and models embedded in code (e.g., from Hugging Face) can introduce malicious dependencies. An inventory tracks these non-SaaS AI assets.
Core Components of a Shadow AI Inventory
A robust Shadow AI Inventory goes beyond a simple list of app names. To be actionable for cybersecurity teams, it must include specific metadata regarding risk and usage.
Tool Identity & Classification: The specific name of the AI service (e.g., ChatGPT, Midjourney, Otter.ai) and its category (Generative Text, Image Generation, Meeting Transcription, Coding Assistant).
User & Department Context: Identification of which employees or business units are using the tool. This helps prioritize risk; for example, R&D using an unvetted code assistant is higher risk than Marketing using an image generator.
Data Sensitivity Level: An assessment of the type of data likely interacting with the tool (e.g., PII, Source Code, Financial Data, Internal Memos).
Model Training Policy: A binary flag indicating whether the vendor uses customer data to train their public models. This is the single most critical risk indicator in the inventory.
Authentication Method: How users are logging in (Corporate SSO vs. Personal Gmail accounts). Personal accounts are harder to revoke during offboarding.
Integration Depth: Whether the tool is accessed via a web browser or if it has API access to internal systems (e.g., a suspicious GitHub app or Slack bot).
How to Build a Shadow AI Inventory
Since Shadow AI is, by definition, hidden from IT, building an inventory requires active discovery techniques known as "Outside-In" and "Inside-Out" scanning.
Network Traffic Analysis
Security teams analyze logs from Secure Web Gateways (SWG), Firewalls, and DNS resolvers to identify high-volume traffic to known AI domains. This reveals browser-based Shadow AI usage.
Financial Audits
Analyzing expense reports and corporate credit card transactions often reveals subscriptions to "Pro" or "Plus" versions of AI tools that employees have purchased independently to bypass procurement.
Code Repository Scanning
In engineering environments, scanning code repositories (e.g., GitHub or GitLab) is necessary to identify "Shadow AI Libraries." This involves looking for import statements for unapproved ML libraries or for hardcoded API keys for external AI services (e.g., OpenAI or Anthropic).
Browser Extension Monitoring
Many Shadow AI tools exist as browser extensions that overlay on top of business apps (e.g., a "Grammar Checker" or "Email Writer" running inside corporate Gmail). Endpoint detection agents can enumerate these extensions to add them to the inventory.
Common Questions About Shadow AI Inventory
How does Shadow AI Inventory differ from Shadow IT Inventory? A Shadow IT inventory tracks all unauthorized software. A Shadow AI inventory is a specialized subset focused on AI risks. It prioritizes risk metrics such as "model training rights" and "hallucination potential," which are irrelevant to standard software such as a PDF editor.
Can a Shadow AI Inventory be automated? Yes. Modern CASB (Cloud Access Security Broker) and SSPM (SaaS Security Posture Management) tools can automatically populate this inventory by continuously monitoring network traffic and API connections for known AI application signatures.
What is the "Golden Record" in Shadow AI? The Golden Record is the finalized, approved inventory that includes vetted tools. The goal of cybersecurity teams is to move tools from the "Shadow" inventory list to the "Sanctioned" Golden Record list, or to block them entirely.
Does this include open-source models? Yes. A complete inventory must track not just SaaS tools (such as ChatGPT) but also local, open-source models (such as Llama 2 or Mistral) running on company servers, as these pose risks from software vulnerabilities and unmanaged compute costs.
Building a Shadow AI Inventory with ThreatNG
ThreatNG empowers organizations to construct a comprehensive Shadow AI Inventory by applying an adversarial, "outside-in" approach to discovery. While internal tools monitor authorized traffic, ThreatNG scans the external digital perimeter to identify the unauthorized AI models, large language model (LLM) integrations, and AI-powered SaaS applications that employees deploy without IT oversight.
By systematically mapping the external attack surface, ThreatNG uncovers the "unknown" AI assets that introduce data privacy, regulatory, and intellectual property risks.
External Discovery
ThreatNG’s External Discovery module serves as the primary detection engine for Shadow AI. It automates the identification of AI services by scanning for digital footprints that link the organization to third-party AI providers.
AI Subdomain Discovery: The solution recursively scans for subdomains that indicate AI usage, such as
gpt.company.com,ai-sandbox.dev.company.net, orchatbot.marketing.com. These often reveal unmanaged "sandbox" environments in which developers test public models on corporate data.SaaS Tenant Identification: ThreatNG identifies federation records and verification tokens associated with AI platforms. Discovering a DNS record validating ownership for a tool like "Jasper.ai" or "Midjourney" confirms the existence of a Shadow AI subscription that bypassed central procurement.
Supply Chain AI Mapping: The discovery engine identifies third-party vendors and partners connected to the organization's digital ecosystem. This helps detect if a marketing agency or software vendor is embedding unvetted AI chatbots or tracking pixels into the organization's public-facing websites.
External Assessment
Once a potential Shadow AI asset is discovered, ThreatNG’s External Assessment module evaluates its configuration and security posture. This step validates whether the AI tool is secure or exposes the organization to immediate data leakage.
Detailed Example (AI API Exposure): ThreatNG assesses a discovered developer portal and identifies an exposed API endpoint intended for an internal chatbot. The assessment reveals that the endpoint accepts public queries without authentication, allowing anyone to interact with the model and potentially extract the proprietary data it was trained on (Model Inversion Attack susceptibility).
Detailed Example (Chatbot Configuration): ThreatNG analyzes the configuration of a customer service chatbot found on a marketing microsite. It validates whether the chatbot is configured to store conversation logs publicly or lacks content filtering, creating a risk of "Prompt Injection" attacks in which the bot could be tricked into revealing internal instructions.
Reporting
ThreatNG consolidates Shadow AI findings into actionable reports that serve as the foundation for the Shadow AI Inventory.
Shadow AI Inventory Generation: The platform generates an inventory report listing all identified external assets associated with AI services. This report categorizes tools by type (e.g., Generative Text, Image Synthesis, Code Assistant) to provide a clear view of the "Shadow AI landscape."
Risk-Based AI Reporting: Reports prioritize AI assets based on their specific risks, such as "Training Data Exposure" or "Lack of Enterprise Controls." This allows the Chief Information Security Officer (CISO) to focus governance efforts on high-risk tools that are actively ingesting data.
Continuous Monitoring
The AI landscape evolves rapidly, with new tools launching weekly. ThreatNG’s Continuous Monitoring ensures that the Shadow AI Inventory remains current and accurate.
New AI Service Alerting: As soon as a new subdomain or digital signature associated with an AI provider appears on the organization’s perimeter, ThreatNG triggers an alert. This ensures "Day Zero" visibility into new Shadow AI adoption.
Drift Detection: ThreatNG monitors known AI integrations for changes. If a secure, internal-only AI sandbox suddenly becomes accessible to the public internet due to a firewall change, ThreatNG detects this drift and alerts the security team immediately.
Investigation Modules
ThreatNG’s Investigation Modules allow analysts to conduct deep forensic analysis to understand the scope and ownership of Shadow AI usage.
Detailed Example (Sensitive Code Exposure Investigation): This module scans public code repositories (e.g., GitHub) for AI-related leaks. If ThreatNG identifies a hardcoded OpenAI API Key or Hugging Face Token in a developer’s public repository, it confirms that the organization has an unmanaged, paid connection to these AI services. The investigation traces the key to a specific user, facilitating immediate revocation and policy enforcement.
Detailed Example (Cloud & SaaS Exposure Investigation): This module deep-dives into the infrastructure hosting the Shadow AI. If a discovered AI tool is hosted on a personal AWS account rather than the corporate cloud environment, the investigation reveals the lack of enterprise security controls (like SSO or logging), validating the need to block the asset.
Intelligence Repositories
ThreatNG enriches the Shadow AI Inventory with external threat intelligence to contextualize each tool's risk.
Vulnerability Intelligence: ThreatNG maps discovered AI libraries and frameworks (e.g., PyTorch, TensorFlow versions) to known vulnerabilities. If a Shadow AI instance is running an outdated version of a library with a known remote code execution flaw, the risk score increases.
Dark Web Correlation: The solution monitors for compromised accounts associated with AI tools. If ThreatNG detects credentials for the organization's "Shadow" ChatGPT Team account for sale on the dark web, it issues an imminent account takeover alert.
Complementary Solutions
ThreatNG acts as the "External Radar" for Shadow AI, feeding discovery data into internal enforcement and management platforms to create a closed-loop governance process.
Complementary Solution (Cloud Access Security Broker - CASB): ThreatNG integrates with CASB platforms by providing them with the list of discovered external AI domains and subdomains. While CASBs monitor user traffic, they often miss API-based connections or instances set up outside the corporate network. ThreatNG fills this gap, allowing the CASB to update its blocking policies to cover the entire Shadow AI spectrum.
Complementary Solution (Secure Web Gateway - SWG): ThreatNG pushes high-risk or unvetted AI application domains to the Secure Web Gateway. This allows the organization to block employee access to these tools at the network layer until they are properly vetted and added to the official inventory.
Complementary Solution (Third-Party Risk Management - TPRM): ThreatNG populates TPRM systems with the list of discovered AI vendors and partners. This ensures that the vendor risk team conducts due diligence on the AI providers the organization is actually using, checking their data privacy policies and training data practices.
Examples of ThreatNG Helping
Helping Prevent IP Leakage: ThreatNG discovered a "Shadow" subdomain (
code-assist.dev.company.com) hosting an open-source LLM for code generation. The External Assessment revealed that the interface was publicly accessible without a password. The discovery allowed the security team to take down the instance before proprietary source code could be exposed to the public internet.Helping Enforce AI Policy: A marketing team purchased a subscription to a video generation AI tool using a corporate credit card. ThreatNG detected the DNS verification record associated with the tool's domain. The report alerted the IT department, who then engaged the marketing team to migrate the subscription to an enterprise plan with proper data privacy guarantees.
Examples of ThreatNG Working with Complementary Solutions
Working with Identity Governance: ThreatNG detects a leaked API key for an AI service in a public code repository. It triggers an alert in the Identity Governance and Administration (IGA) solution, which automatically revokes the compromised identity and forces a credential rotation for the developer.
Working with GRC Platforms: ThreatNG pushes the validated Shadow AI Inventory into the Governance, Risk, and Compliance (GRC) platform. This ensures that the organization’s "Record of Processing Activities" (RoPA) for GDPR compliance accurately reflects all AI tools that process personal data, including those previously unknown.
