External AI Asset Inventory

An External AI Asset Inventory is a comprehensive, continuously updated catalog of all artificial intelligence and machine learning components that belong to an organization and are exposed to the public internet. In the context of cybersecurity, this inventory is a critical subset of External Attack Surface Management (EASM), with a focus on the rapid proliferation of AI technologies.

This inventory does not just list internal tools; it identifies every point where an organization’s AI footprint touches the outside world, creating a potential entry point for cyber threats.

Core Components of an External AI Asset Inventory

To effectively secure an organization, an External AI Asset Inventory must account for various elements that attackers can discover and probe.

• Public-Facing AI Models: This includes chatbots embedded on company websites, recommendation engines, and customer support agents powered by Large Language Models (LLMs).

• Exposed AI APIs: Many organizations integrate third-party AI services (like OpenAI or Anthropic) into their public applications. The inventory tracks these API endpoints and the keys used to access them.

• Training Datasets: Large sets of data stored in cloud storage buckets (such as AWS S3 or Azure Blob Storage) that are inadvertently left public.

• AI Infrastructure: Specialized compute resources, such as GPU-powered instances or vector databases (e.g., Pinecone, Milvus), that are accessible via the internet.

• Code Repositories: Publicly accessible code repositories (like GitHub or Hugging Face) that may contain proprietary model weights, training logic, or hardcoded credentials for AI services.

• Shadow AI: Unauthorized AI tools and plugins that employees use and connect to corporate networks or data without IT approval.

Why External AI Asset Inventory is Critical

The rapid adoption of AI has outpaced traditional security controls. An accurate inventory is the foundation for several key security functions.

Combating Shadow AI Employees often sign up for new AI productivity tools to help with coding or writing. If these tools are not vetted and cataloged, they create "Shadow AI" risks where sensitive corporate data is uploaded to unsecure third-party servers. An inventory helps security teams see these assets and bring them under governance.

Mitigating Supply Chain Risks Modern AI applications rely heavily on open-source libraries and pre-trained models. An inventory tracks which models and versions are in use, enabling organizations to respond quickly if a vulnerability is discovered in a specific library (e.g., a flawed version of PyTorch or TensorFlow).

Regulatory Compliance New regulations, such as the EU AI Act, require organizations to maintain strict documentation of their AI systems. An external inventory is the first step in demonstrating control and compliance with these legal standards.

Risks of an Incomplete Inventory

Failing to maintain an accurate External AI Asset Inventory exposes an organization to specific AI-related threats:

• Model Inversion and Extraction: Attackers can query publicly available models to reverse-engineer the training data, potentially exposing PII or trade secrets.

• Prompt Injection: Without knowing where chatbots are deployed, security teams cannot test them for prompt injection vulnerabilities, where attackers manipulate the AI into performing unauthorized actions.

• Resource Hijacking: Exposed AI infrastructure is a prime target for "cryptojacking," where attackers steal expensive GPU compute cycles for their own use.

Frequently Asked Questions

What is the difference between an AI Registry and an External AI Asset Inventory? An AI Registry is typically an internal governance tool used by data science teams to track model versions and performance. An External AI Asset Inventory is a security tool that identifies what is visible to attackers on the open internet.

How does Shadow AI affect the External AI Asset Inventory? Shadow AI represents the "unknown" portion of the inventory. These are assets that exist and are exposed but are not officially recorded. A primary goal of building this inventory is to discover and classify Shadow AI to reduce risk.

Does an External AI Asset Inventory include internal models? Generally, no. It focuses on internet-facing assets. However, if an internal model is accidentally exposed via a misconfigured firewall or public API, it becomes part of the external inventory and a high-priority risk.

Why can't traditional asset management tools handle AI? Traditional tools look for servers, IP addresses, and software versions. They often lack context to identify specific AI components, such as model weights, vector embeddings, or the subtle signatures of AI-driven API endpoints.

ThreatNG and External AI Asset Inventory

ThreatNG plays a pivotal role in building and maintaining an External AI Asset Inventory by leveraging its external attack surface management (EASM) capabilities to detect Artificial Intelligence and Machine Learning (AI/ML) technologies. It automates the discovery of "Shadow AI"—unauthorized or forgotten AI tools, models, and APIs exposed to the public internet—without requiring internal agents or authenticated connectors.

External Discovery of AI Technologies

ThreatNG uses purely external unauthenticated discovery to map an organization's digital footprint, specifically categorizing and identifying technologies, including those in the AI ecosystem.

• AI Vendor and Platform Identification: ThreatNG explicitly identifies specific AI and Machine Learning vendors operating within an organization's external infrastructure. Its Domain Record Analysis and Technology Identification modules can detect the presence of AI model and platform providers, including Anthropic, Cohere, CustomGPT, Hugging Face, OpenAI, Stability AI, and Weights & Biases.

• AI Development and MLOps Discovery: Beyond models, ThreatNG identifies the supporting infrastructure for AI development and MLOps. It detects AI Development & MLOps tools, including CassidyAI, GenTrace (AI), GoSpace, GPT-Trainer, LangChain, MetaTrust, Pinecone, ElevenLabs, and ReadAI.

• Shadow AI Detection: By continuously scanning for these specific technologies across subdomains and web assets, ThreatNG reveals "Shadow AI" instances where employees may have deployed AI tools or connected to third-party AI services without IT knowledge or approval.

External Assessment of AI Risks

Once AI assets are identified, ThreatNG assesses the risks of their exposure, focusing on how they connect to the broader attack surface.

• Supply Chain & Third-Party Exposure: ThreatNG evaluates the Supply Chain & Third-Party Exposure Security Rating by analyzing the Technology Stack and SaaS Identification. This allows organizations to see exactly which third-party AI vendors are integrated into their external environment and assess the aggregate risk of these dependencies.

• Non-Human Identity (NHI) Exposure: AI applications heavily rely on API keys and service accounts for machine-to-machine communication. ThreatNG’s Non-Human Identity (NHI) Exposure rating quantifies vulnerability to threats originating from high-privilege machine identities, such as leaked API keys for AI services, which are often invisible to traditional internal security tools.

• Data Leak Susceptibility: AI models require vast amounts of data. ThreatNG assesses Data Leak Susceptibility by uncovering Cloud Exposure, specifically looking for exposed open cloud buckets that may contain sensitive training datasets or model weights left accessible to the public.

Investigation Modules for AI Asset Verification

ThreatNG’s investigation modules provide granular details to verify and contextualize discovered AI assets.

• Domain and Subdomain Intelligence: The Domain Intelligence module facilitates the identification of vendors and technologies, including the specific AI/ML categories mentioned above. Subdomain Intelligence further refines this by analyzing HTTP Responses and Headers to identify applications and APIs hosted on subdomains, where many AI-driven microservices reside.

• Sensitive Code Discovery: The Cyber Risk Exposure rating includes findings from Sensitive Code Discovery, which searches for "code secret exposure". This is critical for identifying hardcoded credentials for AI services (e.g., an OpenAI API key) that are inadvertently pushed to public repositories or embedded in client-side code.

• Mobile App Exposure: ThreatNG evaluates Mobile App Exposure by scanning marketplaces for mobile apps and analyzing them for embedded Access Credentials. This includes specific checks for AI- and data-service keys, such as Amazon AWS Access Key ID, Google API Key, and Hugging Face tokens (which are implied by general API token checks).

Intelligence Repositories and Vulnerability Correlation

ThreatNG enriches its inventory with threat intelligence to prioritize risks associated with AI assets.

• Vulnerability Mapping (DarCache): ThreatNG cross-references discovered technologies with its DarCache Vulnerability repository. This fuses data from NVD, EPSS (predictive scoring), and KEV (Known Exploited Vulnerabilities) to determine if the specific versions of AI libraries or platforms in use have known vulnerabilities.

• Compromised Credentials (DarCache Rupture): By monitoring for Compromised Credentials, ThreatNG helps organizations determine whether email accounts used to register for third-party AI services (such as an admin account for an OpenAI enterprise plan) have been breached.

Continuous Monitoring and Reporting

Building an inventory is not a one-time task. ThreatNG ensures the AI asset inventory remains current through:

• Continuous Monitoring: The platform continuously monitors the external attack surface, ensuring that as soon as a new AI service is spun up or a new AI vendor is integrated, it is detected and added to the inventory.

• Inventory Reporting: ThreatNG generates detailed Inventory reports that list discovered technologies, enabling security teams to export a list of all externally facing AI assets for compliance and governance.

Cooperation with Complementary Solutions

ThreatNG acts as a source of truth for external AI visibility, feeding data into internal security and management platforms.

• Integration with GRC Platforms: ThreatNG’s External GRC Assessment maps findings to frameworks such as ISO 27001, NIST AI RMF (via NIST CSF), and GDPR. This helps GRC teams validate that the organization’s use of external AI tools complies with regulatory requirements regarding data privacy and vendor risk management.

• Integration with SIEM and SOAR: While ThreatNG identifies Security Monitoring (SIEM/XDR) vendors like Splunk, Datadog, and Rapid7 within an organization's stack, it complements these systems by feeding them data on Exposed Ports, New Subdomains, and Tech Stack Changes. This allows the SIEM to alert on anomalous traffic destined for newly discovered, unvetted AI API endpoints.

• Integration with Vulnerability Management: ThreatNG integrates with vulnerability and risk management vendors, including Tenable, Qualys, and Rapid7. It enhances these internal scanners by providing a list of external targets (External AI Assets) that they may not be aware of, ensuring comprehensive scan coverage.

Frequently Asked Questions

Can ThreatNG detect specific AI models being used? ThreatNG identifies AI platforms and vendors (e.g., OpenAI, Hugging Face) and supporting infrastructure (e.g., Pinecone, LangChain) by analyzing external digital signatures, headers, and page content.

How does ThreatNG find "Shadow AI"? It uses External Discovery to identify subdomains and web assets that IT may not be aware of. It then applies Technology Identification to these assets to see if they are running known AI/ML software or connecting to AI/ML cloud providers.

Does ThreatNG check for leaked AI API keys? Yes. Through its Sensitive Code Discovery and Mobile App Exposure capabilities, ThreatNG scans for hardcoded secrets, including API keys and access tokens that could be used to access AI services.