Unmanaged AI Assets
Unmanaged AI Assets are a critical cybersecurity category encompassing any Artificial Intelligence (AI) or Machine Learning (ML) component that is deployed, operational, and accessible within an organization's digital environment, but which is not subject to the established security, governance, and operational policies of the central IT and security teams.
These assets represent a massive blind spot and are typically the result of Shadow AI—rapid, unsanctioned deployments by departmental users or developers prioritizing speed over security.
Detailed Characteristics of Unmanaged AI Assets
An AI asset can be a model, a data pipeline, or a service endpoint. When unmanaged, it possesses several high-risk characteristics:
Lack of Inventory and Visibility: The asset is not listed in any official configuration management database or asset inventory. The security team does not know it exists, where it is hosted, or what data it processes. This makes it impossible to apply security controls.
Absence of Security Hardening: Since the asset bypasses security review, it lacks fundamental protections:
No or Weak Authentication: The API endpoint may be exposed without proper access control, allowing unauthenticated or low-privilege users to interact with the model.
No Rate Limiting: This makes the model vulnerable to Denial-of-Service (DoS) or efficient Model Stealing attacks, where an adversary can send unlimited queries to reconstruct the proprietary model logic.
Vulnerable Infrastructure and Dependencies: The underlying software (e.g., Python, ML frameworks such as PyTorch or Scikit-learn, and container environments such as Docker) is often not monitored for patch status.
This leaves the asset vulnerable to exploitation via known Common Vulnerabilities and Exposures (CVEs) that are not being addressed because the asset is outside the patch management process.
Improper Data Handling: The asset may be processing, storing, or transmitting highly sensitive data (PII, IP, trade secrets) without proper encryption or compliance checks.
If the model's training data or model weights are stored in a publicly accessible cloud bucket, the asset creates a massive data leakage risk.
Exposed Business Logic (Prompt Exposure): For generative AI models, the critical system prompt that defines the model's behavior may be inadvertently exposed in error messages or API metadata. This allows attackers to easily perform Prompt Injection attacks, forcing the model to reveal confidential information or perform malicious actions.
An Unmanaged AI Asset functions as a ghost door into the organization's network, running powerful, complex, and uninspected code with access to sensitive data, making it a priority target for external attackers. Unmanaged AI Assets are a critical cybersecurity category encompassing any Artificial Intelligence (AI) or Machine Learning (ML) component that is deployed, operational, and accessible within an organization's digital environment, but which is not subject to the established security, governance, and operational policies of the central IT and security teams.
These assets represent a massive blind spot and are typically the result of Shadow AI—the unsanctioned use of AI tools, models, or platforms by departmental users or developers who prioritize speed over security. Unmanaged AI introduces significant risks because you cannot protect or govern what you are unaware of.
Detailed Characteristics and Cybersecurity Risks of Unmanaged AI Assets
An unmanaged AI asset can be a model, a data pipeline, or a service endpoint. When left outside security governance, it poses several high-risk characteristics:
Vulnerability to Specialized AI Attacks: Unmanaged models introduce unique attack vectors that traditional security tools often cannot detect.
Prompt Injection Attacks: Malicious inputs can be designed to manipulate the AI behavior, causing the model to disclose sensitive information or bypass security controls.
Model Inversion and Extraction Attacks: Attackers can systematically query unhardened models to reconstruct their proprietary algorithms or training data, thereby stealing intellectual property (IP) and sensitive information.
Data Poisoning: A lack of security controls during training allows adversaries to inject malicious data, which can degrade model performance, introduce bias, or embed hidden backdoors that activate when triggered by specific inputs.
Uncontrolled Data Exposure: Unmanaged AI assets lack Data Loss Prevention (DLP) or privacy policies.
Sensitive Data Leakage: Employees may inadvertently share confidential business information, intellectual property, or regulated data with external or unsanctioned AI systems, which may retain or use it for training future models.
Unsecured Data Storage: The training data or model artifacts are often stored in unencrypted or publicly misconfigured cloud storage, creating a direct data breach risk.
Lack of Security Hardening and Patching: Unmanaged AI components fail to meet security standards.
Exposed API Keys: Developers may inadvertently expose API keys and tokens to AI services (such as OpenAI or Hugging Face) in public code repositories, leading to unauthorized use, data breaches, and unexpected financial impacts.
Vulnerable Infrastructure: The underlying ML libraries and frameworks often run without proper vulnerability management, leaving them exposed to known Common Vulnerabilities and Exposures (CVEs).
In summary, an Unmanaged AI Asset functions as a ghost door into the organization's network, running powerful, complex, and uninspected code with access to sensitive data, making it a priority target for external attackers.
ThreatNG is an all-in-one external attack surface management (EASM) and digital risk protection solution designed to identify and assess Unmanaged AI Assets. Since unmanaged assets exist outside internal security controls, ThreatNG's strength lies in its ability to perform purely external, unauthenticated discovery, replicating the methods an attacker uses to find these hidden exposures.
External Discovery
ThreatNG’s External Discovery module ensures that every publicly exposed AI asset is brought into the security scope, addressing the core problem of insufficient inventory and visibility.
How it helps: Unmanaged AI Assets frequently reside on obscure subdomains or utilize third-party platforms without official tracking. The Technology Stack Identification module provides exhaustive, unauthenticated discovery of nearly 4,000 technologies, including 265 categorized as Artificial Intelligence, as well as specific vendors in AI Model & Platform Providers and AI Development & MLOps. The detection of these AI-specific technological fingerprints on any public asset confirms the presence of an unmanaged system.
Example of ThreatNG helping: ThreatNG discovers an untracked subdomain, analytics-llm-tool.company.com, and the Technology Stack module identifies it as running an AI Development & MLOps vendor service. This immediately flags the asset as a probable unmanaged AI service, forcing the security team to initiate governance.
External Assessment
ThreatNG’s external assessments focus on the exploitable weaknesses inherent in unmanaged assets, particularly around authentication, access, and data leakage.
Highlight and Examples:
Unsecured Access Credentials: The Non-Human Identity (NHI) Exposure Security Rating quantifies the vulnerability posed by high-privilege machine identities, such as leaked API keys. These credentials are often left exposed when an asset is unmanaged.
Example: The Sensitive Code Discovery and Exposure capability, a component of the Cyber Risk Exposure rating, scans public code repositories and mobile apps for leaked Access Credentials (such as LLM API keys, AWS Access Key IDs, and generic credentials). Finding a publicly exposed service account key for a discovered AI asset is definitive external proof of the asset's lack of security hardening.
Model/Data Leakage: The Data Leak Susceptibility rating is derived from identifying external digital risks, specifically Cloud Exposure from open cloud buckets. Unmanaged AI models often store training data in these misconfigured, publicly accessible locations.
Example: ThreatNG identifies a public Google Cloud Bucket associated with the organization that contains files labeled model-weights.h5 and customer-data-train.csv. This confirms the unmanaged AI asset is directly exposing proprietary IP and sensitive data, confirming the external attack vector.
Continuous Monitoring
ThreatNG provides Continuous Monitoring of the external attack surface, digital risk, and security ratings, ensuring that any changes to an unmanaged AI asset's risk posture are immediately detected.
How it helps: An unmanaged AI model might suddenly become vulnerable if its hosting service is patched with a zero-day exploit, or if a developer removes a temporary security control. Continuous monitoring ensures that the asset's security rating is instantly updated upon detection of a new exposure, maintaining up-to-date visibility of its risk.
Investigation Modules
ThreatNG’s Investigation Modules provide security teams with the context and irrefutable evidence needed to compel immediate remediation of unmanaged AI assets.
Highlight and Examples:
Online Sharing Exposure: This module identifies an organization's presence on code-sharing platforms such as Pastebin and GitHub Gist. This is a frequent source of leaks from unmanaged development efforts.
Example: An analyst uses this module to find a snippet of the unmanaged AI model’s configuration file posted on a forum by a developer. This snippet includes the model's internal API path and service name, providing the necessary intelligence to shut down the ghost asset.
External Adversary View and MITRE ATT&CK Mapping: ThreatNG aligns an organization’s security posture with external threats, automatically mapping unauthenticated findings to MITRE ATT&CK techniques.
Example: ThreatNG discovers an unmanaged AI endpoint with an exposed port and maps this finding to a MITRE Initial Access technique, prioritizing the vulnerability based on how a real attacker would use it to breach the network and subsequently access the unmanaged model.
Intelligence Repositories
ThreatNG’s Intelligence Repositories (DarCache) provide contextual data to validate and prioritize risks associated with unmanaged AI assets.
How it helps: The Vulnerabilities repository integrates external intelligence, including NVD, EPSS (Exploit Prediction Scoring System), and KEV (Known Exploited Vulnerabilities) data. If a discovered unmanaged AI asset is running on an outdated, vulnerable framework, the KEV data confirms that the model's infrastructure poses an immediate, proven threat, prioritizing the unmanaged asset for decommissioning or urgent patching.
Cooperation with Complementary Solutions
ThreatNG's external discovery and high-certainty data are crucial for activating internal security tools to manage these hidden risks.
Cooperation with the Configuration Management Database (CMDB): ThreatNG's Technology Stack discovery provides an accurate, unauthenticated external inventory of unmanaged AI assets.
Example: When ThreatNG discovers a subdomain running an unauthorized AI Development platform, this external discovery information is used to update the complementary CMDB, immediately marking the asset as unmanaged or unauthorized, which triggers governance workflows for tracking and decommissioning.
Cooperation with Secrets Management Platforms: ThreatNG identifies leaked credentials from the external attack surface.
Example: ThreatNG finds a leaked LLM API key or a service account credential via NHI Exposure. This high-fidelity, external finding is routed to the complementary Secrets Management platform, which automatically revokes the exposed key and forces a rotation across all linked AI services, immediately protecting the unmanaged asset from external credential abuse.
