Stop Shadow AI: The Only EASM Solution for the External AI Attack Surface

ThreatNG provides the unauthenticated, outside-in visibility needed to inventory Generative AI risks, exposed models, and leaked credentials before attackers do.

The threat landscape has fundamentally changed with the rise of AI. The External AI Attack Surface, which includes unintentionally exposed AI endpoints, misconfigured cloud storage holding proprietary data, and leaked access keys, is the new, unmonitored frontier for attackers. ThreatNG's solution addresses this by using purely external, unauthenticated discovery to assess and provide Legal-Grade Attribution for these critical risks continuously. By prioritizing exposures like Sensitive Code Exposure (exposed secrets) and Cloud Exposure (open buckets), ThreatNG empowers security leaders to eliminate Shadow AI and secure their organization's most valuable assets from the attacker's perspective.

Unauthenticated AI Discovery

The Blind Spot: Securing What You Can’t See

ThreatNG's external assessments and intelligence repositories are specifically tuned to uncover the core components of the External AI Attack Surface.

AI Security Challenge: Shadow AI & Inventory

ThreatNG addresses the challenge of shadow AI by mapping your organization's complete digital presence through AI Technology Stack Mapping. The solution uncovers the full technology stack across nearly 4,000 technologies. This includes the subdomain level, specifically identifying 265 vendors in the Artificial Intelligence category, along with specific AI Model & Platform Providers and AI Development & MLOps tools.

AI Security Challenge: Leaked Credentials

ThreatNG mitigates the threat of leaked credentials through Non-Human Identity (NHI) & Sensitive Code Exposure.

  • The Non-Human Identity (NHI) Exposure Security Rating quantifies the organization's vulnerability to threats originating from high-privilege machine identities, such as leaked API keys and service accounts.

  • ThreatNG also uncovers Access Credentials and Security Credentials that are exposed in both public code repositories and mobile apps.

AI Security Challenge: Exposed Model Data

The risk of exposed model data is assessed through Cloud and Data Leak Exposure capabilities. ThreatNG finds exposed cloud buckets (such as AWS, Azure, and Google Cloud) that may inadvertently contain sensitive AI training data and model weights.

AI Security Challenge: Vendor AI Risk

Vendor AI risk is managed through Supply Chain & Third-Party Exposure. The platform assesses risk based on the unauthenticated enumeration of vendors in Domain Records, including those identified as running AI/ML technologies. This process contributes to the overall Supply Chain and Third-Party Exposure Security Rating.

Contextual Certainty and Prioritization

Move Beyond Findings with Legal-Grade Attribution

ThreatNG’s Context Engine™ provides the certainty required to accelerate remediation and justify security investments.

  • Irrefutable Attribution: The Context Engine™ is a patent-backed solution that achieves Legal-Grade Attribution by fusing external technical findings with decisive legal, financial, and operational context, eliminating the guesswork across the entire digital attack surface.

  • Adversary Prioritization: Findings from the external attack surface are automatically translated and mapped to specific MITRE ATT&CK techniques (e.g., initial access and persistence) to prioritize threats based on their likelihood of exploitation.

  • External GRC Assessment: Maps exposed risks directly to frameworks like PCI DSS, HIPAA, GDPR, NIST CSF, and ISO 27001, ensuring compliance teams use relevant external data.

Frequently Asked Questions (FAQ)

  • ThreatNG is an EASM solution that performs purely external unauthenticated discovery using no connectors. This is achieved by mapping the external attack surface through Subdomain Intelligence and AI Technology Stack Mapping to identify the publicly exposed technology and the presence of the endpoint.

  • ThreatNG focuses on uncovering risks associated with the Data Leak Susceptibility of an organization , including misconfigured Cloud Exposure (exposed open cloud buckets) and Sensitive Code Exposure (leaked API keys/credentials).

  • Yes. Risks that contribute to the external AI attack surface—such as Cloud Exposure, Compromised Credentials, and Sensitive Code Exposure—are factored into ThreatNG’s various A-F Security Ratings, including Cyber Risk Exposure and Data Leak Susceptibility.

  • Yes. We continuously scan for common misconfigurations and publicly exposed API endpoints associated with popular vector databases used in RAG architectures.

  • AI Security Platforms focus on internal security and prompt testing. ThreatNG focuses on external EASM, finding the unauthenticated entry points, misconfigurations, and leaked credentials that enable the initial breach.

  • ThreatNG finds exposed endpoints using its Subdomain Intelligence module to check HTTP Responses, Header Analysis, and Server Headers for Identified Technologies. This unauthenticated discovery process uncovers the full stack, including vendors in the Artificial Intelligence category, that may be hosting the exposed endpoint.

  • ThreatNG's Subdomain Intelligence includes Content Identification for APIs , and the Domain Intelligence module identifies related SwaggerHub instances, which often contain API documentation and specifications. Furthermore, the investigation of Archived Web Pages can uncover API directories and specifications that may be publicly exposed.

  • The primary risk associated with exposed vector databases is Data Leak Susceptibility resulting from Cloud Exposure (exposed open cloud buckets), which could contain unauthenticated vector data used for retrieval-augmented generation (RAG). ThreatNG's Subdomain Intelligence also identifies the presence of various database technologies, including large databases like Elasticsearch and MongoDB.

  • Traditional EASM often lacks the specialized intelligence to classify an exposed asset as AI. ThreatNG goes further by having a granular Technology Stack Investigation Module that specifically identifies and categorizes hundreds of technologies as Artificial Intelligence and tracks vendors in categories like AI Model & Platform Providers and AI Development & MLOps.

  • ThreatNG assesses for Cloud Exposure by specifically uncovering exposed open cloud buckets on major platforms like AWS, Microsoft Azure, and Google Cloud Platform. The presence of these publicly exposed buckets is a key factor that contributes to the Data Leak Susceptibility Security Rating.

  • ThreatNG provides a Supply Chain & Third-Party Exposure Security Rating that is based on the unauthenticated enumeration of vendors within Domain Records, the identification of all associated SaaS applications ("SaaSqwatch") , and the discovery of the underlying technologies used by those third parties. This comprehensive approach includes identifying external vendors running AI/ML technologies.