EASM for GenAI
EASM for GenAI (External Attack Surface Management for Generative AI) is a specialized application of cybersecurity principles that continuously discovers, inventories, prioritizes, and secures all publicly exposed, internet-facing assets within an organization's Generative AI infrastructure and usage.
This discipline is essential because the adoption of GenAI introduces new and non-traditional attack vectors that exist entirely outside the corporate network.
The core activities of EASM for GenAI include:
Unauthenticated Discovery: This is the foundational step, where the entire external digital footprint is scanned without credentials to identify all subdomains, API endpoints, and public-facing services linked to GenAI usage. This process aims to identify Shadow AI, such as unmanaged chatbots, experimental LLM endpoints, or development environments accidentally exposed to the internet.
Inventorying GenAI Components: Once discovered, assets are categorized. This inventory includes identification of the specific GenAI technologies being used (e.g., foundation models, MLOps tools, vector databases), the public-facing API interfaces, and any associated cloud storage.
Misconfiguration and Leak Detection: A primary focus is on identifying critical external security failures:
Data Leakage Risk: Detecting misconfigured, publicly open cloud storage buckets that may contain sensitive training data, model weights, or proprietary prompt templates.
Credential Exposure: Searching the open, deep, and dark web for leaked LLM API keys, service account credentials, or configuration files that grant external access to the GenAI environment.
Prioritization: Assigning risk scores to discovered GenAI exposures based on their potential impact (e.g., the ease of prompt injection or the severity of a data leak) to ensure limited security resources are focused on the most critical external threats first.
In essence, EASM for GenAI provides the necessary external vigilance to control the perimeter of the AI environment, validating that the organization is not unknowingly exposing its most valuable AI assets to the public internet.
ThreatNG, as an all-in-one external attack surface management (EASM), digital risk protection (DRP), and security ratings solution, is an indispensable tool for securing the EASM for GenAI perimeter. It is strategically designed to perform continuous, unauthenticated discovery and risk assessment to identify and prioritize publicly exposed assets in a Generative AI environment.
External Discovery and Inventory
ThreatNG’s foundational capability is its purely external unauthenticated discovery using no connectors, which directly mirrors the unauthenticated scanning required for EASM for GenAI.
Technology Stack Identification: ThreatNG provides exhaustive, unauthenticated discovery of nearly 4,000 technologies, including hundreds of technologies categorized as Artificial Intelligence, as well as vendors in AI Model & Platform Providers and AI Development & MLOps. This capability provides an inventory of exposed GenAI frameworks and services.
Subdomain Intelligence: ThreatNG uncovers subdomains and identifies the cloud and web platforms hosting them. This helps locate the public-facing API endpoints or applications that interact with the GenAI models.
Example of ThreatNG Helping: ThreatNG discovers an unmanaged subdomain, genai-experiment.company.com, running a technology identified as a vendor under AI Model & Platform Providers. This immediately flags the public presence of a Shadow AI asset that must be secured or taken offline.
External Assessment for GenAI Risks
ThreatNG's security ratings and assessment modules highlight critical misconfigurations that make GenAI assets vulnerable to an unauthenticated attacker.
Data Leak Susceptibility: This security rating is derived from uncovering external digital risks across Cloud Exposure, specifically exposed open cloud buckets. These buckets are common storage locations for proprietary LLM training data and model weights.
Non-Human Identity (NHI) Exposure: This critical governance metric quantifies vulnerability to threats from high-privilege machine identities, such as leaked API keys and service accounts. The discovery of exposed LLM API keys or service credentials is a primary EASM concern for GenAI.
Cyber Risk Exposure (Sensitive Code): This rating is based on findings that include Sensitive Code Discovery and Exposure (code secret exposure). This immediately reveals if proprietary prompt templates or configuration secrets have been externally exposed, enabling an unauthenticated attacker.
Example of ThreatNG Helping: ThreatNG flags a high Data Leak Susceptibility rating. The underlying reason is the discovery of an exposed cloud bucket linked to the Technology Stack module, which points to a Data Warehousing & Processing vendor, strongly suggesting thatproprietary GenAI data is at risk of unauthenticated access.
Reporting and Continuous Monitoring
ThreatNG provides Continuous Monitoring of the external attack surface, ensuring that any new GenAI exposure is flagged immediately.
External Adversary View and MITRE ATT&CK Mapping: ThreatNG aligns the security posture with external threats by performing unauthenticated, outside-in assessment. It automatically maps these findings to specific MITRE ATT&CK techniques, showing how an external exposure could be exploited to gain initial access to the GenAI environment.
Reporting (Security Ratings): The Data Leak Susceptibility and Cyber Risk Exposure Security Ratings (A-F scale) provide easily digestible metrics for executives to understand the external risk to their GenAI assets.
Investigation Modules
ThreatNG's Investigation Modules allow security teams to gather granular, unauthenticated evidence to complete the discovery process.
Sensitive Code Exposure: This module discovers public code repositories and specifically looks for Access Credentials (various API Keys, Cloud Credentials) and Configuration Files. Finding a leaked LLM key here is a primary EASM for GenAI success.
Cloud and SaaS Exposure: This module directly identifies and validates Open Exposed Cloud Buckets and the associated SaaS implementations (SaaSqwatch) used for data and analytics, such as Snowflake or Splunk.
Online Sharing Exposure: This module identifies organizational entity presence within code-sharing platforms like Pastebin and GitHub Gist. An attacker often finds LLM API keys or configuration snippets in these forums, and ThreatNG automates the discovery of these secrets.
Example of ThreatNG Helping: An analyst uses the Sensitive Code Exposure module and finds a Gist containing an Authorization Bearer token and a reference to an API. This critical external leak is immediately flagged, allowing the security team to revoke the token before it is used to compromise the GenAI service.
Complementary Solutions
ThreatNG's external discovery provides essential, unauthenticated intelligence to complementary solutions like AI Security Platforms (focused on internal prompt injection testing) and Data Loss Prevention (DLP) systems.
Complementary Solutions (AI Security Platforms): ThreatNG's discovery of an exposed GenAI API endpoint via Subdomain Intelligence or a leaked key via NHI Exposure provides the security platform with an externally validated, high-priority target. The external discovery of the asset allows the internal security platform to prioritize its internal model-level checks, such as running specific prompt injection tests against that exposed endpoint.
Complementary Solutions (DLP Systems): ThreatNG’s detection of an exposed open cloud bucket provides the external validation needed by a DLP system. This external signal can instruct the DLP system to immediately perform an internal content inspection and policy check on that specific bucket, confirming whether proprietary GenAI training data is present and classifying the severity of the leak.
