GenAI Security Visibility
GenAI Security Visibility, in the context of cybersecurity, refers to the comprehensive, continuous capability to observe, monitor, and map the entire landscape of an organization's Generative Artificial Intelligence (GenAI) usage and infrastructure.
The core purpose of this visibility is to eliminate blind spots and ensure that security teams have a complete, real-time understanding of every GenAI asset and the associated risks.
Adequate GenAI security visibility encompasses three main areas:
Asset Inventory and Discovery: This involves the automatic identification and cataloging of all GenAI components, both sanctioned and unsanctioned (Shadow AI). This includes finding all deployed large language models (LLMs), exposed API endpoints, vector databases, MLOps platforms, and any cloud storage buckets used to house sensitive training data or proprietary prompt templates.
Interaction and Behavior Monitoring: This involves gaining insight into how users and external systems interact with the deployed GenAI models. It requires monitoring the prompts submitted to the models (to detect prompt-injection attacks), logging the model's responses, and tracking the use of external tools or functions (known as "tool-use" or "function-calling") that the agent is authorized to execute.
Risk and Configuration Status: This means continuously assessing the security posture of the GenAI infrastructure. Visibility must extend to checking for misconfigurations (e.g., publicly open cloud storage), monitoring the status of internal security guardrails (to detect bypasses or failures), and correlating the GenAI assets with known external threats or vulnerabilities (like leaked credentials or exposed model secrets).
Without robust GenAI Security Visibility, security teams cannot effectively enforce policies, detect sophisticated logical attacks (like data leakage through manipulation), or manage the governance and compliance risks introduced by rapidly evolving AI technology.
ThreatNG, which is an all-in-one external attack surface management, digital risk protection, and security ratings solution, provides essential capabilities to achieve GenAI Security Visibility by continuously monitoring and assessing the entire external digital footprint of the GenAI environment. It is focused on finding the unauthenticated exposures and shadow AI that traditional internal tools miss.
External Discovery and Inventory
ThreatNG's ability to perform purely external unauthenticated discovery using no connectors is the foundation for establishing GenAI Security Visibility, as it mimics an attacker's reconnaissance to discover all exposed assets.
Technology Stack Identification: ThreatNG provides exhaustive, unauthenticated discovery of nearly 4,000 technologies. This includes the 265 technologies categorized as Artificial Intelligence, as well as vendors in AI Model & Platform Providers and AI Development & MLOps. This capability directly inventories the exposed GenAI frameworks and services to establish visibility.
Subdomain Intelligence: ThreatNG uncovers all associated 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 in its Technology Stack as an AI Model & Platform Provider. This immediately flags a Shadow AI asset, establishing visibility over a previously unseen GenAI resource.
External Assessment for Visibility Gaps
ThreatNG's security ratings and assessment modules highlight critical external configuration gaps that limit GenAI Security Visibility.
Data Leak Susceptibility: This rating is derived from uncovering external digital risks across Cloud Exposure, specifically exposed open cloud buckets. These buckets are often used for LLM training data and model weights. Their discovery provides critical visibility into data exposure risks.
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 unauthenticated discovery of exposed LLM API keys is a primary concern for DRP and immediately gives visibility into external access threats.
Cyber Risk Exposure (Sensitive Code): This rating is based on findings that include Sensitive Code Discovery and Exposure (code secret exposure). This reveals whether proprietary GenAI prompt templates or configuration secrets have been externally exposed, providing visibility into exposed intellectual property.
Example of ThreatNG Helping: ThreatNG flags a high Data Leak Susceptibility rating. The underlying issue is the discovery of an exposed cloud bucket linked to the Technology Stack module by a Data Warehousing & Processing vendor, confirming the external visibility of sensitive GenAI data.
Reporting and Continuous Monitoring
ThreatNG provides Continuous Monitoring of the external attack surface, ensuring that any new GenAI exposure or visibility gap is flagged immediately.
External Adversary View and MITRE ATT&CK Mapping: ThreatNG aligns the security posture with external threats by performing an 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 (GRC): The External GRC Assessment provides a continuous, outside-in evaluation of an organization's Governance, Risk, and Compliance (GRC) posture. It maps external findings directly to relevant GRC frameworks, including HIPAA and GDPR, ensuring visibility into compliance risks introduced by GenAI.
Investigation Modules
ThreatNG's Investigation Modules allow security teams to gather granular, unauthenticated evidence to complete the visibility picture.
Sensitive Code Exposure: This module discovers public code repositories and specifically looks for Access Credentials and Configuration Files. Finding a leaked LLM key here is a primary method of gaining visibility into compromised credentials.
Cloud and SaaS Exposure: This module directly identifies and validates Open Exposed Cloud Buckets. It also identifies 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. This provides visibility into secrets or proprietary prompts leaked by developers.
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 gives the security team immediate visibility into a potentially compromised GenAI token.
Complementary Solutions
ThreatNG's external discovery provides essential, unauthenticated intelligence to complementary solutions like AI Security Platforms (focused on internal prompt injection testing) and Identity and Access Management (IAM) tools.
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 visibility 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 (IAM Tools): ThreatNG’s discovery of a leaked service account credential via NHI Exposure provides definitive external proof of compromise. This external finding is routed to the IAM system, triggering an automated workflow to revoke the exposed key and tighten the access permissions for all remaining keys, thereby using external visibility to control internal access.
