AI Technology Stack Mapping
The AI Technology Stack Mapping is a specialized capability within cybersecurity, typically found in External Attack Surface Management (EASM) and Digital Risk Protection (DRP) solutions, that focuses on the systematic identification and inventory of all hardware, software, platforms, and services used to build, train, deploy, and operate an organization's Artificial Intelligence and Machine Learning (AI/ML) systems.
It is a core technical process that provides granular visibility into the AI environment from the perspective of an external attacker.
Detailed Process and Objectives
The mapping process goes beyond standard web or infrastructure discovery by specifically fingerprinting technologies relevant to the modern AI pipeline.
Unauthenticated Fingerprinting: The process is executed without internal credentials or access. It relies on techniques like:
Header and Banner Analysis: Examining HTTP response headers, server banners, and API metadata on public-facing endpoints to identify software versions, programming languages, and framework signatures (e.g., detecting a Python environment or a specific web server configuration).
Signature Matching: Cross-referencing observed file paths, error messages, and URL structures against an extensive, curated database of digital signatures belonging to known AI/ML vendors and open-source frameworks.
Specific AI Component Identification: The goal is to identify and categorize every component, including those unique to the AI lifecycle:
AI Model Platforms: Identifying commercial services used for hosting and serving models (e.g., Azure OpenAI, Google Vertex AI, or specific open-source serving frameworks).
MLOps Tools: Identifying development and operational tooling used for deployment, monitoring, and scaling (e.g., tools for data labeling, model versioning, or continuous integration/continuous delivery).
Data Store Technologies: Identifying specialized databases used for AI applications, such as Vector Databases (critical for RAG systems) and large-scale data warehouses.
Mapping and Contextualization: Once a technology is identified, it is mapped to the asset it runs on (e.g., a specific subdomain or IP address) and linked to the owning organization. This establishes a clear, contextual view of the external AI environment.
Cybersecurity Value
The primary value of AI Technology Stack Mapping is to eliminate Shadow AI and enable proactive security:
Attack Surface Inventory: It provides the critical inventory necessary to manage Unmanaged AI Assets. If the security team doesn't know which MLOps platform the Data Science team is using, they cannot monitor it for vulnerabilities.
Vulnerability Prioritization: Once a technology is identified (e.g., a specific version of a Python library), the security team can immediately cross-reference it against vulnerability databases for known Common Vulnerabilities and Exposures (CVEs) that could compromise the AI Supply Chain.
Target Assessment: It allows security teams to understand what an attacker sees. For instance, mapping the presence of a specific vector database tells an attacker (or a defender) exactly where the proprietary knowledge base is located and which specialized ports to target.
ThreatNG provides a robust defense against the risks of AI Technology Stack Mapping by leveraging its External Attack Surface Management (EASM) capabilities to continuously, unauthenticatedly discover an organization's AI components, thereby neutralizing the information advantage of an attacker.
External Discovery
ThreatNG’s External Discovery is the mechanism that creates the inventory necessary to manage the AI technology stack, fulfilling the fundamental security requirement: you cannot secure what you cannot see.
How it helps: The core of the solution is the Technology Stack Identification module, which performs exhaustive, unauthenticated discovery across the entire digital footprint. This module identifies nearly 4,000 technologies, critically including those categorized as Artificial Intelligence, as well as specific vendors in AI Model & Platform Providers and AI Development & MLOps. It uses techniques like banner grabbing and signature matching on exposed endpoints to identify the exact components in use.
Example of ThreatNG helping: ThreatNG discovers a subdomain, data-api.company.com, running a technology identified as a major AI Development & MLOps vendor. This confirms the presence of an AI asset without authentication and allows the security team to map it to the shadow IT inventory, eliminating the blind spot created by the unmanaged stack.
External Assessment
ThreatNG assesses the risk of discovered technologies by checking for common misconfigurations and attack vectors specific to the identified stack components.
Highlight and Examples:
Vulnerability Prioritization: The Supply Chain & Third-Party Exposure Security Rating assesses risk based onidentified technologies.
Example: ThreatNG identifies the organization is using an older version of a specific open-source Python ML framework on a public endpoint. This technology stack detail is cross-referenced with known vulnerabilities (CVEs). If a remote code execution vulnerability exists for that specific framework version, the assessment immediately rates the asset as high-risk, enabling the team to prioritize patching the exposed component of the technology stack.
Unsecured Access: The Cyber Risk Exposure rating and Subdomain Intelligence assess the security of the hosting infrastructure for the discovered stack.
Example: ThreatNG discovers the exposed technology stack is running on an endpoint that lacks basic security headers or has an Exposed Port (e.g., a specific port for a vector database). This external assessment shows that the technology stack is not only visible but also potentially accessible via unauthenticated methods, making it a direct target for Unauthenticated Model Theft or Cloud Bucket Poisoning.
Continuous Monitoring
Continuous Monitoring ensures the security team is alerted immediately to changes in the AI Technology Stack, addressing the dynamic nature of development environments.
How it helps: If a developer switches from an approved AI Model & Platform Provider to an unvetted, unauthorized open-source deployment overnight, continuous monitoring detects the immediate change in the technology signature running on the public-facing IP address. This immediate alert prevents the new, unmanaged technology stack from becoming a persistent security vulnerability.
Investigation Modules
These modules provide the granular evidence needed to validate the integrity of the discovered technology stack and its associated secrets.
Highlight and Examples:
Sensitive Code Discovery and Exposure: This module scans public code repositories for configuration details and secrets.
Example: An analyst uses this module and finds a configuration file that explicitly names the discovered technology stack component (e.g., specifying the exact internal API endpoints for a proprietary MLOps tool) and reveals Leaked AI Agent Credentials used by that tool. This provides the definitive proof that the entire technology stack, though external, is exposed via its administrative secrets.
Online Sharing Exposure: This module tracks organizational presence on public forums and paste sites.
Example: ThreatNG discovers a developer's request for technical support on a forum regarding the specific MLOps platform identified by Technology Stack Identification. This conversation might reveal details about the platform's internal architecture, data storage paths, or sensitive system versions, providing an attacker with contextual intelligence.
Intelligence Repositories
The Intelligence Repositories (DarCache) provide crucial threat context for the identified components of the technology stack.
How it helps: The Vulnerabilities (DarCache Vulnerability) repository integrates KEV (Known Exploited Vulnerabilities) data. When ThreatNG identifies a specific technology in the AI stack, it instantly cross-references it with KEV data. If the identified component is known to be actively exploited, the risk is immediately elevated, prioritizing the remediation of that component over theoretical risks.
Cooperation with Complementary Solutions
ThreatNG's detailed identification of the external AI technology stack enables targeted enforcement by internal security systems.
Cooperation with Configuration Management Database (CMDB) Tools: ThreatNG’s Technology Stack Identification provides an unauthenticated inventory of Shadow AI assets that are often missing from internal CMDBs.
Example: When ThreatNG identifies a new subdomain running an unauthorized AI Development platform, this external discovery information is used to update the complementary CMDB. This action forces the organization to formally recognize and begin governance and asset tracking for the previously unknown technology stack component.
Cooperation with Network Security/Firewall Management: ThreatNG flags externally visible ports associated with the stack.
Example: ThreatNG identifies that an exposed vector database technology stack is communicating over a publicly visible port. This intelligence is routed to the complementary Firewall Management solution, which automatically creates a rule to block external traffic to that specific port, immediately securing the vector database component of the technology stack from unauthenticated access.
