AI Supply Chain Mapping
What is AI Supply Chain Mapping?
AI supply chain mapping is the systematic discovery, documentation, and continuous visualization of all upstream, runtime, and downstream components, dependencies, data flows, and trust boundaries that comprise an artificial intelligence or machine learning system.
Unlike conventional software supply chain mapping—which primarily catalogs static source code libraries, open-source packages, and compiled binaries—AI supply chain mapping accounts for the non-deterministic, probabilistic nature of machine learning. It traces data lineage, foundation model weights, fine-tuning datasets, machine learning frameworks, orchestration layers, vector databases, model registries, inference APIs, and autonomous agent tool connections. The objective is to identify hidden single points of failure, unverified third-party dependencies, and inherited exploit vectors before they compromise production workflows.
Core Components Documented in an AI Supply Chain Map
A comprehensive AI supply chain map inventories and connects dependencies across six distinct layers:
Data Provenance and Training Sets: Tracks raw data collection sources, public web scrapers, synthetic data pipelines, third-party labeling vendors, and document embeddings used in Retrieval-Augmented Generation (RAG).
Foundation Models and Weights: Documents base model architectures, checkpoint provenance, parameter files, adapter layers (such as LoRA weights), and model serialization formats (such as Safetensors or PyTorch files).
Machine Learning Pipelines and Frameworks: Catalogs developer frameworks (such as PyTorch, TensorFlow, or vLLM), container base images, continuous integration/continuous deployment (CI/CD) pipelines, and MLOps build scripts.
Orchestration and Context Protocols: Identifies middleware frameworks (such as LangChain or LlamaIndex), Model Context Protocol (MCP) servers, and vector databases (such as Milvus, Qdrant, or Pinecone) that manage context injection.
Non-Human Identities and Programmatic Secrets: Inventories API bearer tokens, OAuth scopes, service account credentials, and webhook endpoints used to authenticate model requests and tool executions.
Downstream Integrations and Agent Tools: Maps write-enabled enterprise database connectors, command-line runners, email gateways, and third-party SaaS integrations granted to autonomous agents.
Critical Cybersecurity Risks Uncovered by AI Supply Chain Mapping
Visualizing the full dependency chain reveals complex, distributed risks that static code reviews fail to detect:
Model Serialization and Insecure Deserialization: Pinpoints dependencies that load unverified model weights in executable serialization formats (such as standard Python pickle files), which can execute arbitrary system code upon loading.
Training and Retrieval Data Poisoning: Identifies untrusted external data sources or public scraping links that adversaries can manipulate to inject sleeper backdoors, alter classification boundaries, or corrupt RAG outputs.
Adversarial LoRA and Adapter Tampering: Highlights imported fine-tuning layers or adapter files hosted on unverified public registries that introduce hidden malicious behaviors while appearing benign on standard benchmarks.
Typosquatting and Dependency Hallucination Exploits: Detects when software pipelines download maliciously registered packages on PyPI, npm, or Hugging Face designed to exploit hallucinated package names generated by AI coding assistants.
Transitive Fourth-Party Exposure: Exposes when a direct third-party AI vendor relies on secondary or tertiary cloud providers, unvetted foundation models, or external infrastructure that lack equivalent security controls.
Silent Behavioral and Guardrail Drift: Traces dynamic dependencies on commercial cloud-hosted APIs where upstream provider updates alter model behavior, bypass internal safety filters, or degrade compliance posture without notice.
AI Supply Chain Mapping vs. Traditional Software Supply Chain Mapping
Understanding the operational differences between traditional and AI supply chain mapping is critical for security architecture:
Traditional Software Supply Chain Mapping: Focuses on deterministic software artifacts. It maps known source code dependencies, open-source libraries, package managers, and compile-time links, tracking vulnerabilities through standardized Common Vulnerabilities and Exposures (CVE) entries.
AI Supply Chain Mapping: Governs probabilistic and dynamic systems. It must map uncompiled model weights, data provenance, semantic prompt templates, inference endpoints, and runtime agent capabilities where compromises (such as data poisoning or backdoor triggers) occur without generating traditional CVE signatures.
The Operational Lifecycle of AI Supply Chain Mapping
Organizations establish an AI supply chain mapping process through a five-stage operational lifecycle:
1. Automated Asset and Dependency Discovery: Continuously discovering internet-facing AI inference servers, model training pipelines, unmanaged cloud storage buckets, and public code repositories hosting machine learning projects.
2. Generation of AI Bills of Materials (AI-BOMs): Generating standardized, machine-readable records that capture base model hashes, training datasets, framework dependencies, system prompts, and license terms.
3. Lineage and Trust Boundary Modeling: Modeling the relationship between data origins, intermediate transformations, compute environments, and downstream consuming applications to establish cryptographic trust boundaries.
4. Continuous Vulnerability and Threat Correlation: Correlating mapped AI components against emerging adversarial threat taxonomies, such as MITRE ATLAS and CISA Known Exploited Vulnerabilities (KEV) catalogs.
5. Choke Point Identification and Blast Radius Analysis: Calculating graph intersections to determine which compromised model, dataset, or API key would cause the greatest operational damage, guiding targeted architectural isolation.
Strategic Benefits of AI Supply Chain Mapping
Implementing continuous AI supply chain mapping provides fundamental advantages across security, risk, and governance teams:
Preemptive Vulnerability Containment: Allows security teams to identify and replace vulnerable open-source machine learning packages or poisoned weights before models deploy to production environments.
Accelerated Incident Response: Enables immediate identification of every internal application, model, or customer workflow affected when an upstream foundation model provider or AI library is breached.
Defensible Regulatory Compliance: Satisfies mandatory governance and supply chain transparency requirements under the European Union AI Act, ISO/IEC 42001, and the NIST AI Risk Management Framework (AI RMF).
Elimination of Shadow AI Infrastructure: Uncovers unsanctioned model endpoints, rogue MLOps clusters, and developer test environments spun up outside central enterprise visibility.
Frequently Asked Questions
What is the difference between an SBOM and an AI-BOM in supply chain mapping?
A Software Bill of Materials (SBOM) details software libraries, versions, and build dependencies. An AI Bill of Materials (AI-BOM) extends this by cataloging foundation model origins, parameter weights, training data provenance, fine-tuning datasets, prompt templates, and external tool connectors.
Why is AI supply chain mapping essential for managing third-party risk?
Modern enterprises rarely build foundation models from scratch; they consume pre-trained weights, hosted APIs, and open-source frameworks. AI supply chain mapping makes these inherited upstream dependencies visible, so security teams can evaluate external providers' security hygiene and regulatory compliance.
How do non-human identities impact the AI supply chain?
AI models and autonomous agents interact with internal databases and external APIs using non-human identities such as API keys and service tokens. Mapping these credentials ensures organizations enforce least privilege, monitor token rotation, and prevent compromised agents from moving laterally across enterprise networks.
Operationalizing AI Supply Chain Mapping with ThreatNG
AI supply chain mapping is the continuous, systematic discovery, inventorying, and relationship modeling of all external, runtime, and third-party dependencies that comprise an enterprise’s machine learning and artificial intelligence ecosystem. Unlike static software supply chains that focus on deterministic code libraries, AI supply chains encompass non-deterministic variables: foundational models, hosted inference Application Programming Interfaces (APIs), third-party Retrieval-Augmented Generation (RAG) vector stores, autonomous agent tool bridges, and Non-Human Identities (NHIs).
Organizations struggle with the Contextual Certainty Deficit because internal security platforms depend on internal connectors, software agents, and self-attested third-party surveys. As a result, security teams remain unaware of unmanaged developer staging environments, third-party model endpoints, public code repository leaks, and shadow Software as a Service (SaaS) integrations visible to external threat actors.
ThreatNG operationalizes outside-in AI supply chain mapping by serving as an unauthenticated external scout. Bringing together External Attack Surface Management (EASM), Digital Risk Protection (DRP), and continuous Security Ratings into a single platform, ThreatNG discovers, evaluates, categorizes, and monitors an enterprise’s entire public perimeter alongside its upstream, runtime, and partner AI dependencies. ThreatNG traces multi-step supply chain exploit paths via DarChain, measures weaponization through its 4-Dimensional (4D) Data Model, aligns findings with MITRE ATLAS and ISO/IEC 42001, and provides Legal-Grade Attribution without requiring internal software agents, API access keys, or administrative credentials.
External Discovery
A comprehensive AI supply chain map requires continuous, unauthenticated discovery of all external touchpoints connecting corporate perimeters to third-party model providers, contractor build environments, and external cloud assets. ThreatNG establishes this inventory baseline through connectorless external discovery.
Connectorless Asset and Perimeter Discovery: ThreatNG maps the public-facing footprint of an organization’s AI ecosystem without deploying software agents, internal connectors, or network credentials. It continuously inspects public domain registries, authoritative Domain Name System (DNS) zone files, Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificate transparency logs, Regional Internet Registry (RIR) databases, and Border Gateway Protocol (BGP) routing announcements to catalog every public Internet Protocol (IP) block, subdomain, cloud host, and web application connecting to AI components.
Patented Recursive Discovery Across Distributed AI Stacks: Starting from an initial seed entity (such as an apex domain, brand name, or Autonomous System Number/ASN), ThreatNG iteratively expands outward. As new subdomains, DNS records, or netblocks emerge, the engine feeds them back in as fresh discovery seeds. This recursive process discovers unmanaged developer staging gateways, external Model Context Protocol (MCP) servers, and shadow AI implementations deployed across multi-cloud environments (AWS, Microsoft Azure, Google Cloud Platform) and regional hosting providers.
Unauthenticated SaaS and Vendor Discovery (SaaSqwatch): ThreatNG evaluates public digital exhaust—including DNS Canonical Name (CNAME) routing chains, Hypertext Transfer Protocol (HTTP) headers, and SSL/TLS certificates—to discover sanctioned and unsanctioned external AI vendors, hosted model endpoints, vector databases, and autonomous agent frameworks used across corporate business units.
Adversary Lookalike and Typosquat Discovery: ThreatNG continuously discovers newly registered, typosquatted, and lookalike domain permutations (such as homoglyphs and transposed characters) across global domain registrars. It identifies active Mail Exchange (MX) records and SSL/TLS certificates configured to impersonate enterprise AI portals, customer-facing LLM chat interfaces, or trusted AI SaaS providers, identifying adversary staging infrastructure before phishing or model-abuse campaigns launch.
Subsidiary and Extended Ecosystem Scoping: Because ThreatNG operates without internal credentials or vendor permissions, organizations can execute unauthenticated discovery across corporate subsidiaries, prospective acquisition targets (M&A due diligence), and third-party software partners. This maps unmanaged AI pipelines across the entire corporate hierarchy and partner network.
External Assessment
ThreatNG elevates supply chain analysis from static questionnaires to deterministic, evidence-backed evaluation using its Known Vulnerability Exposure Verification (KVEV) engine, proprietary Security Ratings, and 4-Dimensional (4D) Data Model. The 4D model cross-references National Vulnerability Database (NVD) baselines, 30-day Exploit Prediction Scoring System (EPSS) probabilities, Cybersecurity and Infrastructure Security Agency (CISA) Known Exploited Vulnerabilities (KEV) listings, and verified Proof-of-Concept (PoC) exploit code in DarCache eXploit.
Detailed Assessment Example 1: Non-Human Identity (NHI) and Leaked Machine Secret Assessment: AI pipelines depend on programmatic machine identities to authenticate model inference, vector database queries, and external tool execution. ThreatNG evaluates external exposure variables—including open non-standard ports, accessible environment variables, public cloud configurations, and unvetted webhook endpoints—to locate exposed programmatic machine identities. It identifies exposed OpenAI API keys, Anthropic tokens, Hugging Face user secrets, and LangChain orchestration credentials, computing an NHI Exposure Rating (A through F). This allows security teams to revoke exposed credentials before adversaries abuse them to poison vector databases, hijack models, or bypass authentication controls.
Detailed Assessment Example 2: Insecure AI Interface and Insecure Header Analysis: ThreatNG inspects public AI application endpoints, web chat widgets, and inference gateways across all discovered subdomains for missing or weak HTTP security headers—specifically evaluating subdomains missing Content-Security-Policy (CSP), HTTP Strict Transport Security (HSTS), X-Content-Type-Options, and X-Frame-Options, as well as deprecated headers. It generates an A through F Web Application Hijack Susceptibility rating to determine whether an AI portal is vulnerable to client-side script injection, clickjacking, or prompt injection via manipulated web contexts.
Detailed Assessment Example 3: Subdomain Takeover Susceptibility on Decommissioned AI Services: When an experimental AI model or temporary tool gateway is decommissioned, DNS CNAME records can be left pointing to unclaimed cloud PaaS, serverless, or storage resources. ThreatNG cross-references discovered subdomains against an extensive catalog of over 60 cloud services (such as AWS, Azure, Heroku, Vercel, and GitHub) and validates whether the resource is unclaimed. It assigns an A through F Subdomain Takeover Susceptibility rating, ensuring threat actors don't hijack abandoned AI endpoints to host malicious models under trusted corporate domains.
Detailed Assessment Example 4: Known Vulnerability Exposure Verification (KVEV) on AI Infrastructure: When ThreatNG discovers an internet-facing AI inference gateway, vector database interface, or model server, the KVEV engine performs live, unauthenticated checks. It evaluates public reachability, checks against the CISA KEV catalog, calculates 30-day EPSS weaponization probabilities, and cross-references active exploit scripts in DarCache eXploit. This confirms whether an exposed AI service is actively vulnerable to remote code execution, identifying systems that need immediate isolation or patching.
Detailed Assessment Example 5: Data Leak Susceptibility on Exposed Vector Stores and Training Buckets: ThreatNG evaluates public cloud storage buckets, open database ports, and external web directories across the perimeter. It assigns an A through F Data Leak Susceptibility rating to pinpoint unprotected cloud buckets containing RAG vector embeddings, model weights, or cached training datasets, ensuring that data containment protocols successfully secure sensitive assets.
Strategic Reporting
ThreatNG standardizes AI supply chain risk communication by converting raw outside-in discoveries, infrastructure graphs, and technical exposure telemetry into structured, auditable records for technical practitioners, executive leadership, and compliance auditors.
Executive Security Ratings Reports: ThreatNG converts complex vulnerability metrics, exposed configurations, and digital risk indicators into standardized A through F security ratings across categories including Cyber Risk Exposure, Data Leak Susceptibility, Supply Chain & Third Party Exposure, and Non-Human Identity (NHI) Exposure. This enables CISOs to present empirical supply chain exposure trends and AI risk reduction metrics directly to executive boards.
Correlation Evidence Questionnaires (CEQs): ThreatNG dynamically generates Correlation Evidence Questionnaires based on confirmed external discovery and assessment results. The CEQ acts as an EASM-to-Audit Translation Layer, transforming unauthenticated outside-in discoveries into targeted, auditable inquiries mapped directly to regulatory frameworks across four functional pillars: Technical, Strategic, Operational, and Financial.
Defensible Regulatory Compliance Mapping: ThreatNG maps external AI discoveries directly to key regulatory frameworks and reporting mandates, including ISO/IEC 42001 (Artificial Intelligence Management System), the EU AI Act, the NIST AI Risk Management Framework (AI RMF), MITRE ATLAS, SEC Form 8-K material breach disclosure rules, and SOC 2. This provides the auditable documentation required to prove that mandatory operational oversight and access-verification controls are enforced across all deployed AI systems.
Forensic Evidence Packages: When ThreatNG verifies an active vulnerability on an AI server, an exposed vector database, an unauthorized chatbot, or a dangling DNS record, it generates a detailed forensic evidence package containing technical markers, DNS resolution histories, HTTP response headers, affected URLs, and proof of ownership to support root-cause investigations, vendor disputes, and legal attribution.
Continuous Monitoring
Because cloud environments drift, data science teams rapidly deploy experimental models, and external dependencies change constantly, static periodic audits miss supply chain non-compliance. ThreatNG provides 24/7 continuous external surveillance across the extended digital footprint.
The platform tracks asset state changes, newly registered subdomains, modified DNS records, fresh certificate issuances, and emerging zero-day vulnerabilities in real time. Furthermore, ThreatNG incorporates its Overwatch capability—a cross-entity vulnerability intelligence system that instantly evaluates exposure across an entire portfolio of subsidiaries, business units, and supply chain partners whenever a new zero-day CVE or AI pipeline vulnerability is disclosed, identifying every affected external system within seconds to coordinate enterprise-wide defense.
Investigation Modules
ThreatNG features specialized investigation modules that allow security analysts to inspect discovered infrastructure, trace developer leaks, and evaluate the full technical context of AI supply chain exposures.
Detailed Module Example 1: Subdomain Infrastructure Exposure Module (AI Framework and Vector Store Detection): Operating within Subdomain Intelligence, this module actively inspects discovered subdomains for exposed AI and agentic infrastructure. It specifically scans for and detects exposed AI Orchestration Frameworks (such as Langflow, self-hosted n8n, AnythingLLM, LM Studio, LiteLLM, Ollama, OpenAI-compatible APIs, and Clawdbot/Moltbot). In the Data Storage category, it detects exposed Vector Databases and Neural Memory stores (such as QDrant, Milvus, local Pinecone, and DuckDB). In Network Protocols, it discovers Model Context Protocols (MCP) and AI Inter-Process Communication channels (such as Server-Sent Events/SSE, Next.js MCP, Browser Automation, General SSE MCP, MCP Inspector, Enterprise MCP, and Playwright MCP). Discovering these endpoints externally immediately exposes the system to model manipulation, RAG data poisoning, and unauthorized tool invocation.
Detailed Module Example 2: The DarChain Exploit Path Mapping Engine: DarChain (Digital Attack Risk Contextual Hyper-Analysis Insights Narrative) chains isolated technical, credential, and environmental exposures into predictive attack graphs. For example, DarChain maps how an attacker discovers an unmanaged staging subdomain hosting an exposed Langflow interface, correlates that finding with a leaked cloud database credential identified in a public code repository, and demonstrates how that path leads directly to proprietary vector embeddings, pinpointing the critical Attack Path Choke Point where remediating a single configuration severs the adversary's progression.
Detailed Module Example 3: Sensitive Code Exposure Module: ThreatNG continuously monitors public code repositories (such as GitHub, GitLab, and Bitbucket) and paste sites for leaked corporate secrets. This module uncovers hardcoded OpenAI keys, Hugging Face tokens, Anthropic credentials, private SSH keys, and database connection strings committed by internal developers or contractors, providing exact commit URLs and author metadata to confirm that credentials used across the AI supply chain are identified and neutralized.
Detailed Module Example 4: Cloud and SaaS Exposure Module (SaaSqwatch): This capability investigates public cloud storage environments and unauthenticated SaaS deployments. It actively scans for exposed open cloud buckets and data repositories across AWS S3, Azure Blob, Azure Data Lake, and Google Cloud Platform, while identifying unsanctioned third-party AI platforms and autonomous agent services, ensuring secondary data channels connected to the supply chain are brought under governance.
Detailed Module Example 5: Cybersecurity AI Prompts (DarcPrompt): DarcPrompt packages verified AI supply chain exposure context and attack path discoveries into structured prompt blueprints. Featuring specialized personas—such as Shadow IT and AI, External Attack Paths, and External GRC Assessment—DarcPrompt applies strict architectural constraints that bind the prompt to ThreatNG's proprietary ground truth. Through an Air-Gapped Handoff, security analysts safely copy these blueprints into their internal private enterprise AI systems to draft vendor risk assessments, AI Bill of Materials (AI-BOM) validations, and executive summaries without exposing sensitive asset data to public AI services.
Intelligence Repositories
ThreatNG centralizes and structures threat intelligence through the DarCache intelligence engine, providing an interconnected dynamic ecosystem that grounds AI supply chain defense in empirical adversary reality:
DarCache Vulnerability & eXploit: Integrates NVD baselines, CISA KEV listings, 30-day EPSS probabilities, and verified PoC exploit pointers to evaluate whether external AI servers host software flaws that are actively weaponized, providing concrete justification for rapid isolation.
DarCache Dark Web & Rupture: Scans underground forums, paste sites, and dark web sources for threats to brand assets and personnel, while tracking compromised corporate credentials, session cookies, and data leaks across all domain permutations.
DarCache Infostealer: Parses dark web logs for compromised corporate credentials and active browser session tokens, allowing teams to determine whether supply chain disruptions or repository leaks originated from stolen employee or contractor identities.
DarCache Ransomware: Tracks active ransomware cartels and their specific tactics, techniques, and procedures (TTPs), monitoring threat actor targeting patterns directly against an organization's extended footprint.
DarCache Bug Bounty: Aggregates and analyzes historical bug bounty program disclosures, researcher activity trends, and crowdsourced exploit patterns to evaluate AI assets and public endpoints under active scrutiny by external researchers.
DarCache Mobile: Detects hardcoded AI access credentials, API keys, and model-serving URLs embedded in public mobile applications.
DarCache 8-K & ESG: Tracks SEC Form 8-K filings and global ESG violations, providing non-technical governance indicators that correlate with corporate cyber risk, vendor instability, and regulatory disclosure liabilities.
DarCache BIN: Monitors Bank Identification Numbers (BINs) to identify and prevent potential payment card fraud across digital transactional services.
Cooperation with Complementary Solutions
ThreatNG functions as an external intelligence scout that cooperates seamlessly with complementary solutions across enterprise governance, risk, and security operations.
Cooperation with Third-Party Risk Management (TPRM) and GRC Platforms: ThreatNG feeds continuous, objective A through F security ratings, supply chain exposure metrics, and dynamically generated Correlation Evidence Questionnaires into complementary solutions (TPRM and GRC software). Risk teams use this outside-in telemetry to replace static vendor questionnaires with real-world technical data, monitoring AI SaaS suppliers continuously rather than relying on annual self-attestations.
Cooperation with AI Security Posture Management (AI-SPM) Solutions: ThreatNG pushes unauthenticated outside-in discovery data—such as discovered shadow AI endpoints, unmonitored MCP tool servers, exposed vector databases, and unmapped subdomains—directly into complementary solutions (internal AI-SPM platforms). While internal AI-SPM tools evaluate prompt safety, guardrails, and model weights within known environments, ThreatNG acts as the external discovery scout, uncovering shadow AI infrastructure where internal tools were never deployed.
Cooperation with Security Orchestration, Automation, and Response (SOAR): ThreatNG delivers pre-correlated Context Objects and verified risk alerts to complementary solutions (SOAR platforms) via an API. When ThreatNG detects an exposed AI model key or an unauthenticated vector database, the SOAR platform executes automated containment playbooks—modifying perimeter firewall access control lists (ACLs), revoking compromised API tokens, and opening high-priority tickets for engineering teams.
Cooperation with Identity and Access Management (IAM) and Secrets Vaults: ThreatNG passes verified leaked Non-Human Identities (NHIs) and programmatic AI tokens discovered in public code repositories or paste sites to complementary solutions (enterprise IAM platforms and secrets management vaults). The IAM system immediately invalidates the affected service principal credentials and rotates secrets, severing an attacker's access privileges across internal databases.
Cooperation with Cyber Asset Attack Surface Management (CAASM) and CMDBs: ThreatNG feeds external asset inventories, newly discovered AI subdomains, and shadow cloud infrastructure into complementary solutions (CAASM platforms and CMDBs). IT and asset management teams use this feed to reconcile external discoveries against internal records, ensuring that every AI pipeline and partner dependency is cataloged and assigned business ownership.
Examples of ThreatNG Helping Organizations
Uncovering an Exposed Vector Database in an Unmanaged Contractor Subdomain: An external analytics consultancy deployed a retrieval-augmented generation pipeline on an unrecorded subdomain (rag-search-dev.enterprise.com). ThreatNG’s recursive discovery engine identified the host during an unauthenticated crawl. The Subdomain Infrastructure Exposure module revealed that the host was running an exposed Milvus vector database without authentication, leaving proprietary enterprise document embeddings open to public indexing. ThreatNG assigned an F Data Leak Susceptibility score and compiled a forensic evidence package. Security leadership required the vendor to take the staging interface offline immediately, preventing threat actors from scraping or poisoning internal enterprise knowledge stores.
Neutralizing Leaked Third-Party AI Pipeline Secrets in Public Version Control: An external software engineer working on an automated customer support bot pushed an application configuration file containing production OpenAI API keys and Pinecone vector database connection strings to a public GitHub repository. ThreatNG’s Sensitive Code Exposure module discovered the repository within minutes of the commit. ThreatNG verified that the credentials granted read and write access to internal customer embeddings and issued an alert with exact repository URLs and commit timestamps. Security engineers revoked the machine token immediately, preventing adversaries from accessing or tampering with the vector database.
Examples of ThreatNG Working with Complementary Solutions
Working with TPRM Platforms to Replace Annual Surveys with Continuous Technical Evidence: ThreatNG continuously monitors a key SaaS vendor that provides customer-facing LLM capabilities. ThreatNG discovers an unpatched remote code execution vulnerability listed on the CISA KEV catalog on the vendor's external API portal, downgrading the vendor's Supply Chain & Third Party Exposure rating to an F. ThreatNG transmits a pre-populated Correlation Evidence Questionnaire and forensic evidence package to complementary solutions (a TPRM platform). The TPRM platform automatically triggers a vendor security review, requiring the vendor to remediate the exposed portal before renewal.
Working with SOAR and Firewalls to Block Reachable AI Exploit Vectors: ThreatNG discovers an exposed AI orchestration gateway running an unpatched software version listed on the CISA KEV catalog with active PoC exploit code in DarCache eXploit. ThreatNG transmits a Context Object to complementary solutions (a SOAR platform). The SOAR system automatically commands complementary solutions (perimeter firewalls and cloud security groups) to revoke public access to the IP address while engineering applies vendor patches.
Frequently Asked Questions
How does ThreatNG discover AI supply chain risks without internal network access?
ThreatNG operates entirely as an unauthenticated external scout. It continuously analyzes public DNS records, SSL/TLS certificate transparency logs, BGP routing announcements, public code repositories, and internet-facing port handshakes across the open internet, discovering exposed inference endpoints, shadow tool gateways, and unmonitored staging environments strictly from an external adversary's viewpoint.
What is the role of Non-Human Identity (NHI) governance in AI supply chain security?
AI models and autonomous agents interact with internal databases and external APIs using machine identities, API keys, and service principals rather than interactive human logins. ThreatNG's NHI Exposure Assessment evaluates whether programmatic secrets are exposed across public repositories or cloud settings, allowing organizations to confirm that compromised machine credentials are identified and revoked before adversaries abuse them to hijack AI workflows.
How does ThreatNG support regulatory compliance for AI supply chain architectures?
Frameworks like the EU AI Act, NIST AI RMF, and ISO/IEC 42001 mandate demonstrable inventory tracking, transparency, and operational risk management for all deployed AI systems. ThreatNG continuously maps external AI assets, vulnerability verifications, and security ratings directly to these frameworks, providing auditors with timestamped forensic evidence proving that all external AI entry points are monitored, verified, and controlled.
Immediate Actionable Verification Checklist
Conduct Recursive Outside-In Perimeter Discovery: Initiate an unauthenticated seed scan across all apex domains and ASNs to establish an exhaustive baseline of external subdomains, cloud hosting blocks, and exposed AI tool gateways.
Review Exposed Non-Human Identities (NHIs): Examine the NHI Exposure Rating and public code repository alerts to locate, isolate, and rotate all exposed AI model API keys, service principal tokens, and webhook secrets.
Audit Dangling DNS Records for Subdomain Takeovers: Inspect all decommissioned AI model subdomains and PaaS routing records against the 60+ vendor service catalog to eliminate unclaimed resources and prevent unauthorized host takeovers.
Deploy Context Objects into Automated Containment Workflows: Configure the delivery of pre-correlated external risk findings into complementary SOAR playbooks and perimeter firewalls to enable machine-speed isolation when high-probability exploit vectors are verified.
Validate External Reachability Post-Decommissioning: Run continuous Subdomain Intelligence and HTTP header analysis following any AI maintenance or decommissioning event to confirm that public endpoints enforce authentication, return terminating status codes, and leave no unprotected data paths exposed.
