ISO 42001
What is ISO 42001?
ISO/IEC 42001 is the international standard that specifies the requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS). Published jointly by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), it serves as a certifiable governance framework for managing artificial intelligence responsibly, safely, and securely.
In cybersecurity, ISO/IEC 42001 provides the governance and control baseline needed to defend AI systems against threats that traditional information security frameworks cannot fully mitigate. While standard frameworks safeguard static code and infrastructure, ISO 42001 addresses the operational and algorithmic vulnerabilities unique to machine learning (ML), large language models (LLMs), and autonomous agents. This includes protecting training data from poisoning, preventing prompt injection and jailbreaks, securing model weights, managing non-human machine identities, and mitigating model evasion attacks throughout the entire AI lifecycle.
Structure of the ISO 42001 Standard
ISO/IEC 42001 uses the Harmonized Structure (formerly Annex SL), making it compatible with other management system standards like ISO/IEC 27001 (Information Security Management) and ISO 9001 (Quality Management). The standard consists of two main sections:
Management System Clauses (Clauses 4 through 10): Define mandatory governance requirements, including organizational context (Clause 4), leadership commitment (Clause 5), AI risk and impact planning (Clause 6), resource allocation and competency (Clause 7), operational planning and control (Clause 8), performance evaluation and audit (Clause 9), and continual improvement (Clause 10).
Annex A Reference Controls: Provides 38 specific technical and operational controls organized across key operational domains to address risks identified during risk assessments. These include policies related to AI, internal organization, resources, AI system lifecycle processes, data for AI systems, information for interested parties, use of AI systems, and third-party relationships.
Annex B and Annex C Guidance: Annex B offers implementation guidance for Annex A controls, while Annex C details potential AI-related organizational objectives and risk sources.
Core Cybersecurity Domains Addressed by ISO 42001
ISO/IEC 42001 addresses cyber risks specific to artificial intelligence architectures:
Training Data Integrity and Poisoning Prevention: Establishes controls over data provenance, data pipeline validation, and data quality assurance. This prevents adversaries from manipulating training corpora, injecting backdoors, or compromising retrieval-augmented generation (RAG) vector embeddings.
Adversarial Robustness and Model Hardening: Requires organizations to test and protect models against adversarial evasion attacks, data extraction techniques, model inversion, and automated prompt manipulation.
Model Supply Chain and Component Security: Enforces security evaluations across third-party foundational models, pre-trained weights, open-source AI frameworks (such as PyTorch and TensorFlow), orchestration tools, and agent tool-calling libraries to prevent supply chain compromises.
Non-Human Identity (NHI) and API Security: Secures programmatic credentials, service accounts, and API tokens connecting AI agents, vector databases, and Model Context Protocol (MCP) servers, preventing attackers from hijacking autonomous workflows.
Continuous Monitoring and Drift Detection: Mandates mechanisms to monitor model outputs for performance degradation, behavioral drift, unauthorized model modifications, and security anomalies in production.
ISO 42001 vs. ISO 27001 in Cybersecurity
Understanding how ISO 42001 compares to ISO 27001 clarifies their complementary roles in enterprise defense:
Scope and Objective: ISO 27001 establishes an Information Security Management System (ISMS) focused on safeguarding the confidentiality, integrity, and availability (CIA) of digital assets and data. ISO 42001 establishes an Artificial Intelligence Management System (AIMS) focused on the governance, safety, fairness, transparency, and specific cyber resilience of AI models and their algorithmic decisions.
Threat Models: ISO 27001 addresses traditional network breaches, access control failures, malware, and data loss. ISO 42001 addresses AI-specific attack vectors, such as prompt injection, model weight theft, training data poisoning, hallucination-induced security failures, and autonomous agent abuse.
Operational Overlap: Rather than replacing ISO 27001, organizations implement ISO 42001 on top of their existing ISO 27001 controls. ISO 27001 secures the underlying cloud servers, containers, and databases, while ISO 42001 governs the AI application logic, data pipelines, and model behavioral controls.
Strategic Significance of ISO 42001 for Enterprise Cyber Resilience
Implementing an ISO/IEC 42001 framework provides essential strategic and operational advantages:
Bridging the AI Governance Gap: Eliminates shadow AI deployments by establishing centralized visibility, organizational ownership, and formal approval processes for all machine learning models across the enterprise.
Preemptive Regulatory Compliance: Aligns an enterprise with major emerging global regulations, including the European Union AI Act, U.S. executive actions on artificial intelligence, and sector-specific financial and healthcare oversight.
Defensible Third-Party Risk Assurance: Delivers independent, auditable verification to customers, insurance underwriters, and enterprise partners that customer data passed into AI models is protected against unauthorized model training and leakage.
Resilient Incident Response for AI: Establishes formal incident management protocols tailored to AI-specific security breaches, such as automated model shutdown mechanisms, prompt rollback procedures, and compromised dataset isolation.
Frequently Asked Questions
Is ISO 42001 a certifiable standard?
Yes. Like ISO 27001 and ISO 9001, ISO/IEC 42001 is a Type A management system standard, meaning organizations can undergo third-party audits by accredited certification bodies to achieve formal ISO 42001 certification.
Can an organization implement ISO 42001 without having ISO 27001?
Yes. ISO 42001 is a standalone standard that any organization developing, deploying, or using AI can implement. However, because ISO 42001 relies on standard security baselines (such as infrastructure access control and encryption), ISO 27001 certification streamlines implementation.
How does ISO 42001 address third-party AI systems and APIs?
Annex A of ISO 42001 contains specific controls governing external suppliers and partners. Organizations must assess the security posture of third-party model providers, evaluate external API dependencies, establish clear data ownership agreements, and verify that third-party AI integrations do not expose proprietary enterprise data.
Operationalizing ISO 42001 Compliance and AI Governance with ThreatNG
ISO/IEC 42001 is the international standard for establishing, implementing, maintaining, and continuously improving an Artificial Intelligence Management System (AIMS). In cybersecurity, ISO 42001 defines the operational, technical, and governance controls required to protect machine learning models, autonomous agents, training datasets, and inference pipelines.
Achieving and maintaining ISO 42001 certification often suffers from the Contextual Certainty Deficit because internal governance teams rely heavily on internal documentation, subjective policy questionnaires, and self-attested developer inventories. These internal assessments overlook how an organization’s artificial intelligence deployments, unmanaged shadow AI instances, exposed vector databases, and leaked machine secrets appear to external adversaries on the open internet.
ThreatNG operationalizes ISO 42001 compliance by functioning as an unauthenticated external scout. By unifying 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 complete public digital perimeter and AI footprint from an outside-in, adversary-centric perspective. It correlates exposed AI infrastructure, machine identities, and data leaks into deterministic adversarial narratives via DarChain, evaluates exploitability through its 4-Dimensional (4D) Data Model, and delivers Legal-Grade Attribution without requiring internal software agents, API access keys, or administrative credentials.
External Discovery
ISO 42001 mandates complete visibility into all AI systems within the organizational scope (Clause 4 and Annex A.5/A.6). ThreatNG establishes this inventory baseline through connectorless external discovery.
Connectorless AI Perimeter Discovery: ThreatNG maps the entire public-facing digital footprint using unauthenticated discovery with zero internal connectors, software agents, or network credentials. It evaluates public domain registries, authoritative DNS zone files, SSL/TLS certificate transparency logs, Regional Internet Registry (RIR) databases, and global BGP routing tables to catalog every public IP block, subdomain, cloud environment, and web application hosting AI components.
Patented Recursive Discovery for Shadow AI: Starting from an initial seed entity (such as an apex domain, brand name, or ASN), ThreatNG iteratively expands outward. As new subdomains, DNS records, or netblocks emerge, the platform automatically feeds them back in as fresh discovery seeds. This recursive process uncovers rogue LLM endpoints, orphaned sub-agent task runners, and unmonitored Model Context Protocol (MCP) tool servers deployed across AWS, Azure, Google Cloud, and regional hosting providers without central IT authorization.
Third-Party AI and Supply Chain Mapping (SaaSqwatch): Supporting ISO 42001 Annex A.10 (Third-Party Relationships), ThreatNG analyzes external routing, DNS CNAME records, and HTTP response headers to identify third-party AI vendors, hosted model endpoints, vector databases, and external API dependencies used across corporate departments.
Adversary Lookalike and Impersonation Discovery: ThreatNG continuously discovers newly registered, typosquatted, and lookalike domain permutations registered across global domain registrars. It flags adversary infrastructure mimicking corporate AI services or internal chatbot interfaces before phishing campaigns or prompt-harvesting schemes launch.
Subsidiary and Extended Ecosystem Scoping: Because ThreatNG operates without internal credentials or vendor permissions, organizations can execute unauthenticated discovery across operating subsidiaries, prospective acquisition targets (M&A due diligence), and supply chain partners, ensuring all AI instances across the extended enterprise are identified for ISO 42001 scoping.
External Assessment
ThreatNG elevates ISO 42001 risk evaluation from theoretical checklists to deterministic, evidence-backed assessment 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, 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 AI Secret Assessment: Supporting ISO 42001 Annex A.6 (Resources) and Annex A.7 (AI Lifecycle), 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 automated LangChain credentials, computing an NHI Exposure Rating (A through F). This allows security teams to revoke exposed credentials before adversaries hijack autonomous agent loops, exhaust cloud budgets, or compromise backend databases.
Detailed Assessment Example 2: Insecure AI Interface and Web Control Assessment: Supporting ISO 42001 Annex A.8 (Use of AI Systems), 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), HSTS, X-Content-Type-Options, and X-Frame-Options, as well as deprecated headers. It assigns an A through F Web Application Hijack Susceptibility rating to determine whether an AI chat portal is vulnerable to clickjacking or cross-site scripting (XSS), which threat actors use to intercept user prompts or inject malicious instructions.
Detailed Assessment Example 3: Subdomain Takeover Susceptibility on Decommissioned AI Services: Supporting ISO 42001 Annex A.7 (AI System Lifecycle), ThreatNG inspects discovered subdomains across multi-cloud environments for dangling CNAME records pointing to decommissioned third-party cloud hosting providers, PaaS platforms, or marketing tools. The platform cross-references hostnames against an extensive catalog of over 60 cloud services (including AWS S3, Microsoft Azure, Heroku, Vercel, and GitHub) and validates whether the resource is unclaimed, assigning an A through F Subdomain Takeover Susceptibility rating to eliminate dangling assets before adversaries hijack them to host malicious models under trusted corporate domains.
Detailed Assessment Example 4: Known Vulnerability Exposure Verification (KVEV) on AI Infrastructure: Supporting ISO 42001 Annex A.6 (Technical Controls), when ThreatNG uncovers an internet-facing AI inference gateway, model server, or vector database interface, the KVEV engine performs live, unauthenticated checks. It confirms public reachability, checks for inclusion on the CISA KEV catalog, evaluates 30-day EPSS weaponization probabilities, and cross-references active exploit scripts in DarCache eXploit. This determines whether an exposed AI service is actively vulnerable to remote code execution.
Detailed Assessment Example 5: Data Leak Susceptibility on Exposed Vector Stores and Training Buckets: Supporting ISO 42001 Annex A.7 (Data for AI Systems), 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 teams secure these assets before adversaries execute data poisoning or model theft.
Strategic Reporting
ThreatNG standardizes the communication of ISO 42001 security posture by converting raw outside-in discoveries, infrastructure graphs, and technical exposure telemetry into structured, auditable records for technical practitioners, executive leadership, and ISO certification 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 Non-Human Identity (NHI) Exposure, Cyber Risk Exposure, Data Leak Susceptibility, and Supply Chain & Third Party Exposure. This equips CISOs to present transparent AI governance progress directly to executive boards and external ISO 42001 audit committees.
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 ISO 42001 clauses and Annex A controls across four functional pillars: Technical, Strategic, Operational, and Financial.
Defensible Regulatory Compliance Mapping: ThreatNG maps discovered external AI exposures directly to key regulatory frameworks and standards, including ISO/IEC 42001, the EU AI Act, NIST AI RMF, MITRE ATLAS, ISO/IEC 27001, SEC Form 8-K material breach disclosure rules, FedRAMP, and SOC 2. This supplies auditors with timestamped proof that mandatory AI inventory and risk management controls are operating effectively.
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, insurance reviews, registrar takedowns, and legal attribution.
Continuous Monitoring
Because engineering teams deploy microservices rapidly and cloud configurations change continuously, static periodic assessments fail to maintain ISO 42001 compliance. ThreatNG delivers 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. If an unauthorized developer team exposes an internal model to public traffic, spins up an unauthenticated vector database, or leaves an AI agent running on a public IP, ThreatNG detects the configuration drift immediately. 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 zero-day vulnerability affecting an AI framework or model server is disclosed, identifying every affected external asset within seconds.
Investigation Modules
ThreatNG features specialized investigation modules that allow security analysts to investigate discovered infrastructure, trace developer leaks, and evaluate the full technical context of AI risks mapped to ISO 42001 controls.
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 detects exposed AI Orchestration Frameworks (including 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 (including 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). Identifying these endpoints externally provides the empirical evidence required to enforce ISO 42001 controls regarding authorized deployment and data protection.
Detailed Module Example 2: 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 AI systems are identified and neutralized in accordance with ISO 42001 Annex A.6.
Detailed Module Example 3: 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 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, providing evidence to satisfy ISO 42001 requirements for third-party risk management and training data governance.
Detailed Module Example 5: Cybersecurity AI Prompts (DarcPrompt): DarcPrompt packages verified AI 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 ISO 42001 audit remediation runbooks, Statement of Applicability (SoA) documentation, 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 ISO 42001 compliance 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 an AI system compromise originated from stolen developer or administrator 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 to safeguard mobile AI application backends.
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 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 unauthenticated external scout that cooperates seamlessly with complementary solutions across enterprise governance, risk, and security operations.
Cooperation with Governance, Risk, and Compliance (GRC) Platforms: ThreatNG pushes unauthenticated outside-in discovery records, objective A through F ratings, and Correlation Evidence Questionnaires directly into complementary solutions (GRC platforms). Compliance officers use this live evidence to substantiate ISO/IEC 42001 Statement of Applicability (SoA) audits, replacing subjective self-attestation with automated, outside-in telemetry.
Cooperation with AI Security Posture Management (AI-SPM) Solutions: ThreatNG feeds discovered shadow AI endpoints, unmonitored MCP tool servers, exposed vector databases, and unmapped subdomains 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 Secrets Vaults and Non-Human Identity Management (NHIM): ThreatNG passes verified leaked Non-Human Identities (NHIs) and programmatic AI tokens discovered in public code repositories or paste sites to complementary solutions (enterprise secrets vaults and NHIM platforms). The secrets vault immediately invalidates the affected service principal credentials and rotates secrets, cutting an attacker's access privileges across internal databases.
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 rogue agent API or an unauthenticated vector database, the SOAR platform executes automated decommissioning playbooks—modifying perimeter firewall access control lists (ACLs), revoking temporary task tokens, and isolating the container.
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 all deployed AI models, vector stores, and third-party AI dependencies are cataloged and assigned business ownership.
Examples of ThreatNG Helping Organizations
Uncovering an Unmonitored Vector Database to Satisfy ISO 42001 Scoping: During preparation for an ISO/IEC 42001 audit, an enterprise used ThreatNG to validate its declared AI asset inventory. ThreatNG’s recursive discovery engine identified an unlisted subdomain (ai-poc-rag.company.com). The Subdomain Infrastructure Exposure module revealed an exposed QDrant vector database containing internal document embeddings accessible to the public internet without authentication. ThreatNG assigned an F Data Leak Susceptibility score and compiled a forensic evidence package. Security engineers immediately placed the vector store behind enterprise SSO and updated their ISO 42001 AIMS asset register before external auditors initiated their evaluation.
Remediating Leaked Model Orchestration Secrets to Satisfy Access Controls: An external contractor working on an autonomous customer service workflow committed an environment file containing production Anthropic and OpenAI API tokens 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 active access to production workloads and issued an alert with exact repository URLs and commit timestamps. Security administrators revoked the machine token immediately, meeting ISO 42001 Annex A.6 controls for machine identity management and preventing an unmonitored API compromise.
Examples of ThreatNG Working with Complementary Solutions
Working with GRC Platforms to Automate ISO 42001 Audit Evidence: ThreatNG discovers all public-facing AI endpoints, validates SSL/TLS cryptographic baselines, and verifies that no dangling subdomains exist across the enterprise perimeter. ThreatNG transmits the resulting Correlation Evidence Questionnaire (CEQ) and forensic evidence packages to complementary solutions (an enterprise GRC platform). The GRC platform automatically links the verified data to ISO 42001 Annex A.7 and A.8 control items, providing third-party certification auditors with verifiable evidence of continuous external monitoring.
Working with SOAR and Firewalls to Block Reachable AI Vulnerabilities: 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, neutralizing the entry point.
Frequently Asked Questions
How does ThreatNG validate ISO 42001 compliance without internal software agents?
ThreatNG operates entirely as an unauthenticated external scout. It continuously evaluates 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.
Which ISO 42001 Annex A controls does ThreatNG support most directly?
ThreatNG directly supports Annex A.5 (AI Policies), Annex A.6 (Internal Organization and Resources), Annex A.7 (AI System Lifecycle), Annex A.8 (Use of AI Systems), and Annex A.10 (Third-Party and Supply Chain Relationships) by providing continuous outside-in discovery, non-human identity tracking, vulnerability exposure verification, and supplier mapping.
How does ThreatNG cooperate with complementary security platforms during an ISO 42001 audit?
ThreatNG acts as an external intelligence scout that feeds pre-correlated Context Objects, verified asset inventories, prioritized risk indicators, and DarcPrompt blueprints directly into complementary solutions like GRC platforms, AI-SPM solutions, secrets vaults, SOAR engines, and CAASM databases, driving automated audit documentation, token revocation, and proactive perimeter defense.
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.
Populate GRC Platforms with Continuous Evidence: Export ThreatNG Correlation Evidence Questionnaires (CEQs) and objective A through F ratings directly into your enterprise GRC platform to substantiate ISO 42001 Annex A controls with timestamped technical proof.
