Zero Trust for AI Agents
What is Zero Trust for AI Agents?
Zero Trust for AI Agents is a cybersecurity architecture and governance model that treats every autonomous agent, tool invocation, prompt input, and machine identity as inherently untrusted, regardless of network location or prior approval.
Extending the core Zero Trust axiom—"never trust, always verify"—to autonomous and semi-autonomous systems, this framework mandates explicit authentication, dynamic runtime authorization, strict least privilege, and continuous behavioral verification for every operational step an AI agent attempts.
Unlike passive chatbots or deterministic software workflows, autonomous AI agents possess agency: they independently evaluate objectives, plan multi-step execution paths, generate and run code, retrieve documents, and invoke external APIs using non-human identities. Zero Trust for AI Agents ensures that an agent cannot exceed its operational mandate, succumb to indirect prompt injection, or become a compromised confused deputy inside the enterprise perimeter.
Core Pillars of Zero Trust for AI Agents
Implementing Zero Trust across agentic architectures requires continuous enforcement across five foundational pillars:
1. Cryptographic Workload Identity and Attestation: Every agent instance must be assigned an immutable, machine-verifiable identity (such as SPIFFE/SPIRE workload identities or ephemeral cryptographic certificates) rather than sharing static API keys or inherited service accounts. This links every downstream action to a verifiable agent ID, runtime environment, and delegating human principal.
2. Dynamic, Context-Aware Authorization (Per-Action Validation): Authentication at the start of a session is insufficient. Policy Enforcement Points (PEPs) must intercept and evaluate every tool invocation, API request, and database query at runtime based on task scope, real-time risk, resource sensitivity, and operational context.
3. Ephemeral Least Privilege and Scoped Delegation: Agents should never hold permanent, blanket write credentials. Trust systems issue scoped, just-in-time (JIT) tokens valid only for a single, named execution step, preventing agents from pivoting into unrelated enterprise infrastructure.
4. Bidirectional Input/Output Guardrails and Data Filtering: Zero Trust treats both prompt inputs and agent outputs as untrusted data streams. Inbound inputs—including retrieved files, web context, and tool responses—are sanitized against prompt injection and jailbreaks. Outbound actions are evaluated to prevent unauthorized data exfiltration, tool tampering, and policy violations.
5. Runtime Behavioral Monitoring and Attestation Drift Detection: Telemetry engines baseline normal agent operation, tracking token consumption rates, tool-call frequencies, reasoning traces, and destination endpoints to identify anomalous behaviors or goal hijacking in real time.
How Zero Trust for AI Agents Works in Practice
The operational lifecycle of an agent request governed by Zero Trust executes in a closed loop:
Task Delegation and Identity Binding: A user or upstream orchestrator assigns an objective to an agent. The security plane binds the request to a unique agent session token containing cryptographic provenance of the human delegator, permitted capabilities, and resource boundaries.
Plan Formulation and Policy Interception: The agent formulates a step-by-step execution plan. Before any step touches internal networks or external tools, a centralized Policy Decision Point (PDP) checks whether the proposed plan aligns with predefined enterprise policies.
Just-In-Time Credential Issuance: If an approved step requires reading a database or sending an email, an identity broker issues a short-lived, downscoped token restricted exclusively to that specific target API and task.
Deterministic Execution in an Isolated Sandbox: The agent executes the tool call in a micro-segmented runtime sandbox with restricted network egress, ensuring potential code execution cannot break out to the host system.
Response Validation and Credential Destruction: The tool's response is inspected for data leakage, the ephemeral credential is immediately invalidated, and the complete audit trail is committed to structured logs.
Zero Trust for Traditional Workloads vs. Zero Trust for AI Agents
Understanding how Zero Trust principles shift between standard applications and autonomous agents highlights key security differences:
Traditional Workload Zero Trust: Focuses on deterministic, pre-compiled code paths, static role-based access control (RBAC), user authentication, network microsegmentation, and device health posture. Network and IAM rules remain largely static once provisioned.
AI Agent Zero Trust: Governs non-deterministic, probabilistic execution paths assembled on the fly by language models. Access control must evaluate semantic intent, tool sequences, changing runtime contexts, and untrusted natural language data sources to prevent privilege abuse and confused-deputy manipulation.
Critical Threats Neutralized by Agentic Zero Trust
Applying Zero Trust architectures to AI agents neutralizes severe operational and architectural threats:
Confused-Deputy Exploitation: An attacker uses an indirect prompt injection embedded in an unauthenticated document to persuade an agent to misuse its legitimate, high-privilege credentials against internal databases. Zero Trust stops this by evaluating context and blocking the out-of-scope tool invocation.
Silent Lateral Movement: In flat environments, a compromised agent can use broad service account keys to query sensitive files outside its primary objective. Microsegmentation and just-in-time tokens limit access exclusively to explicitly permitted resources.
Credential Scraping and Replay: Attackers compromising memory dumps or logs to steal embedded API keys find no persistent secrets because the agent operates only with short-lived, single-use cryptographic tokens.
Excessive Agency and Unauthorized Execution: Without Zero Trust, agents can autonomously execute destructive actions (such as dropping database tables or transferring funds). Zero Trust enforces out-of-band human-in-the-loop (HITL) gates for high-impact actions.
Frequently Asked Questions
Why is traditional Role-Based Access Control (RBAC) inadequate for AI agents?
Traditional RBAC grants an identity broad, static permissions for long periods. Because AI agents assemble workflows dynamically and can be manipulated by malicious inputs at runtime, static permissions allow an agent compromised by prompt injection to execute any action permitted by its broad role. Zero Trust replaces RBAC with context-aware, per-action authorization.
What is the confused deputy problem in autonomous AI agents?
A confused deputy problem occurs when an agent holding legitimate, elevated credentials is tricked by an unauthorized third party (often via indirect prompt injection) into using its authority to access, alter, or exfiltrate resources that the third party cannot reach directly.
How do Non-Human Identities (NHIs) factor into Agentic Zero Trust?
AI agents interact with software systems exclusively through Non-Human Identities, such as API tokens, OAuth credentials, and service accounts. Zero Trust mandates continuous discovery, cryptographic attestation, strict scoping, and immediate revocation for all machine identities used by agents.
Enforcing Zero Trust for AI Agents with ThreatNG
Zero Trust for AI Agents extends the security axiom "never trust, always verify" to autonomous Large Language Models (LLMs), multi-agent swarms, Model Context Protocol (MCP) servers, and agentic workflows. Because autonomous agents possess execution agency—generating dynamic tool calls, querying databases, and utilizing programmatic machine credentials—they represent an active, non-deterministic attack surface. When security teams evaluate agent trust boundaries only within internal environments, they suffer from the Contextual Certainty Deficit: internal policy engines cannot see exposed staging gateways, public MCP endpoints, leaked developer secrets, and lookalike domains that external adversaries use to compromise agent workflows from the open internet.
ThreatNG operationalizes Zero Trust for AI Agents by acting 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 agentic footprint from an outside-in, adversary-centric perspective. By translating external technical discoveries into the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) taxonomy and the ISO/IEC 42001 standard, ThreatNG delivers Legal-Grade Attribution without requiring internal software agents, API access keys, or administrative credentials.
External Discovery
A Zero Trust architecture requires an exhaustive, continuously verified inventory of every exposed interface before enforcing trust boundaries. ThreatNG identifies public agentic infrastructure through connectorless external discovery.
Connectorless Agent and Tool Surface Discovery: ThreatNG maps the entire public-facing digital footprint using unauthenticated discovery with zero internal connectors, software agents, or network credentials. It queries 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 agents or tool interfaces.
Patented Recursive Discovery of Ephemeral Agent Staging: Starting from a single seed entity (such as an apex domain, brand name, or ASN), ThreatNG iteratively expands outward. As newly uncovered subdomains, DNS records, or netblocks emerge, the engine feeds them back in as fresh discovery seeds. This recursive process uncovers developer staging sandboxes, temporary agent execution endpoints, and shadow MCP implementations across multi-cloud environments (AWS, Azure, Google Cloud Platform) and regional hosting providers.
Unauthenticated SaaS and Third-Party Agent Discovery (SaaSqwatch): ThreatNG evaluates public digital exhaust—such as DNS CNAME routing chains, HTTP headers, and SSL/TLS certificates—to discover sanctioned and unsanctioned external agent platforms, hosted model endpoints, and third-party AI tools employees use. This catalogs unvetted external dependencies that violate Zero Trust boundaries.
Adversary Infrastructure and Lookalike Discovery: ThreatNG continuously discovers newly registered, typosquatted, and lookalike domain permutations (such as homoglyphs and transposed characters) registered across global domain registrars. It flags dormant domains and emerging SSL/TLS certificates configured to impersonate corporate agent gateways or prompt interfaces, detecting adversary staging environments before phishing or confused-deputy attacks 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 partners. This establishes baseline visibility across the extended ecosystem to ensure partner agent deployments adhere to enterprise Zero Trust standards.
External Assessment
ThreatNG elevates agent security from theoretical assumptions 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, 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 Agent Secret Assessment: AI agents rely on machine credentials to interact with backend databases and external APIs. 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 detects exposed OpenAI API keys, Anthropic tokens, Hugging Face user secrets, and LangChain orchestration credentials, computing an NHI Exposure Rating (A through F). This assessment enables organizations to revoke exposed machine identities, preventing adversaries from using leaked tokens to bypass Zero Trust per-action authorization.
Detailed Assessment Example 2: Insecure Agent 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), 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 agent interface is vulnerable to client-side script injection, clickjacking, or indirect prompt injection via manipulated web contexts.
Detailed Assessment Example 3: Subdomain Takeover Susceptibility on Abandoned Agent Services: When an experimental agent microservice or temporary tool endpoint 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 and validates whether the resource is unclaimed. It assigns an A through F Subdomain Takeover Susceptibility rating, ensuring that abandoned agent endpoints are not hijacked by threat actors to host malicious models under trusted corporate domains.
Detailed Assessment Example 4: Known Vulnerability Exposure Verification (KVEV) on Agent 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 agent service is actively vulnerable to remote code execution, identifying systems that require immediate isolation or patching to maintain Zero Trust verification.
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 agent interaction logs, ensuring sensitive corporate memory stores remain within strict Zero Trust boundaries.
Strategic Reporting
ThreatNG standardizes the communication of agent-related security postures by converting raw outside-in discoveries, infrastructure graphs, and technical exposure telemetry into structured, auditable records for technical teams, 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 lets CISOs communicate verified agent attack surface health and exposure-reduction metrics directly to executive leadership.
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 agent 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 agent 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 autonomous agents can spawn sub-agents dynamically, CI/CD pipelines push frequent tool definitions, and cloud configurations drift constantly, static periodic audits fail to enforce Zero Trust. 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 a developer temporarily exposes an agent execution port or connects an experimental model to an unauthenticated public IP address, ThreatNG detects the configuration drift instantly. 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 agent 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 agent exposures.
Detailed Module Example 1: Subdomain Infrastructure Exposure Module (Agent Orchestration and MCP 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 proves an immediate breakdown in Zero Trust isolation, confirming that agent tools and execution contexts are directly accessible from the open internet.
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 enforcing Zero Trust access controls 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 targeted for Zero Trust revocation are completely 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 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 agentic workflows fall under Zero Trust 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 agent Zero Trust access policies, executive summaries, and regulatory disclosures 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 agentic Zero Trust 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 agent hosts run software flaws that are actively weaponized, providing concrete justification for revoking execution privileges.
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 unauthorized agent request 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.
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 external intelligence scout that cooperates seamlessly with complementary solutions across enterprise governance, risk, and security operations.
Cooperation with Policy Decision Points (PDPs) and Agent Gateways: ThreatNG feeds external risk telemetry—such as confirmed public reachability, lack of HTTP security headers, and domain reputation scores—directly into complementary solutions (enterprise Policy Decision Points and AI Gateway proxies). When an agent attempts an outbound tool call or API request, the PDP uses ThreatNG's outside-in intelligence to block requests destined for unverified external endpoints or domains exhibiting lookalike traits.
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, ensuring that static, leaked credentials cannot bypass Zero Trust per-action verification.
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 agent execution port or an unauthenticated MCP server, the SOAR platform executes automated Zero Trust containment playbooks—modifying perimeter firewall access control lists (ACLs), revoking temporary task tokens, and isolating the container.
Cooperation with Web Application Firewalls (WAFs) and API Gateways: ThreatNG discovers exposed subdomains and API routes hosting AI interfaces that lack proper authentication or security headers. It shares these URLs and technical markers with complementary solutions (enterprise WAFs and API gateways). Security teams use this data to deploy blocking rules, enforce strict token authentication, and prevent untrusted external webhooks from triggering agent execution loops.
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 agent deployment is accounted for in Zero Trust directory catalogs.
Examples of ThreatNG Helping Organizations
Identifying an Exposed Model Context Protocol (MCP) Server on an Unmonitored Staging Host: A development team deployed an experimental browser automation agent using Playwright MCP on an unmanaged subdomain (agent-runner-01.dev.enterprise.com) to automate data collection. ThreatNG’s recursive discovery engine identified the host during an unauthenticated crawl. The Subdomain Infrastructure Exposure module confirmed that the MCP endpoint was exposed to the public internet without authentication, violating Zero Trust boundary isolation. ThreatNG assigned an F Web Application Hijack Susceptibility score and generated an urgent forensic evidence package. Security engineers immediately terminated the public DNS record and rebound the service to localhost behind enterprise identity proxies, preventing external threat actors from issuing unauthorized browser automation commands.
Neutralizing Leaked Agent Master API Keys in a Public Git Repository: A software engineer working on an autonomous customer support workflow hardcoded production OpenAI API keys and QDrant vector database connection strings into an orchestration script and pushed it 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 broad 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 using the static credentials to bypass Zero Trust per-action authentication.
Examples of ThreatNG Working with Complementary Solutions
Working with SOAR and Firewalls to Enforce Zero Trust Isolation on Unmanaged Agent Ports: ThreatNG discovers an internet-facing host running an unauthenticated agent orchestration server (such as Ollama or Langflow) with an unpatched software vulnerability listed 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 port, enforce network microsegmentation, and require all future agent requests to route through an authenticated API gateway.
Working with CAASM and IAM to Revoke Compromised Agent Machine Identities: ThreatNG detects an exposed environment configuration file containing production Anthropic and vector database credentials committed to a public Git repository. ThreatNG passes the findings to complementary solutions (a CAASM platform and IAM directory). The CAASM system maps the asset's business ownership, and the IAM directory revokes the compromised service account, ensuring that the compromised agent cannot execute lateral tool calls across internal networks.
Frequently Asked Questions
How does ThreatNG discover AI agent exposures without internal software agents?
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 MCP tool gateways, and unmonitored staging environments strictly from an external adversary's viewpoint.
What is the role of Non-Human Identity (NHI) governance in Zero Trust for AI Agents?
Autonomous AI models and agents execute actions 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 bypass Zero Trust access boundaries.
How does ThreatNG support regulatory compliance for AI agent 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 enterprise 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.
