AI Security Posture Management
AI Security Posture Management (AI-SPM) is a comprehensive discipline within cybersecurity that continuously manages, monitors, and optimizes the security posture and risk of an organization's entire AI and machine learning (ML) environment.
Its central objective is to ensure that AI systems—including models, data, infrastructure, and agents—adhere to security policies, regulatory requirements, and industry best practices throughout their lifecycle, from development to deployment.
AI-SPM typically operates across three key dimensions:
Visibility and Inventory: This involves discovering and maintaining a real-time inventory of all AI assets, including proprietary models, foundation models (LLMs) in use, training and inference data pipelines, vector databases, and all associated configurations. The goal is to eliminate "Shadow AI" and map the entire AI attack surface.
Risk Assessment and Prioritization: This involves evaluating each discovered asset's posture against predefined risks. These risks include technical vulnerabilities in the supporting infrastructure (such as misconfigured cloud services), logical threats specific to the model (such as prompt injection susceptibility or data leakage), and policy violations (such as unauthorized access or non-compliance with data privacy laws). Findings are scored and prioritized based on severity and business impact.
Governance and Remediation: AI-SPM enforces security and compliance policies across the AI environment. It involves deploying and managing security controls (often called guardrails) to monitor the model's behavior at runtime. It translates identified risks into actionable remediation workflows, ensuring that security gaps are closed quickly and that all AI development and deployment practices remain aligned with the organization's risk tolerance and regulatory obligations.
Ultimately, AI-SPM shifts the security focus from reactive incident response to proactive risk management for the entire AI application portfolio.
ThreatNG, as an External Attack Surface Management (EASM), Digital Risk Protection (DRP), and security ratings solution, provides critical external visibility and continuous assessment to support AI Security Posture Management (AI-SPM). While AI-SPM is a broad discipline, ThreatNG excels at delivering the unauthenticated, attacker-centric data that informs the posture's external integrity.
External Discovery and Inventory
The foundation of AI-SPM is inventorying all assets, which ThreatNG achieves through purely external unauthenticated discovery using no connectors.
Technology Stack Identification: ThreatNG's Technology Stack Investigation Module uncovers the full technology stack, detailing technologies across categories such as Artificial Intelligence (265 technologies), AI Model & Platform Providers, and AI Development & MLOps. This process directly identifies and inventories externally exposed AI assets and the frameworks they rely on.
Subdomain Intelligence: ThreatNG's Subdomain Intelligence uncovers subdomains and identifies the cloud and web platforms hosting them. This helps locate the public-facing endpoints that represent the perimeter of the AI environment.
Example of ThreatNG Helping: ThreatNG discovers a subdomain, ai-research.company.com, which the security team was not tracking. ThreatNG's Technology Stack identifies this site as using a vendor in the AI Development & MLOps category, automatically adding a crucial, previously unmanaged AI asset to the external posture inventory.
External Assessment for Posture Validation
ThreatNG's security ratings and assessment modules validate the security posture by highlighting critical external control failures.
Data Leak Susceptibility: This rating is derived from identifying external digital risks, such as Cloud Exposure, including exposed open cloud buckets. These misconfigured storage assets frequently contain sensitive AI training data or model weights, representing a massive posture failure.
Non-Human Identity (NHI) Exposure: This critical governance metric quantifies vulnerability to threats arising from high-privilege machine identities, such as leaked API keys and service accounts. The NHI exposure rating itself is based on unauthenticated discovery across 11 exposure vectors, including Sensitive Code Exposure and misconfigured Cloud Exposure. The exposure of an AI agent's service credential is a direct reflection of a poor AI-SPM.
Cyber Risk Exposure (Sensitive Code): The rating is based on findings that include Sensitive Code Discovery and Exposure (code secret exposure). This immediately reveals if configuration secrets or proprietary prompt logic have been externally exposed, which is a fundamental posture weakness.
Example of ThreatNG Helping: ThreatNG flags a high NHI Exposure rating because it discovered a service account credential for an AI-related cloud platform exposed in an archived web page. This external evidence provides irrefutable proof that the AI-SPM policy regarding secrets management has failed.
Reporting and Continuous Monitoring
ThreatNG provides Continuous Monitoring of the external attack surface, ensuring that any deviation in the security posture is flagged immediately.
External GRC Assessment: ThreatNG provides a continuous, outside-in evaluation of an organization's Governance, Risk, and Compliance (GRC) posture. It maps external findings directly to relevant GRC frameworks, including PCI DSS, HIPAA, GDPR, and NIST CSF. This directly validates the AI-SPM's external health against compliance requirements.
Prioritized Reporting: ThreatNG provides executive, technical, and prioritized reports. The Risk levels in the Knowledgebase help organizations prioritize security efforts and allocate resources effectively by focusing on the most critical risks.
Investigation Modules
ThreatNG's Investigation Modules allow security teams to gather granular, unauthenticated data to validate the external posture of AI assets.
Sensitive Code Exposure (Code Repository Exposure): This module discovers public code repositories and specifically identifies exposed Access Credentials (various API Keys, Cloud Credentials) and Configuration Files. These findings directly expose weaknesses in the AI asset development posture.
Cloud and SaaS Exposure: This module identifies Sanctioned and Unsanctioned Cloud Services and Open Exposed Cloud Buckets. It also identifies all associated SaaS implementations (SaaSqwatch), including those used for Data Analytics (Snowflake, Splunk) and Identity and Access Management, all of which are essential components of the AI environment.
External Adversary View: This capability aligns the organization's security posture with external threats by performing unauthenticated, outside-in assessment. ThreatNG's assessments directly map to MITRE ATT&CK techniques by uncovering how an adversary might achieve initial access and establish persistence.
Example of ThreatNG Helping: An analyst uses the Reconnaissance Hub and Advanced Search to investigate a publicly exposed API endpoint. The Subdomain Intelligence confirms the presence of missing security headers (such as HSTS and X-Frame-Options), which directly affects the Web Application Hijack Susceptibility rating and reveals a posture weakness that an attacker could leverage to tamper with the AI interface.
Intelligence Repositories
ThreatNG’s Intelligence Repositories (DarCache) provide necessary contextual data for risk prioritization within AI-SPM.
Vulnerabilities (DarCache Vulnerability): This repository integrates NVD, KEV, EPSS, and Proof-of-Concept Exploits. If the exposed infrastructure hosting an AI model has a known, exploitable vulnerability, the EPSS score helps predict the likelihood of exploitation, allowing the AI-SPM team to prioritize remediation based on actual external threat likelihood.
Complementary Solutions
ThreatNG's external posture assessment data can provide essential intelligence to complementary solutions like Cloud Security Posture Management (CSPM) platforms and AI Guardrail tools.
Complementary Solutions (CSPM): When ThreatNG’s external discovery flags a significant posture gap, such as an exposed open cloud bucket or a poor Supply Chain & Third-Party Exposure rating, it provides the CSPM platform with the specific evidence of an external failure. This external signal can then direct the CSPM to immediately perform an internal security review of the configuration for that particular resource and any associated IAM policies, validating the end-to-end security control effectiveness.
Complementary Solutions (AI Guardrails/Monitoring): The intelligence gained from ThreatNG’s Sensitive Code Exposure or NHI Exposure could identify the external presence of a configuration or credential linked to a production AI agent. This critical data can be used by an AI Guardrail solution to flag the agent's identity as potentially compromised and trigger immediate, enhanced internal monitoring, runtime analysis, or even temporary deactivation of the agent until the external leak is contained.
