AI Security Posture Management
AI Security Posture Management (AI-SPM) is a cybersecurity framework that continuously discovers, monitors, and protects artificial intelligence and machine learning systems. It secures the entire AI lifecycle by identifying vulnerabilities, misconfigurations, and compliance risks across AI models, training data, and infrastructure.
Why is AI-SPM Important in Cybersecurity?
As organizations integrate generative AI and large language models into their operations, they introduce unique security risks that traditional cybersecurity tools cannot effectively monitor. AI-SPM is essential because it eliminates "shadow AI" blind spots—unauthorized or unmonitored AI tools used by employees—and prevents the exposure of sensitive data. It ensures that an organization can safely adopt AI technologies without introducing new attack vectors, such as prompt injection, malicious data poisoning, or accidental intellectual property leaks.
What Are the Core Capabilities of AI-SPM?
A comprehensive AI-SPM solution relies on several interconnected capabilities to secure the AI ecosystem from development to deployment:
AI Asset Discovery and Inventory: Automatically maps all proprietary, open-source, and third-party AI models, applications, and agents across cloud and on-premises environments to establish a complete system inventory.
Data Governance and Privacy: Scans training pipelines, prompt inputs, and vector databases to classify sensitive data. This prevents personally identifiable information (PII) or protected corporate data from being inadvertently exposed in AI model outputs.
Risk and Vulnerability Assessment: Continuously evaluates AI configurations against security baselines. It identifies excessive user permissions, exposed API endpoints, and unpatched software libraries within machine learning pipelines.
Runtime Monitoring and Anomaly Detection: Analyzes model behavior in live production environments to identify unauthorized access, usage policy violations, and malicious interactions in real time.
Regulatory Compliance Enforcement: Automates policy checks to ensure AI systems align with complex industry regulations and governance frameworks, such as the NIST AI Risk Management Framework or the EU AI Act.
How Does AI-SPM Differ from CSPM and DSPM?
While Cloud Security Posture Management (CSPM) focuses on securing underlying cloud infrastructure and Data Security Posture Management (DSPM) protects static or transient data, AI-SPM provides targeted, AI-specific context. AI-SPM bridges the gap by governing exactly how AI models process data, how applications interact with dynamic machine learning pipelines, and how users query AI systems. It adds a specialized layer of defense specifically tailored to the behavior and architecture of artificial intelligence.
How Does ThreatNG Enable AI Security Posture Management (AI-SPM)?
As artificial intelligence rapidly integrates into enterprise environments, organizations face a critical blind spot: the external exposure of their AI infrastructure. ThreatNG is an all-in-one External Attack Surface Management (EASM), Digital Risk Protection (DRP), and Security Ratings platform that plays a foundational role in AI Security Posture Management by acting as the external adversary's viewpoint.
By automating the discovery and validation of external exposures without requiring internal agents, ThreatNG identifies Shadow AI, exposed machine learning pipelines, and leaked training data before threat actors can exploit them.
Here is how ThreatNG delivers comprehensive AI-SPM from the outside in.
ThreatNG External Discovery for AI Assets
To secure an AI ecosystem, an organization must first know what exists. ThreatNG performs purely external, unauthenticated discovery requiring zero connectors and zero permissions.
This agentless approach is critical for AI-SPM because developers often spin up experimental AI models, vector databases, and testing environments outside of sanctioned, continuously monitored IT infrastructure. ThreatNG continuously scans the internet to map this Shadow AI footprint. It identifies unregistered domains, unmanaged cloud storage, and undocumented APIs that connect to large language models (LLMs), ensuring no AI asset remains hidden from the security team.
Detailed External Assessment of AI Infrastructure
Once AI assets are discovered, ThreatNG conducts rigorous external assessments to determine if they are actually exploitable. It goes beyond static hygiene scores by validating vulnerabilities from an unauthenticated attacker's perspective.
Subdomain Takeover Susceptibility
AI development often relies on transient cloud environments. A development team might spin up a custom application (e.g., ai-test.company.com) hosted on a third-party Platform as a Service (PaaS) like Heroku or AWS Elastic Beanstalk. If the AI project is abandoned but the DNS CNAME record remains active, an attacker can claim that subdomain. ThreatNG uses DNS enumeration to find these dangling records and cross-references them against its comprehensive vendor list. By identifying this susceptibility, ThreatNG prevents attackers from taking over a trusted corporate subdomain to host malicious AI phishing agents or rogue data collection portals.
Web Application Hijack Susceptibility
AI applications often feature chat interfaces or data input portals. ThreatNG evaluates the presence of critical security headers on these subdomains, such as Content-Security-Policy (CSP), HTTP Strict-Transport-Security (HSTS), and X-Frame-Options. If an external AI application lacks a Content-Security-Policy, ThreatNG flags this as a high-severity Web Application Hijack Susceptibility. An attacker could exploit this missing header to execute Cross-Site Scripting (XSS) or prompt-injection attacks, bypassing the AI's intended guardrails to extract sensitive data.
Web Application Firewall (WAF) Identification
ThreatNG identifies the WAFs protecting exposed AI endpoints and applications. By analyzing the WAF from an external perspective, ThreatNG provides objective evidence of its effectiveness and reveals potential bypasses, ensuring that basic security controls are actively shielding vulnerable AI API endpoints.
Reporting and Continuous Monitoring
The AI landscape changes daily as developers push new code and spin up new environments. ThreatNG provides continuous visibility, eliminating the fatigue of multi-day manual fire drills.
Instead of overwhelming security teams with isolated technical alerts, ThreatNG uses a feature called DarChain to map the complete external attack path. DarChain transforms dry technical logs into real-world adversarial narratives. For example, a report will not just show an "open S3 bucket." It will show how an abandoned subdomain leads to an open S3 bucket containing AI training data, directly mapping the exploit path to frameworks like MITRE ATT&CK. This provides the Board of Directors and security leadership with decisive, prioritized evidence of business risk.
Investigation Modules and Intelligence Repositories
ThreatNG uses a robust set of Investigation Modules to pull critical context from various intelligence repositories. These modules are vital for identifying the collateral damage and data leakage associated with AI development.
Sensitive Code Exposure: Developers frequently commit code containing hardcoded secrets. This module scans public repositories (such as GitHub) to find leaked API keys for services like OpenAI and Anthropic. If an LLM API key is exposed, attackers can use it to incur massive charges or access proprietary AI models.
Cloud and SaaS Exposure (SaaSqwatch): This module externally identifies vendor use across the digital supply chain. It identifies unauthorized generative AI SaaS applications being used by employees, allowing security teams to rein in Shadow IT and prevent corporate data from being fed into public AI tools.
Archived Web Pages and the Dark Web: ThreatNG scrapes archived versions of websites (such as the Wayback Machine) and dark web forums. For AI-SPM, this means uncovering deprecated developer documentation, API blueprints, or sensitive AI training datasets that were accidentally published and later removed from production, but still exist in archives or are being traded by threat actors.
Cooperation with Complementary Solutions
ThreatNG is designed to work seamlessly alongside internal security investments, providing the missing "Outside-In" intelligence to create a complete AI security posture.
ThreatNG and Cloud Security Posture Management (CSPM)
While CSPM tools are excellent "Quartermasters" that manage the security of known, sanctioned cloud infrastructure, they cannot protect what they cannot see. ThreatNG complements CSPM by performing external discovery to locate "unwired" AI entry points—such as forgotten cloud instances spun up on personal credit cards. ThreatNG finds these rogue AI assets and feeds them back into the enterprise, allowing the security team to deploy CSPM agents and bring them under internal management.
ThreatNG and Breach and Attack Simulation (BAS)
BAS tools are highly effective at testing an organization's internal defenses against simulated attacks. ThreatNG enhances BAS platforms by providing the real-world external intelligence needed to make those simulations accurate. Instead of a BAS tool guessing where an attack might start, ThreatNG supplies the exact "forgotten side doors"—such as a specifically identified exposed AI API or a vulnerable subdomain. The BAS tool can then use this intelligence to simulate exactly how an attacker would move laterally from that exposed AI asset into the core network.
ThreatNG and Cyber Risk Quantification (CRQ)
CRQ platforms calculate financial risk to help businesses understand their cyber liability. ThreatNG acts as the real-time "telematics chip" for these platforms. Instead of the CRQ relying on static questionnaires about AI policies, ThreatNG feeds it behavioral facts. If ThreatNG discovers an exposed machine learning database or a leaked AI vendor credential on the dark web, it dynamically updates the CRQ platform, allowing the organization to adjust its financial risk models based on actual, external AI exposure rather than statistical guesses.
