Shadow AI
Shadow AI refers to the unsanctioned use of artificial intelligence tools, applications, and models within an organization without the IT and security departments' explicit knowledge, approval, or oversight. This phenomenon is a subset of "Shadow IT," but it introduces unique risks related to data privacy, intellectual property, and decision-making integrity.
In many cases, employees use Shadow AI in good faith to increase productivity, automate repetitive tasks, or analyze data more quickly. However, because these tools bypass corporate security vetting and governance, they create significant blind spots in an organization's risk profile.
The Core Risks of Shadow AI
The use of unmanaged AI tools presents several critical threats to an organization’s security posture and legal standing.
Data Leakage and Privacy Breaches: The most common risk involves employees pasting sensitive corporate data—such as financial records, customer PII, or internal strategy—into public AI models. Many public generative AI tools use input data to train future models, potentially revealing proprietary information to other users outside the company.
Intellectual Property Theft: Developers may use unvetted AI coding assistants to debug or optimize proprietary source code. If that code is absorbed into the AI’s training set, the organization’s "secret sauce" could be exposed to competitors or the public.
Regulatory Non-Compliance: Regulations such as GDPR, HIPAA, and CCPA require strict controls over how data is processed. Using an unapproved AI tool that processes data in an unauthorized jurisdiction or lacks sufficient security controls can lead to heavy fines and legal liability.
Model Bias and Inaccuracy: Shadow AI often involves using consumer-grade tools that may produce "hallucinations"—confidently stated but false information. If business decisions are made based on unverified AI outputs, the organization faces operational risks and reputational damage.
Account Takeover and Credential Risk: Employees may create accounts for AI services using corporate credentials or weak passwords, creating new entry points for attackers to compromise corporate identities.
Common Examples of Shadow AI Usage
Shadow AI is not always a standalone application; it often hides within existing workflows or tools.
Public Chatbots: Employees using tools like ChatGPT, Claude, or Gemini for drafting emails, summarizing confidential contracts, or generating reports without corporate accounts or data protection agreements.
Browser Extensions: Productivity extensions that offer AI-powered grammar checks, translation, or note-taking, which often scrape data directly from the browser window.
AI-Enabled SaaS Features: Existing approved software (like project management or CRM tools) that have added new AI features that the IT department has not yet reviewed or disabled.
Bring Your Own AI (BYO-AI): Employees using personal premium subscriptions to AI tools on work devices to bypass the limitations of free versions.
How to Mitigate Shadow AI Risks
Organizations can manage the risks of Shadow AI by moving from a policy of total restriction to one of guided enablement.
Establish a Clear AI Acceptable Use Policy: Define which AI tools are permitted, what types of data can be shared, and the process for requesting new tools.
Implement AI Discovery Tools: Use Cloud Access Security Brokers (CASB) and network monitoring to identify traffic to known AI endpoints and discover which unsanctioned tools are being used.
Provide Sanctioned Alternatives: The best way to reduce Shadow AI is to offer "Enterprise" versions of popular tools that include data privacy guarantees, such as ensuring that company data is not used for model training.
Continuous Employee Education: Train staff on the specific risks of AI, specifically the danger of inputting sensitive data into public prompts.
Zero Trust Architecture: Apply zero-trust principles to treat all unvetted AI applications as potential threats, requiring strict identity verification and data loss prevention (DLP) controls.
Frequently Asked Questions About Shadow AI
How is Shadow AI different from Shadow IT?
Shadow IT is the use of any unauthorized hardware or software. Shadow AI is a specific category of Shadow IT that involves the use of artificial intelligence. It is considered more dangerous because it doesn't just involve unvetted software, but also the active "feeding" of corporate data into external models that learn and evolve from it.
Can Shadow AI lead to a data breach?
Yes. If an employee uploads sensitive data to a public AI, and that data is later surfaced in a response to another user or if the AI provider suffers a breach, it constitutes a data leak. Organizations have already reported incidents where proprietary source code was leaked via public AI tools.
Why do employees use Shadow AI if it is risky?
Most employees use these tools to be more efficient. If the company’s official tools are slow or lack modern features, employees will often seek out faster, more capable AI solutions to meet their performance goals, usually unaware of the underlying security implications.
Shadow AI refers to the unsanctioned use of artificial intelligence tools and models within an organization without IT or security oversight. ThreatNG serves as an all-in-one solution for external attack surface management, digital risk protection, and security ratings, designed to identify and disrupt the "Exploitable Path" created by these unmanaged tools. By mapping how an adversary can use exposed AI assets to compromise mission-critical data, ThreatNG provides the visibility needed to secure the modern digital footprint.
Proactive External Discovery of AI Exposure
ThreatNG performs purely external, unauthenticated discovery to identify an organization's digital footprint without requiring internal connectors or agents. In the context of Shadow AI, this outside-in approach is critical for uncovering tools that bypass traditional internal controls.
Asset Identification: Automatically discovers subdomains and cloud environments where employees may have deployed unsanctioned AI models or testing interfaces.
Shadow IT Detection: Uncovers "forgotten" or unmanaged AI experiments, such as an abandoned subdomain hosting a prototype LLM interface.
Technology Profiling: Identifies specific AI/ML stacks used across the environment, including providers like OpenAI, Hugging Face, Anthropic, and LangChain.
Comprehensive External Assessments for AI Risks
External assessments provide granular security ratings (A-F) that quantify an organization's susceptibility to attack vectors often introduced by Shadow AI.
Web Application Hijack Susceptibility
ThreatNG assesses the presence of key security headers on subdomains, such as Content-Security-Policy (CSP) and HSTS.
Detailed Example: An employee might use an unvetted AI tool to create an internal data visualization dashboard quickly. If ThreatNG discovers this dashboard on a subdomain missing CSP headers, it highlights an exploitable path that allows an attacker to inject malicious scripts to steal the session tokens of users accessing the AI tool.
Data Leak Susceptibility
This assessment uncovers external digital risks across cloud exposure, compromised credentials, and externally identifiable SaaS applications.
Detailed Example: ThreatNG can identify an exposed open cloud bucket (e.g., AWS S3) used by a team to store AI training datasets. If these datasets contain sensitive customer information, ThreatNG alerts the organization to a critical data breach path before an adversary exploits it.
Subdomain Takeover Susceptibility
ThreatNG uses DNS enumeration to find CNAME records pointing to third-party services and cross-references them against a comprehensive vendor list.
Detailed Example: A developer might have linked a corporate subdomain to a trial AI hosting service. If the trial ends but the DNS record remains, ThreatNG identifies this as a "dangling DNS" state. An attacker could then claim the AI service resource to host a malicious clone of the tool, leading to credential harvesting.
Investigation Modules for AI Contextual Intelligence
ThreatNG uses specialized investigation modules to transform raw discovery data into actionable risk narratives.
Domain and Subdomain Intelligence
These modules identify exposed ports, private IPs, and the specific technology providers used by discovered assets.
Detailed Example: The Domain Intelligence module can identify if an organization is using "AI Development & MLOps" vendors such as Pinecone, Weights & Biases, or ElevenLabs. Identifying these tools allows security teams to verify if they are sanctioned and properly configured.
Social Media and Reddit Discovery
ThreatNG monitors the "Conversational Attack Surface" by transforming public chatter into early warning intelligence.
Detailed Example: An employee might post on Reddit or LinkedIn asking for help with a specific, proprietary AI script. ThreatNG's discovery capabilities can alert security teams to this information leakage, which an attacker could use to understand internal AI architectures and tailor their exploits.
Sensitive Code Exposure
This module scans public code repositories for leaked secrets, such as API keys and cloud credentials.
Detailed Example: A developer might accidentally commit an OpenAI API key or AWS Access Key to a public GitHub repository. ThreatNG identifies these exposed secrets, which are critical pivot points in an exploitable path that could allow an attacker to access internal AI models or cloud infrastructure.
Intelligence Repositories and Continuous Monitoring
ThreatNG maintains the DarCache repositories, which are continuously updated to feed the assessment engine with real-world threat context.
DarCache Vulnerability: Correlates discovered AI assets with known vulnerabilities (NVD), confirmation of active exploitation (KEV), and future exploitation likelihood (EPSS).
DarCache Ransomware: Tracks over 100 ransomware gangs, including those using AI-driven tactics (e.g., AiLock) to target organizations.
Continuous Monitoring: ThreatNG provides ongoing visibility into the external attack surface, ensuring that new Shadow AI deployments are identified in real-time as they appear.
Strategic Reporting and Remediation Guidance
ThreatNG generates diverse reports—including executive, technical, and prioritized summaries—to help organizations allocate resources effectively. These reports map findings to MITRE ATT&CK techniques and GRC frameworks like GDPR and NIST CSF, providing the necessary business context to justify security investments.
Cooperation with Complementary Solutions
ThreatNG works most effectively when used alongside complementary solutions to provide a multi-layered defense against Shadow AI.
Cloud Access Security Brokers (CASB): ThreatNG identifies the presence of unsanctioned AI tools outside the corporate network, while complementary CASB solutions can use these findings to block employee access to those endpoints from within the corporate network.
Security Orchestration, Automation, and Response (SOAR): When ThreatNG identifies a leaked AI API key in a public repository, it can trigger an automated workflow in a complementary SOAR tool to immediately rotate the key and alert the developer.
Vulnerability Management Platforms: ThreatNG discovers the external "Exploitable Path," and complementary internal scanners can use these alerts to prioritize patching on the internal systems that those AI tools connect to.
Cloud Security Posture Management (CSPM): If ThreatNG detects an exposed cloud bucket containing AI data, complementary CSPM solutions can be used to trace the misconfiguration back to its root cause in the cloud console for permanent remediation.
+1Frequently Asked Questions
How does ThreatNG detect Shadow AI?
ThreatNG uses purely external, unauthenticated discovery to find subdomains, cloud environments, and SaaS applications that may be hosting unauthorized AI tools. It also monitors social media and public code repositories for mentions of internal AI projects or leaked API keys.
What is an "Exploitable Path" in Shadow AI?
It is the sequence of steps an attacker takes to compromise an organization using a Shadow AI asset as a starting point. For example: finding an unmanaged AI test server (Discovery) -> exploiting a missing security header (Assessment) -> using an exposed API key found on that server to access customer data (Exploitation).
Can ThreatNG identify specific AI vendors?
Yes, ThreatNG's Domain Record Analysis can externally identify a wide range of AI models, Platform providers, and AI Development and MLOps vendors.
