Unsanctioned AI
Unsanctioned AI, frequently referred to as Shadow AI, is the use of artificial intelligence tools, models, or applications by employees without the explicit approval, knowledge, or security oversight of their organization's IT department.
While traditional "Shadow IT" involves employees downloading unauthorized software or using personal cloud storage, Unsanctioned AI specifically deals with the unvetted use of generative AI, large language models (LLMs), and AI-powered browser extensions. Employees typically adopt these tools to increase personal productivity—such as drafting emails, summarizing meetings, or generating code—without realizing they are bypassing corporate security controls and data governance protocols.
Why Unsanctioned AI is a Critical Cybersecurity Risk
The decentralized and unmonitored adoption of AI introduces severe vulnerabilities to an organization's digital environment.
Data Leakage and Exfiltration: The most immediate threat of unsanctioned AI is the uncontrolled flow of sensitive data into third-party systems. When an employee pastes a proprietary document into a public AI chatbot, that data leaves the secure corporate perimeter. Many public AI models retain user inputs to train future iterations of their software, potentially exposing confidential information to external users.
Regulatory and Compliance Violations: Regulations like GDPR, HIPAA, and CCPA require strict oversight of how sensitive data is processed and stored. Using unapproved AI tools to handle regulated data bypasses required data processing agreements and audit trails, resulting in immediate compliance failures and risking substantial financial penalties.
Intellectual Property Loss: Developers and engineers who use unauthorized AI coding assistants to debug or optimize software risk exposing proprietary algorithms, trade secrets, and internal application logic to third-party vendors.
Expanded Attack Surface: Unvetted AI tools, browser extensions, and API integrations often lack basic security controls like multi-factor authentication or encryption. These integrations can introduce vulnerabilities, such as prompt-injection risks, in which attackers manipulate AI inputs to extract sensitive information or trigger unauthorized actions.
Common Examples of Unsanctioned AI in the Workplace
Unsanctioned AI usage spans all departments and is almost always driven by convenience rather than malicious intent. Common scenarios include:
Software Development: Engineers pasting chunks of internal, closed-source code into free AI models to troubleshoot errors or generate new functions.
Human Resources: Staff uploading candidate resumes or performance reviews into unapproved AI summarizers, inadvertently exposing Personally Identifiable Information (PII).
Marketing and Sales: Inputting customer lists, financial projections, or upcoming campaign strategies into generative AI platforms to draft proposals.
Customer Service: Representatives are relying on unauthorized AI chatbots to quickly draft responses to customer inquiries, risking the exposure of sensitive account details.
How Organizations Can Detect and Prevent Unsanctioned AI
To regain visibility and control, security teams must implement a framework that balances innovation with strict data governance.
Provide Sanctioned AI Alternatives: The most effective way to prevent the use of unapproved tools is to deploy secure, enterprise-grade AI solutions that have been strictly configured for data privacy and zero-retention.
Establish an AI Acceptable Use Policy: Create and enforce clear guidelines that explicitly prohibit the sharing of certain types of corporate data with external AI platforms.
Implement Network Monitoring and DLP: Use Data Loss Prevention (DLP) tools, Cloud Access Security Brokers (CASBs), and secure web gateways to monitor, alert on, and block data uploads to known, unsanctioned AI applications.
Conduct Targeted Employee Training: Educate the workforce on how AI models process data, emphasize that AI prompts are a data egress channel, and explain the real-world consequences of bypassing IT procurement.
How ThreatNG Mitigates Unsanctioned AI in Cybersecurity
ThreatNG provides the unauthenticated, outside-in visibility needed to inventory Generative AI risks, exposed models, and leaked credentials before attackers do. It maps the external attack surface to secure the unmonitored frontier of exposed AI endpoints and misconfigured cloud storage. By operating as an intelligence layer, ThreatNG uncovers the shadow infrastructure that traditional security tools miss.
Here is a detailed breakdown of how ThreatNG combats Unsanctioned AI.
External Discovery and Continuous Monitoring
Agentless Discovery: ThreatNG can perform purely external, unauthenticated discovery without connectors. This means it can find shadow AI assets without needing administrative privileges or internal agents installed on the devices.
Persistent Vigilance: Continuous monitoring of the external attack surface, digital risk, and security ratings ensures organizations have real-time visibility into their shifting digital footprint.
Comprehensive External Assessment
ThreatNG can perform all of the following assessments to quantify the risks associated with Unsanctioned AI:
Data Leak Susceptibility: This security rating is derived from uncovering external digital risks across exposed open cloud buckets and compromised credentials. This helps identify misconfigured cloud storage that may inadvertently contain sensitive AI training data.
Supply Chain and Third-Party Exposure: This assessment is based on unauthenticated enumeration of vendors in domain records and identification of all associated SaaS applications. This helps manage vendor AI risk by identifying external vendors that run artificial intelligence and machine learning technologies.
Non-Human Identity (NHI) Exposure: This critical governance metric quantifies an organization's vulnerability to threats originating from high-privilege machine identities, such as leaked API keys and service accounts.
Cyber Risk Exposure: This rating is based on findings across cloud exposure, compromised credentials, and the discovery of sensitive code.
Deep Investigation Modules
ThreatNG uses granular investigation modules to systematically uncover Unsanctioned AI usage:
Subdomain Intelligence: This module analyzes HTTP responses and headers to identify server technologies. It uncovers subdomains hosted on cloud platforms like AWS, Microsoft Azure, and Google Cloud Platform. It also identifies the presence of databases like Elasticsearch and MongoDB, which are often used in vector database architectures.
Technology Stack Investigation: ThreatNG provides exhaustive, unauthenticated discovery of nearly 4,000 technologies. Crucially, it specifically identifies 265 vendors in the Artificial Intelligence category. It tracks specific vendors categorized as AI Model and Platform Providers, as well as AI Development and MLOps tools.
Sensitive Code Exposure: This module scans public code repositories to uncover digital risks, including access credentials such as API keys, Google OAuth tokens, Stripe keys, and cloud credentials like AWS Access Key IDs.
Cloud and SaaS Exposure (SaaSqwatch): This module identifies sanctioned and unsanctioned cloud services, as well as exposed open cloud buckets, across major providers. It tracks SaaS implementations to find unauthorized tools being adopted by the workforce.
Intelligence Repositories (DarCache)
ThreatNG maintains continuously updated intelligence repositories, branded collectively as DarCache, to provide deep context without exposing the user's infrastructure:
DarCache Dark Web: This repository provides the first level of the Dark Web, archived, normalized, sanitized, and indexed for searching. It allows teams to see a sanitized Dark Web mirror connected to their specific open cloud buckets in a single view.
DarCache Rupture: This repository continuously tracks all organizational emails associated with compromised credential breaches.
DarCache Vulnerability: This strategic risk engine transforms raw vulnerability data into a validated verdict by fusing foundational severity from the National Vulnerability Database (NVD), predictive foresight via the Exploit Prediction Scoring System (EPSS), real-time urgency from Known Exploited Vulnerabilities (KEV), and verified Proof-of-Concept (PoC) exploits.
Actionable Reporting
Boardroom-Ready Attribution: ThreatNG translates highly complex technical vulnerabilities into Boardroom-Ready Attribution. It uses a patent-backed Context Engine to achieve Irrefutable Attribution by correlating technical security findings with decisive legal, financial, and operational context.
External GRC Assessment: This capability maps exposed risks directly to frameworks such as PCI DSS, HIPAA, GDPR, and NIST CSF, ensuring that compliance teams use relevant external data.
Cooperation with Complementary Solutions
ThreatNG serves as the external intelligence layer, feeding highly objective data into internal security platforms, creating a synergistic defense strategy.
Cyber Asset Attack Surface Management (CAASM): CAASM tools provide full visibility into managed assets, but ThreatNG is the only one to find the unmanaged shadow estate that API connectors cannot reach. ThreatNG completes the picture by securing the perimeter that CAASM cannot see.
Governance, Risk, and Compliance (GRC): A GRC platform governs the organization's authorized state in accordance with internal policies and documented assets. ThreatNG provides the satellite feed that continuously scans the external environment to detect shadow IT and policy violations, transforming GRC into a dynamic system.
Continuous Control Monitoring (CCM): CCM solutions monitor the efficacy of controls on known, managed assets. ThreatNG performs external discovery to identify unwired entry points, such as forgotten cloud instances, and feeds the system the assets it is currently missing.
Breach and Attack Simulation (BAS): BAS platforms simulate sophisticated attacks to validate defenses on known infrastructure. ThreatNG expands the scope of the simulation by identifying the neglected, vulnerable assets that attackers actually target, ensuring simulations test the path of least resistance.
Cyber Risk Quantification (CRQ): CRQ platforms calculate financial risk using industry baselines and internal questionnaires. ThreatNG replaces statistical guesses with behavioral facts by feeding the risk model real-time indicators of compromise, such as open ports and dark web chatter, to dynamically adjust the likelihood variable.
