Shadow AI
What is Shadow AI?
Shadow AIThe use of AI tools, models, or services by employees without the knowledge or approval of an organisation's security, privacy, or governance functions.
Shadow AI is the AI-era successor to shadow IT. Employees paste source code, contracts, customer records, or strategic documents into consumer chatbots; install unsanctioned browser copilots and IDE extensions; or fine-tune local models on confidential data. Risks include data exfiltration, intellectual-property loss, regulatory non-compliance (GDPR, HIPAA), prompt injection through unmanaged tools, and uncontrolled model output flowing back into production decisions. Effective mitigation combines an AI inventory and policy, sanctioned alternatives (enterprise GenAI with DLP and audit), egress and CASB controls, browser-isolated workflows, and clear user training. Programs typically align with NIST AI RMF and ISO/IEC 42001 to integrate Shadow AI controls into broader AI governance.
● Examples
- 01
Engineers pasting proprietary code into a free consumer chatbot to debug it.
- 02
A marketing team using an unvetted AI translation service that stores submitted text on third-party servers.
● Frequently asked questions
What is Shadow AI?
The use of AI tools, models, or services by employees without the knowledge or approval of an organisation's security, privacy, or governance functions. It belongs to the AI & ML Security category of cybersecurity.
What does Shadow AI mean?
The use of AI tools, models, or services by employees without the knowledge or approval of an organisation's security, privacy, or governance functions.
How does Shadow AI work?
Shadow AI is the AI-era successor to shadow IT. Employees paste source code, contracts, customer records, or strategic documents into consumer chatbots; install unsanctioned browser copilots and IDE extensions; or fine-tune local models on confidential data. Risks include data exfiltration, intellectual-property loss, regulatory non-compliance (GDPR, HIPAA), prompt injection through unmanaged tools, and uncontrolled model output flowing back into production decisions. Effective mitigation combines an AI inventory and policy, sanctioned alternatives (enterprise GenAI with DLP and audit), egress and CASB controls, browser-isolated workflows, and clear user training. Programs typically align with NIST AI RMF and ISO/IEC 42001 to integrate Shadow AI controls into broader AI governance.
How do you defend against Shadow AI?
Defences for Shadow AI typically combine technical controls and operational practices, as detailed in the full definition above.
What are other names for Shadow AI?
Common alternative names include: Unsanctioned AI, BYOAI.
● Related terms
- ai-security№ 027
AI Governance
The policies, processes, roles, and controls organisations and regulators use to ensure AI systems are developed, deployed, and operated responsibly and lawfully.
- ai-security№ 025
AI Bill of Materials (AIBOM)
A machine-readable inventory of every component that goes into an AI system — datasets, base models, fine-tuning data, libraries, prompts, and evaluation artifacts — used for security, compliance, and accountability.
- ai-security№ 034
AI Supply Chain Risk
The set of threats arising from the third-party datasets, base models, libraries, plug-ins, and infrastructure that organisations combine to build and deploy AI systems.
- cloud-security№ 148
CASB (Cloud Access Security Broker)
A policy enforcement point that sits between users and cloud/SaaS applications to enforce visibility, data protection, and threat controls.
- ai-security№ 281
Data Poisoning
An attack on a machine-learning system in which adversaries inject, alter, or relabel training data so the resulting model behaves incorrectly or contains hidden backdoors.
- ai-security№ 029
AI Incident Response
The set of processes, roles, and playbooks an organisation uses to detect, contain, investigate, communicate, and recover from incidents involving AI systems.