Govern and secure every GenAI deployment
Unified security for generative AI — LLM risk controls, shadow AI discovery, data leakage prevention, and content safety monitoring across every ChatGPT, Copilot, and custom LLM deployment in your organisation.
What is GenAI Security — Secure Generative AI in the Enterprise?
GenAI security is the set of controls, policies, and monitoring capabilities needed to manage the security risks introduced by generative AI systems in an enterprise environment. It covers sanctioned LLM deployments (custom applications, API integrations), unsanctioned shadow AI usage (employees using consumer AI tools with company data), and the AI supply chain (LLM providers, fine-tuning pipelines, AI APIs).
Why GenAI creates security risks at scale
- Employees routinely paste sensitive data, source code, and customer information into consumer GenAI tools — creating uncontrolled data leakage at scale
- Shadow AI proliferates faster than security teams can track — new AI tools are adopted daily without security review or data handling agreements
- Custom LLM applications ship with insufficient security controls — prompt injection, insecure output handling, and over-permissioned tool access are common
- GenAI supply chain risk: LLM providers, fine-tuning datasets, and embedded AI models introduce third-party trust dependencies that traditional vendor risk processes do not cover
A four-step operational model
Shadow AI Discovery
Identify all GenAI tools in use across the organisation — sanctioned and unsanctioned — through network telemetry, DNS analysis, and endpoint monitoring.
- Shadow AI tool inventory
- Network traffic analysis for AI endpoints
- Employee AI usage visibility
Data Leakage Prevention
Monitor and control what data enters GenAI systems — blocking or alerting when sensitive data categories are detected in AI-bound requests.
- Sensitive data classification
- AI-bound DLP policy enforcement
- Real-time leakage alerting
LLM Application Security
Test and continuously monitor custom LLM applications for prompt injection, insecure output handling, and access control weaknesses.
- Custom LLM application testing
- Runtime output monitoring
- Access control validation
AI Governance Reporting
Executive-level visibility into AI risk posture: tool inventory, data exposure incidents, control coverage, and framework compliance status.
- AI risk posture dashboard
- Data exposure incident tracking
- Compliance framework mapping
Outcomes for security teams
Shadow AI is the #1 GenAI data risk
Most data leakage to GenAI tools comes not from breaches but from employees using consumer AI products with sensitive business data — without policies or controls.
Sanctioned AI needs security just like any other application
Custom LLM applications and AI-enabled features require the same security review, testing, and monitoring as any other production application.
Regulators are watching AI data processing closely
GDPR, data protection authorities, and sector regulators increasingly scrutinise how AI systems handle personal and sensitive data.
Direct answers
What is GenAI security?+
GenAI security covers the controls, policies, and monitoring needed to manage security risks from generative AI — including shadow AI governance, data leakage prevention, LLM application security, and AI supply chain risk.
What is shadow AI?+
Shadow AI refers to unsanctioned AI tools and applications that employees adopt without IT or security review — typically consumer GenAI products used with company data.
How does Threatstealth detect shadow AI usage?+
Through network traffic analysis, DNS telemetry, and endpoint monitoring to identify AI service endpoints being accessed from corporate networks and devices.
Does GenAI security cover third-party LLM APIs?+
Yes — risk controls cover both internal LLM deployments and third-party LLM API integrations, including supply chain risk assessment for AI providers.