Use Cases
LangGuard helps security and compliance teams gain visibility into AI operations across the organization. Here's how teams use LangGuard to address common AI security challenges.
Security & Threat Detection
Detect Prompt Injection
Identify malicious prompts targeting your AI systems, correlate with user and session context, and enforce policies to block or alert on violations.
Identify Data Exfiltration
Detect sensitive data being sent to AI services, trace it back to source endpoints or users, and trigger incident response workflows.
Governance & Compliance
Monitor AI Coding Assistants
See high-risk code suggestions and policy violations from tools like Claude Code or Cursor, alert your security team, and provide context for SOC analyst review.
Track Shadow AI Usage
Identify unauthorized AI tools and API calls across endpoints and cloud environments, assess risk, and enforce governance policies.
Why LangGuard?
Unified Visibility
Most organizations use multiple AI tools and platforms. LangGuard aggregates data from all your observability sources into a single view, eliminating blind spots.
Policy-Based Automation
Define policies once and apply them consistently across all AI interactions. Automatically detect violations without manual review of every trace.
Security-First Design
Built for security teams, not just developers. LangGuard provides the context and workflow integration that SOC analysts need to investigate and respond to AI-related incidents.
Getting Started
- Connect your integrations - Start ingesting AI trace data
- Enable policies - Turn on built-in detection rules
- Review violations - Investigate flagged activity
Need help with a specific use case? Contact support@langguard.ai.