GardLayer
Protect enterprise data from generative-AI risks through real-time discovery, browser controls, LLM gateway security, AI asset inventory, and centralized monitoring.
Visibility and control for enterprise AI use.
GardLayer addresses Shadow AI, limited visibility into AI activity, sensitive-data leakage, and regulatory exposure. Its five modules form a lifecycle from discovery and prevention to centralized governance and incident response.
A layered control model for generative AI.
Shadow AI Discovery
Agentless DNS traffic analysis identifies access to more than 200 generative-AI services, classifies application risk, and produces recurring usage reports and alerts.
Browser DLP
Detect and block sensitive information pasted into AI chats, including personal data, financial information, source code, and trade secrets, while warning users in real time.
LLM Gateway Firewall
Centralize LLM API access with rate limiting, prompt-injection and jailbreak filtering, TLS protection, and request-and-response audit logs.
AI Asset Inventory
Catalog detected AI services, map data flows, assign risk scores, apply compliance tags, and export structured inventory reports.
Security Console
Manage dashboards, alerts, security policies, role-based access, and security-operation integrations from a centralized console.
Trust and AI Security Layer
Combine network discovery, endpoint enforcement, API controls, asset governance, and centralized response instead of managing isolated point solutions.
Reduce blind spots without blocking responsible AI adoption.
Discover unauthorized AI
Identify which AI services are being accessed, by whom, and how usage changes over time.
Protect sensitive data
Apply preventive controls at the browser and gateway layers before information reaches public AI services.
Build an audit trail
Centralize activity, policy, risk, and incident evidence for security, privacy, and compliance reviews.
Designed for security, privacy, risk, and AI governance teams.
Shadow AI baseline
- Discover unsanctioned AI applications
- Rank services by organizational risk
- Establish usage trends and reporting
- Prioritize policy and awareness actions
Sensitive-data protection
- Prevent personal-data exposure
- Protect confidential code and trade secrets
- Provide real-time user guidance
- Maintain interaction audit records
Enterprise LLM gateway
- Centralize approved model access
- Apply rate and content controls
- Filter prompt injection and jailbreak attempts
- Integrate multiple model providers
AI governance evidence
- Maintain an AI asset inventory
- Map data flows and risk ownership
- Tag assets against compliance requirements
- Feed incidents and risks into GRC workflows
See and control how AI is used across your organization.
Discuss a Shadow AI discovery pilot, browser protection rollout, LLM gateway implementation, or integrated enterprise deployment.