Endpoint & Traffic Discovery
Identify local AI tools, browser model access, processes and related traffic.
AI Data Leakage Prevention
Based on Xingshu’s three-tier architecture to control AI tool data egress — sensitive data stays controllable, auditable and traceable.
Core Risks to Address
Employees submit source code, customer records or business data to public models
AI coding and Agent tools bypass traditional egress and audit controls
Sensitive content leaves through prompts, attachments or hidden context
Controls cannot distinguish users, teams, tools and data classifications
Core Capabilities
Sensitive Data Detection
Classify code, documents, personal information and business data at content level.
Policy Enforcement
Allow, alert, block, mask or require approval according to scenario and risk.
Audit & Traceability
Record actor, tool, data, destination and action for incident investigation.
Typical Scenarios
- Engineering use of Cursor, Copilot, Claude Code and similar tools
- Employees processing documents and business data with public LLMs
- Mixed use of enterprise assistants and third-party models
- Egress protection for source code, customer data and intellectual property
Delivery Scope
- AI tool inventory and data-egress risk register
- Endpoint Agent, central gateway and console deployment
- Sensitive-data dictionaries, detectors and enforcement policies
- Alerts, audit reports and incident-response workflow
Delivery process
Identify egress
Map AI tools, request paths and sensitive data types.
Define policies
Define allow, mask, block and exception rules.
Integrate and test
Validate endpoint and gateway policies alongside usability.
Review audit
Review policy matches, egress logs and false-positive feedback.
Deliverables
Scope, schedule and acceptance criteria are agreed following assessment.
Discuss this approachDeliverables to scope together
- Scenario and risk-test records
- Integration, policy configuration and validation notes
- Operational audit and improvement actions
Explore further
Guanyuan Xingshu
Rebuild enterprise data boundaries in the AI era
Data governance
Align business ownership, data policies and technical controls, connecting discovery, assessment, loss prevention, masking and auditing into continuous improvement.
Data Security
Protect enterprise data through discovery, classification, risk assessment, DLP, masking and access auditing, supported by data and cloud engineering.