Eigent has three key differentiators in data privacy protection:
- Full-stack controllable localization solutions: Unlike most tools that rely on cloud-based APIs, Eigent allows models and data processing to be run completely locally. Users can run the model and data processing in
config.yamlset up indeployment: localand specify the local model path (e.g., LLaMA) to ensure that sensitive data does not exit the intranet. - Fine-grained privilege control: The Enterprise Edition supports Role-Based Access Control (RBAC), which precisely restricts intelligences' access to databases and APIs. For example, in medical scenarios, document intelligences can be configured to access only desensitized patient data.
- Transparent Code Audit: As an open source project (based on CAMEL-AI), all data processing logic can be reviewed. Users can verify the existence of data outgoing code, in contrast to closed-source commercial tools.
Real-world tests show that when dealing with PII data such as bank transaction records, Eigent's vulnerability scanning pass rate in local mode is 47% higher than that of mainstream cloud-based tools, and its privacy design is especially compliant with stringent compliance requirements such as GDPR.
This answer comes from the articleEigent: an open source desktop application for automated multi-intelligence collaborationThe
May not be reproduced without permission:AI productivity tools " What are the unique advantages of Eigent over similar AI automation tools in terms of data privacy?
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