Trusting Open Source AI Models with Company Data
When considering the use of open-source AI models with your company’s data, it’s natural to question the security and trustworthiness of these models. The good news is that open-source AI models can be deployed on your own hardware, a third-party infrastructure, or a cloud-based platform like Eolaris.
Understanding Data Privacy with Open Source Models
In all deployment scenarios, the creators of open-source models do not design them to harvest your data or use your requests to enhance their models. Instead, they provide the models solely for hosting and deployment.
Key Benefits
1. Data Control
- Complete control over your data
- Secure deployment options
- Protected data processing
2. Transparency
- Open-source code review
- Auditable algorithms
- Verifiable model behaviour
3. Deployment Options
- On-premises hosting
- Third-party infrastructure
- Cloud-based platforms
Security Measures
Data Protection
- Local data processing
- Encrypted communications
- Access control mechanisms
Compliance
- Audit capabilities
- Security reviews
- Regulatory alignment
Infrastructure Control
- Customisable deployment
- Resource management
- Security configurations
Best Practices for Implementation
- Regular security audits
- Data encryption at rest and in transit
- Access control implementation
- Monitoring and logging
- Regular updates and patches
Deployment Considerations
- Hardware requirements
- Network security
- Data storage
- Access management
- Backup procedures
Conclusion
Open-source AI models provide a secure and transparent solution for companies looking to leverage AI capabilities while maintaining control over their sensitive data. By understanding the deployment options and implementing proper security measures, organisations can confidently utilise these models while ensuring their data remains protected.