h4cker/ai_research/AI Security Best Practices/secure-deployment.md

# AI Secure Deployment

High-level list of AI Secure Deployment best practices:

| Best Practice | Description |
| --- | --- |
| Use Secure APIs | All communication with the AI model should be done using secure APIs that use encryption and other security protocols. |
| Implement Authentication and Access Controls | Ensure only authorized individuals can access the deployed AI models and associated data. |
| Use Secure Communication Channels | All data exchanged with the AI model should be done over secure, encrypted communication channels. |
| Regular Updates and Patching | Ensure the software, libraries, and dependencies used by your AI model are up to date and patched for known vulnerabilities. |
| Monitor System Usage and Performance | Monitor for any anomalies that could indicate a security breach, such as unexpected spikes in system usage or a sudden decline in model performance. |
| Test for Robustness | Regularly test your AI model's robustness to adversarial attacks and other types of unexpected inputs. |
| Implement Secure Data Storage | Ensure that data used by your AI model, both for training and inference, is stored securely. |
| Privacy-preserving Techniques | If your AI model handles sensitive data, consider using privacy-preserving techniques such as differential privacy or federated learning. |
| Plan for Incident Response | Have a plan for how to respond if a security incident does occur, including steps for identifying the breach, containing it, investigating it, and recovering from it. |
| Regular Audits | Regularly audit your AI system for potential security vulnerabilities. |