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+# 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. |