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.