AI protection goes beyond simple functional security issues to include complex elements such as model protection, supply chain safety, and control of excessive autonomy. 

Companies must prioritize security when developing or implementing AI. Continuous red team activities or proactive security testing are necessary to minimize threats. 

As AI is adopted, granting high privileges to AI can increase security risks. 

For example, what if an AI assistant could access all files on OneDrive to summarize Microsoft Teams meeting notes? This vulnerability significantly increases the likelihood of hackers exploiting it to steal confidential data. 

Companies should inspect generative AI security both initially and regularly. Perform quarterly security tests to protect APIs and models. If using open source models, continue malware detection and detailed analysis. Do not overlook verifying cloud environments, containers, and network configurations where AI models operate. 

Security Methods 

✔ Implement AI Trust and Risk Management (AI TRiSM) Framework

📍 Validate the data AI processes, and continuously evaluate the security of AI models

📍 Enhance features for detecting anomalies in AI model behavior (Content Anomaly Detection)

✔ Strengthen AI Model and API Security

📍 Apply encryption and access control to data processed by AI models

📍 Conduct API vulnerability analysis and security testing

✔ Strengthen Cloud-based AI Security

📍 Apply cloud security policies when AI models operate in the cloud

📍 Ensure thorough access management for data repositories used by AI

✔ Establish AI Usage Policies and Governance

📍 Clearly define how AI interacts with external systems

📍 Enhance security awareness training for AI users