The knowledge that we’ve reached a new AI maturity level opens up new options, results, and cost advantages for society
AI models’ increased “attack surface” new to everyone and changing data make safeguarding them difficult
To understand how to protect AI data, models, apps, and the complete process we must understand how it works and how it is distributed across multiple contexts
Using current controls to secure AI model routes is essential
Similar to how AI adds visibility to corporate apps, threat detection and response must be extended to AI applications
Even though AI lifecycle management standards are still emerging, enterprises may use current guardrails to protect the AI journey
Privacy and security must be enforced throughout AI development and deployment, including training and testing data
Securing the AI lifecycle requires adding ML to DevSecOps procedures while creating integrations and deploying AI models and apps