A Comprehensive Guide to Delivery and Security for AI Applications

It is important to note that one of the most significant consequences of AI applications will be the increase of both E-W and N-S traffic, with much of the N-S traffic originating from the AI and thus introducing the outbound N-S data path as a strategic point of control in addition to the traditional inbound N-S data path.

AI applications will be additive to existing portfolios for the next 2-3 years, with consolidation occurring as organizations understand consumer demand for NLI (natural language interface).

Increasing distribution on the N-S data path will drive greater demand for security as a service at the corporate boundary while the increasing distribution on the E-W data path across environments is driving the need for multicloud networking. Internally, the sensitivity of data on the E-W data path is accelerating the need for security and access capabilities.

The result is two new insertion points in AI application architectures where application delivery and security will be valuable, and an opportunity to reconsider where application delivery and security are deployed with an eye toward efficiency, cost reduction, and efficacy.

This is important given that we're starting to see CVEs logged against inference servers. That's the server part of the "model" tier that communicates with clients via an API. The use of API security here is important in the overall AI security strategy because it is here that capabilities to inspect, detect, and protect AI models and servers against exploitation are best deployed. It is the "last line of defense" and, given a programmable API security solution, the fastest means to mitigating new attacks against AI models.