Fortinet today extended the reach of its generative artificial intelligence (AI) capabilities to include support for its network detection and response (NDR) and cloud native application protection platform (CNAPP).

John Maddison, chief marketing officer at Fortinet, said FortiAI will ultimately make the entire Fortinet networking and security portfolio more accessible to a wider range of IT professions.

Previously, FortiAI has been integrated with FortiAnalyzer, FortiManager, FortiSIEM, FortiSOAR and Forti DLP to provide a consistent method for employing generative AI, he added.

Fortinet is making a case for an integrated portfolio of networking and cybersecurity tools and platforms that cybersecurity teams can mix and match as they see fit. The overall goal is to make it simpler to identify and mitigate cybersecurity threats at a lower total cost.

It’s not clear to what degree generative AI will drive organizations to standardize on a single platform. Most cybersecurity teams are not going to be able to collect enough data to train, deploy and manage AI models. In contrast, a vendor can more easily collect data from across its customer base to train multiple types of AI models.

Over time, the lines that currently exist between various cybersecurity and networking tools and platforms will continue to blur in the age of AI. Cybersecurity teams are not necessarily going to care whether the answer provided to a query was pulled from a specific tool or platform, so long as the output generated is reliable.

While it’s still early days so far as the adoption of generative AI is concerned, it’s already apparent that many cybersecurity teams would prefer to rely on it to automate many routine manual tasks, such as creating an incident management report.

In the long term, as AI agents are trained to automate specific tasks, the range and scope of those tasks will steadily increase. Ultimately, IT teams will find themselves orchestrating workflows that will be performed by multiple AI agents. The rise of AI is likely to accelerate the pace at which the management of networking and cybersecurity has already been converging, noted Maddison.

It’s not likely those AI agents will ever eliminate the need for IT professionals, but the nature of the job is going to evolve. There will, for example, be a lot less reliance on command line interfaces (CLI) to launch queries that hopefully surface what the root cause of an issue might be. Instead, AI agents will have already identified that issue and surfaced recommendations to resolve it.

Before too long, AI capabilities will soon be considered table stakes for any set of tools or platforms that an IT organization might consider adopting. The next challenge will be finding a way to orchestrate workflows across various types of AI agents that IT teams are employing to automate specific tasks.

In the meantime, IT teams might want to come up with a list of tasks they perform today that they would generally prefer to have a machine handle tomorrow.