How AI and Machine Learning Improve Fortinet NetOps Security

By Sanjay Seth From my desk, third coffee in, still buzzing from DefCon and hardware hacking village vibes!

Introduction

I began as a network admin way back in 1993—when the devices on our tableland of devices and technologies were still being wrestled into control as we converged voice and data over PSTN. Those were the days of sweaty palms when the Slammer worm struck. Fast-forward several decades, and here I am, running my own cybersecurity company, P J Networks Pvt. Ltd. These days, I am up to my neck in AI-driven NetOps powered by Fortinet solutions, sunsetting the attack surface for banks (three recently and yep, zero trust all the way for those who are wondering) and enterprise across the country.

Here’s the thing about AI in cybersecurity: It’s not magic. It’s math, training, lots of data — but boy, does it change the game when you finally get it right.

The Role of AI in NetOps

For the past 20 years, NetOps (for network operations, in case you didn’t know) has meant manually keeping an eye on things, manually pushing changes through, and manually firefighting problems — that’s after the screaming starts, of course. That system … well, it just doesn’t work anymore.

That’s where AI and machine learning come in. With these tools, we can sort through mountains of network data more quickly than any human — sniff out anomalies and, just maybe, foresee problems before they reach the fan.

NetOps AI does several important ones:

• Automates workaday tasks — allowing admins to concentrate on security forethought.
• Learns typical network behavior — in order to be able to identify the strange patterns.
• Works across devices and platforms — which is especially important when your infrastructure resembles a spaghetti monster.

Fortinet explains why in a report entitled Cyber AI: Threat Predictions for 2020. Their NetOps offerings leverage immense telemetry, and use machine learning models to drown out the noise and drop insights in the lap of NetOps engineers.

But here’s my perspective — when vendors toss around AI-powered like Halloween candy, be careful! All too often, it is just a buzzword with little real meaning. The true differentiation is in the extent of the integration, and the quality of data you are feeding into the system.

Predictive Threat Detection

Remember Slammer? You had nothing but signatures and hope back then. Today, AI-based predictive threat detection brings to us what is essentially a crystal ball that shows us where the next attack could be coming from — before it comes knocking.

Considering a task, the machine learning models learn to analyze:

• Odd spikes traffic patterns
• New associations not fitting with past user/device behavior
• Subtle protocol deviations

This can be useful for identifying zero-day attacks or advanced persistent threats (APTs) already within your network before they erupt.

When I was assisting these banks in adopting zero-trust security, we layered in Fortinet’s AI-based NetOps to continuously monitor for and predict vulnerabilities. It cut response times by more than half of what they were before.

No more waiting for a breach to trigger a warning. Instead:

• Alerts are triggered early.
• This is identified with context to potentially suspect behaviour.
• Security operations (SecOps) field actionable insights, not just noise.

It’s kind of like having an experienced network admin who never sleeps, never gets sick, never overlooks anything, and learns every day. But nothing substitutes for human judgment — the AI is only a force multiplier.

Automation and Response Based on AI

Here’s the nice part: And here’s where it gets very interesting. AI and ML combined with automation is doing the grunt work in incident response. And honestly? It’s about time.

We used to have to furious print out the manual playerbooks, wait for glacial change windows, and do lots of finger pointing. Now the system can:

• Automatically isolate devices when they are suspicious
• Ability to update firewall rules in realtime
• Blocks IPs with intelligence based on real time risk scoring

And the integration with Fortinet Cyber AI enables your NetOps to stretch across physical and virtual environments to orchestrate responses with no boundary.

But — and here’s where I’ll insert a note of caution — dependence on automation can be too much of a good thing. Misconfiguration or false positives can create unnecessary outage or worst, make your team complacent. Automation is supposed to help, not replace.

I mean, I still wake up some mornings with the warm glow of the amber alert we sent (with tones of explanation since we were aware and half the team was still fixing things, but still) after a testing tool decided to lock us out due a false alarm once — that was 168 of manual work to sort the mess out!

AI-Driven NetOps From PJ Networks

Here at P J Networks we don’t only sell Fortinet products – we tailor them. How we do it with AI-enabled NetOps security It is hands-on, because every network has its eccentricities.

And here’s what we do differently:

• Customized telemetry ingestion so the AI models are taught your own network fabric, not some baseline framework.
• Strategic anomaly detection thresholds: too loose, you get noise; too tight, you miss threats.
• Ongoing tuning and training — AI is not set-and-forget.
• Hybrid monitoring — making a mix of AI insights and veteran NetOps expertise.

Lately, after deploying Fortinet’s Cyber AI-driven NetOps in those three banks, security operations teams did less not just in responding to incidents but also in prioritizing them. The AI separates the wheat from the chaff — sifting noise and directing our attention to signals worth exploring — and it saves hours of the monotonous labor.

And the icing on the cake?

• Speedier zero-trust policy enforcement
• Enhancements Patch Management via Predictive Alerts Improved
• Decreased risk profile for multi-cloud environments

I make not all this noise to toot P J Networks’ horn but that I have seen the after – the before and then the after of smart AI in NetOps. It’s not flawless. No solution is. But when it works, it’s a transformative thing.

Quick Take

If you’re scanned jumped to this (I know, time is short), here’s what you need to know:

• AI in NetOps is not hype — but not all AI is created equal AI in NetOps is not hype — but only works when deeply integrated.
• Anticipatory threat detection, which helps to transform reactive security to a proactive stance.
• Automation accelerates the response, but it requires close human supervision.
• Cyber AI from Fortinet is a mature and Field tested solution.
• P J Networks personalizes these tools to YOUR wacky network.

Conclusion

I’ve been at this since the time when network security just meant keeping the bad guys out of a few routers. What a difference a decade makes — attacks are more complex, networks more sophisticated, and user expectations out of this world.

Thinking of AI and machine learning as a shovel is what you are supposed to do. Not silver bullets. But in the Fortinet NetOps realm, particularly when combined with experienced human acumen (hi, that’s me again), they can transform security postures.

Yes, there’s skepticism (I share it) about AI-powered claims, but watching the models learn, adjust and catch what humans might miss — that’s a convincing vision.

So, whether you are a bank or an organization or you just want to convert the weird traffic spikes into the actual breaches, investing in AI solutions like Fortinet NetOps makes a lot of sense.

And hey — if you need someone who’s been in the trenches since the days of dial-up, who seen worms explode and zero-trust evolve, give me a holler. For the same reason that protecting your network isn’t just a matter of tech; it’s a matter of experience and a healthy sense of skepticism.

Ok, coffee number four beckons—off to the logs!

Stay safe out there,

Sanjay Seth
P J Networks Pvt Ltd.