Erhan Giral

speaker

389 appearances 1 recordings 1 series first heard Aug 2026 last heard 3 Aug

Erhan Giral’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Aug 2026 with 1.

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Eye On A.I. · AI Agents Fixing Your IT Before You Even Know Something Broke | Erhan Giral & Ryan Manning, BMC Helix · 3 Aug 2026 podcast

We have a funnel that deals with that.

Let's take the observable data and run that through various machine learning models so that we can understand, oh, that spike that you see right now at 8.30 AM on a Monday on the bank's ATM network is actually kind of unusual.

We don't expect that spike to appear at 8.30 perhaps, maybe it's supposed to come in later.

So something needs to essentially constantly reason about, hey, you know, what's unusual or given the time we are in or given the workload we are dealing with, given the circumstances in general we are.

So that's one part of the big use case we try to do.

And of course, once you detect an anomaly, once you detect an availability problem, you

You then first ask, okay, so why?

What is the root cause of this issue?

Why is this router so busy where it should be close to idle state, for instance?

So then we call that the root cause analysis.

So it's actually something that needs to dig into all that telemetry data, dig into all that observability data to say, ah, it's actually not that.

That's a symptom.

what's really going on is such and such file system or such and such network queue in broker somewhere is now full and dropping the traffic or what have you.

You know, we always ask, you know, once we detect something, we always ask the data, why, you know, why, why, why, until we get to a point where you can take an actionable step to mitigate or remedy that or perhaps even remedy that issue.

And while doing that, of course, data centers and IT is very targeted.

All kinds of stuff happens all the time.

So you need to be able to do this by sifting away all the noise, all the background noise that

typically happens in that environment.

There's always this cosmic background noise that you have to essentially first detect and then push away to then focus on the real signals in the data to understand what the root cause is.

And then we also do impact analysis, like, okay, so is this a big problem?

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