Data Overload: A Collaborative Deep Dive with Tony Gonzalez

I recently had a fascinating discussion with @Tony Gonzalez, Fortune 50/500 Cyber Security Executive and Advisor, about a challenge that's been nagging at me for years: our industry's complicated relationship with data. The conversation turned into an in-depth article that I'd love to share some thoughts from.

Here's the thing: we're drowning in data yet somehow struggling to stay secure. It's a bit like trying to find a needle in a haystack, except someone keeps adding more hay every few seconds.?

What really struck me during my discussion with Tony was how we've created this perfect storm in cyber security.??

First, there's what Tony brilliantly calls the "shiny bubble syndrome" — our tendency to chase after the latest tools and technologies, generating vast amounts of data without really thinking through how we'll make sense of it all.?

Then, there's our obsession with metrics. We measure everything possible, often without asking the crucial question: "What actually matters for our specific context?" Every organisation is different, yet we often fall into this one-size-fits-all approach to measurement.

Third: the human factor. We hire brilliant, creative minds to drive innovation and tackle complex security challenges. Then what do we do? We bog them down with mundane log reviews and routine vulnerability assessments. It's like having a neurosurgeon working in the ER — technically capable, but not the best use of their specialised skills.?

Where does AI fit into all this? Well, it's promising but not the panacea many are hoping for. Yes, it could help reduce analyst burnout by automating mundane tasks. But there's a catch: if we're not careful, we might just end up with AI systems demanding even more data, more tools, more everything. As Tony put it perfectly, "It's not going to be a one-and-done; you just deploy it in and walk away."

The path forward isn't about collecting more data but about being smarter with the data we have. This means:

• Prioritising quality over quantity in our data collection
• Shifting left in security instead of always playing catch-up
• Thoughtfully embracing AI while maintaining human oversight
• Most importantly, matching the right skills to the right tasks

I've seen too many organisations struggling with this data overload, missing critical security events because they're buried under mountains of irrelevant information.

If you agree that it’s time we rethink our approach, check out the full collaborative essay on data overload, where Tony and I explore these challenges in detail and offer practical strategies for turning the tide.

I'd love to hear your thoughts on this.

Have you experienced similar challenges in your organisation? How are you dealing with the balance between data collection and actual security insights?

Disclaimer

The views expressed in this article represent the personal insights and opinions of Dan Haagman and Tony Gonzalez. Dan Haagman’s views also reflect the official stance of Chaleit , while Tony Gonzalez’s views are his own and do not necessarily represent the official position of his organisations. Both authors share their perspectives to foster learning and promote open dialogue.