In the second episode of Inside Cybersecurity, Cylake founder and CEO Nir Zuk joins René Bonvanie to discuss one of the biggest misconceptions shaping cybersecurity today: the belief that AI itself is the solution.
While much of the industry is focused on AI agents, copilots, and automation, Nir argues that the real challenge lies elsewhere. AI is only as effective as the data it can access, and most cybersecurity platforms were never designed to provide the complete visibility modern AI systems require.
The conversation explores the differences between machine learning and generative AI, why attackers and defenders are adopting AI in fundamentally different ways, and how fragmented data architectures continue to limit what security teams can accomplish. Nir also explains why organizations chasing the latest AI capabilities may be overlooking a more foundational problem: incomplete data leads to incomplete security outcomes.
As AI becomes increasingly central to cybersecurity, the discussion shifts from what AI can do to what it needs in order to work.
This is the second episode of Inside Cybersecurity, a series exploring the architectural shifts reshaping cybersecurity, AI infrastructure, security operations, and data governance for large enterprises and regulated institutions.
Nir Zuk:
Let’s say you build a machine learning pipeline. That’s not too difficult to do. Who is going to write the thousands of machine learning models that you need?
And then policy and all that stuff. You’ll basically have to do what Cylake is doing, which is rebuild the entire cybersecurity stack on top of your custom data pipeline and your custom on-prem data lake.
We’re going to spend $100 million doing it. You’ll probably have to spend $100 million doing it too. You’ll do it for yourself. We’ll do it for enough customers to justify the investment.
René Bonvanie:
Exactly.
Nir Zuk:
I understand the frustration. Customers are disappointed with existing solutions. But the answer isn’t trying to rebuild the entire cybersecurity stack yourself.
