Cybersecurity at AI Speed
Anyone can attack with AI today. Defending takes someone who knows both sides.
So the defence is technically sound and makes business sense.
I separate what's genuinely dangerous from what only looks that way – and I stay until the gap is closed, not until the report is finished.
The analysis is only the beginning. What follows is prioritisation, implementation during live operations, and verification that the measure actually works.
Situation
AI scales attacks. Experience decides what protects against them.
A model finds vulnerabilities and builds attacks. It doesn't know which of them will hit your business – or which measure actually works.
- 01
What attackers once had to learn now comes from a model on demand – a vulnerability and an exploit in minutes, not weeks.
- 02
More findings and alerts don't create security on their own. Without experience, teams work through what's visible – not necessarily what's dangerous.
- 03
I connect the attack path, the business risk and the measure that will actually work. So what's closed first is what actually protects your company.
Why me
I don't just examine the technology. I examine the assumptions behind it.
I have attacked systems as a penetration tester — and defended them too. That's why I don't see a gap as a line in a report, but as a path someone will take – including the technical chains and dependencies behind it.
The question isn't whether a vulnerability exists. The question is which assumption makes it dangerous — and whether that assumption still holds.
A tool finds the gap. Whether it hits your business is something only someone who examines both sides can see.
Process
Use the speed. Keep the judgement.
I translate technical findings into a clear order of priority for your business: what's actually reachable, what can cause serious damage, and what needs to be closed first? I assign ownership and deadlines, and verify afterwards that the measure actually reduces the risk. That keeps the focus on what actually protects the business, instead of trying to fix everything at once.
- 01
Make the attack surface visible.
Consider systems, vulnerabilities and existing security assumptions together.
- 02
Assess relevance.
Check which gap is reachable and what consequences it could have in operation.
- 03
Assign ownership.
Clearly define the action, owner and deadline.
- 04
Drive the fix through to closure.
Verify that the measure was implemented and that the risk has actually gone down.
Assessment
A security assumption has to hold up under pressure.
AI doesn't need to invent a new class of vulnerabilities. It's enough to test known paths faster and more often. That turns a long-overlooked gap into a concrete problem. What matters is whether architecture, detection and ownership can keep up with that pace.
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