Founder & Managing Director
I’ve been taking technology apart and putting it back together since I was a kid — first computers, then software, then systems.
What always interested me was understanding how things actually work.
That mindset followed me into business. When I started building an e-commerce company, I saw how much time was being lost to repetitive, manual work. Processes that required people, not because they needed judgment, but because nobody had automated them yet.
That was what first drew me to automation.
Cybersecurity added another perspective.
It taught me not only to ask, "Does this work?" but also, "What happens when it fails, gets misused, or is deliberately attacked?"
That perspective became especially important with AI.
AI is one of the most powerful technologies companies have ever had access to. But the more capable a system becomes, the more important it is to understand the risks that come with it.
History shows the same pattern repeatedly.
The early internet was built largely on trust until incidents like the Morris Worm in 1988 exposed how vulnerable connected systems could be. Businesses moved onto the web long before application security became a structured discipline. Cloud adoption followed the same path, and even today many security incidents come from basic configuration mistakes rather than highly sophisticated attacks.
Technology moves fast. Security usually catches up later.
AI is moving even faster.
Companies are introducing AI into critical workflows, giving systems access to company data, tools and decision-making processes, often before the necessary controls are fully in place.
That creates a familiar gap between what technology can do and how safely it is being used.
And with AI, the consequences can be significant: data exposure, security failures, regulatory risk, operational mistakes and loss of customer trust.
That is why Autorea exists.
We help companies adopt AI without treating security and governance as something to solve afterwards.
Every engagement starts with understanding what already exists: the systems, the data, the risks, the processes worth automating and the controls that need to be in place first.
Because AI adoption is happening now.
And this time, security should not have to catch up later.
The early internet ran for years on pure trust, with no firewalls or intrusion detection. Then a single piece of self-replicating code took down roughly a tenth of every connected computer in one night. Cybersecurity as a field exists largely because of what happened after that.
Businesses were taking payments and storing customer data online for years before the first organized effort to secure web applications even existed, in 2001.
Still true today: roughly two-thirds of cloud security incidents come down to basic misconfiguration, not sophisticated attacks. Companies moved fast; the basics didn’t always move with them.
Companies are spending well over ten times more on adopting AI than on securing it, and AI agents are entering production far faster than the controls meant to govern them.