Censinet really came from seeing a problem in healthcare that wasn’t being solved well. Health systems were becoming increasingly dependent on thousands of vendors / technologies but the process for understanding and managing the risk around them was still incredibly manual and fragmented.

We started with third-party risk management and made the decision early on to go all-in on healthcare. What makes Censinet different is the network. Instead of every health system assessing the same vendors independently and starting from zero, we’ve built a collaborative risk network where healthcare organizations can benefit from shared intelligence across more than 55,000 vendors and products. And we’ve expanded that approach beyond third-party risk into enterprise risk, AI governance, and systemic risk.

A lot of our growth has historically been relationship-driven. Healthcare is a trust-based industry, particularly when you’re talking about cybersecurity and risk, so referrals, existing customers, partners, and the broader healthcare leadership community are incredibly important.

Where I’d like to see even more growth come from is the market recognizing the problem before we have to explain it. The industry is moving from thinking about cybersecurity as a collection of individual controls and assessments to understanding systemic risk, how a disruption at one vendor or technology can affect an entire health system. So the opportunity for us is to create more of that category-level awareness. I’d love for more organizations to come to us already understanding that traditional, point-in-time risk management isn’t enough and looking specifically for a healthcare-focused, network-based approach.

One thing I’m very comfortable handing over to AI is the repetitive, documentation-heavy work. That’s actually a big part of how we think about AI at Censinet. If a risk analyst is spending hours doing something the machine can do in minutes, that’s probably not the best use of that person’s expertise.

Where I still pull it back to the human is judgment. I’ve experimented with using AI further into decision-making, and that’s where you realize very quickly that context matters. Particularly in healthcare, the technically correct answer isn’t always the right answer for the organization, the clinician, or the patient. I’ve said for a while that AI should be the copilot, not the autopilot. I think that distinction is becoming even more important as the technology gets better.

Absolutely in terms of what buyers know before they reach us. The amount of research somebody can do before ever talking to a salesperson has changed dramatically. They can understand the category, compare approaches, research our leadership, listen to a podcast, read about a problem, and increasingly ask an AI assistant to help them make sense of all of it.

I wouldn’t overstate ChatGPT as a major acquisition channel for us yet. I think we’re still early there. But I do think the behavior is changing, and that’s what matters. Buyers are going to expect an answer before they ever visit your website or fill out a form.

I’d tell them to start with the workflows, not the AI.

Find the repetitive work your best people are doing that doesn’t actually require their expertise. Automate that first. Then put very clear governance around what data can go into these systems, which tools employees can use, who owns the output, and where a human needs to remain in the loop.

I’d also tell them to build an inventory. You can’t govern AI you don’t know you have, and in healthcare a lot of AI is going to enter the organization through vendors and products you already use, not through some big enterprise AI initiative.