Credal’s Category Creation Playbook: Defining Enterprise AI Security

Creating a new product category isn’t just about having the right technology – it’s about helping the market understand a problem they haven’t fully grasped yet. In a recent Category Visionaries episode, Credal founder Ravin Thambapillai revealed how they’re shaping the narrative around enterprise AI security by focusing on where the market is heading, not where it is today.

Moving Beyond Traditional Security Definitions

Rather than pitch AI security as just another cybersecurity tool, Credal frames it as essential infrastructure for the future of work. As Ravin explains: “What I actually believe we are building is very different, I think, to people in general think… from my perspective, what we’re really building is the safe and access controlled data environment for your AI employees.”

This vision stems from a clear prediction: “We believe that five years from now, every company is going to have hundreds of these AI employees running around doing work. They’re going to need to access company data, and they’re going to need to access that company data in a way that’s controlled and secure.”

Educating Through Specific Use Cases

Instead of abstract discussions about AI security, Credal focuses on concrete customer implementations. “We have this AI security guides sort of content series on our website, and we have a lot of blog posts about the specific use cases that customers have solved with us,” Ravin notes.

This approach works because different industries search for different solutions. For example: “To take the example of Transferwise, there’s a big financial services company, they were trying to solve, like, a KYC AML problem… And so they would be searching, like, how do I protect customer data when using LLMs to do KYC?”

Meeting Customers Where They Are

Credal recognizes that most companies aren’t ready for their full vision yet. As Ravin explains: “If I go out and say to a customer, hey, this is what you need, they’re like, but I don’t have hundreds of AI employees. Right. What are you talking about?”

Instead, they start with current pain points: “Most companies have not actually figured out what that really means… You can talk about prompt injections, you can talk about hallucinations. There’s this entire universe of AI security that people don’t really understand.”

Focusing on Real Problems vs. Theoretical Ones

Their category creation strategy emphasizes practical over theoretical concerns. Ravin illustrates: “When you’re building an internal AI application, there’s some sort of theoretical worry that one of your employees might go rogue and try and inject some nonsense into a prompt. But typically that’s something that you can risk accept.”

Instead, they focus on concrete risks: “What you actually can’t risk accept is the idea that an honest employee might log into the system and ask a question like, what’s the company’s strategy? And the AI accidentally retrieves an email from the CEO’s inbox that’s like, hey, we’re going to fire the entire engineering tomorrow.”

Building Trust Through Results

Rather than trying to convince the market through marketing alone, Credal lets results speak for themselves. Growing “20% to 30% per month” and processing “over a million LLM queries every month,” they’re demonstrating the reality of their vision through actual enterprise adoption.

For founders creating new product categories, Credal’s approach offers valuable lessons: start with concrete problems customers already recognize, educate through specific implementations rather than theory, and maintain a clear vision of where the market is heading while meeting customers where they are today.

The key is patience in category education while delivering immediate value. As Ravin puts it, they’re “taking our customers on that journey,” gradually helping them understand what AI security really means while solving their current pressing needs.