From Alert Generation to AI Analysts: Dropzone AI’s Journey to Product-Market Fit

Sometimes the best product insights come from building the wrong thing first. In a recent episode of Category Visionaries, Edward Wu revealed how eight years of creating security alerts led to a crucial realization: SOCs don’t need more alerts – they need help processing them.

The Alert Generation Years

Before founding Dropzone AI, Edward spent eight years as “a senior principal scientist at another cybersecurity startup.” During this time, he “built Actual Hub’s AI, ML and detection product from scratch.” This experience provided crucial insights into the security operations center (SOC) ecosystem.

Recognizing the Real Problem

Working closely with SOCs, Edward “really came away with the realization that most SOCs are already struggling to properly investigate all these security alerts they are receiving.” The problem wasn’t a lack of alerts – it was the inability to process them effectively.

The Market Reality

The scale of the problem became clear through market research: “Around the world in aggregate, has around 10 million cybersecurity job openings. But the world talent pool around cybersecurity is only 6 million.” This 4-million-person talent shortage wasn’t just a hiring challenge – it was an operational crisis.

Understanding SOC Operations

Edward compares modern SOCs to “police departments in large US cities.” Like their physical counterparts, these cyber police departments are “overwhelmed by the number of security alerts that the security analysts have to investigate each and every day.”

The Pivot to AI Analysts

Rather than adding to the flood of alerts, Edward decided to “switch sides and solve this opportunity to build technology that’s specialized in processing of security alerts.” The goal wasn’t to replace human analysts but to “offload the voluminous, repetitive analytical work and tier one work to our AI system as the human cyber defenders and the human SOC analysts get to focus only the real threats as well as critical projects.”

Building Trust in the New Approach

The pivot required a new approach to market trust. Instead of traditional security vendor tactics, Dropzone AI embraced radical transparency: “We are the only vendor in our market, large or small, that has a publicly facing test drive on our website. And that’s ungated, where anybody on the Internet can try and play with our technology.”

Finding the Right Early Adopters

Success required identifying “practitioners in the cybersecurity space who are early adopters, the people who to some extent have almost as much conviction and kind of trust and faith in where the technology can ultimately deliver.”

Rather than targeting every potential customer, they focused on security leaders who could “see the big picture and can have interests and affinity to testing out new technologies to help the operation to move the needle while simultaneously is closer to the day to day.”

The Broader Technology Shift

Dropzone AI positions their pivot within a larger technological transformation: “What we are building at Dropzone AI is part of the bigger tectonic shift or the tectonic wave of AI insert job role here. So AI software developer, AI SDR, AI customer support.”

Vision for the Future

Looking ahead, Edward sees their pivot as crucial for the future of cybersecurity: “If we look at the number of attacks, the intensity of the attacks, and that’s also when we know attackers today are not yet fully utilizing newer technologies like generative AI, it’s clear that human cyber defenders alone are insufficient to protect our shared digital future.”

For B2B founders, Dropzone AI’s pivot offers valuable lessons about product-market fit. Sometimes the best opportunities come from intimately understanding the problems created by existing solutions. The key is recognizing when to stop adding to a problem and start solving it instead.