Edward Wu is the founder & CEO of Dropzone AI, creator of the world’s first autonomous AI SOC analyst that force-multiplies cybersecurity teams with armies of AI agents. Previously, Edward spent eight years at ExtraHop Networks, leading AI/ML and detection engineering and developing behavioral network attack detection. He also worked on automated binary analysis and software defenses at the University of Washington, Seattle, and UC Berkeley. Edward holds 30+ patents in applied AI for cybersecurity and is a contributor to the MITRE ATT&CK framework.

What traction metrics or milestones are you most proud of from the last year?

Our product currently processes security alerts from over 100 enterprises, ranging from Fortune Global 100 organizations to insurance companies and tech scale-ups.

Can you walk through how you plan to utilize the new funding?

We will first focus on scaling our GTM team to better field the overwhelming demand we have been receiving from the field. Part of that also involves working with more technology integration partners as well as channel partners to get our technology into the hands of as many security teams as possible. We are also planning to expand the development of additional AI agents for other types of high-volume and repetitive manual work within a typical security team.

What are you seeing in the broader market right now that validates your approach?

We have repeatedly heard from channel partners and industry analysts that our category, AI SOC analysts, is becoming a top-inquired topic among security leaders. We envision our segment to follow the typical S-shaped adoption curve of agentic AI applications. Currently, we are not yet at the point of inflection, so security teams are still taking risks when they adopt our technology. However, I foresee reaching the point of inflection within the next 12 months, where every security team will risk being left behind if they do not adopt our technology. We have seen this play out in several mature Gen AI applications, ranging from call center automation to AI coding tools.

Where and how is AI core to the product? How do you see that evolving over time?

We are developing a software tool that can replicate the techniques and investigative mindsets of expert human security analysts, allowing cyber defenders to be force-multiplied and regain the advantage against attackers. What makes this difficult is that human security analysts rely heavily on improvisation, intuition, and judgment calls that are impossible to automate using traditional if-then-else decision trees. With LLM and, especially, its cognitive reasoning capabilities, we are finally able to make this technology work and produce results that approach human-level performance. We will continue to push the envelope of what LLMs can do in cybersecurity, from building AI agents that match the analytical capabilities of a senior human resource to those that exceed the capabilities of the most brilliant human security analyst on the planet.

Is your AI strategy more about proprietary models, orchestration of existing tools, or something else?

We are currently leveraging multiple commercial LLM providers and hand-selecting the best model for respective distinct uses within our application. We will continue to lean heavily on the rapid advancements from the commercial model providers, and will also invest in our own models for specific high-value uses that have unique latency, cost, and intelligence requirements.

What trends or shifts are you seeing in the market that are influencing your product or positioning?

The expectations of customers on the maturity and breadth of capabilities of AI agents continue to increase. For example, a year ago, having a chatbot that enables natural language interactions with a cybersecurity data lake was considered very cool and differentiating. However, at this moment, this same feature is already considered a table stake, and security teams expect human-level analytical qualities on complex multi-step tasks and the ability to influence and direct AI agents to follow specific approaches and rules. There are still numerous product innovation opportunities as first movers like us gain more real-world adoption and experiment with exactly how security teams expect to interact with AI agents.

How has customer demand or behavior evolved over the last 6–12 months

We have seen a significant uptick in customer demand in the last 6 to 12 months. Every single board is asking the executive team about their function’s Gen AI strategy, and our technology is one of the most obvious applications of Gen AI for cybersecurity teams.

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