If you had time to walk the expo floor at this year’s RSA Conference, it was impossible to miss the shift in our industry. Artificial intelligence has moved from an emerging layer to the foundation of what powers cybersecurity companies. But from our vantage point as investors who work closely with founders and operators, the bigger shift is how AI is changing how these companies are formed, funded and scaled.
The past year marked an inflection point. A surge in venture funding and headline acquisitions underscored a market moving faster than many expected. Startups that once spent years iterating toward product-market fit are now emerging from stealth with mature products and raising large early rounds almost immediately. Meanwhile, the traditional progression from seed to Series A is compressing into a much shorter, higher-stakes window, and legacy companies are being forced to move faster than ever to stay relevant in today’s landscape.
Venture funding is concentrating around fewer, larger AI bets
The acceleration reflects real capability. AI has cut the time and cost of building and iterating on cybersecurity products, allowing small teams to move at unprecedented speed. But faster development doesn’t change the basics: durable businesses still require clear differentiation, strong go-to-market execution and proven customer demand.
What has changed is how capital is being deployed. Venture funding in cybersecurity is increasingly concentrated into fewer companies, with larger rounds and higher valuations. The market is increasingly binary: startups are expected to either secure AI systems or use AI to deliver clear, measurable improvements in security outcomes. Companies that can’t clearly stake out one of those positions are finding it harder to attract attention from both investors and acquirers.
Higher valuations can accelerate momentum, but they also raise the bar for performance. When growth does not materialize as expected, the path forward becomes more difficult, particularly in a market that is moving as quickly as this one.
AI-native startups are operating with smaller, more technical teams
AI is also reshaping how cybersecurity companies are staffed and operated. The most effective teams today are smaller and more technical, relying heavily on automation to extend their capabilities. Engineers are increasingly focused on orchestrating AI systems rather than building every component from scratch, shifting the nature of technical work toward higher-level problem solving and system design. They can iterate faster than ever before, putting pressure on fast-paced innovation and high-capacity outputs.
This is creating a widening gap between companies that are built around AI from the start and those trying to retrofit it into existing models. For newer startups, this approach is often foundational. For incumbents, it can require significant changes to both technology and culture, leading to an upcoming M&A wave that’s already in the early innings.
Threat actors are using AI to scale attacks and lower barriers to entry
At the same time, the threat landscape is evolving. AI is lowering the barrier to entry for offensive cyber capabilities, enabling less sophisticated actors to execute attacks that previously required significant expertise. This is increasing both the volume and complexity of threats facing organizations. We’re seeing early responses to that with things like Anthropic’s Project Glasswing, which aims to bring together leading organizations to protect critical software.
The expansion is not limited to traditional network or endpoint attacks. AI is introducing new attack surfaces, from machine identities to autonomous agents and decision-making systems. It is also unleashing new forms of risk, including more advanced disinformation campaigns and other narrative-driven attacks that can impact markets and corporate reputations as much as technical systems.
Cyber defense is shifting toward autonomous, machine-driven models
As attackers scale their use of AI, defenders are being forced to do the same. Cybersecurity is moving toward a model where machine-driven systems play a central role in both detecting and responding to threats. In many cases, the dynamic is moving from human vs. machine, to machine vs. machine.
This shift is driving innovation across the market. New categories are emerging around securing AI systems and workloads, while established areas like endpoint security, data protection and vulnerability management are being rebuilt with AI at their core. These changes are enabling new capabilities but also increasing the pace of competition across the industry.
M&A and platform strategies are accelerating alongside AI innovation
The speed of innovation is also reshaping consolidation across cybersecurity. Larger platforms are moving to incorporate AI capabilities more quickly, while startups are building toward platform strategies earlier in their lifecycle. This is compressing timelines for both growth and acquisition. When incumbents can’t innovate quickly enough, they can buy instead.
Capital continues to play a central role in this dynamic. Strong funding environments are enabling companies to scale quickly, but they are also introducing risk when valuations outpace underlying performance. Some of the largest rounds are functioning as signals of market leadership as much as sources of operating capital.
There is growing awareness that not all these companies will meet expectations. The same conditions that enable rapid growth can also expose weaknesses quickly, particularly if customer adoption and revenue do not keep pace.
What founders and investors are watching for the rest of 2026
The defining characteristic of the current market is speed. The gap between companies that can adapt to these changes and those that cannot is widening quickly.
For founders, that means balancing urgency with discipline – building AI-native products while staying focused on real customer problems. For investors, it means identifying teams that can execute in a rapidly changing environment and build companies that endure beyond the current cycle.
The cybersecurity landscape has always evolved alongside technology and threat activity, but the pace of change today is different. The companies that emerge as leaders in the next phase of the market will be those that can operate effectively in that reality, where AI is foundational, competition is global, and the timeline for success is shorter than ever.
