Cybersecurity has always been a race. Now it feels more like a chase, and the defenders are falling behind. 

According to a Cyber Risk and Insurance Survey (2024), 87% of global decision makers say their company is not adequately protected against cyberattacks. That number isn’t surprising, but it should be alarming. Most organizations know they’re not ready, especially as threats evolve faster than the tools designed to stop them. 

The driving force behind this shift is artificial intelligence. Attackers are using public models and automation tools to generate malware that is unique to every campaign. It doesn’t look like anything we’ve seen before. There are no reused payloads or shared infrastructure. Every sample is a zero-day. 

Everything will become brand new. And that’s the problem. 

For years, defenders have relied on pattern matching, behavioral analysis and heuristic rules. But when every attack is previously unseen, those patterns don’t exist. Even behavior-based detection, once considered cutting edge, has a critical flaw: It only works after the malware runs. That means defenders are stuck waiting for something to go wrong. Dormant malware sitting quietly in your environment? It slips through. And by the time malicious behavior is detected, the damage is often already done. 

Instead of watching what a file does, defenders need to ask what it’s designed to do. That means analyzing the file itself, not just its behavior. Antivirus verdicts and EDR classifications are no longer sufficient. If the malware was built to evade those systems, they won’t be the ones to catch it. 

The Cost of Creating Malware

At the same time, the economics of attacks have collapsed. The cost of creating malware has dropped dramatically. What once required skill and infrastructure can now be automated. With a few prompts and a language model, attackers can generate polymorphic code that adapts to each target. 

The development cost that used to throttle attackers is gone. 

When attackers can spin up a new tool per operation, there’s no need to reuse infrastructure or rely on “good enough” code. The volume and complexity of attacks will only increase because nothing is slowing them down. 

The one advantage defenders still have is visibility. But not just more logs or alerts. What matters is having a searchable, historical context — an internal record of what entered your environment, when it appeared and where it went. If you know what doesn’t belong, you don’t have to wait for it to behave badly. Most teams can’t answer those questions today, not because they lack data, but because their systems weren’t built for that kind of inquiry. 

This is where the mindset needs to shift. AI can be a force multiplier for defenders, but only if it’s paired with better questions and clearer intent. Understanding will always beat detection. Knowing what doesn’t belong and why it shouldn’t be there matters more than chasing alerts that come too late. 

Innovation is no longer optional. Your appetite for trying new things doesn’t matter to the adversary. They’re already experimenting, adapting and moving forward. The only way to stay in the fight is to rethink how we approach it. 

The teams that succeed won’t be the ones with the most alerts or the biggest dashboards. They’ll be the ones who stop guessing and start analyzing. The ones that treat visibility not as a log file, but as a foundation for understanding. 

And the ones that remember: If you don’t know what’s in your environment, you’re not defending it. You’re just hoping you get lucky. 

At Stairwell, we believe security starts with knowing what’s in your environment, not just reacting to it. That’s why our approach has always focused on the actual things that could be bad. We gather, store and analyze every file even remotely executable: The known good, the known bad and everything in between. By doing so, we eliminate the guesswork and take back an advantage adversaries have relied on for far too long. By asking questions about the files, you can find the security answers you need.