All current application security is based on the same design approach: it is focused on threat vectors. This represents a ‘fatal flaw’.

By constraining response to a threat or vulnerability, you limit your ability to respond to things that have ostensibly already happened. Even the most sophisticated of security applications that have predictive modelling built into them, are still focusing on known threats. No matter how sophisticated the modelling, it is still limited to what we know and therefore leaves you exposed to unknown vulnerabilities and zero-day attacks.

Once upon a time that was enough, but the world has changed. The explosion of open-source code means no one truly knows what vulnerabilities lie hidden, and AI-driven zero-day exploits have made the race to keep up unwinnable.

The only way to solve this flaw is to reverse the logic: stop reacting to what’s happened (the threat vector) and start controlling what will (the behavior vector).

By defining and enforcing expected behavior, Program Behavior Intelligence™ (PBI™) replaces uncertainty with assurance, transforming security from endless reaction to continuous confidence.

Open source has become the lifeblood of modern software—and its greatest liability. Every application is now an assembly of countless open-source components written by strangers, updated unpredictably, and stacked on dependencies no one fully maps. This creates an ‘incompleteness problem’, unknown unknowns: exceptions and vulnerabilities buried so deep that no scanner can see them and no patch cycle can keep pace. The openness that fuels innovation also guarantees instability. Until behavior itself becomes the control surface, open source will remain both your engine and your biggest risk factor.

Application security starts and ends with good software hygiene, but many organizations struggle with developing rigor and discipline around this. Gaps inevitably creep in, especially around testing and validation, usually for no other reason than resources are stretched. The problem is akin to the incompleteness problem of open source libraries; how do you know that you’ve mapped and tested the complete set of program behaviors. Solving this problem should not be any more complicated than observing the program’s behavior under captive normal use and then building a model of this.

Keeping up with the constant flow of vulnerabilities was always difficult, but at least exploitation was limited by human speed. Zero-day attacks were serious but not systemic. Now that’s changed. AI-generated exploits remove the natural cap on frequency and scale, overwhelming even the fastest response teams so that it is now no longer a zero-day exploit, but a zero-second exploit. Unless our approach evolves, application security risks becoming unmanageable. The only way to regain control is to neutralize vulnerabilities at their source, not by chasing exceptions, but by enforcing how your program is meant to behave.