Headline after headline in early 2026 tells a terrifying story: Super-Intelligence is nearly here, and it is coming to find every vulnerability in your IT system before you do. With the emergence of models capable of autonomously hunting zero-day bugs and agents that operate with unrestricted permissions, many business and IT leaders are paralyzed by a fear: Modern AI has broken the rules of internet security forever.
But this fear, while understandable, is largely based on hype rather than technical reality. If we look into the true nature of these breakthroughs, we discover that the rules of defense haven't actually changed. We aren't being outsmarted by super-intelligent AI - we are being outpaced.
Significant changes in how we treat application security are ahead of us, that part is true. But to understand what they mean for us, however, we need to understand what is really changing. We must right-size our expectations and re-center on human architectural choices.
Here are five theses with my assessment that shifts the focus from AI magic to the sustainable realities of secure software.
Thesis 1: It's not the best and latest AI model that creates best-in-class IT security.
As LLMs get better, they do not create human-level intelligence and create a truly deeper understanding of IT security and software. They improve their ability to remember historic bugs, and thus are able to formulate better hypotheses on how a hacker potentially could exploit a software. AI memorizes more historic bugs. Whatever reasoning capability they have, the practical security gain comes overwhelmingly from speed and tool integration, not from a deeper conceptual understanding.
These improvements are accompanied by a landscape of tools that unravels the true level of innovation. As software development was arguably the area where generative AI created the most value in our business world, significant research went into improving the tool ecosystem around it.
And this is important: These setups get more efficient with executing the commands that human software developers use as well. And they play out the major strengths which they have: Computers are much faster in what they do compared to humans.
They don't need to get really intelligent and get a good understanding in what is happening: As long as the AI is good in guessing what could be wrong, and the tools can test it at a blazing fast speed, they can identify software bugs.
This means: The best AI on its own is worth nothing without good software around it. Don't trust vendors which want to sell you IT Security tools only because it is AI-enabled.
Thesis 2: Software is not getting less secure - what changes is the speed of iteration and the tools we use.
With the release of Anthropic's Mythos model (and for sure comparably powerful models of other vendors very soon), we will face a short period where a lot of software bugs will be found, before the situation will calm down again.
Why is that? The internet is built on Open Source software. Many of which are maintaineed by only one or two developers in their free-time. Having this in mind, it can easily be understood why large parts of internet software faces bugs that have not been closed since years or even decades. And this historic debt of bugs is exactly what will be unraveled in the near future.
It's all about efficiency - not about uncovering novel types of vulnerabilities.
We will hit a point where these kind of bugs are closed. Then, what comes afterwards? New software development paradigms get more prominent, especially in the context of AI Agents. This introduces new types of security threats which require us to take action.
Existing AI models will not be good in identifying such novel types of attacks. Therefore, the IT landscape will be forced to iterate faster in improving their AI models. The speed of iteration will increase, which will demand new tools that enable us to keep pace.
What does this mean for us? Investing into modern developer tools and pipelines that allow for fast update cycles will become crucial. Manual patching of software will be a bottle neck in the future.
Thesis 3: New AI models will not define the end of secure internet applications.
Workflows for developing software will change, and so will the tools and ecosystems that developers use. Over time, these ecosystems will find ways to provide developers with the latest security updates in a convenient way.
The speed of automation has been increasing over the past decades steadily. And with that, the speed of new attack scenarios on software has been increasing. Until now, we have always found a way to make our software ecosystems keep up with this speed. And there is no reason to believe that this will be different this time.
Yet, an important precondition is that the tech scene invests into the development of new tools, security research and education of developers. History shows that adaption is the result of the conscious decisions we take.
Thesis 4: Not everything that can be automated shall be automated.
One key principle of secure software has always been the application of "least privileges". This means: Algorithms and users shall only have the rights they need to realize functionalities for customers.
What we have seen throughout the last years is the rise of Vibe Coding, where developers have AI create software quickly without human interference. And without thorough human checks afterwards.
Further, AI Agents are introduced in software. With Open Claw earlier this year, we have seen what happens: Developers are tempted to grant AI Agents unrestricted permissions and access to data, because it makes them more powerful. This "automation-first" approach improves the user experience.
But it contradicts one basic principle of secure software: Always think twice which rights you grant a software.
Creating applications for yourself through quick vibe coding and with unrestricted OpenClaw agents is much fun. Doing the same for productive usage is dangerous. We have to get back to architecting software properly before we hand them over to our customers. AI cannot release us from this burden.
We must think functionality- and customer-first instead of automation-first.
Thesis 5: Established best practices for IT security still hold true.
In an era where AI models can discover and exploit vulnerabilities at machine speed, the definition of a "safe" system must evolve. "Safe" can no longer mean "completely impenetrable", because an AI that never sleeps will eventually find a flaw. Instead, the gold standards of classical IT security shift from being highly recommended to being existentially mandatory.
Because we must operate under an "Assume Breach" mentality, our defense strategies must focus on containment.
- Minimal third-party library dependencies: Every piece of external code is an attack surface an AI can scan in seconds.
- Enforce strict Zero Trust architectures and network microsegmentation: If an AI-driven attack compromises a frontend web application, it must be logically isolated, preventing it from pivoting laterally to the core database.
- Spend time on good software architecture: AI does not reinvent the rules of defense. It simply punishes bad architecture much faster.
Adhering to the fundamentals - least privilege, encryption, and rapid patching pipelines - remains the most reliable way to limit the threats for modern web applications.
Summary: Security FAQ
Can modern AI models like Claude Mythos hack any website?
No. It is a misconception that AI is an omniscient synthetic brain that fundamentally understands software logic better than humans. In reality, modern LLMs are just powerful pattern matchers that have improved their ability to remember historic bugs. They are good at forming hypotheses about where a hacker might exploit software, but they cannot prove a bug exists on their own. The breakthrough is that these models are now successfully wired into ecosystems of deterministic tools (like debuggers) that can verify these hypotheses at blazing fast speeds. Don't trust vendors selling "AI security" as standalone magic.
Is the internet doomed by an AI hacking epidemic?
No - if we take action. While the release of advanced AI models has introduced speed, we have historically always found ways to make our software ecosystems keep up. We will likely face a short period where the large historic debt of legacy bugs (especially in Open Source libraries) is cleared out by symmetric AI tools used by both attackers and defenders. Once this historical technical debt is resolved, the landscape will plateau, and the "cat-and-mouse" game will shift toward entirely new types of security threats specific to AI agents. The dangers of an AI hacking epidemic are real - but we are able to fight them. Therefore, a period with immense changes lies ahead of us. But we will reach a state where we can treat application security like in the past: serious, but without doomsday panic.
Are established IT security best practices useless against fast AI attacks?
No. AI does not reinvent the rules of defense. In fact, established gold standards of classical IT security shift from being highly recommended to being existentially mandatory. AI cannot magically bypass strict Zero Trust architectures, network microsegmentation, or strong encryption. It simply punishes bad architectural decisions much faster. Sticking to fundamentals - least privilege, encryption, and rapid patching pipelines - remains the most reliable way to limit the threats.
Should I give my autonomous AI agents full administrative access to maximize helpfulness?
Absolutely not. This "automation-first" approach, tempting to developers seeking convenience, contradicts one basic principle of secure software: least privilege. Algorithms and agents should only have the rights they strictly need to perform their defined functionality for customers. Granting agents unrestricted permissions creates terrifying new vulnerabilities, such as Agent Goal Hijacking. We must get back to architecting software properly and thinking customer-first instead of automation-first. AI cannot release us from this burden.
Will AI eventually find a way to break into every system?
We must operate under an "Assume Breach" mentality. In an era where AI can discover vulnerabilities at machine speed, "safe" can no longer mean "completely impenetrable", because an AI that never sleeps will eventually find a flaw. Instead, the gold standards must focus on containment to limit the impact. We must focus on good software architecture and adhering to the fundamentals to remain the most reliable way to limit these threats.