Why do you think there are so few mature AI-driven autonomous pentesting solutions on the market, and why does this topic seem to generate more hype than in-depth technical discussion?

The challenge is that security has largely become theoretical. Companies are mostly hiring auditors and focusing on certifications, with very little emphasis on real research. The market is flooded with checklists and yes/no compliance exercises, but there are hardly any concrete technical solutions. Now with AI, the same pattern is repeating—people are trying to understand it only at a theoretical level, which again limits real technological progress in the industry.

Autonomous pentesting is hard because real attacks require judgment, context, and creative chaining, not just exploit discovery. Production environments are noisy, dynamic, and legally constrained, which limits safe autonomy.
Hype grows because controlled demos show clean wins in lab environments, but real enterprises expose harder realities. Autonomous tools must handle fragile systems, ambiguous signals, and incomplete data without breaking production or flooding teams with false positives. When something goes wrong, responsibility, auditability, and legal ownership become far more complex than the demo implies.

Well autonomous pentesting requires Agency (planning a campaign), Reasoning (understanding business intent), and Safety (knowing when to stop). Till the end of 2025, we have solved the easy part (generating exploit code), but the hard part (strategic reasoning) is yet to work on. Until AI can reliably understand context without hallucinating, the hype will continue to be a sales pitch for what is essentially "super-charged scanning.

Honestly, the main reason we don’t see many truly autonomous AI pentesting tools is that AI excels at automation repetitive tasks like scanning, mapping, and identifying known vulnerabilities, but it’s still far from real autonomy. It can’t replicate a human tester’s instinct, creative problem-solving, or deep understanding of a business’s unique logic to chain together complex attacks.

Getting an AI model to reason through uncertainty and make safe, high-stakes decisions in a completely new environment is a major technical challenge we simply haven’t solved yet.

Unfortunately, the public conversation is often overshadowed by hype. It’s much more exciting to sell the idea of an “AI hacker” than to talk about the unglamorous work behind the scenes: reducing false positives, enforcing ethical safeguards, and ensuring the system works reliably across countless different client setups.

So for now, the reality is that most effective solutions are still AI-powered, not AI-autonomous tools that support human testers rather than replace them.

I love this topic ,today IT security is moving around certification, Audit. Which is very strange. IT security was never technical, it is all theoretical. Whereas hacker are more technical and technical knowledge comes with theory. So hackers are more prepared than us. AI is something which is increasing worry of security, so there is less. We have to be more technical.