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?

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.

Few mature autonomous AI pentesting tools exist because the core task is far harder than the hype suggests. Offensive security depends on contextual judgment, long-chain reasoning, and safe exploit execution abd these are the areas where current models remain brittle. On top of that, organizations are wary of the operational and legal risks of letting an AI conduct unsupervised intrusion.

Fundamentally due to the nature of LLMs being a product of semantics-based generation and semi-rigid reasoning. This will not change until the technology evolves to effective use of concept-based models incorporating System 1 and 2 thinking within shared context.

That in mind, hype will be more incessant the more those who produce it would try to push inherently deficient solutions for otherwise very real problem - scale of AI-enabled attack surface, in conjunction with complexity of AI-powered penetration, simply leave no space for humans to react and respond.

When it comes to AI-driven autonomous pentesting, I see three fundamental challenges:

1. The Attack Surface Problem
Pentesting isn't just about finding vulnerabilities—it's about understanding business context, prioritizing risk, and avoiding operational disruption. Current AI models lack the contextual judgment to distinguish between a critical production database and a test environment, or to recognize when "success" would actually cause patient care disruption. AI-augmented security tools can be extremely useful, but we still require human oversight at key decision points.

2. The Training Data Paradox
Unlike other domains where AI excels, we can't openly share our penetration testing data—it literally maps our vulnerabilities. This creates a data scarcity problem that's antithetical to how modern ML works. Vendors claiming "autonomous" capabilities are often just wrapping traditional rule-based tools in AI marketing language, which explains the hype-to-substance gap observed.

3. The Liability Question
Who's responsible when an autonomous pentesting tool causes an outage or exposes PHI during its scan? Our legal and compliance teams wouldn't sign off on truly autonomous security testing without clear accountability frameworks, and frankly, neither would I. We need AI that augments our red teams' efficiency—faster reconnaissance, better vulnerability correlation—not AI that operates independently.

The technical discussions are often thin because most vendors haven't solved these problems—they've just rebranded existing tools with "AI-powered" labels to capture budget dollars.