Artificial intelligence is no longer just a lab experiment. It’s quietly becoming part of everyday software, helping developers write code, assisting analysts with research, and powering tools inside banks, hospitals, and tech companies. Over the last few years, large language models (LLMs) have moved from curiosity to core infrastructure for many digital products.
But while companies rushed to build smarter systems, one important piece lagged behind: security. The way AI systems behave is very different from traditional software, and that difference is forcing the cybersecurity world to rethink how protections actually work. As a result, a new discipline is emerging within the security community: AI penetration testing, often referred to as AI pentesting.
Why AI Systems Create New Security Risks
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Most software behaves in predictable ways. You give it an input, the code follows a set of rules, and it produces an output. Security testing has always relied on this predictable structure.
Large language models don’t work that
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