Imagine an AI security guard tasked with protecting a vast digital fortress. This guard has been trained to spot any threat but hasn’t learned the specific weaknesses each part of the fortress has. It might be great at spotting general suspicious activity, but it could easily miss subtle vulnerabilities that a specialist would catch. This, in essence, is the current problem with AI-powered vulnerability detection. Existing AI models often treat all vulnerabilities as the same, using a binary "vulnerable/not vulnerable" approach. But vulnerabilities are as diverse as the software they exploit, each with unique characteristics and code patterns. A SQL injection vulnerability, for instance, is vastly different from a buffer overflow, yet current AI models might lump them together. This one-size-fits-all approach often leads to a high rate of false positives—the AI cries wolf too often—and misses crucial, specific vulnerabilities. New research explores a different tactic: training specialized AI detectors for each Common Weakness Enumeration (CWE), a standardized list of software vulnerabilities. This is like giving our AI guard specialized training for each part of the fortress, making them experts at recognizing those specific weaknesses. By building CWE-specific classifiers, the AI can learn the nuances of each vulnerability type, improving its ability to spot real threats. The results are promising. These specialized AI detectors are significantly better at catching their target vulnerability within a controlled test. They also outperform generalist AI detectors at spotting their specific CWE. These specialized classifiers do face a critical challenge. They need to generalize well to broader datasets, containing diverse vulnerability types they haven't seen before. While the results show that the number of vulnerabilities classified as "true positives" increases with specialized detectors, it also leads to a higher false-positive rate. The problem, it turns out, lies in the balance of training data. When training these specialist AIs, researchers must create balanced datasets where both vulnerable and non-vulnerable code have an equal share in training to avoid bias toward vulnerabilities and improve learning effectiveness. However, this downsampling limits the exposure of these classifiers to the sheer diversity of non-vulnerable code patterns in the real world. As a result, when these AI models see real software, which is mainly non-vulnerable, they tend to overestimate the threat and classify many non-vulnerable pieces of code as potential risks. This research highlights a vital shift in how we approach AI-powered vulnerability detection. Building specialized AI detectors, like training specialized security guards, is a significant step towards making software safer. But more research needs to focus on how these specialized AI models can generalize across a wide range of vulnerabilities while keeping a tight leash on those false positives. This journey towards accurate and efficient vulnerability detection is ongoing. As research continues to refine these techniques, we can envision AI systems that can automatically spot even the most subtle and complex vulnerabilities, making our digital world significantly more secure.