Social media platforms are constantly battling harmful content. From hate speech and profanity to more nuanced issues like self-harm discussions and spam, the challenge is immense. Traditional moderation tools often struggle with customization, accuracy across diverse categories, and privacy concerns. Existing AI models, while good at catching toxic language, often miss other sensitive content. Researchers are tackling this problem head-on. A new study introduces "X-Sensitive," a comprehensive dataset specifically designed to train AI models for a wider range of sensitive content. This dataset covers six key categories: conflictual language, profanity, sexually explicit material, drug-related content, self-harm, and spam. The results are promising. By fine-tuning large language models (LLMs) on X-Sensitive, researchers significantly boosted detection performance, outperforming even established commercial APIs by 10-15%. Interestingly, even smaller, specialized LLMs showed impressive results, suggesting that highly scaled models aren't always necessary. However, challenges remain. The study highlighted the difficulty AI models have with subtler forms of sensitive content, particularly within the "conflictual language" category. For example, deciphering the intent behind certain phrases remains a complex task. The research also underscores the need for larger, more diverse datasets and continued refinement of AI models. Future research will likely explore different model architectures and prompt engineering to improve accuracy and address the nuances of online communication. This work represents a significant step towards creating safer online environments. As AI models become more sophisticated, they can provide valuable support to human moderators, helping to identify and address a broader range of harmful content, ultimately leading to more positive and inclusive online experiences.