This study introduces two specialized datasets for legal violation identification, generated using large language models (LLMs) and validated by domain experts. The datasets are designed for class-action cases and cover a range of legal violations. The study evaluates various language models, including BERT-based models and LLMs, across two NLP tasks: Named Entity Recognition (NER) for identifying violations and Natural Language Inference (NLI) for associating these violations with affected individuals. The results show that the datasets and setups can be used for both tasks, with an F1-score of 62.69% for violation identification and 81.02% for associating victims. The study also presents a two-setup approach employing NER and NLI tasks, providing a methodology for legal violation detection and resolution. The datasets and code are publicly released to advance research in legal natural language processing (NLP). The study highlights the effectiveness of LLMs in generating synthetic data that closely mimics legal language, offering a scalable and ethically sound alternative to manual data crafting. The research also addresses the challenges of distinguishing between machine-generated and human-generated text in the context of legal violation identification, showing that the two types of content are strikingly similar in terms of average sentence lengths and character count. The study's findings suggest that LLMs can adapt effectively to new, unseen data for identifying legal violations and correlating them with past resolved cases across different legal domains. The study also emphasizes the importance of human expert annotations in validating the datasets and ensuring their quality and reliability. The research contributes to the field of legal NLP by introducing a new set of entity types that have not been previously explored in legal NER research, thereby expanding the scope and applicability of NER in legal contexts. The study also highlights the limitations of the current dataset, which focuses on US common law and may not apply to civil law jurisdictions or non-US legal systems. The research underscores the ethical considerations of deploying machine learning models in the legal domain, emphasizing the need for responsible use and a thorough understanding of the limitations and biases inherent in automated systems. The study's findings have implications for improving the efficiency and accuracy of legal violation identification and resolution, contributing to a safer and more equitable digital society.This study introduces two specialized datasets for legal violation identification, generated using large language models (LLMs) and validated by domain experts. The datasets are designed for class-action cases and cover a range of legal violations. The study evaluates various language models, including BERT-based models and LLMs, across two NLP tasks: Named Entity Recognition (NER) for identifying violations and Natural Language Inference (NLI) for associating these violations with affected individuals. The results show that the datasets and setups can be used for both tasks, with an F1-score of 62.69% for violation identification and 81.02% for associating victims. The study also presents a two-setup approach employing NER and NLI tasks, providing a methodology for legal violation detection and resolution. The datasets and code are publicly released to advance research in legal natural language processing (NLP). The study highlights the effectiveness of LLMs in generating synthetic data that closely mimics legal language, offering a scalable and ethically sound alternative to manual data crafting. The research also addresses the challenges of distinguishing between machine-generated and human-generated text in the context of legal violation identification, showing that the two types of content are strikingly similar in terms of average sentence lengths and character count. The study's findings suggest that LLMs can adapt effectively to new, unseen data for identifying legal violations and correlating them with past resolved cases across different legal domains. The study also emphasizes the importance of human expert annotations in validating the datasets and ensuring their quality and reliability. The research contributes to the field of legal NLP by introducing a new set of entity types that have not been previously explored in legal NER research, thereby expanding the scope and applicability of NER in legal contexts. The study also highlights the limitations of the current dataset, which focuses on US common law and may not apply to civil law jurisdictions or non-US legal systems. The research underscores the ethical considerations of deploying machine learning models in the legal domain, emphasizing the need for responsible use and a thorough understanding of the limitations and biases inherent in automated systems. The study's findings have implications for improving the efficiency and accuracy of legal violation identification and resolution, contributing to a safer and more equitable digital society.