Organisations often rely on Cyber Threat Intelligence (CTI) for collective defence against evolving threats. Therefore, it is important that these shared reports are both compliant with data protection legislation and analytically useful. Pseudonymisation is a recognised data protection measure, yet its effect on downstream utility remains underexplored in cybersecurity. Unlike other domains where personal identifiers carry predictive value, personal data in cyber incident reports may act as noise rather than signal — suggesting pseudonymisation could improve classification accuracy. This hypothesis is evaluated in two steps. First, we derive three new datasets by applying Data Masking, Data Tokenisation and Data Substitution to a subset of the CECILIA-10C-900 dataset. We then evaluate 21 models — spanning traditional Machine Learning (ML) classifiers, encoder transformers and QLoRA fine-tuned Large Language Models (LLMs) — on these datasets, for a CTI classification task based on the Spanish National Cybersecurity Institute’s incident taxonomy. The RoBERTa-base model achieved the highest overall weighted F1-score of 87.35% when Data Tokenisation was applied, while Llama-3.1-8B demonstrated the largest gain (+12.63 pp) with Data Masking. These findings reframe pseudonymisation from only a compliance measure into a preprocessing step that may simultaneously protect privacy and improve classification in specific model-technique pairings.