## 6 Privacy Considerations and Regulatory Alignment  
Balancing effective security monitoring with user privacy has emerged as a central challenge for machine learning (ML)-based intrusion detection systems, especially in an era where regulatory frameworks and ethical considerations are evolving rapidly. As security solutions increasingly rely on large datasets to detect anomalies or malicious behaviors, they must do so in full compliance with data protection regulations, including the EU General Data Protection Regulation (GDPR) and the EU AI Act. These regulations place stringent requirements on data confidentiality and user consent, mandating that personal information be minimized, de-identified, or otherwise protected before any algorithmic processing occurs (Regulation (EU) 2016/679 and Regulation (EU) 2024/1689) [53,54]. Privacy-preserving methodologies, such as differential privacy and secure multiparty computation, have gained prominence for their ability to ensure that individual data points remain confidential while still allowing for robust pattern analysis and threat detection [55]. In differential privacy, statistical noise is injected into the data analysis process to obscure individual contributions, thereby reducing the risk of re-identification or unauthorized disclosures [56]. Meanwhile, secure multiparty computation enables multiple parties to jointly compute a function over their inputs without revealing those inputs to each other, preserving confidentiality even in collaborative threat intelligence settings [57]. In addition to these privacy-enhancing techniques, federated learning has emerged as a promising paradigm for addressing ethical concerns around data centralization. Rather than collecting raw data in a single repository, federated learning trains models across distributed edge devices, allowing each participant to retain ownership of their data while contributing to a global model [58]. This decentralized approach mitigates risks associated with large-scale data breaches and fosters greater trust among stakeholders who might otherwise be reluctant to share sensitive information. With the EU AI Act moving toward responsible AI development, such privacy considerations are going to be more imperative in aligning technical innovations with legal and societal expectations. Such commitment to transparent data governance, proactive privacy-risk assessments, and ethical impact reviews not only protects from potential reputational harm but also ensures effectiveness and compliance of machine-learning-based security solutions in increasingly dynamic regulatory environments [59]. Privacy-preserving techniques like federated learning and differential privacy enhance compliance with the EU AI Act and GDPR, significantly reducing the risk of personal data exposure. However, implementing these techniques adds complexity, computational overhead, and can reduce model performance due to the introduction of statistical noise or decentralized training procedures. In anticipation of the EU AI Act, WIDA has been designed to satisfy the five core obligations listed in Article 5 for high-risk AI systems. First, a continuous risk-management plan is realised through the edge-resident SHAP drift monitor, which triggers retraining whenever concept shift exceeds a preset threshold. Second, robust data-governance measures ensure that only anonymised traffic statistics leave tenant domains, with synthetic-to-real transfer protocols preventing leakage of personally identifiable information. Third, all releases are accompanied by full technical documentation--model cards, version history, and evaluation reports--hosted in a public repository. Fourth, the platform enforces human oversight via a \"four-eyes\" policy: every automated mitigation proposed by the IDS must be confirmed by a Security Operations Centre analyst. Finally, WIDA strengthens robustness and accuracy through blockchain-anchored model checkpoints and optional adversarial training on synthetically generated attack samples.