XAI-Compliance-by-Design: A Modular Framework for GDPR- and AI Act-Aligned Decision Transparency in High-Risk AI Systems

High-risk Artificial Intelligence (AI) systems deployed in cybersecurity and privacy-critical contexts must satisfy not only demanding performance targets but also stringent obligations for transparency, accountability, and human oversight under the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (AI Act). Existing approaches often treat these concerns in isolation as follows: Explainable Artificial Intelligence (XAI) methods are added ad hoc to machine learning pipelines, while governance and regulatory frameworks remain largely conceptual and weakly connected to the concrete artefacts produced in practice. This article proposes XAI-Compliance-by-Design, a modular framework that integrates XAI techniques, compliance-by-design principles and trustworthy Machine Learning Operations (MLOps) practices into a unified architecture for high-risk AI systems in cybersecurity and privacy domains. The framework follows a dual-flow design that couples an upstream technical pipeline (data, model, explanation, and monitoring) with a downstream governance pipeline (policy, oversight, audit, and decision-making), orchestrated by a Compliance-by-Design Engine and a technical–regulatory correspondence matrix aligned with the GDPR, the AI Act, and ISO/IEC 42001. The framework is instantiated and evaluated through an end-to-end, Python-based proof of concept using a synthetic, intrusion detection system (IDS)-inspired anomaly detection scenario with a Random Forest (RF) classifier, Shapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), drift indicators, and tamper-evident evidence bundles and decision dossiers. The results show that, even in a modest, toy setting, the framework systematically produces verifiable artefacts that support auditability and accountability across the model lifecycle. By linking explanation reports, drift statistics and compliance logs to concrete regulatory provisions, the approach illustrates how organisations operating high-risk AI for cybersecurity and privacy can move from model-centric optimisation to evidence-centric governance. The article discusses how the proposed framework can be generalised to real-world high-risk AI applications, contributing to the operationalisation of European digital sovereignty in AI governance. This article does not introduce a new intrusion detection algorithm; instead, it proposes an evidence-centric governance pipeline that captures decision provenance and compliance artefacts so that decisions can be audited and justified against regulatory obligations.