Lack of high‑quality public cyber incident data hinders empirical research and predictive modeling for cyber risk. Companies' reluctance to disclose incidents, fearing reputational damage, perpetuates this challenge. Actuarial solutions focus on enhancing existing datasets and employing advanced modeling. A new InsurTech framework is proposed to enrich cyber incident data with entity‑specific attributes, addressing the gap in publicly available information. Machine learning models predict incident types and estimate frequencies, demonstrating improved robustness when incorporating InsurTech‑derived features. This framework aims to generate transparent, entity‑specific cyber risk profiles, supporting tailored underwriting and proactive risk mitigation for insurers and organizations.