The management of technical debt related to non-functional properties such as security, reliability or other trustworthiness dimensions is of paramount importance for critical systems (e.g., safety-critical, systems with strong privacy constraints etc.). Unfortunately, diverse factors such as time pressure, resource limitations, organizational aspects, lack of skills, or the fast pace at which new risks appears, can result in an inferior level of trustworthiness than the desired or required one. In addition, there is increased interest in considering trustworthiness characteristics, not in isolation, but in an aggregated fashion, as well as using this knowledge for risk quantification. In this work, we propose a trustworthiness debt measurement approach using 1) established categories and subcategories of trustworthiness characteristics from SQuaRE, 2) a weighting approach for the characteristics based on an AHP method, 3) a composed indicator based on a Fuzzy method, and 4) a risk management and analysis support based on Monte Carlo simulations. Given the preliminary nature of this work, while we propose the general approach for all trustworthiness dimensions, we elaborate more on security and reliability. This initial proposal aims providing a practical approach to manage trustworthiness debt suitable for any life cycle phase and bringing the attention to aggregation methods.