# A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly
## 6 Cybersecurity and Privacy Risk Assessment: The Road Ahead
Especially when aiming to privacy preservation, the actions of physical actors (e.g., data processor) could lead to security incidents that may put data subjects in position to encounter significant, or even irreversible consequences, which they may not overcome. In this context, there is an open research question on how to assess, and possibly quantify, those privacy related cascading risks. Physical-equivalent scoring systems like those developed for the quantification of the impact of technical vulnerabilities and weaknesses, like CVSS and CWE would contribute in this direction. In the context of APSIA method and tool, the interdependency graphs can be used for defining dependencies between the physical and cyber world using the isUsedBy and isLocatedIn relations. This feature could be used as an enabler for building future methods that consider this connection between the physical and cyber world. **Risk Mitigation through Optimal Countermeasure Selection:** In light of the complex and demanding task of risk mitigation, one of the current hurdles that both the scientific community and ICT industry are trying to overcome pertains to the identification of appropriate mitigation actions towards increasing the robustness of cyber defense solutions. In this context, there have been several mechanisms proposed for supporting automated reaction; delivering a set of mitigation suggestions to the decision makers [58]. The vast majority of such frameworks capitalise on multi-objective optimisation techniques and constraint solvers in order to find an optimal mitigation action, or a set of actions, that can eliminate the identified risks. More, complex techniques consider game theoretic methods for emulating the engagement between defenders and aggressors [59]. This specific field has a number of challenges to tackle in order to bridge the gap from theory to practice. The scalability of optimisation solutions, in cases where the environment poses several objectives to be met, in conjunction with graph modeling techniques and live feeds of intrusion detection systems [60; 61], create wide search areas for the optimal solution and has been reported as a major challenge to overcome. In addition, these optimisation processes do not consider any standard representation of remediation actions or a clear mapping between well-correlated sets of mitigation actions and threats in order to provide interoperable and effective solutions [62]. Notably, the aforementioned endeavors widely neglect the privacy preservation aspect in their objectives. This is mainly due to the fact that cybersecurity and privacy risk assessments are treated as distinct management processes, but also there is a lack of scoring systems that can capture and quantify the interdependence between these two aspects. Therefore, the instantiation of tools like APSIA can enable the model-based risk workflow, scoring and assessment for both the security and privacy assurance of mixed-criticality applications. Risks that have been identified in core assets (comprising the target application), can be weighted according to their criticality and impact degree, allowing the resolution of an optimization problem for the selection, deployment and placement of the best set of possible mitigation actions; ranging from the enactment of security solutions based on traditional cryptographic primitives (i.e., symmetric/asymmetric encryption, digital signatures [63], etc.) to more advanced trust assurance services [64; 65] including network-based intrusion detection systems or (remote)attestation controls. However, despite the clear benefits of such approaches, their applicability in environments with resource constrained devices, especially when it comes to privacy-related mitigation actions, is rather challenging [66]. For instance, strong cryptographic protocols can be used to increase trust, by not letting privacy risks be technically possible.