# A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly
## 5 Case Study for the Healthcare Sector
### Assessment Results
Moreover, Figures 13 and 13 present the data flows visualisation of the two identified processing activities. Each data flow consists of the PII, Data Subjects, Processing Activities, the supporting assets and the corresponding Privacy Risks. This type of figures enables the assessor to associate the identified privacy threats, based on the privacy scoring system, with the assets that trigger the threat, the data and the individuals which are being threaten. This graph enables the assessor not only to prioritise the mitigation actions for specific assets, but crucially, to keep track of the data of subjects that may face an impact on their fundamental rights and freedoms. Taking a closer look at Figure 13, the Patient Registration activity and the corresponding data subjects can be affected by vulnerabilities identified in assets PC0002 and SQLServer. The total risk score assigned to the specific process is \"Very High\" and overshadows the \"Low\" risk of PC0002, following the approach described in Section 4.3.3. The same approach is illustrated in Figure 13 for the Patient Monitoring activity. In this case, more supporting assets bring threats against the data processing activity. In the case of the PC0001, which was found vulnerable to DoS and Code Execution attacks, the latter privacy risk overshadows the \"Medium\" risk of the DoS vulnerability, following the approach described in Section 4.3.3. Apart from the data flow graphs, APSIA offers a set of adjustable visualisation tools that enhance the risk assessment process. Indicatively, Figure 14 presents the heat-maps and histograms with information regarding both the privacy and the cyber risks in order to provide an holistic view of the privacy and cybersecurity levels of the organisation. More specifically the Threat Probability-Vulnerability Impact heat-map provides an overview of the cybersecurity risk assessment based on the CVSS scores given in Table 6. The privacy impact assessment process is supported by the heat-map in Figure 14, where the correlation of the Privacy Impact-Vulnerability Characterisation is given. As can be seen, based on the scores of Table 6, there are risk associations that reveal a \"Very High\" privacy impact even if the vulnerability characterisation is \"Medium\". By utilising this view, the risk assessor can properly prioritise the mitigation actions based on the heat of the privacy impact axis. In addition, in an effort to provide an holistic view of the Privacy scoring results of the organisation, the histogram in Figure A4 presents the distributional characteristics of the privacy scores for all the processing activities of the organisation. This view complements the data flows in which some of the identified privacy scores may be overshadowed, as only the highest score is illustrated following the approach described in Section 4.3.3. Last but not least, APSIA is able to represent the evolution of risks among consecutive risk assessments by highlighting the differences in the histograms. Such differences may occur as a result of mitigation actions or the emergence of new cybersecurity and privacy threats. Overall, APSIA offers a gamut of enablers to converge and automate the privacy and cybersecurity risk assessment by integrating asset inventory and vulnerability detection tools to support the assessment on a dynamic manner. The visualisation options based on the use of the interdependency graphs and the data processing flows constitute one of the competitive advantages of APSIA. In fact, as highlighted by the authors in [21], the CNIL method, which is one of the most prominent methods in the PIA field, does not use visual representations of the information flows. Such a feature is the foundation stone for the whole PIA and is facilitated by APSIA to a great extend.