Was ist das eigentlich? Cyberrisiken verständlich erklärt

Es wird viel über Cyberrisiken gesprochen. Oftmals fehlt aber das grundsätzliche Verständnis, was Cyberrisiken überhaupt sind. Ohne diese zu verstehen, lässt sich aber auch kein Versicherungsschutz gestalten.

Beinahe alle Aktivitäten des täglichen Lebens können heute über das Internet abgewickelt werden. Online-Shopping und Online-Banking sind im Alltag angekommen. Diese Entwicklung trifft längst nicht nur auf Privatleute, sondern auch auf Firmen zu. Das Schlagwort Industrie 4.0 verheißt bereits eine zunehmende Vernetzung diverser geschäftlicher Vorgänge über das Internet.

Anbieter von Cyberversicherungen für kleinere und mittelständische Unternehmen (KMU) haben Versicherungen die Erfahrung gemacht, dass trotz dieser eindeutigen Entwicklung Cyberrisiken immer noch unterschätzt werden, da sie als etwas Abstraktes wahrgenommen werden. Für KMU kann dies ein gefährlicher Trugschluss sein, da gerade hier Cyberattacken existenzbedrohende Ausmaße annehmen können. So wird noch häufig gefragt, was Cyberrisiken eigentlich sind. Diese Frage ist mehr als verständlich, denn ohne (Cyber-)Risiken bestünde auch kein Bedarf für eine (Cyber-)Versicherung.

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Exam Number : Qlik-Sense-Business-Analyst
Exam Name : Qlik Sense Business Analyst Certification (QSBA) 2025
Vendor Name : Qlik
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Qlik-Sense-Business-Analyst test Format | Qlik-Sense-Business-Analyst Course Contents | Qlik-Sense-Business-Analyst Course Outline | Qlik-Sense-Business-Analyst test Syllabus | Qlik-Sense-Business-Analyst test Objectives


Exam Name: Qlik Sense Business Analyst Certification
Exam Format: Multiple-choice, multiple-response, and scenario-based questions
Number of Questions: ~40-60 questions
Duration: ~60-90 minutes (varies by test version)
Passing Score: ~70% (may vary)
Delivery Method: Online proctored or at a Pearson VUE test center

- Based on business requirements, identify Qlik products, needed for the solution (QSE, QDC, QABDI, ODAG, Qlik Core, Attunity, Insight Bot, NPrinting, etc.)
- Explain Qlik Sense features (e.g., smart search, responsive design, accessibility features, themes, etc.) and how they impact app design
- Describe the differences between the Qlik deployment options
- Based on business requirements, identify KPIs, dimensions and measures

- Ask appropriate questions to understand the needs of the potential users of the Qlik Sense application
- Identify user types and their typical devices to use to access apps
- Explain the purpose of APIs, server-side extensions (such as R, Python), Qlik GeoAnalytics, web mashups, widgets, section access
- Explain and interpret results from Qlik Associative Engine
- Identify Qlik Cognitive Engine uses
- Consider performance during data loading and application development (removing unused columns, consider calculation condition)

- Create a basic data model using Data Manager (e.g., spreadsheets, CSV, flat files, QVDs, etc.)
- Define the steps to load and associate data
- Apply filters while loading data in Data Manager
- Manually create data table in Data Manager (creates Inline Load script)
- Explain synthetic keys and possible methods of resolution
- Create folder and file data connections
- Leverage data connections already in Qlik Sense Enterprise

- Based on the business requirements and desired insight, determine the optimal number of charts, correct chart type, order of dimensions, use of color, and measures
- Design the order of the sheets in an application to provide an overall flow matching the expectations of the user journey (DAR concept)
- Select object types appropriate for the required data analysis (e.g., a line chart to show a trend over time)
- Understand when and why to use an alternate chart recommendation
- Identify the proper expression to create the appropriate visualization to meet the business requirements

- Explain the impact of using mobiles devices on app design
- Leverage appropriate method for data comparisons (filters, bookmarks, alternate states, set analysis)
- Distinguish between situations when data security requirements need to use section access or security rules
- Add dimensions, measures, and objects
- Use the latest objects and features in the Visualization Bundle, e.g., trellis chart, Profit & Loss pivot table
- Use Advanced Authoring features, e.g., specify a default landing sheet, native container object, etc.
- Set up visualizations with alternate states

- Change the grid size after adding objects
- Create and use variables
- Use custom color in charts (e.g., ColorMix, CSS colors, If statements, etc.)
- Know what extensions are and when to use them
- Create a story
- Create master items

- Apply filters and create bookmarks
- Use advanced functions like AGGR and set analysis
- Perform tasks around making a production ready application (e.g., create a thumbnail image, appropriate title, etc.)
- Use Insight Advisor to generate visualizations
- Use chart suggestions to change visualization type
- Select appropriate screen layout



- Understanding Qlik Senses associative model
- Differences between QlikView and Qlik Sense
- Qlik Sense deployment options (Qlik Sense Desktop, Enterprise, Cloud)
- Qlik Sense Hub and its components (Sheets, Stories, Apps)
- Data model optimization techniques

- Associative Engine – Qliks in-memory data indexing technology
- In-memory processing – Data loaded into RAM for fast analysis
- QIX Engine – The underlying engine powering Qlik Sense
- QMC (Qlik Management Console) – Admin tool for managing Qlik Sense Enterprise
- Qlik Sense Hub – Central interface for accessing apps and visualizations

- Loading data from various sources (Excel, CSV, SQL, Web, REST APIs)
- Using the Data Load Editor and Script Editor
- Understanding ETL (Extract, Transform, Load) processes
- Optimizing data models (Star Schema, Snowflake Schema, Synthetic Keys, Circular References)
- Handling incremental loads and partial reloads
- Using variables and parameters in scripts

- Data Load Script – Script used to extract and transform data
- Resident Load – Loading data from an already loaded table
- Concatenate – Combining tables with similar fields
- Qualify / Unqualify – Handling field name conflicts
- Synthetic Key – An automatically created key due to multiple linking fields
- Circular Reference – A loop in table relationships

- Best practices for dashboard design
- Choosing the right chart types (Bar, Line, Pie, Scatter, Gauge, KPI, etc.)
- Using Master Items (Dimensions, Measures, Visualizations)
- Creating interactive filters (List Box, Dropdown, Slider, Search)
- Implementing Set Analysis for advanced filtering
- Using Bookmarks and Selections for user navigation

- Master Items – Reusable dimensions, measures, and visualizations
- Set Analysis – Advanced filtering syntax (e.g., ={$})
- Alternate States – Multiple selection states in the same app
- Bookmark – Saved selection state for quick access
- Storytelling – Creating guided analytics narratives

- Writing expressions using Aggregation Functions (Sum, Avg, Min, Max, Count)
- Using Conditional Functions (If, Pick, Match, etc.)
- Implementing Advanced Aggregations (Set Analysis, Aggr())
- Working with Date and Time Functions
- Creating Calculated Dimensions and Calculated Fields

- AGGR() – Nested aggregation function
- Set Analysis – Dynamic filtering in expressions (e.g., Sum({$} Sales))
- Calculated Dimension – A dimension derived from an expression
- Measure – A calculated metric (e.g., Sum(Sales))
- Variables – Storing reusable expressions (e.g., vTotalSales = Sum(Sales))

- Understanding Section Access for row-level security
- User roles in Qlik Sense (Admin, Developer, Analyst, User)
- Managing security rules in QMC (Qlik Management Console)
- Best practices for app security and governance
- Section Access – Script-based row-level security
- Data Reduction – Limiting data visibility based on user roles
- Stream – A folder-like structure for organizing apps
- Task – Scheduled reloads and distribution tasks

- Identifying and resolving performance bottlenecks
- Optimizing data models (reducing synthetic keys, minimizing joins)
- Using Qlik Sense Performance Analyzer
- Best practices for script optimization
- RAM Utilization – Monitoring memory usage
- Cache Management – Handling cached data for faster reloads
- QVD (Qlik Data File) – Optimized Qlik-native data storage format
- Partial Reload – Updating only changed data

- Publishing apps to the Qlik Sense Hub
- Sharing insights via Stories and Snapshots
- Exporting data and visualizations (Excel, PDF, Image)
- Using Qlik Sense Mobile for on-the-go analytics
- Publish – Making an app available to end-users
- Snapshot – A static image of a visualization with current selections
- Mashups – Embedding Qlik Sense apps in external websites
- Qlik Sense Mobile – Accessing dashboards on mobile devices



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Warum sind Cyberrisiken so schwer greifbar?

Als mehr oder weniger neuartiges Phänomen stellen Cyberrisiken Unternehmen und Versicherer vor besondere Herausforderungen. Nicht nur die neuen Schadenszenarien sind abstrakter oder noch nicht bekannt. Häufig sind immaterielle Werte durch Cyberrisiken in Gefahr. Diese wertvollen Vermögensgegenstände sind schwer bewertbar.

Obwohl die Gefahr durchaus wahrgenommen wird, unterschätzen viele Firmen ihr eigenes Risiko. Dies liegt unter anderem auch an den Veröffentlichungen zu Cyberrisiken. In der Presse finden sich unzählige Berichte von Cyberattacken auf namhafte und große Unternehmen. Den Weg in die Presse finden eben nur die spektakulären Fälle. Die dort genannten Schadenszenarien werden dann für das eigene Unternehmen als unrealistisch eingestuft. Die für die KMU nicht minder gefährlichen Cyber­attacken werden nur selten publiziert.

Aufgrund der fehlenden öffentlichen Meldungen von Sicherheitsvorfällen an Sicherheitsbehörden und wegen der fehlenden Presseberichte fällt es schwer, Fakten und Zahlen zur Risikolage zu erheben. Aber ohne diese Grundlage fällt es schwer, in entsprechende Sicherheitsmaßnahmen zu investieren.

Erklärungsleitfaden anhand eines Ursache-Wirkungs-Modells

Häufig nähert man sich dem Thema Cyberrisiko anlass- oder eventbezogen, also wenn sich neue Schaden­szenarien wie die weltweite WannaCry-Attacke entwickeln. Häufig wird auch akteursgebunden beleuchtet, wer Angreifer oder Opfer sein kann. Dadurch begrenzt man sich bei dem Thema häufig zu sehr nur auf die Cyberkriminalität. Um dem Thema Cyberrisiko jedoch gerecht zu werden, müssen auch weitere Ursachen hinzugezogen werden.

Mit einer Kategorisierung kann das Thema ganzheitlich und nachvollziehbar strukturiert werden. Ebenso hilft eine solche Kategorisierung dabei, eine Abgrenzung vorzunehmen, für welche Gefahren Versicherungsschutz über eine etwaige Cyberversicherung besteht und für welche nicht.

Die Ursachen sind dabei die Risiken, während finanzielle bzw. nicht finanzielle Verluste die Wirkungen sind. Cyberrisiken werden demnach in zwei Hauptursachen eingeteilt. Auf der einen Seite sind die nicht kriminellen Ursachen und auf der anderen Seite die kriminellen Ursachen zu nennen. Beide Ursachen können dabei in drei Untergruppen unterteilt werden.

Nicht kriminelle Ursachen

Höhere Gewalt

Häufig hat man bei dem Thema Cyberrisiko nur die kriminellen Ursachen vor Augen. Aber auch höhere Gewalt kann zu einem empfindlichen Datenverlust führen oder zumindest die Verfügbarkeit von Daten einschränken, indem Rechenzentren durch Naturkatastrophen wie beispielsweise Überschwemmungen oder Erdbeben zerstört werden. Ebenso sind Stromausfälle denkbar.

Menschliches Versagen/Fehlverhalten

Als Cyberrisiken sind auch unbeabsichtigtes und menschliches Fehlverhalten denkbar. Hierunter könnte das versehentliche Veröffentlichen von sensiblen Informationen fallen. Möglich sind eine falsche Adressierung, Wahl einer falschen Faxnummer oder das Hochladen sensibler Daten auf einen öffentlichen Bereich der Homepage.

Technisches Versagen

Auch Hardwaredefekte können zu einem herben Datenverlust führen. Neben einem Überhitzen von Rechnern sind Kurzschlüsse in Systemtechnik oder sogenannte Headcrashes von Festplatten denkbare Szenarien.

Kriminelle Ursachen

Hackerangriffe

Hackerangriffe oder Cyberattacken sind in der Regel die Szenarien, die die Presse dominieren. Häufig wird von spektakulären Datendiebstählen auf große Firmen oder von weltweiten Angriffen mit sogenannten Kryptotrojanern berichtet. Opfer kann am Ende aber jeder werden. Ziele, Methoden und auch das Interesse sind vielfältig. Neben dem finanziellen Interesse können Hackerangriffe auch zur Spionage oder Sabotage eingesetzt werden. Mögliche Hackermethoden sind unter anderem: Social Engineering, Trojaner, DoS-Attacken oder Viren.

Physischer Angriff

Die Zielsetzung eines physischen Angriffs ist ähnlich dem eines Hacker­angriffs. Dabei wird nicht auf die Tools eines Hackerangriffs zurückgegriffen, sondern durch das physische Eindringen in Unternehmensgebäude das Ziel erreicht. Häufig sind es Mitarbeiter, die vertrauliche Informationen stehlen, da sie bereits den notwendigen Zugang zu den Daten besitzen.

Erpressung

Obwohl die Erpressung aufgrund der eingesetzten Methoden auch als Hacker­angriff gewertet werden könnte, ergibt eine Differenzierung Sinn. Erpressungsfälle durch Kryptotrojaner sind eines der häufigsten Schadenszenarien für kleinere und mittelständische Unternehmen. Außerdem sind auch Erpressungsfälle denkbar, bei denen sensible Daten gestohlen wurden und ein Lösegeld gefordert wird, damit sie nicht veröffentlicht oder weiterverkauft werden.

Ihre Cyberversicherung sollte zumindet folgende Schäden abdecken:

Cyber-Kosten:

  • Soforthilfe und Forensik-Kosten (Kosten der Ursachenermittlung, Benachrichtigungskosten und Callcenter-Leistung)
  • Krisenkommunikation / PR-Maßnahmen
  • Systemverbesserungen nach einer Cyber-Attacke
  • Aufwendungen vor Eintritt des Versicherungsfalls

Cyber-Drittschäden (Haftpflicht):

  • Befriedigung oder Abwehr von Ansprüchen Dritter
  • Rechtswidrige elektronische Kommunikation
  • Ansprüche der E-Payment-Serviceprovider
  • Vertragsstrafe wegen der Verletzung von Geheimhaltungspflichten und Datenschutzvereinbarungen
  • Vertragliche Schadenersatzansprüche
  • Vertragliche Haftpflicht bei Datenverarbeitung durch Dritte
  • Rechtsverteidigungskosten

Cyber-Eigenschäden:

  • Betriebsunterbrechung
  • Betriebsunterbrechung durch Ausfall von Dienstleister (optional)
  • Mehrkosten
  • Wiederherstellung von Daten (auch Entfernen der Schadsoftware)
  • Cyber-Diebstahl: elektronischer Zahlungsverkehr, fehlerhafter Versand von Waren, Telefon-Mehrkosten/erhöhte Nutzungsentgelte
  • Cyber-Erpressung
  • Entschädigung mit Strafcharakter/Bußgeld
  • Ersatz-IT-Hardware
  • Cyber-Betrug