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 : DA-100
Exam Name : Analyzing Data with Microsoft Power BI
Vendor Name : Microsoft
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DA-100 exam Format | DA-100 Course Contents | DA-100 Course Outline | DA-100 exam Syllabus | DA-100 exam Objectives


Exam Number : DA-100

Exam Name : Analyzing Data with Microsoft Power BI



This course will discuss the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will also show how to access and process data from a range of data sources including both relational and non-relational data. This course will also explore how to implement proper security standards and policies across the Power BI spectrum including datasets and groups. The course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution. Finally, this course will show how to build paginated reports within the Power BI service and publish them to a workspace for inclusion within Power BI.



Skills Gained

- Ingest, clean, and transform data

- Model data for performance and scalability

- Design and create reports for data analysis

- Apply and perform advanced report analytics

- Manage and share report assets

- Create paginated reports in Power BI



Specifically:

- Understanding core data concepts.

- Knowledge of working with relational data in the cloud.

- Knowledge of working with non-relational data in the cloud.

- Knowledge of data analysis and visualization concepts.



Course outline

Module 1: Get Started with Microsoft Data Analytics

This module explores the different roles in the data space, outlines the important roles and responsibilities of a Data Analysts, and then explores the landscape of the Power BI portfolio.



Lessons

Data Analytics and Microsoft

Getting Started with Power BI

Lab : Getting Started

Getting Started

After completing this module, you will be able to:



Explore the different roles in data

Identify the tasks that are performed by a data analyst

Describe the Power BI landscape of products and services

Use the Power BI service

Module 2: Prepare Data in Power BI

This module explores identifying and retrieving data from various data sources. You will also learn the options for connectivity and data storage, and understand the difference and performance implications of connecting directly to data vs. importing it.


Lessons

Get data from various data sources

Optimize performance

Resolve data errors

Lab : Preparing Data in Power BI Desktop

Prepare Data



After completing this module, you will be able to:



Identify and retrieve data from different data sources

Understand the connection methods and their performance implications

Optimize query performance

Resolve data import errors

Module 3: Clean, Transform, and Load Data in Power BI

This module teaches you the process of profiling and understanding the condition of the data. They will learn how to identify anomalies, look at the size and shape of their data, and perform the proper data cleaning and transforming steps to prepare the data for loading into the model.



Lessons

Data shaping

Enhance the data structure

Data Profiling

Lab : Transforming and Loading Data

Loading Data



After completing this module, students will be able to:



Apply data shape transformations

Enhance the structure of the data

Profile and examine the data

Module 4: Design a Data Model in Power BI

This module teaches the fundamental concepts of designing and developing a data model for proper performance and scalability. This module will also help you understand and tackle many of the common data modeling issues, including relationships, security, and performance.



Lessons

Introduction to data modeling

Working with tables

Dimensions and Hierarchies

Lab : Data Modeling in Power BI Desktop

Create Model Relationships

Configure Tables

Review the model interface

Create Quick Measures

Lab : Advanced Data Modeling in Power BI Desktop

Configure many-to-many relationships

Enforce row-level security



After completing this module, you will be able to:



Understand the basics of data modeling

Define relationships and their cardinality

Implement Dimensions and Hierarchies

Create histograms and rankings

Module 5: Create Model Calculations using DAX in Power BI

This module introduces you to the world of DAX and its true power for enhancing a model. You will learn about aggregations and the concepts of Measures, calculated columns and tables, and Time Intelligence functions to solve calculation and data analysis problems.



Lessons

Introduction to DAX

DAX context

Advanced DAX

Lab : Introduction to DAX in Power BI Desktop

Create calculated tables

Create calculated columns

Create measures

Lab : Advanced DAX in Power BI Desktop

Use the CALCULATE() function to manipulate filter context

use Time Intelligence functions



After completing this module, you will be able to:



Understand DAX

Use DAX for simple formulas and expressions

Create calculated tables and measures

Build simple measures

Work with Time Intelligence and Key Performance Indicators

Module 6: Optimize Model Performance

In this module you are introduced to steps, processes, concepts, and data modeling best practices necessary to optimize a data model for enterprise-level performance.



Lessons

Optimze the model for performance

Optimize DirectQuery Models

Create and manage Aggregations



After completing this module, you will be able to:



Understand the importance of variables

Enhance the data model

Optimize the storage model

Implement aggregations

Module 7: Create Reports

This module introduces you to the fundamental concepts and principles of designing and building a report, including selecting the correct visuals, designing a page layout, and applying basic but critical functionality. The important Topic of designing for accessibility is also covered.



Lessons

Design a report

Enhance the report

Lab : Designing a report in Power BI

Create a live connection in Power BI Desktop

Design a report

Configure visual fields adn format properties

Lab : Enhancing Power BI reports with interaction and formatting

Create and configure Sync Slicers

Create a drillthrough page

Apply conditional formatting

Create and use Bookmarks



After completing this module, you will be able to:



Design a report page layout

Select and add effective visualizations

Add basic report functionality

Add report navigation and interactions

Improve report performance

Design for accessibility

Module 8: Create Dashboards

In this module you will learn how to tell a compelling story through the use of dashboards and the different navigation tools available to provide navigation. You will be introduced to features and functionality and how to enhance dashboards for usability and insights.



Lessons

Create a Dashboard

Real-time Dashboards

Enhance a Dashboard

Lab : Designing a report in Power BI Desktop - Part 1

Create a Dashboard

Pin visuals to a Dashboard

Configure a Dashboard tile alert

Use Q&A to create a dashboard tile



After completing this module, students will be able to:



Create a Dashboard

Understand real-time Dashboards

Enhance Dashboard usability

Module 9: Create Paginated Reports in Power BI

This module will teach you about paginated reports, including what they are how they fit into Power BI. You will then learn how to build and publish a report.



Lessons

Paginated report overview

Create Paginated reports

Lab : Creating a Paginated report

Use Power BI Report Builder

Design a multi-page report layout

Define a data source

Define a dataset

Create a report parameter

Export a report to PDF



After completing this module, you will be able to:



Explain paginated reports

Create a paginated report

Create and configure a data source and dataset

Work with charts and tables

Publish a report

Module 10: Perform Advanced Analytics

This module helps you apply additional features to enhance the report for analytical insights in the data, equipping you with the steps to use the report for actual data analysis. You will also perform advanced analytics using AI visuals on the report for even deeper and meaningful data insights.



Lessons

Advanced Analytics

Data Insights through AI visuals

Lab : Data Analysis in Power BI Desktop

Create animated scatter charts

Use teh visual to forecast values

Work with Decomposition Tree visual

Work with the Key Influencers visual



After completing this module, you will be able to:



Explore statistical summary

Use the Analyze feature

Identify outliers in data

Conduct time-series analysis

Use the AI visuals

Use the Advanced Analytics custom visual

Module 11: Create and Manage Workspaces

This module will introduce you to Workspaces, including how to create and manage them. You will also learn how to share content, including reports and dashboards, and then learn how to distribute an App.



Lessons

Creating Workspaces

Sharing and Managing Assets

Lab : Publishing and Sharing Power BI Content

Map security principals to dataset roles

Share a dashboard

Publish an App



After completing this module, you will be able to:



Create and manage a workspace

Understand workspace collaboration

Monitor workspace usage and performance

Distribute an App

Module 12: Manage Datasets in Power BI

In this module you will learn the concepts of managing Power BI assets, including datasets and workspaces. You will also publish datasets to the Power BI service, then refresh and secure them.



Lessons

Parameters

Datasets



After completing this module, you will be able to:



Create and work with parameters

Manage datasets

Configure dataset refresh

Troubleshoot gateway connectivity

Module 13: Row-level security

This module teaches you the steps for implementing and configuring security in Power BI to secure Power BI assets.



Lessons

Security in Power BI



After completing this module, you will be able to:



Understand the aspects of Power BI security

Configure row-level security roles and group memberships



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Microsoft integrates the Python programming language into Excel

Microsoft Corp. is releasing a new version of Excel that will enable users to write and run Python code directly in the spreadsheet editor’s interface. 

Stefan Kinnestrand, a general manager for product marketing at Microsoft, detailed the update in a blog post today. The Python support is currently available in public preview.

Excel includes data science features that can be used for tasks such as visualizing business information and finding the average value of customer purchases. According to Microsoft, the newly added Python support will extend those features. The programming language can be used to create more advanced data science workflows than those that Excel supports out of the box.

A worker could, for example, write a Python script that scans the data in an Excel spreadsheet and finds duplicate fields. Those fields can then be deleted to prevent them from lowering the accuracy of analyses. Furthermore, Python can be used to create data visualizations and train machine learning models.

“You can manipulate and explore data in Excel using Python plots and libraries, and then use Excel’s formulas, charts and PivotTables to further refine your insights,” Kinnestrand wrote. 

Many of Python’s data science features are powered by Python modules. Those are prepackaged code components developed by the open-source community. Python modules usually have to be installed and configured manually by users, which can be a time-consuming process.

The new version of Excel skips that step. According to Microsoft, it runs on a version of Python called Anaconda Python that includes many popular data science modules in a preinstalled form. As a result, users don’t have to obtain any additional components or change settings to use Excel’s Python features.

One of the Python modules that Excel includes is Matplotlib. It allows users to run advanced statistical analyses on the data in a spreadsheet. Matplotlib supports several analysis methods including linear regression, which is commonly used in the enterprise to generate sales forecasts. Excel also supports seaborn, a module that can visualize the results of a Matplotlib-powered analysis in graphs.

Both Excel and Python are widely used in the enterprise. As a result, some data science teams already rely on Python scripts to analyze data in their spreadsheets. Until now, those scripts had to be written and run in a separate code editor, which required data scientists to regularly switch tabs.

Excel’s built-in Python support should simplify the workflow. According to Microsoft, users can add Python code to a spreadsheet without leaving the Excel interface. They must simply select a spreadsheet cell, type in their code with the prefix “=py” and specify the data on which the code should run.

Workers can import data into Excel for analysis using a set of built-in connections. Alternatively, they may use Microsoft’s Power Query tool for the task. The tool can not only import records into Excel from external applications but also change the data’s format to simplify processing.

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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