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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PEOPLECERT PC0-006 : Management of Risk (MoR) Foundation exam Dumps

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Exam Number : PC0-006
Exam Name : Management of Risk (MoR) Foundation
Vendor Name : PEOPLECERT
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PC0-006 exam Format | PC0-006 Course Contents | PC0-006 Course Outline | PC0-006 exam Syllabus | PC0-006 exam Objectives


Exam: PC0-006 Management of Risk (MoR) Foundation

Exam Details:
- Number of Questions: The exact number of questions may vary, but the exam typically consists of multiple-choice questions.
- Time: Candidates are usually given a specific time duration to complete the exam.

Course Outline:
The PC0-006 Management of Risk (MoR) Foundation course provides an introduction to risk management principles and practices. It equips candidates with the knowledge and understanding required to apply the MoR framework effectively. The course outline includes the following topics:

1. Introduction to MoR
- Overview of risk management and its importance
- Understanding the MoR framework and its key concepts
- Roles and responsibilities in risk management

2. MoR Principles
- Understanding the MoR principles and how they guide risk management activities
- Embedding risk management in the organization's culture and decision-making processes
- Applying the principles to identify, assess, and manage risks

3. MoR Approach
- Developing a structured approach to risk management
- Defining risk management policies, processes, and strategies
- Establishing risk management responsibilities and accountabilities

4. MoR Processes
- Describing the MoR process model and its components
- Identifying risks and opportunities
- Assessing and evaluating risks
- Planning and implementing risk responses
- Monitoring and reviewing risk management activities

5. MoR Techniques and Documentation
- Overview of common risk management techniques and tools
- Using risk registers, risk exam matrices, and other documentation
- Applying qualitative and quantitative risk analysis techniques

Exam Objectives:
The PC0-006 exam aims to assess candidates' understanding of risk management principles and their ability to apply the MoR framework. The exam objectives include:

1. Understanding the key concepts, principles, and benefits of risk management.
2. Applying the MoR framework to identify, assess, and manage risks effectively.
3. Demonstrating knowledge of the MoR approach and its components.
4. Applying the MoR processes, techniques, and documentation in practical scenarios.
5. Understanding the roles and responsibilities of individuals involved in risk management.

Exam Syllabus:
The exam syllabus covers the following topics:

- Introduction to MoR
- MoR Principles
- MoR Approach
- MoR Processes
- MoR Techniques and Documentation

Candidates are expected to have a comprehensive understanding of these subjects and demonstrate their ability to apply the MoR framework in real-world risk management scenarios. The exam assesses their knowledge, critical thinking skills, and ability to make informed decisions in managing risks within an organization.



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How Machine Learning Will Transform Your Industry

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Machine learning is a rapidly growing field with endless potential applications. In the next few years, they will see machine learning transform many industries, including manufacturing, retail and healthcare.

In manufacturing, machine learning can be used for quality control, automation and customization. For example, machine learning can be used to detect defects in products before they reach consumers. It can also be used to automate repetitive tasks such as assembly line work. And finally, manufacturers will increasingly use machine learning to customize products for individual consumers.

In retail, machine learning can be used for data analysis to help businesses make better decisions about inventory and pricing. Personalization will become more common, with retailers using machine learning to recommend products to customers based on their past behavior. Robotics will also become more prevalent, with machine learning being used to automate tasks such as shelf stocking and order picking.

In healthcare, machine learning can be used for diagnostics, treatment and prevention. For example, machine learning can be used to diagnose diseases earlier and more accurately. It can also be used to develop personalized treatments based on a patient's characteristics. Machine learning can also be used for preventative care, such as identifying risk factors for disease and providing tailored recommendations for healthy living.

So far they have only scratched the surface of what is possible with machine learning. As technology continues to evolve, they will see even more amazing applications of this transformative technology.

Machine Learning In Manufacturing

In the past, quality control for manufactured goods was a time-consuming and expensive process that required human inspectors to examine each item for defects. However, machine learning can be used to automate this process by training algorithms to identify defects from images or other data sources. This can help reduce the cost of quality control while also increasing the accuracy of the inspection process.

Automation

Machine learning can also be used to automate manufacturing processes. For example, robots that are equipped with machine learning algorithms can be trained to perform tasks such as welding or fabricating parts. This can lead to a more efficient manufacturing process and can free up human workers for other tasks.

Customization

Another way that machine learning is transforming manufacturing is by enabling customization at scale. In the past, it was difficult and expensive to create customized products due to the need for manual labor and individualized production lines. However, machine learning algorithms can now be used to automatically generate custom designs based on customer specifications. This allows manufacturers to quickly and easily produce personalized products without incurring significant additional costs.

Machine Learning In Retail

In the past, retailers have relied on data from customer surveys and transactions to make decisions about their business. However, this data is often incomplete and doesn't provide a full picture of customer behavior. Machine learning can help solve this problem by analyzing large data sets to identify patterns and trends. This information can be used to Strengthen customer service, optimize stock levels and make other strategic decisions.

Personalization

Machine learning can also be used to personalize the shopping experience for customers. For example, Amazon uses machine learning to recommend products that customers may be interested in based on their previous purchase history. This helps shoppers find what they're looking for more quickly and makes the overall shopping experience more enjoyable.

Robotics

Robots are increasingly used in retail settings to perform shelf stocking and order fulfillment tasks. While these machines cannot replace human workers completely, they can free up employees' time to focus on more critical tasks, such as helping customers. In the future, robots may become even more involved in the retail sector as machine learning technology develops.

Machine Learning In Healthcare

Machine learning is already being used in healthcare to diagnose diseases. For example, Google has developed an algorithm that can detect breast cancer based on images. In the future, machine learning will be used to diagnose more complex conditions such as Alzheimer's disease and cancer.

Treatment

Machine learning can also be used to develop new treatments for diseases. For example, a company called Insilico Medicine is using machine learning to develop new drugs for cancer and other diseases. In the future, machine learning will be used to develop more effective and personalized treatments for patients.

Prevention

In addition to diagnosing and treating diseases, machine learning can also be used to prevent them. For example, IBM's Watson system is being used to predict patients' risk of developing certain diseases. In the future, machine learning will be used to create more personalized and effective prevention plans for individual patients.

Conclusion

Machine learning is set to transform a wide range of industries in the coming years. In retail, machine learning will enable more accurate data analysis, personalization of products and services and even the use of robotics in stores. In healthcare, machine learning will revolutionize diagnostics, treatment and prevention. And in manufacturing, machine learning will Strengthen quality control, automate processes and allow for greater customization. These are just a few examples of how machine learning will change the landscape of the industry as they know it. So whatever sector you're in, it's time to start preparing for the machine learning revolution.

While ML and associated technologies like natural language processing are gaining traction in current workflows, it's important to pay close attention to ethical standards that differentiate humans from machines. Today, ML has come to a point where it can replace humans in many intelligent tasks. The future is clearly AI/ML-driven, and it will eventually become part of their lives to the degree the mobile phone is. They will take it for granted. Given all of this, those using and developing AI must keep ethics in mind when dealing with it, whether that's focusing on consumer privacy rights or keeping up to date with laws and regulations surrounding the technology in this space.

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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