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.

Wo erhalte ich vollständige Informationen über CITP?

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Financial CITP : Certified Information Technology Professional (CITP) ACTUAL EXAM QUESTIONS

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CITP test Format | CITP Course Contents | CITP Course Outline | CITP test Syllabus | CITP test Objectives


The content of the Certified Information Technology Professional (CITP) test was developed to test a candidates understanding of the fundamental sections of the CITP body of knowledge. The content of each of the topical sections is described in outline form and provides an overview of the knowledge and skills tested on the CITP Exam.

The examination questions are intended to test each content area and its logical extensions.

The percentage following each major content area in the outline represents the approximate weighting for that content area. The examination is fully computerized and consists of multiple-choice questions only



Module I: Information Security & Cyber Risks

A. Information Security Governance (25%)

1. Information security strategy

2. Policy, procedures, processes, and standards

3. Logical access controls

4. Hardware and physical access controls

5. Security authorization & authentication

6. Business continuity & disaster recovery

B. Cybersecurity Risk Management (12%)

1. Cybersecurity threats

2. Data breaches and privacy

3. Vulnerability management

C. SOC for Cybersecurity (3%)

1. Purpose

2. Content

3. Target audiences

4. How to use in conjunction with cybersecurity risk mitigation

Module II: Business Intelligence, Data Management and Analytics

A. Data Management (5%)

1. Information lifecycle management

2. Infrastructures and platforms

3. Data preparation/manipulation

4. Data governance

B. Data Analysis & Reporting (11%)

1. Data analytics

2. Predictive analytics

3. Audit data analytics

C. Business Intelligence Management (4%)

1. Digital transformation & technology disruptors

2. Data integration

3. Data warehousing

Module III: IT Governance, Risks & Controls

A. IT Governance & Strategy (15%)

1. Role of IT governance within an organization

2. IT governance principles

3. IT governance roles & responsibilities

4. IT governance implementation

5. Benefits of effective IT governance

B. IT Risks, Process & Controls (15%)

1. IT risk identification and test

2. IT control frameworks

3. IT general controls

4. Application controls

5. Business process management

6. Change management

7. Assessment of IT controls

C. System and Organization Controls Reporting (10%)

1. System and Organization Controls Reporting Overview

2. Types of Reporting



Detailed content specification outline

Module 1. Information Security & Cyber Risks

This module focuses on the security and risk management of systems and environments, including the use of the SOC for Cybersecurity report as a tool for reporting IT security and risk management for companies.

Information Security Governance — Covers the key areas of information security, including strategy, policies/procedures, control environments, and business continuity/disaster recovery; includes fundamental knowledge of various IT governance frameworks, logical access at the various levels of the “stack,” and the internal control structure of design, implementation, monitoring, and detection/reporting

Cybersecurity Risk Management — Covers the major threat vectors for systems, including cyber adversaries, the cybercrime economy

and various types of attacks; also includes data breaches and their impact on information privacy, as well as how to manage system vulnerabilities

SOC for Cybersecurity — Covers the SOC for Cyber report, including report content, target users and use of the report in conjunction with an entitys overall cybersecurity risk mitigation strategy



A. Information Security Governance (25%)

1. Information security strategy

a. Objectives

b. Components

c. Alignment with organizational strategy, IT strategy

Information Security Governance

CPE self-study

Authors: Gwen Bettwy, Mark Williams,

Mike Beavers

Publisher: AICPA

Module 1 — Information Security Governance

2. Policy, procedures, processes, and standards

a. Frameworks

b. Compliance with applicable laws and regulations

c. Roles and responsibilities

Information Security Governance

CPE self-study

Authors: Gwen Bettwy, Mark Williams,

Mike Beavers

Publisher: AICPA

Module 1 — Information Security Governance

3. Logical access controls

a. Objectives

b. Data (transactional. level

c. Application and financial system level

d. Network level

e. Identifying, designing, implementing, monitoring, detecting and reporting

Information Security Governance

CPE self-study

Authors: Gwen Bettwy, Mark Williams,

Mike Beavers

Publisher: AICPA

Module 3 — Logical access controls

4. Hardware and physical access controls

a. Objectives

b. Identifying, designing, implementing, monitoring, detecting and reporting

Information Security Governance

CPE self-study

Authors: Gwen Bettwy, Mark Williams,

Mike Beavers

Publisher: AICPA

Module 4 — Physical access controls

5. Security authorization and authentication Information Security Governance

CPE self-study

Authors: Gwen Bettwy, Mark Williams,

Mike Beavers

Publisher: AICPA

Module 2 — Identity and access management

6. Business continuity and disaster recovery

a. Business continuity plan (BCP)

b. Disaster recovery plan (DRP)

c. Incident response plan (IRP)

d. Data backup and recovery

Information Security Governance

CPE self-study

Authors: Gwen Bettwy, Mark Williams,

Mike Beavers

Publisher: AICPA

Module 6 — Business continuity management



B. Cybersecurity Risk Management (12%)

1. Cybersecurity threats

a. Primary types of cyber adversaries (how to identify, what is their motivation.

1. How to identify

2. What is their motivation

3. How to manage/mitigate risk

4. Terms to use — Hacktivists, Nation states, Cybercriminals, Insider threat,

Competitors

b. Cybercrime economy (what could potentially drive a cybercrime against

a company.

c. Types of attacks

1. How to identify

2. Effect on the business/financials

3. How to manage/mitigate risk

4. Terms to use — Classic buffer overflow, Web-based application attacks,

Denial of Service/DDoS, Malware, ransomware, and spyware,

phishing/spear phishing, Social engineering

Cybersecurity Fundamentals for Finance &

Accounting Professionals Certificate Program

CPE self-study

Author: Christopher J. Romeo

Publisher: AICPA

2. Data breaches and privacy

a. Causes of a data breach

b. Organizational impact of a data breach

c. Post breach response (business/financial point of view)

d. Personally Identifiable Information (PII)

Cybersecurity Fundamentals for Finance and

Accounting Professionals Certificate Program

CPE self-study

Author: Christopher J. Romeo

Publisher: AICPA

3. Vulnerability management

a. Gap analysis, readiness and risk exams, vulnerability exams,

penetration testing (identification of vulnerabilities and how they could impact

business/financials.

b. Security policy & plan development (input regarding business/financial

implications in the policies/procedures.

1. Identity and access management (IAM)

2. Data loss management and prevention

Cybersecurity Fundamentals for Finance and

Accounting Professionals Certificate Program

CPE self-study

Author: Christopher J. Romeo

Publisher: AICPA

C. AICPA Cybersecurity Risk Management Reporting Framework (SOC for Cybersecurity) (3%)

1. Purpose

SOC for Cybersecurity Certificate Program

CPE self-study

Authors: Tony Chapman, Anurag Sharma

Publisher: AICPA

2. Content

SOC for Cybersecurity Certificate Program

CPE self-study

Authors: Tony Chapman, Anurag Sharma

Publisher: AICPA

3. Target audiences

SOC for Cybersecurity Certificate Program

CPE self-study

Authors: Tony Chapman, Anurag Sharma

Publisher: AICPA

Detailed content specification outline

Module II. Business Intelligence, Data Management & Analytics

This module focuses on information management and the utilization of information to provide value in decision-making and other
managerial needs.

Data Management — Covers the information lifecycle, from identification of system information through destruction and the various types

of infrastructures and ERPs to support data; also discusses how data is collected and manipulated, including consolidation, cleaning, transformation, reduction, processing, etc.; lastly, covers the governance of data including objectives, strategy, and policies Data Analysis & Reporting — Covers the various types of data analytics, the tools and procedures to perform an analysis, and the methods of reporting and performance indicators; also covers the use of predictive analytics, including the various models, techniques, applications and deployment; lastly, covers the integration of analytics in the audit process, including risks and assertions, and continuous assurance Business Intelligence Management — Covers the various forms of technology disruptors, including cloud tech, IoT, and AI; also covers the use of data integration (ETL, EAI and EDR) as well as data warehousing (Active, OLAP, ROLAP, MOLAP, HOLAP and DOLAP)



A. Data Management (5%)

1. Information Lifecycle Management

a. Identify

b. Capture

c. Manage

d. Utilize

e. Archive

f. Retention

g. Destruction

Data Analysis Fundamentals Certificate Program

CPE self-study

Publisher: AICPA

Data Analytics Modeling Certificate Program

CPE self-study

Publisher: AICPA

2. Infrastructures & platforms

a. Types of Infrastructure/Platforms typically employed

1. ERP or other enterprise software

i. ERP implementation

2. Data warehouse infrastructure

Data Analytics Modeling Certificate Program

CPE self-study

Publisher: AICPA

Data Visualization Certificate Program

CPE self-study

Publisher: AICPA

Analytics and Big Data for Accountants

CPE self-study

Author: Jim Lindell

Publisher: AICPA

3. Data preparation/manipulation

a. Data consolidation

b. Data mapping and collection

c. Data selection

d. Data cleaning

e. Data transformation

f. Data reduction

g. Data processing

Data Analytics Modeling Certificate Program

CPE self-study

Publisher: AICPA

Analytics and Big Data for Accountants

CPE self-study

Author: Jim Lindell

Publisher: AICPA



A. Data Management (5%)

4. Data governance

a. Objectives

b. Principles

c. Strategy

d. Policy

e. Architecture

Data Analysis Fundamentals Certificate Program

CPE self-study

Publisher: AICPA

Analytics and Big Data for Accountants

CPE self-study

Author: Jim Lindell

Publisher: AICPA

Information Security Governance

CPE self-study

Authors: Gwen Bettwy, Mark Williams,

Mike Beavers

Publisher: AICPA

Module 1 — Information Security Governance

B. Data Analysis & Reporting (11%)

1. Data analytics

a. Types

1. Quantitative analysis

2. Descriptive statistics

3. Data visualization

b. Tools, techniques, and procedures

c. Performance metrics and reporting

Data Analysis Fundamentals Certificate Program

CPE self-study

Publisher: AICPA

Data Visualization Certificate Program

CPE self-study

Publisher: AICPA

Analytics and Big Data for Accountants

CPE self-study

Author: Jim Lindell

Publisher: AICPA

2. Predictive analytics

a. Types

1. Predictive models

2. Descriptive models

3. Decision models

b. Techniques

1. Regression

2. Machine learning

c. Applications of predictive analytics

d. Deployment

Forecasting and Predictive Analytics Certificate

Program

CPE self-study

Publisher: AICPA

Data Analytics Modeling Certificate Program

CPE self-study

Publisher: AICPA

Analytics and Big Data for Accountants

CPE self-study

Author: Jim Lindell

Publisher: AICPA

3. Audit data analytics

a. Integrating analytics into the audit process

1. Audit applications of data analytics

2. Correlating audit tasks to risks and assertions

3. Continuous assurance

Integrating Audit Data Analytics into the Audit

Process

CPE self-study

Publisher: AICPA

Analytics and Big Data for Accountants

CPE self-study

Author: Jim Lindell

Publisher: AICPA



C. Business Intelligence Management (4%)

1. Digital transformation & technology disruptors

a. Cloud

b. Internet of Things (IoT)

c. Artificial intelligence

Data Analysis Fundamentals Certificate Program

CPE self-study

Publisher: AICPA

Analytics and Big Data for Accountants

CPE self-study

Author: Jim Lindell

Publisher: AICPA

2. Data integration

a. Extract, Transform, and Load (ETL)

b. Enterprise Application Integration (EAI)

c. Enterprise Data Replication (EDR)

Data Analytics Modeling Certificate Program

CPE self-study

Publisher: AICPA

Analytics and Big Data for Accountants

CPE self-study

Author: Jim Lindell

Publisher: AICPA

Data Analysis Fundamentals Certificate Program

CPE self-study

Publisher: AICPA

3. Data warehousing

a. Role in supporting BI

b. Architecture and components

c. Types

1. Active Data Warehousing

2. Multi-dimensional Analysis — OLAP

3. ROLAP, MOLAP, HOLAP and DOLAP

Data Analytics Modeling Certificate Program

CPE self-study

Publisher: AICPA

Data Visualization Certificate Program

CPE self-study

Publisher: AICPA

Analytics and Big Data for Accountants

CPE self-study

Author: Jim Lindell

Publisher: AICPA



Detailed content specification outline

Module III: IT Governance, Risks & Controls

This includes knowledge pertaining to information technology risk and advisory services, engagement compliance, and IT controls and exam. It also covers knowledge of various IT frameworks and related controls, including the use of SOC reporting as a framework to showcase a service organizations internal control environment.

IT Governance & Strategy — Covers the objectives, strategic planning, implementation and management of the IT function within an organization, as well as mitigation of risk; focuses on the management of value, resources, and performance in relation to key components and best practices of the IT function IT Risks, Process, & Controls — Discusses various IT frameworks, including COSO and COBIT, and the integration of frameworks with IT exams; covers a variety of key control areas for IT exams, including ITGCs, application, business process and change management controls System and Organizational Controls (SOC) Reporting — Focuses on the purposes for SOC reporting, the users of SOC reports, and the responsibilities of user auditors



A. IT Governance & Strategy (15%)

1. Role of IT governance within an organization

a. IT governance objectives

b. Management of the IT function

c. Mitigation of IT risk

d. IT strategic plan

1. Alignment with organizational strategy

IT Governance, Risks & Controls

CPE self-study

Publisher: AICPA

Module 1 — Role of IT Governance

Information Strategy

CPE self-study

Author: Kaplan Publishing Limited

Publisher: AICPA

2. IT governance principles

a. Strategy and planning

1. Key components

2. Best practices

b. Value delivery management

1. Key components

2. Best practices

c. Resource management

1. Key components

2. Best practices

d. Risk management

1. Key components

2. Best practices

e. Performance management

1. Key components

2. Best practices

IT Governance, Risks, and Controls

CPE self-study

Publisher: AICPA

Module 1 — Role of IT Governance

3. IT governance roles and responsibilities IT Governance, Risks, and Controls

CPE self-study

Publisher: AICPA

Module 1 — Role of IT Governance

4. IT governance implementation IT Governance, Risks, and Controls

CPE self-study

Publisher: AICPA

Module 2 — Implement and Assess IT Governance

5. Benefits of effective IT governance IT Governance, Risks, and Controls

CPE self-study

Publisher: AICPA

Module 2 — Implement and Assess IT Governance



B. IT Risks, Process & Controls (15%)

1. IT risk identification and test IT Governance, Risks, and Controls

CPE self-study

Publisher: AICPA

Module 3 — IT Risk Management

Risk and Control of Information Systems

CPE self-study

Author: Kaplan Publishing Limited

Publisher: AICPA

2. IT control frameworks

a. COSO

1. Categories of objectives

2. Integrated components & principles

b. COBIT

1. Domains

c. Integration of control frameworks

COSO Internal Control Certificate Program

CPE self-study

Publisher: Committee of Sponsoring Organizations

(COSO.

Internal Control and COSO Essentials for Financial

Managers, Accountants and Auditors

CPE self-study

Author: Glenn L. Helms

IT Governance, Risks, and Controls

CPE self-study

Publisher: AICPA

Module 4 — IT Controls

3. IT general controls

a. Objectives of IT general controls

b. Types of IT general controls (including ERP)

IT Governance, Risks, and Controls

CPE self-study

Publisher: AICPA

Module 4 — IT Controls

Risk and Control of Information Systems

CPE self-study

Author: Kaplan Publishing Limited

Publisher: AICPA

Information Security Governance

CPE self-study

Authors: Gwenn Bettwy, Mark Williams, Mike

Beavers

Publisher: AICPA

Module 3 — Logical access controls

4. Application controls

a. Objectives of application controls

b. Input controls

c. Processing controls

d. Output controls

IT Governance, Risks, and Controls

CPE self-study

Publisher: AICPA

Module 4 — IT Controls

Risk and Control of Information Systems

CPE self-study

Author: Kaplan Publishing Limited

Publisher: AICPA

Information Security Governance

CPE self-study

Authors: Gwen Bettwy, Mark Williams, Mike Beavers

Publisher: AICPA

Module 3 — Logical access controls




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Financial (CITP) cheat sheet

 

IBM Watson: A Cheat Sheet

IBM Watson is designed to make businesses more intelligent, so every member can perform at their best. It first garnered worldwide attention in 2011 as the computerized “brain” that won one million dollars on the TV game show Jeopardy! by beating human contestants. Since then, IBM has widely deployed Watson in many industry verticals and areas of application, and it has become the gold standard for purposeful analytics processing.

IBM Watson is an excellent data analysis platform integrated with advanced application programming interfaces, software-as-a-service application and specialized tooling. It leverages these tools for complex data analysis use cases and can be integrated with different platforms for optimizing daily tasks and enabling businesses to make the right decisions.

Jump to:

What is IBM Watson?

IBM Watson is a data analytics processor, named after IBM CEO Thomas J. Watson, that leverages natural language processing. This technology offers speech analysis to understand the syntax, context and meaning while translating it into actionable and practical answers.

SEE: Explore all of TechRepublic’s cheat sheets and smart person’s guides.

Watson was originally developed in IBM’s DeepQA research project. The primary goal was to develop a natural language-responsive system that could interpret questions asked in a human language, analyze vast amounts of data and return answers that it would take human researchers days, weeks or even months to derive.

Key features of IBM Watson Cloud environment

IBM Watson’s cloud availability means companies can start small and pay for what they use. In addition, this means businesses won’t have to invest in in-house computing devices or hardware, which can be expensive.

API integration

IBM Watson is integrated with various APIs, allowing developers to combine different features of Watson into the business apps.

More must-read AI coverage

In addition, Watson APIs make incorporating conversation, language and advanced text analytics into applications easy. For example, Watson’s Natural Language Understanding can analyze text and return a five-level taxonomy of the content, concepts, emotion, sentiment, entities and relations.

Watson Orchestrate

Orchestrate provides automation for employees by streamlining processes and repetitive tasks accessed through open APIs and robotics process automation integrations. With Orchestrate, employees free up time to pursue more things on their “want-to-do” list, making them more effective.

Watson Assistant

Assistant builds better virtual agents to quickly get accurate answers across applications and devices from customer service to internal IT help desk and human resources teams. It delivers consistent and intelligent customer care across all channels and touchpoints with conversational AI.

Watson Code Assistant

Code Assistant enables developers with various levels of expertise to write code with AI-generated recommendations, making it easier for anyone to write code. Code Assistant brings the power of IT automation to the entire organization as a strategic, accessible asset for more users — not just the subject-matter experts.

How does IBM Watson work?

IBM Watson is deployed across a cluster of IBM Power servers, each running an instance of the software, and these servers communicate and work together to process large amounts of data and carry out complex tasks.

As a result, Watson can leverage parallel processing, meaning it can perform many tasks simultaneously by distributing them across multiple servers. Its parallel processing capability makes Watson extremely scalable, as additional servers can be added to the cluster to increase its processing power.

Use cases of IBM Watson

IBM Watson was initially developed to take months of research and provide answers within a few seconds or minutes. As a showcase of its processing power, in 2011, Watson won against human contestants on the game show Jeopardy!

Two years later, IBM announced the first commercial application of Watson with cancer research. Since then, IBM Watson has since become a common choice for various industries, including healthcare, engineering, education and entertainment.

Healthcare

Watson’s ability to process and understand large amounts of complex data makes it extremely valuable in healthcare. Watson can analyze medical literature, clinical guidelines and patient records to assist doctors in diagnosing diseases and suggesting treatments.

For example, the Watson for Oncology application can analyze a patient’s medical information and provide a list of potential treatment options based on current medical guidelines and data from similar patients.

Finance

Watson has been used in the financial industry to enhance customer service, risk management and financial forecasting. Watson’s natural language processing capabilities in customer service can build sophisticated chatbots that handle customer inquiries.

In addition, Watson can analyze various data sources for risk management to detect potential signs of fraud or other financial risks. Watson’s machine learning algorithms can also create more accurate financial forecasts by analyzing market trends and other relevant data.

Retail

Watson’s machine learning and natural language processing abilities create personalized shopping experiences in retail. For instance, Watson can analyze a customer’s shopping history and preferences to recommend products they might be interested in. Watson can also optimize inventory management by analyzing sales data and predicting future demand trends.

Who should use IBM Watson?

IBM Watson is one of the best choices for data scientists who want to develop unique algorithms and queries, as it can derive answers based on a variety of industry knowledge bases. As such, IBM Watson can be applied to different public sector organizations, companies and institutes.

IBM Watson APIs allows developers to incorporate advanced AI functionalities into their applications without developing and maintaining complex AI models. This can significantly accelerate development timelines and empower developers to build more intelligent and innovative solutions.

IBM Watson vs. other analytics applications

IBM Watson stands out from other analytics applications because it focuses on artificial intelligence and cognitive computing capabilities. While traditional analytics applications provide valuable insights based on historical data and statistical methods, Watson goes beyond that by incorporating AI, machine learning, natural language processing and other advanced technologies:

  • AI-powered insights: IBM Watson’s AI offers the ability to analyze unstructured data like text, images and audio to provide deeper insights from diverse data sources, unlocking valuable information that would otherwise remain untapped.
  • Natural language understanding: NLU capabilities enable users to interact with systems using natural language queries, making IBM Watson more user-friendly and accessible to a broader range of users and allowing nontechnical stakeholders to gain insights and make data-driven decisions easily.
  • Machine learning integration: IBM Watson seamlessly integrates with ML algorithms, allowing users to build predictive models and perform advanced analytics tasks as well as streamlining the process of developing and deploying AI-driven solutions.
  • Domain-specific solutions: IBM Watson’s domain-specific applications come with pretrained models, making it quicker for businesses to adopt AI in their specific fields.
  • Natural language generation: IBM Watson’s NLG capabilities enable it to generate human-like written responses or summaries, which can be beneficial for creating reports, communicating insights and automating content generation.
  • Other analytics applications may excel in providing traditional business intelligence and data analysis capabilities, but IBM Watson’s AI and cognitive computing capabilities set it apart. All in all, IBM Watson allows organizations to derive deeper insights, automate processes and drive innovation in various industries and use cases.

    IBM Watson’s competitors MATLAB

    MATLAB is a high-level programming language and interactive environment primarily used for numerical computing, data analysis, visualization and algorithm development. It allows users to perform complex mathematical operations and create applications for various scientific and engineering applications.

    RapidMiner

    RapidMiner is a powerful and user-friendly data science platform that enables organizations to efficiently build, deploy and operationalize predictive models and advanced analytics. It offers a visual workflow interface that simplifies the entire data mining and machine learning process, making it accessible to data scientists and business users.

    Vertex AI

    Vertex AI is a machine learning platform offered by Google Cloud that simplifies and accelerates the development and deployment of AI models. It provides a unified interface for data preparation, training, evaluation and deployment. This makes it easier for developers and data scientists to build scalable and production-ready machine-learning solutions.

    Alteryx

    Alteryx is a self-service data analytics platform that empowers users to prepare, blend and analyze data from various sources without the need for complex coding. Alteryx also offers a visual workflow interface that enables data blending, advanced analytics and data science tasks. This makes it accessible to both data analysts and business users for faster and more insightful decision-making.

    SEE: Explore how IBM Watson compares to other competitors like Microsoft Azure and AWS IIoT Core.

    How can I get IBM Watson?

    Companies that can afford an investment into multiple millions of dollars can purchase an in-house IBM Watson system, which consists of several servers tethered together into a processing cluster. For companies without these resources, Watson can be accessed for free through the IBM cloud. For example, IBM offers a software developer’s cloud powered by Watson. It also provides a cloud-based global healthcare analytics cloud.


    Net worth

    Learn about the net worth of celebrities from movies, TV and streaming, music, social media, and even the royal family. 

    Read about how much the richest celebrities get paid for blockbuster movies, album sales, investments, and endorsements. Stay up-to-date on breakout stars as they build their fortunes. And then see how these celebrities spend that wealth in extravagant ways as Showbiz Cheat Sheet takes you behind the numbers.

    Go inside celebrity spending habits like Kylie Jenner‘s $72.8 million custom private jet, Jennifer Lawrence‘s mansion in an exclusive Beverly Hills neighborhood, and Yellowstone creator Taylor Sheridan‘s quarter-million-acre ranch.

    Get the lowdown on the list of celebrities with the highest net worth. See side-by-side comparisons of stars from the Marvel Cinematic Universe, pop music, and more. Get the scoop on celebrity chefs and reality TV stars earning millions with their careers.

    Curious about your favorite celebrity couple? The numbers get astronomical as you discover the combined assets of Jay-Z and Beyoncé, Tom Brady and Giselle Bündchen, or Prince Harry and Kate Middleton.


     




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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