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 : MB-240
Exam Name : Microsoft Dynamics 365 for Field Service
Vendor Name : Microsoft
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MB-240 test Format | MB-240 Course Contents | MB-240 Course Outline | MB-240 test Syllabus | MB-240 test Objectives


Candidates for this test are Microsoft Dynamics 365 Customer Engagement functional consultants with Field Service expertise. Candidates are responsible for implementing solutions that manage resources that complete the field service lifecycle.



Candidates implement the field service processes designed in collaboration with internal and external teams. This collaboration includes configuring the default administration areas of the Field Service application, deploying the Connected Field Service (IoT) solution and the mobile application, and implementing any additional needed customizations. Candidates are responsible for the configuration and deployment of the Field Service application in conjunction with the core customer service application.



Candidates must have knowledge of how to configure and customize components of the Field Service application, including configuring services, resources, characteristics, incidents, inventory integration, service agreements, resource scheduling, work orders, service tasks, field service user roles, field service settings, the Connected Field Service (IoT) solution, and configuration of the Field Service Mobile app.



Configure field service applications (20-25%)

Manage work orders (15-20%)

Schedule and dispatch work orders (20-25%)

Manage field service mobility (10-15%)

Manage inventory and purchasing (10-15%)

Manage assets and agreements (10-15%)



Configure field service applications (20-25%)

Configure general settings

 determine and configure field service security roles

 determine and configure resource scheduling options

 identify and configure field service entities

 determine and configure product and service categories

 configure territories, postal codes and organizational units

 determine and configure skill types

 configure characteristics and skills

 configure proficiency models

 implement resource roles and categories

 set defaults for work orders, bookings, the schedule board, and agreements

Configure product and service pricing

 create a product or service

 configure minimum charge amount and duration

 add products and services to incidents

 determine when a product has a default list price and a work order price list

 associate products and services with price lists

 define tax codes

 set up price list validity dates

Configure bookable resources

 manage bookable resources

 enable mapping functionality

 configure geocoding

 define start and end locations for resources

 define work hours for resources

 set up bookable resource categories, characteristics, and resource pay types

 set up bookable resource territories

 set up resource pools and crews

Integrate other tools with Field Service

 determine uses for Remote Assist

 benefits of using Resource Scheduling Optimization

 benefits of Connected Field Service

 using Push Notifications on the mobile app



Manage work orders (15-20%)

Describe the work order lifecycle

 configure work orders, work order types, and priority

 configure work order lifecycle stages

 configure booking status and work order status values

 configure uses and capabilities for billing accounts, service accounts, and sub-accounts

Create and manage work orders

 create a work order from an incident or an opportunity

 add status and sub-status information to a work order

 organize work orders and resources by geography

 associate a work order and a price list

 identify processes to close a work order

Manage incidents

 configure incident types

 configure service task types

 add service tasks, products, and services

 configure Requirement Groups

 attach Guides to Service Tasks



Schedule and dispatch work orders (20-25%)

Manage scheduling options

 schedule work orders using Schedule Board and Schedule Assistant

 determine when to use each scheduling option

 enable scheduling board geocoding

 manually schedule work orders

 reassign and reschedule work orders

 move incomplete work orders

 understand resource utilization

Implement the Schedule Board

 identify features and uses for Booking Requirements view

 implement the integrated map feature

 create additional schedule boards

 configure schedule boards

 customize the schedule board

 define booking rules

 facility Scheduling

Implement the Schedule Assistant

 apply constraints to resource queries

 filter data

 specify a search radius

Configure Resource Scheduling Optimization (RSO)

 create optimizing profiles

 define objectives and constraints

 configure optimization of resources

 geocoding for travel time

Configure Universal Resource Scheduling

 determine Universal Resource Scheduling use scenarios

 components of Universal Resource Scheduling

 configure URS for field service

 fields passed from work order to requirements

 create requirement views

 configure an entity to be schedulable



Manage field service mobility (10-15%)

Install and configure the mobile app

 configure actions that field agents can perform

 configure the mobile app

 configure display customizations

 assign security roles

 administer connected devices

 integrate with Dynamics 365 Guides

 add Guides to HoloLens app

Manage mobile projects

 identify project artifacts that can be modified or edited

 modify configuration settings

 publish a mobile project

 Mobile Device Management, security and synchronization



Manage inventory and purchasing (10-15%)

Manage inventory

 set up inventory and warehouses

 view product inventory

 adjust inventory levels

 transfer inventory

 determine when to integrate inventory with Enterprise Resource Planning application

Manage purchasing and product returns

 determine the purchase order process

 create purchase orders

 create a list of receivable products for a purchase order

 determine product return options

 create return merchandise authorizations (RMAs) and return to vendor (RTV)

 finalize returns



Manage assets and agreements (10-15%)

Create and manage agreements

 determine when to use agreements

 define and configure agreement preferences and settings

 configure automatic generation of bookings

 configure automatic generation of invoices

 define Service Level Agreements (SLAs)

 enable SLAs

 add an SLA to a Work Order

 using Entitlements

Manage customer assets

 create and register customer assets

 configure products to auto-create customer assets

 associate work orders with customer assets

 create child assets

 determine opportunities for Internet of Things (IoT)

 configure Connected Field Service

 manage IoT devices



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What Microsoft’s custom silicon means for Azure

The history of modern software development has been a dance between what hardware can deliver and what software demands. Over the decades, the steps in this dance have moved us from the original Intel 8086, which they now consider very basic functionality, to today’s multi-faceted processors, which provide virtualization support, end-to-end access to encrypted memory and data, and extended instruction sets that power the most demanding application stacks.

This dance swings from side to side. Sometimes their software has to stretch to meet the capabilities of a new generation of silicon, and sometimes it has to squeeze out every last ounce of available performance. Now, we’re finally seeing the arrival of a new generation of hardware that mixes familiar CPUs with new system-level accelerators that provide the ability to run complex AI models on both client hardware and servers, both on premises and in the public cloud.

You’ll find AI accelerators not only in the familiar Intel and AMD processors but also in Arm’s latest generation of Neoverse server-grade designs, which mix those features with low power demands (as do Qualcomm’s mobile and laptop offerings). It’s an attractive combination of features for hyperscale clouds like Azure, where low power and high density can help keep costs down while allowing growth to continue.

At the same time, system-level accelerators promise an interesting future for Windows, allowing us to use on-board AI assistants as an alternative to the cloud as Microsoft continues to Excellerate the performance of its Phi series of small language models.

Azure Boost: Silicon for virtualization offload

Ignite 2023 saw Microsoft announce its own custom silicon for Azure, hardware that should start rolling out to customers in 2024. Microsoft has been using custom silicon and FPGAs in its own services for some time now. The use of Zipline hardware compression and Project Brainwave FPGA-based AI accelerators are good examples. The most recent arrival is Azure Boost, which offloads virtualization processes from the hypervisor and host OS to accelerate storage and networking for Azure VMs. Azure Boost also includes the Cerberus on-board supply chain security chipset.

Azure Boost is intended to deliver your virtual machine workloads access to as much of the available CPU as possible. Instead of using CPU to compress data or manage security, dedicated hardware takes over, allowing Azure to run more customer workloads on the same hardware. Running systems at high utilization is key to the economics of the public cloud, and any investment in hardware will quickly be paid off.

Maia 100: Silicon for large language models

Large language models (and generative AI generally) show the importance of dense compute, with OpenAI using Microsoft’s GPU-based supercomputer to train its GPT models. Even on a system like Microsoft’s, big foundation models like GPT-4 require months of training, with more than a trillion parameters. The next generation of LLMs will need even more compute, both for training and for operation. If we’re building grounded applications around those LLMs, using Retrieval Augmented Generation, we’ll need additional capacity to create embeddings for their source content and to provide the underlying vector-based search.

GPU-based supercomputers are a significant investment, even when Microsoft can recoup some of the capital costs from subscribers. Operational costs are also large, with hefty cooling requirements on top of power, bandwidth, and storage. So, they might expect those resources to be limited to very few data centers, where there’s sufficient space, power, and cooling.

But if large-scale AI is to be a successful differentiator for Azure, versus competitors such as AWS and Google Cloud, it will need to be available everywhere and it will need to be affordable. That will require new silicon (for both training and inferencing) that can be run at higher densities and at lower power than today’s GPUs.

Looking back at Azure’s Project Brainwave FPGAs, these used programmable silicon to implement key algorithms. While they worked well, they were single-purpose devices that acted as accelerators for specific machine learning models. You could develop a variant that supported the complex neural networks of a LLM, but it would need to implement a massive array of simple processors to support the multi-dimensional vector arithmetic that drives these semantic models. That’s beyond the capabilities of most FPGA technologies.

Vector processing is something that modern GPUs are very good at (not surprisingly, as many of the original architects began their careers developing vector processing hardware for early supercomputers). A GPU is basically an array of simple processors that work with matrices and vectors, using technologies like Nvidia’s CUDA to provide access to linear algebra functions that aren’t commonly part of a CPU’s instruction set. The resulting acceleration lets us build and use modern AI models like LLMs.

Microsoft’s new custom AI accelerator chip, Maia 100, is designed for both training and inference. Building on lessons learned running OpenAI workloads, Maia is intended to fit alongside existing Azure infrastructure, as part of a new accelerator rack unit that sits alongside existing compute racks. With over 100 billion transistors delivered by a five-nanometer process, the Maia 100 is certainly a very large and very dense chip, with much more compute capability than a GPU.

The development of the Maia was refined alongside OpenAI’s models, and uses a new rack design that includes custom liquid-based cooling elements. That last part is key to delivering AI workloads to more than the largest Azure data centers. Adding liquid cooling infrastructure is expensive, so putting it in the Maia 100 racks ensures that it can be dropped into any data center, anywhere in the world.

Installing Maia 100 racks does require readjusting rack spacing, as the cooling system makes them larger than Azure’s typical 21-inch racks, which are sized for Open Compute Project servers. In addition to the liquid cooling hardware, the extra space is used for 4.8 Tb high-bandwidth interconnects, essential for pushing large amounts of data between CPUs and accelerators.

There are still questions about how applications will get to use the new chips. Absent additional details, it’s likely that they’ll run Microsoft-provided AI models, like OpenAI’s and Hugging Face’s, as well as their own Cognitive Services and the Phi small language models. If they become available to train your own models, expect to see a new class of virtual machines alongside the current range of GPU options in Azure AI Studio.

Cobalt 100: Azure’s own Arm processor

Alongside the unveiling of Maia, Microsoft announced its own Arm server processor, the Cobalt 100. This is a 128-core 64-bit processor, designed to support high-density, low-power applications, based on Arm’s Neoverse reference design. Azure is already using Arm processors for some of its platform services, and Cobalt 100 is likely to support these and more services, rather than being used for infrastructure as a service.

There’s no need to know if your Azure App Service code is running on Intel, AMD, or Arm, as long as it performs well and your users get the results they expect. They can expect to see Cobalt processors running internet-facing services, where density and power efficiency are important requirements, as well as hosting elements of Azure’s content delivery network outside of its main data centers.

Microsoft describes its silicon engineering as a way of delivering a “systems approach” to its Azure data centers, with end-to-end support from its initial storage and networking offerings to its own compute services. And it’s not only Azure. Better silicon is coming to Windows too, as NPU-enabled processors from Intel and Qualcomm start to arrive in 2024’s desktops and laptops. After many years of software leading hardware, it will be interesting to see how they can push these new platforms to their limits with code.

Copyright © 2024 IDG Communications, Inc.


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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