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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VMware-Nvidia Team Core Infrastructure To Acclererate Generative AI

VMware CEO Rangarajan Raghuram (right) & Nvidia CEO Jensen Huang (left).


AI is everywhere. It’s in their apps, it’s on their smartphones, it’s developing new strains of neural know-how within the confines of the cloud datacenter and it’s rolling out across the ‘edge’ compute estate of smart devices in the Internet of Things (IoT). Because Artificial Intelligence (AI) is so ubiquitous, it is also various, multifarious and occasionally precarious (when they fail to eradicate AI bias and explainability) in its nature. Today, the biggest driving force in AI comes from the fact that it is now capable of working in ways that deliver not just predictive intelligence, but also generative intelligence.

We’re talking about generative-AI of course.

While they see every enterprise technology vendor on the planet now work to deliver a degree of this new smartness in its platform, applications and tools, it is compelling to look into the mechanics and infrastructure behind its delivery. Why is this so? Because this is not just an oil change, this is an engine refit in many senses i.e. while an AI accelerator can be a simple turbo-charge for some applications, many deployments of this technology will require data workload management at the infrastructure level in order for the technology to work to its full potential - or in some cases to work at all.

AI infrastructure first, smart apps second

Having spent the majority of its 25-year history working to provide IT infrastructure choice across storage services, networking and application management & virtualization, as well as being a key player in the cloud infrastructure management space, VMware is now continuing its systems level development by working with Nvidia to deliver core services that underpin new AI deployments. VMware and Nvidia have expanded their existing partnership to help enterprises that run on VMware’s cloud infrastructure to be ready for the era of generative AI.

While they normally understand the term ‘foundation’ to refer to some type of institution, enterprise IT firms sometimes like to use it to denote a base-level framework competency (Microsoft did it with Windows Communication Foundation back in 2006). Using that same style of naming protocol, VMware Private AI Foundation with Nvidia is designed to enable enterprises to customize foundational models (a technology they have explained here) and run generative AI applications. This could be smart apps that might include chatbots, intelligent assistants, search and summarization services - the latter being a way of using AI to categorize and filter masses of information that might exist in emails, for example. In this case, they see a platform that will exist as an integrated product featuring generative AI software and accelerated computing from Nvidia, built on VMware Cloud Foundation and optimized for AI.

“Generative AI and multi-cloud are the perfect match,” said Raghu Raghuram, CEO, VMware. “Customer data is everywhere — in [company] datacenters, at the [IoT] edge and in their clouds. Together with Nvidia, we’ll empower enterprises to run their generative AI workloads adjacent to their data - with confidence - while addressing their corporate data privacy, security and control concerns.”

Aligning AI adjacency

That point noting ‘adjacency’ to data’ is important. Speaking to press and analysts on a video call this week, VMware’s Paul Turner, vice president of product management vSphere and cloud platform echoed Raghuram’s sentiment by explaining how and why this adjacency is a reality.

“One of the things they believe companies will do now is to bring more of their generative AI workloads to their data, versus moving their data into public cloud services,” said Turner. “These same company’s may run some form of generative AI services with cloud service providers [in more public arenas], but they believe quite a lot of companies and a lot of major enterprises will want to run these technologies in relatively small [more restricted] environments. That way they protect themselves and they protect their data, which is their key asset.”

Jensen Huang, founder and CEO of Nvidia backs up the central messages being delivered here and says that, in the ‘race’ to integrate generative AI into businesses, his firm’s expanded collaboration with VMware will offer organizations the full-stack software and computing they need to unlock the potential of generative AI using custom applications built with their own data. All well and good so far then, but they wanted to know more about how these new strains of generative AI will be adequately supported at the infrastructure level.

A difference in inference

We know that the true power of generative AI happens when it can apply the scope of Large Language Model (LLM) data assets and produce human-like levels of inference i.e. this is intelligence that creates contextualized 'things' that have been inferred from an understanding of the other information around them. Talking about this area and how his firm's Graphical Processing Units (GPUs) work to accelerate the speed at which this intelligence is delivered, Justin Boitano, vice president of enterprise computing at Nvidia explained that his firm's latest Bluefield 3 GPUs deliver 1.4 times extra performance for generative AI on the inference side, more in some cases too.

"As they all know now, corporate data is the new asset, so how you manage your data, how you optimize your data, how you take models like LLaMA and bring that model to colocate within your data in your datacenter, all matters a lot," said Boitano. "We're seeing great innovation in this space [with technologies like pre-training, fine-tuning and in-context learning] to optimize generative AI tuning so that it is relevant to each and every business and is able to create new business value offerings. We're seeing this insight in VMware where we're looking at auto encoders so that they can take their API's and their SDKs, then feed them through an automation mechanism driven by Nvidia - and we're able to actually generate pretty good code samples. It needs further work. Of course, you need to then work on optimizing the model, but the capability and the capacity is there," he noted, in the same call to press and analysts.

As they move ahead here then, what do VMware and Nvidia think the key enabling technologies and identifying functions will be? For certain, analyst house McKinsey estimates that generative AI could add up to US$4.4 trillion annually to the global economy. Looking again at the technologies on offer here, VMware Private AI Foundation with Nvidia is designed to enable organizations to customize essentially open data based Large Language Models; produce more secure and private models for internal usage; and offer generative AI as-a-Service to users.

All of which, on a paper and in practice, will lead to an ability to securely run ‘inference workloads’ (the computing guts that delivers new human-like AI) at major scale. The platform is expected to include integrated AI tools to deliver customers what VMware calls ‘proven models’ trained on their private data in a cost-efficient manner. Being finalized now and rolled out through 2024, the technology here is built on VMware Cloud Foundation and Nvidia AI Enterprise software.

According to the Nvidia team, “The platform will feature Nvidia NeMo, an end-to-end, cloud-native framework included in Nvidia AI Enterprise — the operating system of the Nvidia AI platform — that allows enterprises to build, customize and deploy generative AI models virtually anywhere. NeMo combines customization frameworks, guardrail toolkits, data curation tools and pretrained models to offer enterprises an easy, cost-effective and fast way to adopt generative AI.”

Why infrastructure is rising

This story circulates around the central ways that generative AI is being enabled at the infrastructure level. Because VMware is also delivering functions as automations and assistants for network engineers and developers (the company likes to define its audience into platform teams, networking teams and end user teams), via natural language, the use of these technologies can also arguably broaden.

It’s a wider democratization of technology trend that VMware chief technology officer (CTO) Kit Colbert has explained very clearly. “The line between applications and infrastructure has changed. Things that used to be considered infrastructure (Kubernetes for cloud container orchestration is a good example) have now become infrastructure. Why? Because of the inherent standardization that has happened to make technologies at this level usable and popular in the first place,” said Colbert. “So now, what they must realize is, the infrastructure line itself is always rising.”

We can dovetail these thoughts with other new products from VMware. The company has now introduced a suite of technologies across VMware Tanzu, its modular cloud-native application development, delivery and operations optimization and visibility platform. Because Tanzu is modular and is underpinned by a common data platform and control with support for open interfaces, it enables broad ecosystem integrations. This is multi-cloud management technology that works with VMware’s own Aria product, a multi-cloud management portfolio for managing the cost, performance, configuration and delivery of infrastructure and applications.

“Tanzu and Aria are now evolving into the next generation of Tanzu Application Platform and the new Tanzu Intelligence Services. With an application-centric focus and integration through common data and controls, VMware Tanzu is providing a streamlined platform engineering and cloud operations experience and better software agility,” said Purnima Padmanabhan, senior vice president and general manager, modern apps and management business group at VMware.

Padmanabhan explains that VMware is announcing Tanzu Application Platform to now combines new innovations for platform engineering and operations with the existing capabilities of Tanzu for Kubernetes operations to help companies deliverwhat she calls a ‘world-class’ internal platform.

“Managing applications across clouds is a web of data and technology complexity. Distributed silos of tools and data make it difficult to gain visibility into the dependencies between applications, infrastructure and services. Centralizing management of these disparate systems and enabling shared data helps eliminate silos. This empowers teams to respond more quickly to issues and to continuously tune applications and environments using deep and actionable insights,” notes Padmanabhan and team, in a technical statement.

What is VMware now?

All of which developments analyzed and offered here hopefully clarify some of how the backroom engines running the new breed of generative AI (and indeed, old fashioned predictive AI) will work.

Does that mean VMware is becoming a company that will start to now offer generative AI applications and services in a tangible sense?

No, says CEO Raghuram… and VMware probably wouldn’t ever want to anyway i.e. it wants to do what it has always done which is to provide a competent and all encompassing infrastructure offering that enables firms to always have choice across server, networks, applications, cloud and now Large Language Models in the world of generative AI. It’s a logical enough progression and there is a large product portfolio here ‘underpinning’ this technology proposition - pun not intended, but useful nonetheless - it all starts with infrastructure.

'Private AI' Leads AI News at VMware Explore 2023


'Private AI' Leads AI News at VMware Explore 2023
  • By David Ramel
  • 08/22/2023
  • The expected deluge of AI-related news at this week's big VMware Explore 2023 conference sees a "Private AI" initiative with chipmaker NVIDIA, along with AI integrations to the Anywhere Workspace platform and much more.

    Private AI is an architectural approach that VMware introduced to help enterprises use generative AI with their private data. The company said it provides privacy, choice, performance and compliance for AI services while also enabling organizations to run various AI solutions on VMware Cloud infrastructure in different environments.

    "The remarkable potential of generative AI cannot be unlocked unless enterprises are able to maintain the privacy of their data and minimize IP risk while training, customizing, and serving their AI models," said company CEO Raghu Raghuram in an Aug. 22 post. "With VMware Private AI, they are empowering their customers to tap into their trusted data so they can build and run AI models quickly and more securely in their multi-cloud environment."

    AI in general and Private AI in particular were subjects addressed in Raghuram's opening keynote address for the conference, which is covered in the Virtualization & Cloud Review article, "VMware Explore: VMware (and NVIDIA) Execs Promise To Deliver on Generative AI's Promise."

    Private AITo realize the new Private AI offerings, VMware announced:

  • VMware Private AI Foundation with NVIDIA, extending the companies' strategic partnership to ready enterprises that run VMware's cloud infrastructure for the next era of generative AI. That's done via a set of integrated AI tools that can help enterprises run proven models trained on their private data in a cost-efficient manner while enabling them to be deployed in datacenters, on leading cloud computing platforms, and at the edge.
  • VMware Private AI Reference Architecture for Open Source to help customers achieve their desired AI outcomes by supporting best-in-class open source software (OSS) technologies today and in the future. The initiative integrates OSS technologies to provide an open reference architecture for building and serving OSS models on top of VMware Cloud Foundation. At the conference, VMware said it's showcasing collaborations with leading companies from across the AI value chain thusly:
  • Anyscale: VMware is bringing the widely adopted open source Ray unified compute framework to VMware Cloud environments. Ray on VMware Cloud Foundation makes it easy for data scientists and MLOps engineers to scale AI and Python workloads much more easily by utilizing their current compute footprints for ML workloads instead of defaulting to the public cloud.
  • Domino Data Lab: VMware, Domino Data Lab and NVIDIA have teamed up to provide a unified analytics, data science, and infrastructure platform that is optimized, validated, and supported, purpose-built for AI/ML deployments in the financial services industry.
  • Global Systems Integrators: VMware is working with leading GSIs such as Wipro and HCL to help customers realize the benefits of Private AI by building and delivering turnkey solutions that combine VMware Cloud with AI partner ecosystem solutions.
  • Hugging Face: VMware is collaborating with Hugging Face to help launch SafeCoder today at VMware Explore. SafeCoder is a complete commercial code assistant solution built for the enterprise that includes service, software and support. VMware is utilizing SafeCoder internally and publishing a reference architecture with code samples to enable the fastest possible time-to-value for customers when deploying and operating SafeCoder on VMware infrastructure. Read the full launch blog here.
  • Intel: VMware vSphere/vSAN 8 and Tanzu are optimized with Intel's AI software suite to take advantage of the new built-in AI accelerators on the latest 4th Gen Intel Xeon Scalable processors.
  • VMware Anywhere Workspace and 'Autonomous Workspaces'In addition to Private AI, VMware announced the advancement of its Autonomous Workspace vision via AI-powered integrations with its Anywhere Workspace platform, which is part of the VMware Cross-Cloud services portfolio that VMware said automatically optimizes employee experience, drives new vulnerability management use cases and simplifies application lifecycle management.

    The Autonomous Workspace idea was introduced in last year's event as covered by Tom Fenton, who noted, "These workspaces will be self-configuring, self-healing and self-securing."

    VMware noted the progress made over the past year since that announcement. "Last year, they announced their Autonomous Workspace vision as a path forward for organizations to navigate the challenges brought on by hybrid work," said EUC exec Shankar Iyer. "We are thrilled to unveil new advancements for their customers that expand data sources and insights, integrate with technology partners for improved security, and unify app delivery strategy across all virtual desktops and apps. These innovations continue to progress their vision of providing the next evolution of digital workspaces."

    AI EverywhereWhile the Private AI initiative and Anywhere Workspace AI integrations were noteworthy announcements at the start of the conference, many other AI-related developments were detailed, including:

  • Tanzu: VMware Tanzu is a modular, cloud-native application platform that accelerates development, delivery and operations across multiple clouds. The offering received several enhancements, two of which are powered by AI:
  • VMware Tanzu with Intelligent Assist (tech preview) will Excellerate operations from reactive to proactive and dramatically streamlines operational workflows by providing a conversational chatbot experience powered by generative AI that leverages the federated data architecture across VMware Tanzu Hub's integrated solutions.
  • VMware Tanzu Insights will empower operations teams to better troubleshoot and resolve issues with holistic visibility and deeper ML/AI powered insights across distributed Kubernetes, Amazon Web Services (AWS), and Microsoft Azure environments. Initial availability planned for Q3 FY24.
  • Digital Exployee Experience (DEX) VMware says its AI-driven DEX solution from VMware Workspace ONE Intelligence is the only platform that delivers a comprehensive solution to create a seamless hybrid work experience for employees and empower IT teams to be more proactive with a holistic tool that measures, analyzes, delivers and remediates workplace experiences.

    The VMware Workspace ONE DEX solution now supports broader data capture that empowers IT teams with holistic visibility across their end-user computing (EUC) environment, said VMware, which listed three examples:

  • New network-based telemetry. The hybrid workforce is reliant on SaaS services for maximum productivity, and outages can have a significant impact. The DEX solution conducts synthetic testing that can detect outages of internal or external services before users experience them. These capabilities can prevent an influx of inbound tickets and automate resolution with proactive communications to affected employees.
  • Expanded Android battery and network analytics. Now collected via Launcher on frontline devices, the near real-time data (every 10 minutes) allows admins to identify devices with problems. The issues can include poor Wi-Fi signal or cellular strength, high latency or jitter, and even batteries that are too hot, have a high discharge rate, or never charge to 100 percent.
  • Horizon in-guest session and network monitoring. This solution alerts VMware Horizon admins to common issues, such as long logon time, to help find root cause via its Insights and Guided Root Cause Analysis features. The network performance data helps Horizon admins quickly determine if the poor experience is caused by a remote user's network or high resource consumption by the virtual machines.
  • Next-Generation Insights:The last item above mentions Insights, described by the company as AI-driven anomaly detection that informs IT admins of environmental issues."We are excited to announce today that they have released an expanded set of out-of-the-box insights across more data sources, with an underlying re-architecture that enables measurement on dynamic thresholds while also accounting for seasonality," the company said, listing insights support for:
  • Horizon last-mile performance
  • Workspace ONE Unified Endpoint Management (UEM) for app or OS install failures and forecasting
  • Workspace ONE Access for logon trends and failures
  • Frontline use cases for long check-in/check-out time or increases in lost devices or devices not checked in
  • Stay tuned for more AI-related news from the conference, which concludes on Thursday.

    About the Author

    David Ramel is an editor and writer for Converge360.

    VMware Announces Plethora of AI Offerings to Support Generative AI Adoption

    Aug 23, 2023

    By Stephanie Simone

    VMware, Inc. is launching new Private AI offerings to drive enterprise adoption of generative artificial intelligence and tap into the value of trusted data. The announcement was made at VMware Explore 2023.

    Private AI is an architectural approach that unlocks the business gains from AI with the practical privacy and compliance needs of an organization, according to the company.

    To make Private AI a reality for enterprises and fuel a new wave of AI-enabled applications, VMware announced:

  • VMware Private AI Foundation with NVIDIA, extending the companies’ strategic partnership to ready enterprises that run VMware’s cloud infrastructure for the next era of generative AI.
  • VMware Private AI Reference Architecture for Open Source to help customers achieve their desired AI outcomes by supporting best-in-class open source software (OSS) technologies today and in the future.
  • VMware Private AI is bringing compute capacity and AI models to where enterprise data is created, processed, and consumed, whether that is in a public cloud, enterprise data center, or at the edge.

    According to the company, with these new offerings, VMware is helping customers combine the flexibility and control required to power a new generation of AI-enabled applications that will help dramatically increase worker productivity, ignite transformation across major business functions, and drive economic impact.

    A multi-cloud environment is the foundation for this new class of AI-powered applications because it makes private yet highly distributed data easier to harness. VMware’s multi-cloud approach provides enterprises with greater choice and flexibility where AI models are built, customized with an enterprise’s private data, and consumed, while still enabling required security and resiliency across any environment.

    “The remarkable potential of generative AI cannot be unlocked unless enterprises are able to maintain the privacy of their data and minimize IP risk while training, customizing, and serving their AI models,” said Raghu Raghuram, CEO, VMware. “With VMware Private AI, they are empowering their customers to tap into their trusted data so they can build and run AI models quickly and more securely in their multi-cloud environment.”

    VMware Private AI Foundation with NVIDIA, comprised of a set of integrated AI tools, will empower enterprises to run proven models trained on their private data in a cost-efficient manner and will enable these models to be deployed in data centers, on leading public clouds, and at the edge.

    VMware Private AI Foundation with NVIDIA will integrate VMware’s Private AI architecture, built on VMware Cloud Foundation, with NVIDIA AI Enterprise software and accelerated computing.

    According to the company, the turnkey offering will provide customers with the accelerated computing infrastructure and cloud infrastructure software they need to customize models and run generative AI applications, including intelligent chatbots, assistants, search, and summarization. VMware Private AI Foundation with NVIDIA will be supported by Dell Technologies, Hewlett Packard Enterprise (HPE), and Lenovo.

    VMware Private AI Reference Architecture for Open Source integrates innovative OSS technologies to deliver an open reference architecture for building and serving OSS models on top of VMware Cloud Foundation.

    In addition, VMware is announcing a new VMware AI Ready program, which will connect ISVs with tools and resources needed to validate and certify their products on VMware Private AI Reference Architecture.

    The program will be available to ISVs focused on ML and LLM Ops, data and feature engineering, developer tools for AI, and embedded AI applications. This new program is expected to be live by the end of 2023.

    The company is also introducing Intelligent Assist, a family of generative AI-based solutions trained on VMware’s proprietary data to simplify and automate all aspects of enterprise IT in a multi-cloud era.

    The Intelligent Assist features will be seamless extensions of the investments enterprises have made in VMware Cross-Cloud Services and will be built upon VMware Private AI.

    VMware products with Intelligent Assist are expected to include:

  • VMware Tanzu with Intelligent Assist (Tech Preview) will address the challenges of multi-cloud visibility and configuration by allowing users to conversationally request and refine changes to their enterprise’s cloud infrastructure.
  • Workspace ONE with Intelligent Assist (Tech Preview) will empower users to create high-quality scripts using natural language prompts for a faster and more efficient script writing experience.
  • NSX+ with Intelligent Assist (Tech Preview) will allow security analysts to determine the relevance of security findings quickly and more accurately and effectively remediate threats.
  • For more information about this news, visit www.vmware.com.


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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 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.


    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:


    • 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


    • 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