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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Amazon SAA-C02 : AWS Certified Solutions Architect - Associate - 2023 exam Dumps

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Exam Number : SAA-C02
Exam Name : AWS Certified Solutions Architect - Associate - 2023
Vendor Name : Amazon
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SAA-C02 exam Format | SAA-C02 Course Contents | SAA-C02 Course Outline | SAA-C02 exam Syllabus | SAA-C02 exam Objectives

Exam Number : SAA-C02

Exam Name : AWS Certified Solutions Architect – Associate

Questions : 65 questions

Questions Type : Either multiple choice or multiple response

Exam Type : Associate

Delivery Method : Testing center or online proctored exam

Time Allowed : 130 minutes to complete the exam

Language : Available in English, Japanese, Korean, and Simplified Chinese

Recommended AWS Knowledge

 1 year of hands-on experience designing available, cost-effective, fault-tolerant, and scalable distributed systems on AWS.

 Hands-on experience using compute, networking, storage, and database AWS services.

 Hands-on experience with AWS deployment and management services.

 Ability to identify and define technical requirements for an AWS-based application.

 Ability to identify which AWS services meet a given technical requirement.

 Knowledge of recommended best practices for building secure and reliable applications on the AWS platform.

 An understanding of the basic architectural principles of building in the AWS Cloud.

 An understanding of the AWS global infrastructure.

 An understanding of network technologies as they relate to AWS.

 An understanding of security features and tools that AWS provides and how they relate to traditional services.

The AWS Certified Solutions Architect - Associate examination is intended for individuals who perform a solutions architect role and have one or more years of hands-on experience designing available, cost-efficient, fault-tolerant, and scalable distributed systems on AWS.

Abilities Validated by the Certification

Effectively demonstrate knowledge of how to architect and deploy secure and robust applications on AWS technologies

Define a solution using architectural design principles based on customer requirements

Provide implementation guidance based on best practices to the organization throughout the life cycle of the project

Recommended Knowledge and Experience

Hands-on experience using compute, networking, storage, and database AWS services

Hands-on experience with AWS deployment and management services

Ability to identify and define technical requirements for an AWS-based application

Ability to identify which AWS services meet a given technical requirement

Knowledge of recommended best practices for building secure and reliable applications on the AWS platform

An understanding of the basic architectural principles of building on the AWS Cloud

An understanding of the AWS global infrastructure

An understanding of network technologies as they relate to AWS

An understanding of security features and tools that AWS provides and how they relate to traditional services

Domain 1: Design Resilient Architectures 30%

Domain 2: Design High-Performing Architectures 28%

Domain 3: Design Secure Applications and Architectures 24%

Domain 4: Design Cost-Optimized Architectures 18%

TOTAL 100%

Domain 1: Design Resilient Architectures

1.1 Design a multi-tier architecture solution

1.2 Design highly available and/or fault-tolerant architectures

1.3 Design decoupling mechanisms using AWS services

1.4 Choose appropriate resilient storage

Domain 2: Design High-Performing Architectures

2.1 Identify elastic and scalable compute solutions for a workload

2.2 Select high-performing and scalable storage solutions for a workload

2.3 Select high-performing networking solutions for a workload

2.4 Choose high-performing database solutions for a workload

Domain 3: Design Secure Applications and Architectures

3.1 Design secure access to AWS resources

3.2 Design secure application tiers

3.3 Select appropriate data security options

Domain 4: Design Cost-Optimized Architectures

4.1 Identify cost-effective storage solutions

4.2 Identify cost-effective compute and database services

4.3 Design cost-optimized network architectures

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Amazon - Study Guide

Amazon turns AI models into study buddies

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Knowledge is power. According to Amazon, that extends to machine learning models, too.

The company wants to patent a system that transfers the knowledge of one machine learning model to another. Essentially, Amazon's patent allows for the knowledge of one machine learning model that's completed its training to be transferred to another model that's undergoing training, thereby speeding up the training of AI models.

"Generally, training a machine learning model, such as a reinforcement learning model, from scratch requires a huge amount of time and computing resources," the filing notes. "Thus, it is desirable to have techniques to Boost the learning speed of a machine learning model."

Amazon outlines two kinds of transfers: a representation transfer and an instance transfer. In a representation transfer, the knowledge that's given (such as how to perform a task or follow a policy) is based on the similarities between the "teacher model" and the "student model." Essentially, this system will distill the knowledge transferred to only the relevant information depending on those similarities.

Alternatively, in an instance transfer, the conveyed knowledge comes from "sampled trajectories" of the teacher model, or basically exact examples of its own inputs and outputs. The system automatically switches between doing representation transfers and instance transfers depending on the circumstance.

Amazon noted that this method could be applied to various AI training processes, including neural networks used for image processing and speech recognition.

Photo via the U.S. Patent and Trademark Office.

Knowledge transfer is far from a new technique in the machine learning community, said Rijul Gupta, co-founder of AI communications company Deep Media. Gupta gave the example of an AI model that can play chess versus one that can play checkers: The games are somewhat similar, so transferring knowledge between them wouldn't be difficult.

Most of Amazon's patent is quite broad, Gupta said, so it's unlikely that it would get approved without a decent amount of pushback and narrowing. In its current form, granting it would "essentially cripple everyone else's machine learning development," Gupta said.

But the filing isn't entirely old news. One piece that's novel is its ability to automatically choose which teacher model is the best fit for training the student model without having to actually go through training, said Gupta. Typically, this process is a costly and tedious process that's done by hand.

Secondly, Amazon's patent discusses a way to bridge the gap between teacher and student models that are more distinct from each other. For example, rather than making the chess algorithm play checkers, Gupta said, it could "take an algorithm that's built to play chess and make it play Fortnite."

Using transfer techniques, he said, could speed up machine learning development by as much as 95%, helping it catch companies like Google, Microsoft and OpenAI. Meanwhile, securing the patent in a more narrow form could send the company's AI development soaring, making it "very difficult for any of Amazon's competitors to catch up to them."

Amazon has thrown its hat into the AI ring in more ways than one: The company pledged $100 million toward a AWS Generative AI Innovation Center, put together a new team focused on large language models, and has filed for multiple AI-related patents. As it stands, though, it remains a step behind the AI heavyweights.

That said, Amazon's real strength lies in AWS, said Gupta, with tools like SageMaker and Bedrock, as well as Inferentia and Trainium, its custom in-house chips for training AI. Plus, as AWS CEO Adam Selipsky told The Verge in a podcast last week, "Cloud and AI are not two different things. They're really just two of the many faces of the same thing."

"There's an old saying that when there's a gold rush, you should sell shovels," said Gupta. "And Amazon, right now, is selling shovels."

Have any comments, tips or suggestions? Drop us a line! Email at [email protected] or shoot us a DM on Twitter @patentdrop. If you want to get Patent Drop in your inbox, click here to subscribe.

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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