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 MLS-C01 : AWS Certified Machine Learning Specialty (MLS-C01) exam Dumps

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Exam Number : MLS-C01
Exam Name : AWS Certified Machine Learning Specialty (MLS-C01)
Vendor Name : Amazon
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Question Bank : Check Questions

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MLS-C01 exam Format | MLS-C01 Course Contents | MLS-C01 Course Outline | MLS-C01 exam Syllabus | MLS-C01 exam Objectives

Format : Multiple choice, multiple answer

Type : Specialty

Delivery Method : Testing center or online proctored exam

Time : 170 minutes to complete the exam

Introduction

The AWS Certified Machine Learning - Specialty (MLS-C01) examination is intended for individuals who perform a
development or data science role. This exam validates an examinees ability to build, train, tune, and deploy
machine learning (ML) models using the AWS Cloud.

It validates an examinees ability to design, implement, deploy, and maintain ML solutions for given business
problems. It will validate the candidates ability to:

 Select and justify the appropriate ML approach for a given business problem.

 Identify appropriate AWS services to implement ML solutions.

 Design and implement scalable, cost-optimized, reliable, and secure ML solutions

Expectations;

- 1-2 years of experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud

- The ability to express the intuition behind basic ML algorithms

- Experience performing basic hyperparameter optimization

- Experience with ML and deep learning frameworks

- The ability to follow model-training best practices

- The ability to follow deployment and operational best practices

Exam Content

There are two types of questions on the examination:

 Multiple choice: Has one correct response and three incorrect responses (distractors).

 Multiple response: Has two or more correct responses out of five or more options.

Select one or more responses that best complete the statement or answer the question. Distractors, or incorrect
answers, are response options that an examinee with incomplete knowledge or skill would likely choose. However,
they are generally plausible responses that fit in the content area defined by the test objective.

Unanswered questions are scored as incorrect; there is no penalty for guessing.

Unscored Content

Your examination may include unscored items that are placed on the test to gather statistical information. These
items are not identified on the form and do not affect your score.

Exam Results

The AWS Certified Machine Learning - Specialty (MLS-C01) examination is a pass or fail exam. The examination is
scored against a minimum standard established by AWS professionals who are guided by certification industry best
practices and guidelines.

Your results for the examination are reported as a score from 100–1,000, with a minimum passing score of 750.
Your score shows how you performed on the examination as a whole and whether or not you passed. Scaled
scoring models are used to equate scores across multiple exam forms that may have slightly different difficulty
levels.

Domain 1: Data Engineering 20%

Domain 2: Exploratory Data Analysis 24%

Domain 3: Modeling 36%

Domain 4: Machine Learning Implementation and Operations 20%

TOTAL 100%

Domain 1: Data Engineering

1.1 Create data repositories for machine learning.

1.2 Identify and implement a data-ingestion solution.

1.3 Identify and implement a data-transformation solution.

Domain 2: Exploratory Data Analysis

2.1 Sanitize and prepare data for modeling.

2.2 Perform feature engineering.

2.3 Analyze and visualize data for machine learning.

Domain 3: Modeling

3.1 Frame business problems as machine learning problems.

3.2 Select the appropriate model(s) for a given machine learning problem.

3.3 Train machine learning models.

3.4 Perform hyperparameter optimization.

3.5 Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

4.2 Recommend and implement the appropriate machine learning services and features for a given problem.

4.3 Apply basic AWS security practices to machine learning solutions.

4.4 Deploy and operationalize machine learning solutions.

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Build Trust in AI Now or Risk Policy Backlash, Tech Leaders Told

Tech leaders have to boost public trust in the use of artificial intelligence or risk increasing backlash by policymakers, analysts said Tuesday during a panel at the National Conference of State Legislatures summit in Indianapolis.

State lawmakers are increasingly eyeing potential regulations of the technology in the absence of federal action. Some states like Connecticut and Texas have established panels to research the topic. North Dakota banned legal personhood for AI. An effort to approve comprehensive regulations stalled in California earlier this year.

“That ability to earn that trust is going to really, I think, enable an ability for this technology to be deployed and used,” said Nicole Foster of Amazon Web Services during a discussion on AI and its policy implications. “I think regulation is going to be an important part of that, but I think they need to make sure that we’re targeting it in a way that they don’t lose the good.”

The panel discussion, which was sponsored by Amazon.com Inc., highlighted the positives of AI and how securing the confidence of consumers will be key to striking a balance between the risks and rewards of the emerging technology.

AI has provoked fears about robots replacing humans in the workplace, but Foster emphasized its current uses are mostly mundane. Zoom blurs out backgrounds. Netflix recommends videos. ChatGPT cranks out everyday correspondence for people on the go.

Connecticut state Sen. James Maroney (D), who moderated the panel, noted that AI has been around for decades in one form or another before the exact advancements. “AI started actually was originally discussed in 1955 at Dartmouth College,” he said.

Widespread public attention to the tech platform is relatively new, following the release of generative AI platforms like ChatGPT and Google’s Bard as well as the proliferation of deep fake images affecting politics, pornography, and even the stock market.

“I think where a lot of this consternation and fear and maybe even some frustration about whether or not they trust AI is coming from is the reality that this space is moving so fast and it is creating a lot of these questions around what is real and what is not,” said Chloe Autio of the Cantellus Group, an advisory firm on tech issues.

Transparency is a key building block for public confidence in wonky tech such as machine learning, large language models, and facial recognition software, Foster said. Disclosing that humans are interacting with AI is a good place to start, but consumers might also need to know whether any automated decision can be appealed to a fellow human, she added.

“There’s lots of privacy disclosures and people just click boxes, but I think it’s really important that as leaders you’ve demonstrated some due diligence,” Foster said. “How should this technology be deployed? How was it designed to be deployed?”

Integrating watermarks into images and videos generated by AI could also keep deep fake videos and images in check, Maroney said.

One audience member spoke of how a single mistake challenged his own confidence in AI as legislatures nationwide continue to debate how best to regulate the technology.

“I asked Bart a couple of months ago: ‘Who was the first female governor of Arkansas?’ And the answer I got was: ‘It was Bill Clinton’s wife, Hillary,’” Arkansas state Rep. Stephen Meeks (R) told the panelists. “Obviously, she was not the governor.”

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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