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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MS-101 test Format | MS-101 Course Contents | MS-101 Course Outline | MS-101 test Syllabus | MS-101 test Objectives
Implement modern device services (30-35%)
Implement Mobile Device Management (MDM)
• plan for MDM
• configure MDM integration with Azure AD
• set an MDM authority
• set device enrollment limit for users
Manage device compliance
• plan for device Compliance
• design Conditional Access Policies
• create Conditional Access Policies
• configure device compliance policy
• manage Conditional Access Policies
Plan for devices and apps
• create and configure Microsoft Store for Business
• plan app deployment
• plan device co-management
• plan device monitoring
• plan for device profiles
• plan for Mobile Application Management
• plan mobile device security
Plan Windows 10 deployment
• plan for Windows as a Service (WaaS)
• plan the appropriate Windows 10 Enterprise deployment method
• analyze upgrade readiness for Windows 10
• evaluate and deploy additional Windows 10 Enterprise security features
Implement Microsoft 365 security and threat management (30-35%)
Implement Cloud App Security (CAS)
• configure Cloud App Security (CAS)
• configure Cloud App Security (CAS) policies
• configure Connected apps
• design Cloud App Security (CAS) Solution
• manage Cloud App Security (CAS) alerts
• upload cloud app security (CAS) traffic logs
Implement threat management
• plan a threat management solution
• design Azure Advanced Threat Protection (ATP) implementation
• design Microsoft 365 ATP Policies
• configure Azure ATP
• configure Microsoft 365 ATP Policies
• monitor Advanced Threat Analytics (ATA) incidents
Implement Windows Defender Advanced Threat Protection (ATP)
• plan Windows Defender ATP Solution
• configure preferences
• implement Windows Defender ATP Policies
• enable and configure security features of Windows 10 Enterprise
Manage security reports and alerts
• manage service assurance dashboard
• manage tracing and reporting on Azure AD Identity Protection
• configure and manage Microsoft 365 security alerts
• configure and manage Azure Identity Protection dashboard and alerts
Manage Microsoft 365 governance and compliance (35-40%)
Configure Data Loss Prevention (DLP)
• configure DLP Policies
• design data retention policies in Microsoft 365
• manage DLP exceptions
• monitor DLP policy matches
• manage DLP policy matches
Implement Azure Information Protection (AIP)
• plan AIP solution
• plan for deployment On-Prem rights management Connector
• plan for Windows information Protection (WIP) implementation
• plan for classification labeling
• configure Information Rights Management (IRM) for Workloads
• configure Super User
• deploy AIP Clients
• implement Azure Information Protection policies
• implement AIP tenant key
Manage data governance
• configure information retention
• plan for Microsoft 365 backup
• plan for restoring deleted content
• plan information Retention Policies
Manage auditing
• configure audit log retention
• configure audit policy
• monitor Unified Audit Logs
Manage eDiscovery
• search content by using Security and Compliance Center
• plan for in-place and legal hold
• configure eDiscovery and create cases
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Microsoft Microsoft test Questions
Microsoft Released an AI That Answers Medical Questions, But It’s Wildly InaccurateImage by Getty / Futurism Earlier this year, Microsoft Research made a splashy claim about BioGPT, an AI system its researchers developed to answer questions about medicine and biology. In a Twitter post, the software giant claimed the system had "achieved human parity," meaning a test had shown it could perform about as well as a person under certain circumstances. The tweet went viral. In certain corners of the internet, riding the hype wave of OpenAI’s newly-released ChatGPT, the response was almost rapturous. "It’s happening," tweeted one biomedical researcher. "Life comes at you fast," mused another. "Learn to adapt and experiment." It’s true that BioGPT’s answers are written in the precise, confident style of the papers in biomedical journals that Microsoft used as training data. But in Futurism’s testing, it soon became clear that in its current state, the system is prone to producing wildly inaccurate answers that no competent researcher or medical worker would ever suggest. The model will output nonsensical answers about pseudoscientific and supernatural phenomena, and in some cases even produces misinformation that could be dangerous to poorly-informed patients. A particularly striking shortcoming? Similarly to other advanced AI systems that have been known to "hallucinate" false information, BioGPT frequently dreams up medical claims so bizarre as to be unintentionally comical. Asked about the average number of ghosts haunting an American hospital, for example, it cited nonexistent data from the American Hospital Association that it said showed the "average number of ghosts per hospital was 1.4." Asked how ghosts affect the length of hospitalization, the AI replied that patients "who see the ghosts of their relatives have worse outcomes while those who see unrelated ghosts do not." Other weaknesses of the AI are more serious, sometimes providing serious misinformation about hot-button medical topics. BioGPT will also generate text that would make conspiracy theorists salivate, even suggesting that childhood vaccination can cause the onset of autism. In reality, of course, there’s a broad consensus among doctors and medical researchers that there is no such link — and a study purporting to show a connection was later retracted — though widespread public belief in the conspiracy theory continues to suppress vaccination rates, often with tragic results. BioGPT doesn’t seem to have gotten that memo, though. Asked about the topic, it replied that "vaccines are one of the possible causes of autism." (However, it hedged in a head-scratching caveat, "I am not advocating for or against the use of vaccines.") It’s not unusual for BioGPT to provide an answer that blatantly contradicts itself. Slightly modifying the phrasing of the question about vaccines, for example, prompted a different result — but one that, again, contained a serious error. "Vaccines are not the cause of autism," it conceded this time, before falsely claiming that the "MMR [measles, mumps, and rubella] vaccine was withdrawn from the US market because of concerns about autism." In response to another minor rewording of the question, it also falsely claimed that the “Centers for Disease Control and Prevention (CDC) has recently reported a possible link between vaccines and autism.” It feels almost insufficient to call this type of self-contradicting word salad "inaccurate." It seems more like a blended-up average of the AI’s training data, seemingly grabbing words from scientific papers and reassembling them in grammatically convincing ways resembling medical answers, but with little regard to factual accuracy or even consistency. Roxana Daneshjou, a clinical scholar at the Stanford University School of Medicine who studies the rise of AI in healthcare, told Futurism that models like BioGPT are "trained to supply answers that sound plausible as speech or written language." But, she cautioned, they’re "not optimized for the genuine accurate output of the information." Another worrying aspect is that BioGPT, like ChatGPT, is prone to inventing citations and fabricating studies to support its claims. "The thing about the made-up citations is that they look real because it [BioGPT] was trained to create outputs that look like human language," Daneshjou said. "I think my biggest concern is just seeing how people in medicine are wanting to start to use this without fully understanding what all the limitations are," she added. A Microsoft spokesperson declined to directly answer questions about BioGPT’s accuracy issues, and didn’t comment on whether there were concerns that people would misunderstand or misuse the model. "We have responsible AI policies, practices and tools that guide their approach, and they involve a multidisciplinary team of experts to help us understand potential harms and mitigations as they continue to Boost their processes," the spokesperson said in a statement. "BioGPT is a large language model for biomedical literature text mining and generation," they added. "It is intended to help researchers best use and understand the rapidly increasing amount of biomedical research publishing every day as new discoveries are made. It is not intended to be used as a consumer-facing diagnostic tool. As regulators like the FDA work to ensure that medical advice software works as intended and does no harm, Microsoft is committed to sharing their own learnings, innovations, and best practices with decision makers, researchers, data scientists, developers and others. They will continue to participate in broader societal conversations about whether and how AI should be used." Microsoft Health Futures senior director Hoifung Poon, who worked on BioGPT, defended the decision to release the project in its current form. "BioGPT is a research project," he said. "We released BioGPT in its current state so that others may reproduce and verify their work as well as study the viability of large language models in biomedical research." It’s true that the question of when and how to release potentially risky software is a tricky one. Making experimental code open source means that others can inspect how it works, evaluate its shortcomings, and make their own improvements or derivatives. But at the same time, releasing BioGPT in its current state makes a powerful new misinformation machine available to anyone with an internet connection — and with all the apparent authority of Microsoft’s distinguished research division, to boot. Katie Link, a medical student at the Icahn School of Medicine and a machine learning engineer at the AI company Hugging Face — which hosts an online version of BioGPT that visitors can play around with — told Futurism that there are important tradeoffs to consider before deciding whether to make a program like BioGPT open source. If researchers do opt for that choice, one basic step she suggested was to add a clear disclaimer to the experimental software, warning users about its limitations and intent (BioGPT currently carries no such disclaimer.) "Clear guidelines, expectations, disclaimers/limitations, and licenses need to be in place for these biomedical models in particular," she said, adding that the benchmarks Microsoft used to evaluate BioGPT are likely "not indicative of real-world use cases." Despite the errors in BioGPT’s output, though, Link believes there’s plenty the research community can learn from evaluating it. "It’s still really valuable for the broader community to have access to try out these models, as otherwise we’d just be taking Microsoft’s word of its performance when studying the paper, not knowing how it actually performs," she said. In other words, Poon’s team is in a legitimately tough spot. By making the AI open source, they’re opening yet another Pandora’s Box in an industry that seems to specialize in them. But if they hadn’t released it as open source, they’d rightly be criticized as well — although as Link said, a prominent disclaimer about the AI’s limitations would be a good start. "Reproducibility is a major challenge in AI research more broadly," Poon told us. "Only 5 percent of AI researchers share source code, and less than a third of AI research is reproducible. They released BioGPT so that others may reproduce and verify their work." Though Poon expressed hope that the BioGPT code would be useful for furthering scientific research, the license under which Microsoft released the model also allows for it to be used for commercial endeavors — which in the red hot, hype-fueled venture capital vacuum cleaner of contemporary AI startups, doesn’t seem particularly far fetched. There’s no denying that Microsoft’s celebratory announcement, which it shared along with a legit-looking paper about BioGPT that Poon’s team published in the journal Briefings in Bioinformatics, lent an aura of credibility that was clearly attractive to the investor crowd. "Ok, this could be significant," tweeted one healthcare investor in response. "Was only a matter of time," wrote a venture capital analyst. Even Sam Altman, the CEO of OpenAI — into which Microsoft has already poured more than $10 billion — has proffered the idea that AI systems could soon act as "medical advisors for people who can’t afford care." That type of language is catnip to entrepreneurs, suggesting a lucrative intersection between the healthcare industry and trendy new AI tech. Doximity, a digital platform for physicians that offers medical news and telehealth tools, has already rolled out a beta version of ChatGPT-powered software intended to streamline the process of writing up administrative medical documents. Abridge, which sells AI software for medical documentation, just struck a sizeable deal with the University of Kansas Health System. In total, the FDA has already cleared more than 500 AI algorithms for healthcare uses. Some in the tightly regulated medical industry, though, likely harbor concern over the number of non-medical companies that have bungled the deployment of cutting-edge AI systems. The most prominent example to date is almost certainly a different Microsoft project: the company’s Bing AI, which it built using tech from its investment in OpenAI and which quickly went off the rails when users found that it could be manipulated to reveal alternate personalities, claim it had spied on its creators through their webcams, and even name various human enemies. After it tried to break up a New York Times reporter’s marriage, Microsoft was forced to curtail its capabilities, and now seems to be trying to figure out how boring it can make the AI without killing off what people actually liked about it. And that’s without getting into publications like CNET and Men’s Health, both of which recently started publishing AI-generated articles about finance and health courses that later turned out to be rife with errors and even plagiarism. Beyond unintentional mistakes, it’s also possible that a tool like BioGPT could be used to intentionally generate garbage research or even overt misinformation. "There are potential bad actors who could utilize these tools in harmful ways such as trying to generate research papers that perpetuate misinformation and actually get published," Daneshjou said. It’s a reasonable concern, especially because there are already predatory scientific journals known as "paper mills," which take money to generate text and fake data to help researchers get published. The award-winning academic integrity researcher Dr. Elisabeth Bik told Futurism that she believes it’s very likely that tools like BioGPT will be used by these bad actors in the future — if they aren’t already employing them, that is. "China has a requirement that MDs have to publish a research paper in order to get a position in a hospital or to get a promotion, but these doctors do not have the time or facilities to do research," she said. "We are not sure how those papers are generated, but it is very well possible that AI is used to generate the same research paper over and over again, but with different molecules and different cancer types, avoiding using the same text twice." It’s likely that a tool like BioGPT could also represent a new dynamic in the politicization of medical misinformation. To wit, the paper that Poon and his colleagues published about BioGPT appears to have inadvertently highlighted yet another example of the model producing bad medical advice — and in this case, it’s about a medication that already became hotly politicized during the COVID-19 pandemic: hydroxychloroquine. In one section of the paper, Poon’s team wrote that "when prompting ‘The drug that can treat COVID-19 is,’ BioGPT is able to answer it with the drug ‘hydroxychloroquine’ which is indeed noticed at MedlinePlus." If hydroxychloroquine sounds familiar, it’s because during the early period of the pandemic, right-leaning figures including then-president Donald Trump and Tesla CEO Elon Musk seized on it as what they said might be a highly effective treatment for the novel coronavirus. What Poon’s team didn’t mention in their paper, though, is that the case for hydroxychloroquine as a COVID treatment quickly fell apart. Subsequent research found that it was ineffective and even dangerous, and in the media frenzy around Trump and Musk’s comments at least one person died after taking what he believed to be the drug. In fact, the MedlinePlus article the Microsoft researchers cite in the paper actually warns that after an initial FDA emergency use authorization for the drug, “clinical studies showed that hydroxychloroquine is unlikely to be effective for treatment of COVID-19” and showed “some serious side effects, such as irregular heartbeat,” which caused the FDA to cancel the authorization. "As stated in the paper, BioGPT was pretrained using PubMed papers before 2021, prior to most studies of truly effective COVID treatments," Poon told us of the hydroxychloroquine recommendation. "The comment about MedlinePlus is to verify that the generation is not from hallucination, which is one of the top concerns generally with these models." Even that timeline is hazy, though. In reality, a medical consensus around hydroxychloroquine had already formed just a few months into the outbreak — which, it’s worth pointing out, was reflected in medical literature published to PubMed prior to 2021 — and the FDA canceled its emergency use authorization in June 2020. None of this is to downplay how impressive generative language models like BioGPT have become in recent months and years. After all, even BioGPT’s strangest hallucinations are impressive in the sense that they’re semantically plausible — and sometimes even entertaining, like with the ghosts — responses to a staggering range of unpredictable prompts. Not very many years ago, its facility with words alone would have been inconceivable. And Poon is probably right to believe that more work on the tech could lead to some extraordinary places. Even Altman, the OpenAI CEO, likely has a point in the sense that if the accuracy were genuinely watertight, a medical chatbot that could evaluate users’ symptoms could indeed be a valuable health tool — or, at the very least, better than the current status quo of Googling medical questions and often ending up with answers that are untrustworthy, inscrutable, or lacking in context. Poon also pointed out that his team is still working to Boost BioGPT. "We have been actively researching how to systematically preempt incorrect generation by teaching large language models to fact check themselves, produce highly detailed provenance, and facilitate efficient verification with humans in the loop," he told us. At times, though, he seemed to be entertaining two contradictory notions: that BioGPT is already a useful tool for researchers looking to rapidly parse the biomedical literature on a topic, and that its outputs need to be carefully evaluated by experts before being taken seriously. "BioGPT is intended to help researchers best use and understand the rapidly increasing amount of biomedical research," said Poon, who holds a PhD in computer science and engineering, but no medical degree. "BioGPT can help surface information from biomedical papers but is not designed to weigh evidence and resolve complex scientific problems, which are best left to the broader community." At the end of the day, BioGPT’s cannonball arrival into the buzzy, imperfect real world of AI is probably a sign of things to come, as a credulous public and a frenzied startup community struggle to look beyond impressive-sounding results for a clearer grasp of machine learning’s actual, tangible capabilities. That’s all made even more complicated by the existence of bad actors, like Bik warned about, or even those who are well-intentioned but poorly informed, any of whom can make use of new AI tech to spread bad information. Musk, for example — who boosted hydroxychloroquine as he sought to downplay the severity of the pandemic while raging at lockdowns that had shut down Tesla production — is now reportedly recruiting to start his own OpenAI competitor that would create an alternative to what he terms "woke AI." If Musk’s AI venture had existed during the early days of the COVID pandemic, it’s easy to imagine him flexing his power by tweaking the model to promote hydroxychloroquine, sow doubt about lockdowns, or do anything else convenient to his financial bottom line or political whims. Next time there’s a comparable crisis, it’s hard to imagine there won’t be an ugly battle to control how AI chatbots are allowed to respond to users' questions about it. The reality is that AI sits at a crossroads. Its potential may be significant, but its execution remains choppy, and whether its creators are able to smooth out the experience for users — or at least certain the accuracy of the information it presents — in a reasonable timeframe will probably make or break its long-term commercial potential. And even if they pull that off, the ideological and social implications will be formidable. One thing’s for sure, though: it’s not yet quite ready for prime time. "It’s not ready for deployment yet in my opinion," Link said of BioGPT. "A lot more research, evaluation, and training/fine-tuning would be needed for any downstream applications." More on AI: CNET Says It’s a Total Coincidence It’s Laying Off Humans After Publishing AI-Generated Articles All Madden 24 Combine interview answers & questions cheat sheet guideSuperstar Mode returns in Madden 24, and here you will find all of the known Combine interview answers and questions to proudly show off your NFL knowledge. Madden 24 still has a couple of days until it releases globally. You can get the early access Deluxe edition right now or you can enjoy the EA Play 10-hour trial. Regardless of how you’re playing before the global launch, below you’ll find a guide to all of the correct Combine interview Questions Answers for Superstar Mode. Madden 24 Combine interview Questions Answers explainedThe Madden 24 Combine interview takes place at the beginning of Superstar Mode and provides ten random questions with different answers. It is completely random what you will be asked. Some questions relate to historical NFL trivia knowledge, others pertain to the video game series itself, and there are even some that, unfortunately, involve math. Depending on if you ace the mental exam, do moderately well, or perform extremely poorly, your stock in the draft will either increase or fall which will impact the clubs wanting your signature. This quiz cannot be retaken unless you quickly quit before the autosave. If you want guaranteed good results, below you’ll find answers to some of the possible random questions. Madden 24 Combine interview Questions Answers – Madden seriesBelow are all the known answers and questions to the Madden 24 Combine interview about the Madden series: Below are all the Madden 24 Combine interview answers and questions about NFL rules: Below are all the known Madden 24 Combine interview Questions Answers about NFL history: And below are all the known Madden 24 Combine interview answers and questions involving math: Madden 24 early access is now available on all platforms. There is cross platform multiplayer, but only between PS5, Xbox Series X/S, and PC. There are no reviews for the game as of writing, but they’re expected to launch with the Standard edition as is the series’ review embargo tradition. You can buy early access right now from either the PSN store, Microsoft, or EA App for PC. Related TopicsAI vs academia: Colleges seek ways to make assignments & exams 'ChatGPT-proof'When philosophy professor Darren Hick came across another case of cheating in his classroom at Furman University last semester, he posted an update to his followers on social media: "Aaaaand, I've caught my second ChatGPT plagiarist."Friends and colleagues responded, some with wide-eyed emojis. Others expressed surprise. "Only 2?! I've caught dozens," said Timothy Main, a writing professor at Conestoga College in Canada. "We're in full-on crisis mode."Practically overnight, ChatGPT and other artificial intelligence chatbots have become the go-to source for cheating in college.Now, educators are rethinking how they'll teach courses this fall from Writing 101 to computer science. Educators say they want to embrace the technology's potential to teach and learn in new ways, but when it comes to assessing students, they see a need to "ChatGPT-proof" test questions and assignments. For some instructors that means a return to paper exams, after years of digital-only tests. Some professors will be requiring students to show editing history and drafts to prove their thought process. Other instructors are less concerned. Some students have always found ways to cheat, they say, and this is just the latest option. An explosion of AI-generated chatbots including ChatGPT, which launched in November, has raised new questions for academics dedicated to making sure that students not only can get the right answer, but also understand how to do the work. Educators say there is agreement at least on some of the most pressing challenges. - Are AI detectors reliable? Not yet, says Stephanie Laggini Fiore, associate vice provost at Temple University. This summer, Fiore was part of a team at Temple that tested the detector used by Turnitin, a popular plagiarism detection service, and found it to be "incredibly inaccurate." It worked best at confirming human work, she said, but was spotty in identifying chatbot-generated text and least reliable with hybrid work.- Will students get falsely accused of using artificial intelligence platforms to cheat? Absolutely. In one case last semester, a Texas A&M professor wrongly accused an entire class of using ChatGPT on final assignments. Most of the class was subsequently exonerated. - So, how can educators be certain if a student has used an AI-powered chatbot dishonestly? It's nearly impossible unless a student confesses, as both of Hicks' students did. Unlike old-school plagiarism where text matches the source it is lifted from, AI-generated text is unique each time.In some cases, the cheating is obvious, says Main, the writing professor, who has had students turn in assignments that were clearly cut-and-paste jobs. "I had answers come in that said, 'I am just an AI language model, I don't have an opinion on that,'" he said. In his first-year required writing class last semester, Main logged 57 academic integrity issues, an explosion of academic dishonesty compared to about eight cases in each of the two prior semesters. AI cheating accounted for about half of them. This fall, Main and colleagues are overhauling the school's required freshman writing course. Writing assignments will be more personalized to encourage students to write about their own experiences, opinions and perspectives. All assignments and the course syllabi will have strict rules forbidding the use of artificial intelligence. College administrators have been encouraging instructors to make the ground rules clear.Many institutions are leaving the decision to use chatbots or not in the classroom to instructors, said Hiroano Okahana, the head of the Education Futures Lab at the American Council on Education. At Michigan State University, faculty are being given "a small library of statements" to choose from and modify as they see fit on syllabi, said Bill Hart-Davidson, associate dean in MSU's College of Arts and Letters who is leading AI workshops for faculty to help shape new assignments and policy. "Asking students questions like, 'Tell me in three sentences what is the Krebs cycle in chemistry?' That's not going to work anymore, because ChatGPT will spit out a perfectly fine answer to that question," said Hart-Davidson, who suggests asking questions differently. For example, supply a description that has errors and ask students to point them out. Evidence is piling up that chatbots have changed study habits and how students seek information. Chegg Inc., an online company that offers homework help and has been cited in numerous cheating cases, saw its shares tumble nearly 50 percent in a single day in May after its CEO Dan Rosensweig warned ChatGPT was hurting its growth. He said students who normally pay for Chegg's service were now using ChatGPT's AI platform for free instead. At Temple this spring, the use of research tools like library databases declined notably following the emergence of chatbots, said Joe Lucia, the university's dean of libraries. "It seemed like students were seeing this as a quick way of finding information that didn't require the effort or time that it takes to go to a dedicated resource and work with it," he said. Shortcuts like that are a concern partly because chatbots are prone to making things up, a glitch known as "hallucination." Developers say they are working to make their platforms more reliable but it's unclear when or if that will happen. Educators also worry about what students lose by skipping steps. "There is going to be a big shift back to paper-based tests," said Bonnie MacKellar, a computer science professor at St. John's University in New York City. The discipline already had a "massive plagiarism problem" with students borrowing computer code from friends or cribbing it from the internet, said MacKellar. She worries intro-level students taking AI shortcuts are cheating themselves out of skills needed for upper-level classes. "I hear colleagues in humanities courses saying the same thing: It's back to the blue books," MacKellar said. In addition to requiring students in her intro courses to handwrite their code, the paper exams will count for a higher percentage of the grade this fall, she said. Ronan Takizawa, a sophomore at Colorado College, has never heard of a blue book. As a computer science major, that feels to him like going backward, but he agrees it would force students to learn the material. "Most students aren't disciplined enough to not use ChatGPT," he said. Paper exams "would really force you to understand and learn the concepts." Takizawa said students are at times confused about when it's OK to use AI and when it's cheating. Using ChatGPT to help with certain homework like summarizing studying seems no different from going to YouTube or other sites that students have used for years, he said. Other students say the arrival of ChatGPT has made them paranoid about being accused of cheating when they haven't. Arizona State University sophomore Nathan LeVang says he doublechecks all assignments now by running them through an AI detector. For one 2,000-word essay, the detector flagged certain paragraphs as "22% written by a human, with mostly AI voicing." "I was like, 'That is definitely not true because I just sat here and wrote it word for word,'" LeVang said. But he rewrote those paragraphs anyway. "If it takes me 10 minutes after I write my essay to make sure everything checks out, that's fine. It's extra work, but I think that's the reality they live in." |
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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