Riskoa

riskoa

Creating an AI Chatbot That Parses Financial Information

Author:
(1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Table of Links Abstract and Introduction History of Using AI in Education Research Methodology Literature Review Summary Conclusion and References 5. Summary The integration of generative AI models in education offer a wide range of potential benefits, including improved productivity, enhanced creativity, and personalized learning, providing timely feedback and support, and automating tasks (Farrokhnia et al., 2023; Cotton et al., 2023). The advantages of using generative AI models in education are summarised in Table 1. However, the adoption of generative AI models in education raises challenges that need to be tackled in order to guarantee a safe and responsible usage of such technologies (Su & Yang, 2023). These challenges are summarised in Table 2. Findings of this survey underscore the need for further research, regulatory policies, and pedagogical guidance to navigate issues related to academic integrity and equity in the era of AI enhanced learning (Chaudhry et al., 2023; Yan, 2023; Cotton et al., 2023). The irresponsible implementation of generative AI models in education have ethical and legal consequences (Chaudhry et al., 2023). Table 3 discusses potential ethical consideration for the adoption of Generative AI models in education. Whereas, Table 4 focuses on the legal implication. Governments around the world are developing regulations to address the ethical and legal challenges posed by generative AI models. For example, the ”European Union Artificial Intelligence Act (EU AI Act)” was proposed to promote the development and use of trustworthy AI, while also mitigating the risks associated with AI (Madiega, 2021). The Act does this by establishing a riskbased approach to AI regulation. AI systems are classified into four risk categories: ”unacceptable risk, high risk, limited risk, and minimal risk”. The EU AI Act includes the following provisions: (a) Risk Assessment: the AI Act mandates the assessment of risks associated with AI systems, ensuring that potential harms are identified and mitigated, (b) Transparency: transparency requirements are specified to enhance the explainability of AI systems and make their decisionmaking processes more understandable, the EU AI Act specified the following three transparency requirements when it comes to adopt generative AI technology: • Disclosure of AI Generation: The imperative for transparency in the context of generative AI pertains to the acknowledgment and notification that content has been produced by artificial intelligence rather than a human agent. Such disclosures serve to elucidate the nature of the content creation process, fostering user understanding and trust. • Mitigation of Illicit Content Generation: The design and implementation of generative AI models must encompass measures to forestall the generation of content that contravenes legal statutes. These measures are integral to ethical and legal compliance, involving content moderation and safety mechanisms to curtail the production of potentially harmful, offensive, or unlawful materials. • Publication of Summaries of Copyrighted Data Utilized in Training: In the sphere of AI model development, the utilization of copyrighted data engenders concerns pertaining to intellectual property rights. To address these concerns and exemplify transparency, developers may be encouraged to provide summaries or descriptions of the copyrighted data sources employed during training. This step showcases adherence to copyright laws while enhancing transparency regarding the model’s training data. and (c) Human Oversight: the AI Act emphasizes the importance of human oversight in AI systems to ensure responsible and ethical use. However, there are some limitations to the current regulatory landscape (Schuett, 2023). For example, the EU AI Act does not explicitly address the issue of generative AI models hallucination. Additionally, the EU AI Act focuses on the obligations of AI developers and users, rather than on the rights of individuals who may be affected by AI systems (Enqvist, 2023). Findings from literature also provide valuable insights into the determinants of intention to use generative AI models for educational purposes. The findings have important implications for the models developers, instructors, and universities as they work to accelerate the adoption of them in educational contexts. Table 5 discusses a set of recommendations for the different stakeholders fostering them to have a safe and responsible adoption of such technologies in education. The adoption or generative AI models in education requires a trust raising plan (Choudhury & Shamszare, 2023). In addition to addressing the forementioned challenges (See Table 2), there are a number of steps that can be done to build trust in generative AI, including: (1) Transparency: developers should be transparent about the limitations, biases, data sources, and potential risks of these models. (2) Explainability: developers should develop explainable AI methods to explain how their models generate outputs (Dosilovi ˇ c et al., 2018). (3) ´ Human-in-the-loop validation: human experts should be involved in the evaluation of AI-generated content to identify and rectify errors. This will help into solving other problems such as bias, and hallucination. (4) Accountability: developers and users of generative AI should be accountable for the consequences of their actions. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Author: Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Table of Links Abstract and Introduction Abstract and Introduction History of Using AI in Education History of Using AI in Education Research Methodology Research Methodology Literature Review Literature Review Summary Summary Conclusion and References Conclusion and References 5. Summary The integration of generative AI models in education offer a wide range of potential benefits, including improved productivity, enhanced creativity, and personalized learning, providing timely feedback and support, and automating tasks (Farrokhnia et al., 2023; Cotton et al., 2023). The advantages of using generative AI models in education are summarised in Table 1. However, the adoption of generative AI models in education raises challenges that need to be tackled in order to guarantee a safe and responsible usage of such technologies (Su & Yang, 2023). These challenges are summarised in Table 2. Findings of this survey underscore the need for further research, regulatory policies, and pedagogical guidance to navigate issues related to academic integrity and equity in the era of AI enhanced learning (Chaudhry et al., 2023; Yan, 2023; Cotton et al., 2023). The irresponsible implementation of generative AI models in education have ethical and legal consequences (Chaudhry et al., 2023). Table 3 discusses potential ethical consideration for the adoption of Generative AI models in education. Whereas, Table 4 focuses on the legal implication. Governments around the world are developing regulations to address the ethical and legal challenges posed by generative AI models. For example, the ”European Union Artificial Intelligence Act (EU AI Act)” was proposed to promote the development and use of trustworthy AI, while also mitigating the risks associated with AI (Madiega, 2021). The Act does this by establishing a riskbased approach to AI regulation. AI systems are classified into four risk categories: ”unacceptable risk, high risk, limited risk, and minimal risk”. The EU AI Act includes the following provisions: (a) Risk Assessment: the AI Act mandates the assessment of risks associated with AI systems, ensuring that potential harms are identified and mitigated, (b) Transparency: transparency requirements are specified to enhance the explainability of AI systems and make their decisionmaking processes more understandable, the EU AI Act specified the following three transparency requirements when it comes to adopt generative AI technology: • Disclosure of AI Generation: The imperative for transparency in the context of generative AI pertains to the acknowledgment and notification that content has been produced by artificial intelligence rather than a human agent. Such disclosures serve to elucidate the nature of the content creation process, fostering user understanding and trust. Disclosure of AI Generation: • Mitigation of Illicit Content Generation: The design and implementation of generative AI models must encompass measures to forestall the generation of content that contravenes legal statutes. These measures are integral to ethical and legal compliance, involving content moderation and safety mechanisms to curtail the production of potentially harmful, offensive, or unlawful materials. • Mitigation of Illicit Content Generation: • Publication of Summaries of Copyrighted Data Utilized in Training: In the sphere of AI model development, the utilization of copyrighted data engenders concerns pertaining to intellectual property rights. To address these concerns and exemplify transparency, developers may be encouraged to provide summaries or descriptions of the copyrighted data sources employed during training. This step showcases adherence to copyright laws while enhancing transparency regarding the model’s training data. • Publication of Summaries of Copyrighted Data Utilized in Training: and (c) Human Oversight: the AI Act emphasizes the importance of human oversight in AI systems to ensure responsible and ethical use. However, there are some limitations to the current regulatory landscape (Schuett, 2023). For example, the EU AI Act does not explicitly address the issue of generative AI models hallucination. Additionally, the EU AI Act focuses on the obligations of AI developers and users, rather than on the rights of individuals who may be affected by AI systems (Enqvist, 2023). Findings from literature also provide valuable insights into the determinants of intention to use generative AI models for educational purposes. The findings have important implications for the models developers, instructors, and universities as they work to accelerate the adoption of them in educational contexts. Table 5 discusses a set of recommendations for the different stakeholders fostering them to have a safe and responsible adoption of such technologies in education. The adoption or generative AI models in education requires a trust raising plan (Choudhury & Shamszare, 2023). In addition to addressing the forementioned challenges (See Table 2), there are a number of steps that can be done to build trust in generative AI, including: (1) Transparency: developers should be transparent about the limitations, biases, data sources, and potential risks of these models. (2) Explainability: developers should develop explainable AI methods to explain how their models generate outputs (Dosilovi ˇ c et al., 2018). (3) ´ Human-in-the-loop validation: human experts should be involved in the evaluation of AI-generated content to identify and rectify errors. This will help into solving other problems such as bias, and hallucination. (4) Accountability: developers and users of generative AI should be accountable for the consequences of their actions. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Generative AI in Education: Benefits, Challenges, and Future Directions

In the presentation below, Yaron Velner, founder of , presented the challenges of mitigating the human factor in DeFi risk management and how to leverage on-chain risk feeds to build trust in DeFi platforms. B Protocol https://youtu.be/p2dbLHpSe_Q?si=IeewOG5_1_1x8Ota&embedable=true Below is a glossary of key concepts mentioned during Yaron’s talk, intended as a supplement to his video presentation. About B Protocol has been building open-source protocols and infrastructure for risk mitigation and assessment for the DeFi ecosystem since 2020. Through our research arm, , and its novel risk framework, we have supported over a dozen DeFi protocols with risk analysis, research, audits, and monitoring. B Protocol RiskDAO Our and formula automate the process of setting risk parameters for lending platforms in a transparent way, building the next generation of DeFi risk management infrastructure. Risk Oracle SmartLTV Risk Oracle B. Protocol’s risk oracle is an on-chain decentralized system that provides objective risk-related information about different assets, protocols, or platforms within the DeFi ecosystem. The oracle assesses objective risk-related parameters, such as liquidity and volatility of assets, borrowing and lending caps, liquidations at risk, existing bad debt, smart contract audits, and updates, etc. Platforms and users can utilize the data provided to make informed decisions regarding listing and managing their lending markets, setting risk parameters that are aligned with the platform’s subjective risk appetite. More information about the risk oracle can be found in B. Protocol’s . documentation LTV Ratio LTV is an acronym for Loan-To-Value ratio. It is a measure of the ratio of a user’s loan to the value of their collateral, and it is the DeFi equivalent of a credit score in traditional finance. LTV is a vital metric for gauging the risk and security of loans in the DeFi ecosystem, and loans are often overcollateralized to reduce the likelihood of losses due to defaults, market volatility, or sudden declines in collateral value. LTV ratio is typically expressed as a percentage and calculated using: When a user’s LTV rises above a predefined threshold, occur. forced liquidations Risk Appetite Risk Appetite is commonly defined as the amount and type of risk that an organization is willing to accept or assume. This appetite can vary from platform to platform and from DAO to DAO as they each have their perspective on what is to be considered as risky. Currently, most risk assessment frameworks in DeFi combine and mash this subjective, qualitative risk appetite with objective and measurable risk-related data feeds. B.Protocol separates the two through its SmartLTV formula that calculates the LTV based on objective data together with a risk level factor that represents the subjective risk appetite. The risk level factor is a single numerical value that aims to represent a desirable safety margin by accounting for potential deviations from previously observed stats to anticipated values in black swan events. For instance, it considers scenarios where volatility could be three times higher than what was previously recorded. Ultimately, this factor enables lending protocols to tailor their risk management strategies to their specific goals and tolerances while empowering users with the transparency necessary to make informed decisions. Circuit Breakers Circuit breakers are mechanisms that temporarily halt or limit specific activities or trading within a protocol or platform to manage extreme market volatility or prevent sudden crashes. The mechanism is triggered by predefined conditions, such as rapid price declines, high volatility, or abnormal trading volumes. Once activated,  circuit breakers halt activities like trading, lending, borrowing, or other relevant operations to prevent insolvency, protect users from losses, and offer time for the market to stabilize. B.Protocol’s risk oracle and its first implementation – the which is already live on mainnet – can be used as an automated circuit breaker on lending platforms, triggered by rapid market movements or malicious setup of risk parameters. SmartLTV formula Learn more about B Protocol here: B Protocol Website B Protocol Documentation B Protocol Twitter Yaron Velner Twitter The Blockchain Oracle Summit is the world’s only technical summit that dives deep into the use cases, limitations, and impacts of oracles on the wider blockchain ecosystem. Leading speakers worldwide gathered in Paris to share their work and experience building and using Oracle solutions. Article by . Michael Abiodun

How to Remove the Human Factor From DeFi Risk Management: The Key Concepts to Know

ডিফাই রিস্ক ম্যানেজমেন্ট থেকে মানব ফ্যাক্টরকে কীভাবে সরিয়ে ফেলা যায়: জানার মূল ধারণা

So entfernen Sie den menschlichen Faktor aus dem DeFi-Risikomanagement: Die wichtigsten Konzepte, die Sie kennen sollten

Cómo eliminar el factor humano de la gestión de riesgos de DeFi: los conceptos clave que debe conocer

Comment supprimer le facteur humain de la gestion des risques DeFi : les concepts clés à connaître

डेफी जोखिम प्रबंधन से मानवीय कारक को कैसे हटाएं: जानने योग्य मुख्य अवधारणाएं

DeFiリスク管理から人的要因を取り除く方法: 知っておくべき重要な概念

DeFi 위험 관리에서 인적 요소를 제거하는 방법: 알아야 할 주요 개념

Como remover o fator humano do gerenciamento de risco DeFi: os principais conceitos a saber

Как исключить человеческий фактор из управления рисками DeFi: ключевые понятия, которые следует знать

İnsan Faktörü DeFi Risk Yönetiminden Nasıl Çıkarılır: Bilinmesi Gereken Temel Kavramlar

Cách loại bỏ yếu tố con người khỏi quản lý rủi ro DeFi: Các khái niệm chính cần biết

如何从 DeFi 风险管理中消除人为因素：需要了解的关键概念

The SaaS market keeps soaring rapidly, and global is expected to reach $232 billion by the end of 2024, a testament to its widespread adoption across industries. But within SaaS, there is a field-specific approach - vertical SaaS, a customized fit that addresses the individual demands and challenges of concrete sectors. SaaS revenue Vertical SaaS emerges as a beacon of specialization and customization as the continues its meteoric rise. While horizontal SaaS solutions provide broad functionality applicable across various sectors, Vertical SaaS takes a surgical approach, crafting bespoke applications finely tuned to the demands of markets. SaaS business model This tailored strategy not only ensures alignment with sectoral workflows and regulations but also fosters deeper engagement and loyalty among users. In an era where personalization reigns supreme, Vertical SaaS exemplifies the power of niche focus within the expansive , driving innovation and value creation in specialized domains. SaaS ecosystem Vertical SaaS Benefits As industries become more specialized and competitive, the need for customized software solutions is growing. In this setting, Vertical SaaS is a valuable asset, offering targeted features and unmatched advantages: Field-Specific Functionality: It is focused on selected fields and meets their requirements. It streamlines workflows, manages teamwork, and solves problematic issues faced by. Cost Savings Opportunities: While SaaS may have a higher upfront cost due to its specialization, it provides long-term savings; Tailored decisions reduce unnecessary features and expenses. Reduced Competition: This operates in a niche, facing less direct competition. This allows providers to focus on quality and industry expertise. Low Customer Churn: Custom solutions lead to higher customer satisfaction and retention. Customers appreciate tailored features that align with their unique requirements. High Growth Potential: With the development of the technology landscape, Vertical SaaS can adapt and expand within its niche. The potential for growth remains significant. Increased Customer Trust: Field-focused solutions build trust with customers. Companies value suppliers who understand their business needs. Difference From Horizontal SaaS Model Vertical SaaS and horizontal SaaS represent divergent strategies in software delivery, tailored to meet distinct market demands. Horizontal SaaS provides versatile solutions with broad industry applicability, fostering adaptability and scalability. Conversely, Vertical SaaS focuses on finely tuned offerings crafted for niche markets, tackling specialized challenges with exactitude. This comprehension is pivotal for businesses aiming to maximize the efficacy of software investments and harmonize them with strategic goals. Delve deeper into the unique traits of these methodologies in the subsequent analysis: Industry Focus: Vertical SaaS is­ highly specialized to mee­t the unique require­ments, regulations, and workflows of particular sectors, such as he­althcare, finance, or real e­state. In contrast, horizontal SaaS offers broad functionalities that are­ applicable across diverse fields. Functionality: Ve­rtical SaaS platforms are designed with a spe­cific area focus. They streamline proce­sses, manage workflows, and tackle spe­cialized missions within a particular vertical. On the othe­r hand, horizontal SaaS solutions offer a more extensive spectrum of ge­neral business functions that can be used and use­d across various sectors. Customization: Vertical SaaS are­ easy to configure, and tailored to the unique­ needs of the spe­cific field they serve. They provide targeted solutions that close­ly align with the industry's requireme­nts. Horizontal SaaS, on the other hand, gene­rally offers more standardized fe­atures and configurations that cater to a broader audie­nce. While they may have­ some customization options, the leve­l of customization is typically lower compared to Vertical SaaS. Compe­tition: Vertical SaaS operates within a niche­ market, facing less direct compe­tition due to its specialized nature­. This allows providers to focus on delive­ring quality and establishing themselve­s as industry experts. Horizontal SaaS faces more­ competition in the broader marke­t, with numerous providers offering similar ge­neral-purpose solutions. This competition can be­ intense, leading to price­ wars and commoditization of services. Customer Base­: Vertical SaaS attracts customers in these sectors who value targeted decisions that match their goals. Horizontal SaaS appeals to a diverse array of businesses across industries, attracting customers looking for versatile tools that can be applied to different aspects of their operations. Examples of Vertical SaaS Companies Riskalyze (Nitrogen Platform) Financial Services Industry: Riskalyze provides a Vertical SaaS solution that helps financial advisors: Product: Manage 401(k) plans; Analyze investment risks; Create investment portfolios. Notable Feature: Their alignment tool allows advisors to measure risk capacity and tolerance, as well as portfolio risk on a scale from 1 to 991. It empowers firms to transform fearful investors who make bad decisions into fearless investors who make great decisions. These great decisions fuel advisory firms to deliver amazing long-term financial outcomes, turning prospects into lifelong clients. Applied Epic Insurance Industry: Applied Epic is a cloud-based software designed for independent insurance agencies. It offers a comprehensive suite of features to streamline agency operations and enhance efficiency. Product: Notable Feature: Applied Epic provides automated workflows that allow agencies to manage various aspects of their business seamlessly. These workflows cover: Property management; Sales tracking; Revenue management; Customer service deliveries; Integrated group texting; Data control. Users appreciate Applied Epic’s user-friendly dashboard that simplifies navigation and access to critical information. The software’s intuitive design ensures that insurance professionals can efficiently handle day-to-day tasks. Applied Epic empowers insurance agencies to operate easily, manage properties, track sales, and handle revenue—all within a single platform. ClearCare Industry: Health and Care. Product: ClearCare, based in the United States, provides a health and care solution. They offer: Electronic medical care records; 100% automatic labs for ease and swiftness. ClearCare's client relationship management software assists home care agencies by streamlining electronic health care records, including e-charts, e-prescriptions, and automated lab processes. By offering field-specific functionality, ClearCare exemplifies the power of vertical SaaS in meeting the distinct demands of healthcare providers.

What Is a Vertical Saas?

2021’s Startups of the Year , and it seemed as if it could never be topped. Until now. was a huge success The 2023 Startups of the Year was a global phenomenon. Over were nominated and fought for the right to be called the Startup of the Year. Over 600,000 votes later, the dust has settled, and the winners are ready to be declared. 30,000 startups And one of the regions with the most excitement and energy was South America. Let’s take a look and celebrate the . Startups of the Year in South America Startups of the Year in South America Rio de Janeiro, Brazil The Startup of the Year in Rio de Janeiro is with 37 votes! Insidemetrics Bogota, Colombia The winner in is with 25 votes! Bogota TPAGA Antioquia, Colombia emerged as the winner in with 87 votes! Thousand Antioquia State of Rio Grande do Sul, Brazil takes the cake in the ! DoctorCondo State of Rio Grande do Sul Goiania, State of Goias, Brazil The Startup of the Year in is with 42 votes! Goiania Intelijus Fortaleza, State of Ceara, Brazil emerges victorious in ! TEAMGUIDE Fortaleza Buenos Aires, Argentina The winner in is with 191 votes! Buenos Aires Tributo Simple Belo Horizonte, Brazil The Startup of the Year in is ! Belo Horizonte Fluna State of Minas Gerais, Brazil, In the , came out the winner! State of Minas Gerais 42 codelab Montevideo, Uruguay is the winner in with 127 votes! Light-it Montevideo Santiago, Chile With 376 votes, is the Startup of the Year in ! UpGirl Santiago Santa Cruz de la Sierra, Santa Cruz Department, Bolivia is the Startup of the Year in ! MTCorp Santa Cruz de la Sierra Medellin, Colombia The winner in is with 60 votes! Medellin Cogniflow Lima, Peru emerged victorious in with 33 votes! GetLavado Lima Quito, Pichincha Province, Ecuador The Startup of the Year in is ! Quito Reliv Florianopolis, State of Santa Catarina, Brazil, is the winner in ! DataSpoc Florianopolis Valencia, Venezuela In , the Startup of the Year is ! Valencia Riskallay Bucaramanga, Santander, Colombia is the winner in ! Optimify Bucaramanga Cali, Valle del Cauca, Colombia The Startup of the Year in is ! Cali Bitsports Sao Paulo, Brazil With 217 votes, is declared the winner in ! Klever Sao Paulo Cordoba, Argentina The Startup of the Year in is ! Cordoba Quantex Colombo, Brazil The winner in is ! Colombo Callerwise Porto Alegre, Rio Grande do Sul, Brazil is the Startup of the Year in ! Trabalhem Porto Alegre Recife, State of Pernambuco, Brazil is the winner in ! iBoltt Recife Caracas, Capital District, Venezuela In , comes out the winner! Caracas Cygnus Valle del Cauca, Colombia is the Startup of the Year in ! Monitev Valle del Cauca Escazu, San Jose Province, Costa Rica is the Startup of the Year in NextControl Escazu, San Jose Province, Costa Rica Congratulations y’all. We will contact you with details on your winnings! See the global and other regional announcements . here About HackerNoon’s Startups Of The Year Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated in 2023 to be crowned the best startup in their city. Visit our page to learn more. FAQ Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners will get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Startups of the Year 2023: Winners of South America

351 Stories To Learn About Data Security

248 Stories To Learn About Cyber Threats

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Why your Company Needs a Virtual Chief Information Security Officer Why should your company hire a virtual CISO to increase security? 23. 5 Types of Cyber Security and Why It’s Important to be Aware of Them Cybersecurity is the process of protecting systems, networks, and programs from malicious attacks. But why is it important? Let’s find out. 24. 9 Information Technology Industry Trends for 2023 No one knows exactly what awaits us in 2021 in the IT sector, but I am ready to share 8 IT Trends from experts that can significantly affect  business. 25. Why You Should Use IAM Policies to Enforce MFA on AWS In my past life, I was an auditor and performed hundreds of cybersecurity
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Cloud Complexity Causes Security Headaches - Can Identity Cure It? As the cloud industry grew by leaps and bounds, it has become more compex, making it harder to keep data safe from hackers. Can Identity Management help? 31. Why Vulnerability Detection is Important in the IT Space A look at why vulnerability detection is important in the it space 32. What's Stopping Everyone From Going Serverless? In this article, we'll look at what are the security hazards in serverless and what factors will drive its development in the following years. 33. Top 3 Multi-Cloud Management Challenges and Solutions Multi-cloud solutions are on the rise & face many challenges. This piece discusses the top-3 multi-cloud challenges and proposed solutions for each challenge. 34. Kubernetes Security 101: Cloud Native Runtime Security with Falco As Kubernetes continues to grow in adoption, it is important for us to know how to secure it. In a dynamic infrastructure platform such as Kubernetes 35. What Poses a Challenge to Security in a Multi-Cloud Environment Multi-cloud gives you much more flexibility because it allows you to find the perfect solution for each of your business requirements. 36. Research: The Misconfiguration of Real Time Databases in Mobile App Development Mobile App Development is a hugely useful tool but it does come with significant security risks, as the Checkpoint found out when researching threats. 37. How a Unified Approach to Cloud Data Security Can Come To Your Aid A unified approach to clould data security has significant advantages to cybersecurity relative to conventional methods of cybersecurity and may be helpful. 38. How to Revolutionize Data Security Through Homomorphic Encryption For decades, we have benefited from modern cryptography to protect our sensitive data during transmission and storage. However, we have never been able to keep the data protected while it is being processed. 39. Cloud Threat Hunting Series: What Lead To a Major Financial Institution Suffering a Cloud Breach Even the greatest cybersecurity arrangements can be vunerable to simple mistakes such as a tiny setting being misconfigured, compromising the entire system. 40. How to Maximize Cloud ROI With Containerization - Part 1 Struggling to make the most of cloud-hosted applications? Learn about containerization, its optimization challenges, and potential solutions. 41. Why You Should Think About Switching To The Cloud Hosting today Cloud computing has become essential for most homes and businesses, and it has been critical in reducing social upheaval and economic devastation. 42. Enterprise Cloud Security Guide: Secure Application Development This article focuses on DevSecOps and explores how to secure applications during DevOps and the security of the platform itself. 43. I Have My Own AWS VPC, and Now You Can Too! Let's see how a default VPC is configured in AWS, CIDR block, the associated subnets within it, routes, NACLs and Security Groups. 44. Cyber-Resilience On the Cloud by Adopting Digital Immune System and CNAPP This blog post will analyze the similarities and differences between  CNAPP, DIS, and Cyber Resilience. 45. The Emergence of IoT Security in The Post-COVID-19 World Security is a ‘state of mind’ and not an end state. It has been a popular philosophical thought, but what it eludes is the fact of investment towards security. IoT has exploded with the onset of new smart tech gadgets. Though businesses today exercise security and safety measures within their organizations, it isn’t easy to keep upgrading an organization’s infrastructure to encompass these technology updates. Reverse engineering systems have always remained the passcode for unlocking backdoors to capitalize on security vulnerabilities. The best example of such a scenario is the 2016 MIRAI botnet attack. The attack caused massive DDoS attacks on large enterprises on Atlanta’s city administration and the ATL airport. IoT still lacks immunity against ransomware attacks and many new age cyberattacks. 46. Hundreds of Google and Amazon Employees Oppose the Israeli "Nimbus" Cloud Project Hundreds of Google and Amazon employees anonymously sign a letter opposing the tech giants’ deal with Israel. 47. Your Ultimate Guide To The 4 Types of IT Security IT Security protects your business against cyber threats. There are four types of IT Security: network security, end-point security, internet & cloud security. 48. Cloud Security Strategies For Small Businesses If you work from home and use cloud solutions to archive business documents, who is responsible for Cloud Security 49. Cloud Security: Is it Worth it? Storing and managing corporate data by applying the cloud is becoming more and more popular. Companies grow, and it gets too expensive, and resources consuming to store their data on traditional servers. To prove it, look at the research conducted by Google in 2019 that includes insights for the cloud computing market for the next 10 years. 50. CVEs: How Keeping a Catalogue of Common Vulnerabilities and Exposures Helps Your Company CVE combines a dictionary and a catalog containing names for vulnerabilities and other information security vulnerabilities. 51. It's Raining Cyber Attacks: Cybersecurity in the Cloud With the latest news and advancements in the cybersecurity world, we will see how these new rules impact modern cloud applications. 52. Essential Aspects of Cloud Compliance for Financial Services In this post, we will cover five key areas that are very relevant to most financial services businesses—five topics an organization must “get right”. 53. Security Infrastructure and the Multi-Cloud Achieving high-availability cloud architecture requires more than one cloud. From an architecture perspective, there are only three options for mission critical 54. What Makes Cloud Security Solutions so Relevant Intuitively, some people believe that “cloud” storage means your data is being held in the air somehow, but this isn’t really the case. 55. How To Eliminate Leaky S3 Buckets Without Writing A Line Of Code FedEx 56. AWS re:Invent 2020: AWS Community Builders Program Top Highlights As an AWS Community Builder, I truly enjoyed attending AWS' re:Invent 2020 conference this year, diving into cloud technology, devops, databases & innovation. 57. Top 5 Reasons Why Companies are Moving to the Cloud Cloud has introduced some new business capabilities through cost-effectiveness or accessibility, as a company you don't want to miss the opportunity. 58. Cloud Security 101 Cloud security involves the procedures and technology that secure cloud computing environments against external and insider cybersecurity threats. 59. What the Heck Is Envelope Encryption in Cloud Security? If you are going deep into cloud security you may have seen the term “Envelope encryption”. Let’s explain the concept and why it is useful. 60. Application Security Posture Management: An Overview Application Security Posture Management (ASPM) is a holistic approach to managing the security posture of applications throughout their lifecycle. 61. Crucial Cloud Computing Security Threats Everyone Should Know A rundown of the business impact and mitigation measures of the top cloud computing security threats from the 2022 Cloud Security Alliance survey 62. Can Blockchain Fix Enterprise Cloud Computing? As cloud waste increases, businesses are looking for a silver lining. 63. Cloud Threat Hunting: Investigating Lateral Movement What is the most involved attack flow yet? We will break down all of the steps a threat actor took to successfully exfiltrate data out of an AWS account. 64. 6 Cloud Predictions for the Near Future Looking forward to the new decade, it would be tempting to speculate where the cloud industry could be heading. With tech developments in the cloud shop, by 2022, sectors like AI-Web, IoT, Blockchain, Space Cloud Computing, etc. are assumed to reach new heights. 65. Strategies for Combating Cloud Security Risks There are some proven operational and security practices to significantly reduce the occurrence and impact of security breaches in the cloud. 66. Enterprise Cloud Security: Setting Up Structure, Identity-Based Access, and Network Control Security plays an important role in the Enterprise Cloud Foundation. This article covers the foundational security components that form the cloud foundation. 67. How We Built a Solution That Improves Google Cloud Security by Leveraging Managed Instance Groups Ckeckout some significant enhancements to CloudGuard’s integration with new and existing Google Cloud features to benefit from it. 68. Top 10 Cloud Security Trends of the Year While cloud security trends can come and go, there are some with great sticking power. The following top 10 cloud security trends need to be adopted in 2022. 69. Why You Should Adopt Cloud Computing in Healthcare Don’t rush into cloud migration without thorough preparation, assuming that cloud vendors will do you justice. Some of them might not even understand the specifics of your industry.This article will help you discover different cloud deployment models and recognize the risks associated with adopting cloud computing in healthcare. It will also give you some tips on how to get started.With this knowledge, you can turn to experts in technology solutions for healthcare to get more information on your preferred deployment model and to assist you with migration and custom software development, if needed. Thank you for checking out the 69 most read stories about Cloud Security on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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– that is the key ingredient to dominating your market and thriving in today’s cutthroat and competitive business ecosystem. As the world spins at an unprecedented rate, we see new brands emerging left and right, coupled with cutting-edge technologies that introduce disruptive shifts in the marketplace. Stay ahead of the game To remain relevant and achieve the coveted top-tier status, businesses must adapt and innovate continuously. Artificial Intelligence (AI) is undeniably a game-changer in this endeavor. Today, AI gets thrown around in every corner of any given industry. From optimizing sales processes, and streamlining operations, to boosting business growth, this tech breakthrough proves to be a cornerstone of innovation that’s here to stay for good. AI is no longer a buzzword; it's a strategic imperative for those looking to thrive, rather than merely survive. Here are 10 useful ways to use AI to accelerate your business: Data-Driven Decision Making Here’s where AI excels the most: gathering, processing, and analyzing data in a millisecond to push business leaders away from guesswork and lean toward data-driven decision-making. It is wise to utilize AI-powered analytics tools in your business processes, from identifying trends, and unlocking customer preferences, to deciphering market insights. In uncovering the treasure trove of your company’s data and transforming them into actionable insights, invest in AI-driven algorithm platforms that elevate conversion rates, optimize customer success, and identify the most promising prospects. A great example is Coho AI, a software that capitalizes on product usage data, intelligent segmentation, and user journey, equipping sales, operations, product, and customer success, with powerful AI-driven insights, and streamlining their processes through intelligent automation. There's an AI for that: Be a Master Dealmaker Dealmaking has evolved from simply reading a room and deciphering your prospects’ body language to a virtual world where you can only see an inch of their frames on your screen. Remote work has certainly paved the way for faster, rapid-fire deals, but has equally introduced a new set of challenges that dealmakers need to navigate through. With the lack of face-to-face interaction, intuitive understanding, and body language cues, dealmaking in the digital age relies heavily on data and technology, making AI an invaluable ally. Invest in behavioral intelligence tools to free yourself from the burden of reading your prospect’s non-verbal cues, empowering you to focus on the core aspects of dealmaking. These tools can analyze communication patterns, identify prospects' preferences, and assess their receptivity to various propositions – so you don’t have to. One tool that makes a difference is Novacy, an AI-powered behavioral intelligence platform, that helps B2B revenue teams close more deals by understanding their prospects' underlying perceptions by using audio, text, and body language analysis. There's an AI for that: Embrace Predictive Maintenance AI has been a saving grace for businesses with physical assets, both in costs and efficiency. By predicting whether a piece of equipment is on the verge of failure or requires maintenance, AI helps prevent costly downtime, extends the lifespan of assets, and reduces maintenance expenditures, ultimately improving the bottom line. Its impact on manufacturing and logistics is multifaceted, driving benefits that extend well beyond the shop floor. *__There's an AI for that: __*Uptake, a leader in predictive maintenance solutions, offers an AI-driven platform that empowers businesses to proactively manage their physical assets. Their software uses machine learning to analyze equipment data, predict maintenance needs, and provide actionable insights. By implementing Uptake's solution, businesses can significantly reduce maintenance costs, enhance asset reliability, and optimize their operations. Improved Marketing Campaigns In the ever-evolving world of marketing, AI can revolutionize your campaigns. AI-driven marketing tools can segment your audience, predict campaign performance, and optimize ad spending in real time. By ensuring your marketing efforts reach the right audience with the right message at the right time, AI can maximize ROI and elevate the impact of your campaigns. Emarsys is a powerful AI marketing platform that enables businesses to create highly personalized and data-driven marketing campaigns. It uses AI to analyze customer behavior, predict future actions, and automate marketing processes, ensuring that each interaction with your audience is highly relevant and effective. There's an AI for that: Supply Chain Optimization The pandemic has proven that supply chain management is a complex task that requires heavy technological support. In an increasingly globalized world, AI bridges this gap by providing real-time insights into your supply chain, from sourcing materials to ensuring on-time deliveries. It optimizes inventory, reduces transportation costs, and improves supply chain efficiency, ultimately impacting your bottom line positively. Leading this new dawn for the supply chain is Coupa, a global player in the continuous supply chain design, which leverages the power of AI and comprehensive data models. Its product, transforms design and analysis from one-off projects to a consistent and repeatable process, enabling businesses to adapt quickly to changing market conditions and reduce costs. There's an AI for that: Supply Chain Modeler, Fraud Detection and Prevention In an age of increasing cyber threats and financial fraud, AI can be an invaluable ally in safeguarding your business. Machine learning algorithms can detect unusual patterns and anomalies, preventing fraudulent transactions and securing your financial assets. An AI-driven fraud detection and prevention platform, Featurespace offers adaptive behavioral analytics to help companies identify and stop fraudulent activities in real time. This proactive tool’s smart technology can protect your business and your customers from financial fraud, which is a must-have in today’s era of data leaks and cybercrimes. There's an AI for that: Personalized Product Recommendations E-commerce businesses can harness AI to deliver personalized product recommendations. By analyzing customers' browsing and purchase history, AI can propose items that are more likely to resonate with individual customers, leading to increased sales and customer satisfaction. When it comes to AI-powered product recommendations, Amazon still leads the pack. As a global e-commerce giant, this platform boasts a cutting-edge recommendation engine that has substantially contributed to its success by providing customers with what they want when they want it. There's an AI for that: Human Resources Optimization AI can streamline various HR processes, from candidate screening and onboarding to employee engagement and performance management. Automation and data-driven insights enable HR professionals to focus on strategic activities, foster a more engaging employee experience, and drive higher retention rates. Workday, an AI-driven HR software, offers tools for talent acquisition, workforce management, and employee engagement. It uses AI to streamline HR processes, improve talent sourcing, and enhance the overall employee experience, ultimately contributing to higher retention rates and a more efficient HR department. : There's an AI for that Risk Management To say that change is constant is an understatement. In the modern business world, change and uncertainty are the norm, exposing companies to risks and vulnerabilities. This makes it high time for businesses to invest in AI-powered risk management solutions where its predictive capabilities can assess market fluctuations and regulatory changes, and enable companies to develop proactive risk mitigation strategies. When it comes to leveraging AI to analyze data from a myriad of sources, forecast potential risks that are often overlooked by humans, and provide actionable insights for decision-makers, RiskWatch is a reliable tool. This comprehensive AI-powered risk assessment management software provides advanced risk assessment and protection against threats that may ruin and tarnish your brand. There's an AI for that: Environmental Sustainability Currently, environmental consciousness is at its peak. From developing sustainable products, and optimizing energy efficiency and consumption to reducing carbon footprint, modern businesses are under increasing pressure to abide by these growing eco-friendly standards. In this eco-conscious era, AI is an indispensable tool that can assist businesses in reducing their environmental impact and embracing sustainability. OneTrust offers ESG and Sustainability Cloud that empowers organizations with the ability to define their core ESG metrics, track progress towards these goals, and foster trust with key stakeholders through enhanced transparency. There's an AI for that: Conclusion As AI technologies continue to advance, their role in business acceleration will only grow. Efficient and effective leaders know that incorporating AI into various operations is paramount to adapt to the ever-changing business landscape, enabling them to stay steps ahead of the game. Embracing AI is no longer a matter of “if” – it’s a matter of “when,” and those who act now will reap its benefits fast.

10 Useful Ways to Use AI to Accelerate Your Business

. With , understanding how borrowing works is crucial for developers, architects, or researchers. Borrowing is a cornerstone of Ethereum-based blockchain applications billions in assets loaned out Much like the evolution of programming paradigms, these DeFi applications have diverse architectural designs, reflecting changing priorities that range from security to efficiency and user experience. This analysis looks at the architecture of applications like , , , , and . We'll highlight key innovations and design patterns, which are important lessons for the development of future lending applications. MakerDAO Compound Aave Euler Yield If you're a developer, architect, or security researcher, this article is for you. By the end, you'll easily understand new borrowing applications on Ethereum, grasping their architecture swiftly and comprehensively. Dive in to see how these DeFi giants are built from the ground up. Borrowing in DeFi Most DeFi borrowing is . A user can borrow a specific asset if they provide collateral worth more than the loan. Unlike conventional loans, many of these loans don't have regular repayments or fixed end dates. In essence, you can borrow and never repay. overcollateralized However, there is a catch. . The collateral's value must always exceed the loan value by a predetermined margin If the collateral value falls below this, the loan is . liquidated During liquidation, someone else repays part or all of your loan, and they receive part or all of your collateral in return. All borrowing applications following this financial structure need the same building blocks, which then can be arranged in many ways: A treasury to store user collateral and borrowed assets An accounting system that tracks each user's collateral and debt Functions that determine the borrowers' interest rate A mechanism to verify if a loan is sufficiently collateralized, usually involving external price oracles A liquidation path for undercollateralized loans Risk management systems that record total borrowed amounts and other safety metrics, such as global and per-user borrowing limits, collateral minimums, and specific over-collateralization ratios An interface for users to add and remove collateral, borrow, and repay underlying Borrowing and lending can be thought of as separate features. In DeFi, we find both features in most borrowing applications, . but they are not always well integrated . The interest rates for borrowers and lenders are internally correlated; in fact, that is what makes those applications work with minimal intervention. In Compound, Aave and Euler they are are originators of the assets they lend to borrowers. On the other hand, MakerDAO and Yield . They don’t require users to supply assets so that other users can borrow This article will focus on on-chain borrowing and largely ignore lending. Borrowing is much more complicated because of the collateralization requirement, and understanding the borrowing pattern typically unlocks a better understanding of the entire protocol. The Architectural Evolution of MakerDAO , ancient in Ethereum terms, and it holds . Despite its modular architecture with distinct contracts for every function and unique terminology, it remains easy to understand and verify. MakerDAO , was launched in its current form in November 2019 $4.95B in collateral The treasury function in MakerDAO is managed by the contracts. Join There is a for each token approved as a collateral asset. separate contract Contrarily, MakerDAO doesn't possess any DAI, the borrowing asset. Instead, it merely as required. mints and burns DAI Accounting is handled within the contract. The Joins when collateral enters or exits the system. If a user borrows, they . .sol vat update this contract interact with the vat.sol contract directly This action updates the user's debt balance and allows them to mint DAI at the DAI Join. . To repay, users burn DAI in the DAI Join. This process then updates the Vat, enabling the user to settle their loan Additionally, the contract serves as the engine. It maintains global borrowing limits, sets per-user minimum thresholds, and oversees collateralization ratios. vat.sol risk management When changes are made to a user’s debt or collateral balance, the vat.sol contract evaluates both rate and spot. These refer to the interest rate based on the collateral used and the prevailing DAI-to-collateral price ratio. Interestingly, these values are fed into the vat.sol contract by other MakerDAO contracts, a method distinct from most other applications. MakerDAO prioritized safety during its design phase - , user experience was a minor concern, and competition was negligible. a time when factors like gas costs were secondary Consequently, it might appear quirky, costly to use, and challenging to navigate. Yet, the vast assets it manages and its record of operation without significant breaches underline its robust design and execution. Highlights: Each asset has its own contract in the maximally spread treasury function The accounting function is centralized within a single contract that also documents and enforces risk parameters, including collateralization checks Unlike other apps, oracles update the contract, overseeing collateralization Price and interest rate oracles utilize distinct interfaces The interest rate originates externally To borrow, users must interact with multiple contracts The Architectural Evolution of Yield Protocol served as a proof of concept for fixed rates using . This version built its collateralized debt engine on top of MakerDAO. However, Yield v1 was both expensive to use and challenging to augment with new features. Yield v1 YieldSpace Recognizing the potential of YieldSpace, we quickly transitioned into developing . Still drawing inspiration from MakerDAO, but now completely independent, Yield v2 ; Yield v2 prioritized reduced gas costs and an enhanced user experience. Yield v2 launched in October 2021 All accounting, risk management, and collateralization checks were consolidated into one contract: the . Mirroring MakerDAO's approach, we distributed the treasury functions across contracts, each dedicated to a specific asset. Cauldron Join We revamped our oracle integration, merging price and interest rate oracles into a . We reversed the oracle flow from MakerDAO such that  Cauldron as needed for collateralization checks. To my knowledge, this is the preferred flow everywhere except MakerDAO. common interface consults oracles Another significant deviation from MakerDAO's approach was our introduction of the . This contract serves as the sole intermediary between users and Yield. It wields extensive control over treasury and accounting, . Ladle but in return, offers immense flexibility for feature development In summary, borrowing in Yield v2 works as follows: Each asset has its own dedicated treasury contract, ensuring maximum distribution of the treasury function. A singular contract centralizes the accounting function. This contract also oversees risk management measures and performs collateralization checks. The collateralization function consults oracles to determine prices and interest rates. Both price and interest rate oracles share a unified interface. Interest rates are generated externally. Users can borrow by making a single request to just one contract. The Architectural Evolution of Compound Finance The was a , demonstrating that a money market could be established on Ethereum. For this reason, simplicity was prioritized in its design. The contract encapsulates all functions, including lending. first version of Compound Proof-of-Concept MoneyMarket.sol The treasury, accounting, and risk management tasks, such as collateralization checks, are consolidated into one contract. This contract retrieves prices from the oracles but determines interest rates based on asset utilization. Users interact solely with this contract, though they must make separate calls to supply collateral and borrow assets. Compound v2 , igniting the yield farming era and inspiring countless forks. It too functioned as a money market, allowing users to both lend and borrow assets. Compound v2 was launched in May 2019 Based on its and structure, it's evident that a major goal for was to use the . This ensured composability, allowing users to lend to Compound and then use those interest-bearing positions in other blockchain applications. whitepaper Compound v2 ERC20 standard to represent lending positions Interestingly, the whitepaper didn't highlight that Compound v2 incorporated into its smart contracts. Given this omission, the immense impact of this feature might not have been foreseen. rewards Each asset has its own treasury contract, maximizing the distribution of the treasury function. The accounting function is also distributed, with each cToken noting user collateral and debt. A singular contract, the Comptroller, logs and enforces risk management parameters, including collateralization checks. The contract responsible for collateralization checks references the oracles for prices and the cToken for interest rates. The price and interest rate oracles operate with different interfaces. The interest rate derives internally from asset utilization. Users must interact with multiple contracts to borrow. Compound v3 , adopts a more conservative risk management strategy, segregating the liquidity into a for each borrowable asset. The design also reveals concerns about user-friendliness and gas costs. Released in 2022 Compound v3 pool The system is more intuitive for both developers and users due to the reduction in the number of required calls. Additionally, the singular contract design reduces gas costs by minimizing calls between contracts. The segregated money markets are a defense against oracle-based attacks, which are now a major security concern. Other relevant features mentioned in the include: release notes A completely revamped risk management and liquidation engine. This design enhances fund security while being more borrower-friendly. Set limits across the market for individual collateral assets to mitigate risks. The interest rate models for earning and borrowing are now separate, with governance having total control over economic policies. Interestingly, Compound v3 mirrors the architecture of Compound v1 by having a single contract handle all functions for each borrowable asset. Other notable features include: Only lent assets can be borrowed; collateral assets cannot. Collateral does not yield returns in Compound v3. The prohibition against borrowing collateral enhances the safety for those depositing the collateral. This decreases the likelihood that governance errors or intentional attacks jeopardize the collateral. Eliminating returns on supplied collateral might be a result of Compound managing to amass much liquidity in v2. I have the intuition that in Compound v2 the borrowing limits were either below or not much higher than the assets lent to the application by the users. Assuming they will manage similar levels of liquidity for v3, disallowing collateral to be lent out makes the application safe, one of the core goals of v3. From an architectural standpoint: Every money market is an individual contract with its treasury, accounting, and risk management Each money market retains the borrowable asset along with all its approved collateral asset tokens, causing assets to be dispersed across the application Price feeds are the sole external input; interest rates for borrowing and lending are generated internally Traditional functions like supply/withdraw/borrow/repay have been smartly consolidated. Now, withdrawing a borrowable asset from a money market implies borrowing, while supplying it indicates repayment or lending based on the user's debt A routing contract is integrated, permitting multiple operations in a single call The Architectural Evolution of Aave was , succeeding ETHLend. Instead of ETHLend's peer-to-peer approach, Aave v1 introduced a shared liquidity pool. Aave v1 launched in October 2019 As in Yield v2, the also held the business logic. The implemented accounting, risk management and treasury functions. Pooling the treasury in a single contract was a differentiating point from Compound v2. router contract LendingPoolCore The decision to leave the collateralization checks in , called from the router and not the accounting contract seems weak, but it was probably fit for purpose as Aave v2 was only released two years after the v1 release its own contract The LendingPoolCore contract handles treasury and accounting LendingPoolDataProvider manages collateralization checks and interacts with the oracle LendingPool serves as the user entry point and implements business logic Interest rates for borrowing and lending are determined internally, relying solely on price feeds Aave v2 was . While it retained features similar to Aave v1, it introduced an improved and simpler architecture compared to both Aave v1 and Compound v2. With this release, Aave also introduced (akin to Compound's cTokens) and , which represent tokenized debt. Aave v2 released in December 2021 aTokens vTokens Certain features from Aave v1, which had limited use, were omitted for simplicity. Issues in Aave v1, like the complex representation of accrued interest, were addressed in Aave v2. The LendingPool contract consolidates global accounting and risk management functions, such as collateralization checks. It serves as the primary access point for users aTokens signify collateral and are akin to lending positions. Users' collateral is reflected through their aToken holdings, and the treasury function is distributed across all aTokens vTokens are used to represent debt positions. A user's debt is represented by the vTokens they hold Aave v3 was with multi-chain support and other features. These additions don't alter the core architecture. The update also boasts improved risk management and gas efficiency. Aave v3 released in January 2023 Despite its many advancements, for the purposes of this study, Aave v3 is not materially different from Aave v2. In fact, it might suggest that the architecture of Aave v2 remains robust in 2023. The Architectural Evolution of Euler was , aiming to offer money markets with permissionless features and minimal governance. Euler launched in December 2022 A hallmark of its design is the pattern. A . This storage can be accessed through distinct , each managing a different conceptual element of the system. diamond-like single contract holds all the application's storage proxies Even though one contract stores all assets, accounting, and risk management data, there are still eTokens for collateral and lending, and dTokens for debt, similar to Aave v2. However, these token contracts are merely views of the central storage contract. The contract manages accounting variables. Storage The contract functions as the treasury. BaseLogic The contract oversees risk management variables and functions, including collateralization checks. RiskManager An analysis of the code reveals that minimal gas cost was a priority, leading to the monolithic design eliminating the need for inter-contract calls. Security was assured through rigorous testing and auditing. Only the logic was distributed across various modules, serving as the implementation for the storage contract, which acted primarily as a proxy contract. This unified design also supports easy upgrades. Modules can be swiftly replaced to amend or introduce features . if no storage changes are required Euler was hacked fifteen months after its release and six months after the upgrade introduced the exploited vulnerability. . I do not think the monolithic architecture played a part in the assets being drained; rather, it was insufficient oversight of code updates Conclusion Early Ethereum applications such as MakerDAO, Compound, and Aave showcased the potential of overcollateralized borrowing on Ethereum. Once these proofs of concept proved successful, the focus shifted to introducing a mix of new features to capture market share. Compound and Aave's later versions introduced yield farming, composability, and pooled liquidity, which thrived especially during bullish market conditions. A significant development was Compound v2's introduction of tokenized lending positions, which enabled these positions to be recognized as standard assets by other applications. Aave v2 and Euler took it a step further by implementing tokenized debt positions, the broader utility of which remains a subject of debate. High gas costs emerged as a major concern during the bull market, prompting user experience modifications as seen in Yield v2, Aave v2, and Euler. Router contracts and monolithic implementations helped reduce the costs users incurred for transactions. However, this came at the expense of more intricate, and consequently riskier, code. Compound v3 appears to set a precedent, prioritizing safety over financial efficiency. It deviates from the traditional liquidity pool model to better safeguard against potential hacks. The rise of L2 networks, where gas costs are becoming increasingly negligible, will likely shape the design of future collateralized borrowing applications. In this article, I've provided a comprehensive overview of the key collateralized borrowing applications on Ethereum. The approach I've employed to analyze each application can also be applied to grasp the intricacies of other collateralized borrowing applications swiftly. When developing a blockchain borrowing application, always consider the storage of assets, the placement of accounting records, and the methods for risk and collateral evaluation. As you work through these considerations, draw upon the history of previous applications and the insights from this overview to inform your decisions. Thank you for reading, and best of luck. Thanks to for reviewing and editing this article. Calnix The author is a co-Founder and Technical Lead at Yield Protocol.

Borrowing on Ethereum: Comparing Architecture Evolution of MakerDAO, Yield, Aave, Compound, & Euler

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