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This article is part 2 of the Blockchain train journal, start reading here: . Catching the Blockchain Train Some reading material to get started A couple of articles that I found useful when learning about decentralized storage: Decentralized Storage: The Backbone of the Third Web HTTP is obsolete. It’s time for the distributed, permanent web What is the difference between Swarm and IPFS? Comparing decentralized storage systems Our decentralized blog pages are in need of some sort of decentralized hosting. To build that from scratch seems like a lot of work and will probably result in a blockchain train crash. So let’s see what is already out there we could use for our project. I searched a bit and came up with: Storj SIA MaidSafe IPFS ZeroNet Ethereum Swarm BigchainDB did I forget a serious contender? Please . let me know I’m not yet qualified to compare these technologies (still trying to catch the train!), but I’ll add some observations for each: Storj From the home page: Blockchain-based, end-to-end encrypted, distributed object storage, where only you have access to your data. That doesn’t sound like something that is handy for a blog, where we want content to be public. Storj looks like a great product though, it is a bit like , but with a blockchain added to create a marketplace for buying or renting out disk space on the network. Resilio Sync SIA SIA is another Dropbox killer, more or less the same as Storj. MaidSafe Where Storj and SIA pitch their service as a faster and cheaper way to store your data, Maidsafe promotes their SAFE network by scaring you. Cloud providers, government, and hackers can not be trusted is the message in their video. Apart from the funny name, it is very similar to the two services above. IPFS Now, this is a different beast. The first three services are , which means they have their own cryptocurrency to support their marketplace. IPFS also has such a thing, , but it is neatly split from IPFS which is more like a decentralized storage protocol. incentive platforms FileCoin IPFS stands for , but they hide the original name from their homepage. Too funny for a serious business! interplanetary file system On the home page it says: IPFS is the Distributed Web A peer-to-peer hypermedia protocol to make the web faster, safer, and more open. IPFS seems like an great option for our project. ZeroNet ZeroNet doesn’t have a cheesy video on their home page, so I had to actually . However, their awesome presentation is 100x better than the four videos from the others combined: . read ZeroNet preso They also seem to operate a lot less like a serious business compared to the others, showing more of the idealistic open source spirit. On the home page: Open, free and uncensorable websites, using Bitcoin cryptography and BitTorrent network Oh, oh, that is what are trying to do here! A competitor! Nah, let’s just keep an eye on the code, maybe we can learn a thing or two. we Ethereum Swarm On our trip to become blockchain developers, we have no other choice than to dive into Ethereum. We will address that in a future episode. But their decentralized file storage system, named Swarm, must be considered here. It took me a while to find the source code for this project, but ! here it is From the : documentation Swarm is a distributed storage platform and content distribution service, a native base layer service of the ethereum web 3 stack. The primary objective of Swarm is to provide a sufficiently decentralized and redundant store of Ethereum’s public record, in particular to store and distribute dapp code and data as well as block chain data. It seems that Swarm is an incentive platform enforced with Ethereum smart contracts, so not the best choice for our purpose. We gotta stay hands-on here. We want to add a coin later ourselves! BigchainDB From their site: BigchainDB is complementary to decentralized storage, processing and communication building blocks. It can be used side by side with higher-level decentralized computing platforms and applications, and protocols for identity, financial assets, intellectual property and sidechains. BigchainDB fills a gap in the decentralized stack. This is a brilliant solution, almost too good to be true. It is not just decentralized storage, but a full blown database. It is built on top of MongoDB and decentralized, so data is immutable, and there is no central authority. For our current project it might be a bit overkill, but let’s keep an eye on it. The winning decentralized storage system OK, I’ll be honest, I already picked one before I did this quick research what is around :-) The winner is IPFS! Yay! The runner-up we might have a look at at a later stage: BigchainDB! Even in retrospect, IPFS seems like a good choice: all competitors have their “flaws” in the context of this project. By applying “selection by deduction”, only IPFS remains. After this very scientific approach in picking IPFS, let’s hope it is indeed useful for us. Let’s move on to part 3: Getting to know IPFS Originally published at decentralized.blog .

Picking a Decentralized Storage System

Penetration Testers’ Guide to Windows 10 Privacy & Security

Guía de los probadores de penetración para la privacidad y seguridad de Windows 10

In my previous article, Managing Architectural Tech Debt, I talked about understanding and managing architectural technical debt. Architectural technical debt is the often ignored, but ironically, one of the most damaging, categories of technical debt. In this article, I want to dive deeper into one way to manage architectural technical debt (and technical debt as a whole)—architectural observability (AO). AO is a new category of observability that I believe is just as important, if not more so, as application performance management (APM). I believe we need to shift left observability—to the architectural stage—where we can not just see symptoms, but fix core problems. Let’s take a look. APM Is (Half) the Answer You already know that APM is important. Gartner defines it as “software that enables the observation and analysis of application health, performance and user experience.” And IDC reports that companies using APM solutions see a 2.5x improvement in mean time to resolution (MTTR) and a 50% reduction in the number of incidents. APM and observability: Helps to ensure better user experiences by monitoring, in real-time, performance and responsiveness.
Can help identify defects.
Can provide data to teams, such as usage patterns, bottlenecks, and overall health to keep systems healthy. Overall, APM has become a necessary tool for troubleshooting and fixing issues in enterprise environments. APM has become table stakes. And APM works! In my current role, we use APM to observe the usage of our APIs to understand the breakdown of URI (uniform resource identifier) requests across all of our consumers. When our APIs are not functioning as expected, we lean to APM in order to gain visibility into performance bottlenecks. In cases where an alert is triggered, the same interface can be utilized for initial troubleshooting efforts in order to help pin down the root cause. But even though APM works, there’s a problem. APM identifies the symptoms of the defects, but not the actual defects themselves. It’s up to the team to track down why the problems are occurring. And with the pressure we often feel in prod to “just fix the problem as fast as you can,” I often see that while symptoms may be addressed, teams don’t have the time (or organizational support) to find and fix the actual core problems. Imagine taking aspirin because you get a headache every night, but never taking the time to figure out why you keep having headaches. To find—and address—the why of our defects, we need architectural observability. Architectural Observability: Getting to the Real Answers We need to shift our processes left, stop focusing on symptoms, and instead focus on the root cause of these problems and actually reduce the number of incidents caught with APM. That’s where architectural observability comes in. Architectural observability is the ability to analyze an application’s architecture (both statically and dynamically), understand how it works, observe changes, and identify and fix architectural technical debt. Architectural observability is the next step in observability tools. Architectural observability gives you visibility into your application architecture, helping you solve problems (not just identify symptoms) earlier in the SDLC by identifying architectural issues. You probably already have the data you need to implement AO—it uses the same data sources as APM (for example, OpenTelemetry (OTel). But AO takes that data and applies a layer of intelligence that focuses on analyzing the architecture and the sources of architectural technical debt. For example, an AO tool might analyze: Architectural complexity - The interdependence and relationships of services within the architecture, the number of flows in a service, identifying multi-hop flows and circular flows. Dependency mappings - Relationships among services including circular dependencies. Architectural drift - What has changed since your last release, what new domains/services were added, what new dependencies and flows were introduced. Technical debt - Such as resource exclusivity, service dependencies, duplicate services that should be merged, and complexity. Technical debt is a huge problem in the industry. 70% of organizations say that technical debt is a major obstacle to innovation. Database-related issues - Examine if multiple services are accessing tables. Architectural observability is proactive and strategic. Where APM tools alert on the leaks in the roof when it is already raining, AO identifies architectural issues that can lead to those leaks, way before they actually occur. Using Tools to Gain Architectural Observability Used well, AO doesn’t just help you find issues earlier, but helps you: Truly discover and understand your architecture and its relationships and dependencies.
Prevent issues caused by architectural changes.
Make systems more resilient and scalable by continually monitoring, modernizing, and strengthening your architecture.
Minimize technical debt. I love that last one. As I pointed out in my last article, architectural debt is a foe of mine that I have been battling for over a decade. Architectural observability is a new field and is starting to gain traction as something teams must have. There aren’t many tools yet built around the concept, but let’s look at how your team might use one of the first AO tools—vFunction—to gain AO. Once you’ve connected to your applications (through the OTel connector or similar), the tool analyzes your system (in this case with vFunction, it’s using AI to understand and analyze your architecture). Then you get a report on the current state of your architecture. You’ll see details such as: A visualization of the architecture across your entire app portfolio
A map of services and entry points, cross services APIs, and external APIs
Exclusivity of database tables (Kafka, Redis, MongoDB) and other resources
Complexity scores
And more… And you can use architectural observability to monitor your architecture not just in the present state, but dynamically as it changes. Changes to your architecture (a.k.a., architectural drift)? You’ll know right away.
Add a dependency impacting resiliency? You’ll catch it before migrating.
Did you create a circular flow? Did you significantly increase the complexity of your system? Find out early. Incurring even more technical debt? You can’t hide from it now. With architectural observability, you now have hard proof of how architectural debt is affecting your systems and how, by using AO, you can identify and prioritize the actual problems in your systems early and often, rather than only addressing the symptoms in production. AO makes applications more resilient, more scalable, and helps your team move faster. And AO can help proactively. Is your team moving from a monolith to a microservice? AO can give you a plan to move your architecture forward. With AO, you could analyze your monolith, understand how its domains and functionality are structured and connected, and then get actionable steps on how to modularize and move the functionality to microservices. And once you have a distributed architecture (microservices or distributed monoliths), you’ll want to make sure your architecture doesn’t drift or get more complex to the point that things start to break, you lose control, and you have to slow down your engineering velocity. AO keeps your applications in check whether monolith or microservices. Architectural Observability Gives You Better Systems My readers may recall that I have been focused on the following mission statement, which I feel can apply to any IT professional: “Focus your time on delivering features/functionality that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else.” - J. Vester Architectural observability adheres to my mission statement perfectly. By moving from plain vanilla observability to architectural observability, we can shift left for more reliable, more resilient, and better-performing systems. This shift affords us more time to spend on the business problems that our teams understand best. As John F. Kennedy once said: “The best time to repair the roof is when the sun is shining.” Architectural observability should be placed on the same level (or higher) as APM. With AO, teams can truly understand their architecture, have a broader and more insightful view of their applications than with APM alone, and fix problems earlier. Have a really great day! In my previous article, Managing Architectural Tech Debt , I talked about understanding and managing architectural technical debt. Architectural technical debt is the often ignored, but ironically, one of the most damaging , categories of technical debt. Managing Architectural Tech Debt architectural the most damaging In this article, I want to dive deeper into one way to manage architectural technical debt (and technical debt as a whole)— architectural observability (AO) . AO is a new category of observability that I believe is just as important, if not more so, as application performance management (APM). I believe we need to shift left observability—to the architectural stage—where we can not just see symptoms, but fix core problems. architectural observability (AO) Let’s take a look. APM Is (Half) the Answer You already know that APM is important. Gartner defines it as “software that enables the observation and analysis of application health, performance and user experience.” And IDC reports that companies using APM solutions see a 2.5x improvement in mean time to resolution (MTTR) and a 50% reduction in the number of incidents. IDC reports APM and observability: Helps to ensure better user experiences by monitoring, in real-time, performance and responsiveness. Can help identify defects. Can provide data to teams, such as usage patterns, bottlenecks, and overall health to keep systems healthy. Helps to ensure better user experiences by monitoring, in real-time, performance and responsiveness. Can help identify defects. Can provide data to teams, such as usage patterns, bottlenecks, and overall health to keep systems healthy. Overall, APM has become a necessary tool for troubleshooting and fixing issues in enterprise environments. APM has become table stakes. And APM works! In my current role, we use APM to observe the usage of our APIs to understand the breakdown of URI (uniform resource identifier) requests across all of our consumers. When our APIs are not functioning as expected, we lean to APM in order to gain visibility into performance bottlenecks. In cases where an alert is triggered, the same interface can be utilized for initial troubleshooting efforts in order to help pin down the root cause. But even though APM works, there’s a problem. APM identifies the symptoms of the defects, but not the actual defects themselves . It’s up to the team to track down why the problems are occurring. And with the pressure we often feel in prod to “just fix the problem as fast as you can,” I often see that while symptoms may be addressed, teams don’t have the time (or organizational support) to find and fix the actual core problems. but not the actual defects themselves why Imagine taking aspirin because you get a headache every night, but never taking the time to figure out why you keep having headaches. To find—and address—the why of our defects, we need architectural observability . why architectural observability Architectural Observability: Getting to the Real Answers We need to shift our processes left, stop focusing on symptoms, and instead focus on the root cause of these problems and actually reduce the number of incidents caught with APM. root cause of these problems That’s where architectural observability comes in. Architectural observability is the ability to analyze an application’s architecture (both statically and dynamically), understand how it works, observe changes, and identify and fix architectural technical debt. Architectural observability Architectural observability is the next step in observability tools. Architectural observability gives you visibility into your application architecture, helping you solve problems (not just identify symptoms) earlier in the SDLC by identifying architectural issues. You probably already have the data you need to implement AO—it uses the same data sources as APM (for example, OpenTelemetry (OTel). But AO takes that data and applies a layer of intelligence that focuses on analyzing the architecture and the sources of architectural technical debt. For example, an AO tool might analyze: Architectural complexity - The interdependence and relationships of services within the architecture, the number of flows in a service, identifying multi-hop flows and circular flows. Architectural complexity - The interdependence and relationships of services within the architecture, the number of flows in a service, identifying multi-hop flows and circular flows. Architectural complexity Dependency mappings - Relationships among services including circular dependencies. Dependency mappings - Relationships among services including circular dependencies. Dependency mappings Architectural drift - What has changed since your last release, what new domains/services were added, what new dependencies and flows were introduced. Architectural drift - What has changed since your last release, what new domains/services were added, what new dependencies and flows were introduced. Architectural drift Technical debt - Such as resource exclusivity, service dependencies, duplicate services that should be merged, and complexity. Technical debt is a huge problem in the industry. 70% of organizations say that technical debt is a major obstacle to innovation. Technical debt - Such as resource exclusivity, service dependencies, duplicate services that should be merged, and complexity. Technical debt is a huge problem in the industry. 70% of organizations say that technical debt is a major obstacle to innovation. Technical debt major obstacle to innovation. Database-related issues - Examine if multiple services are accessing tables. Database-related issues - Examine if multiple services are accessing tables. Database-related issues Architectural observability is proactive and strategic. Where APM tools alert on the leaks in the roof when it is already raining, AO identifies architectural issues that can lead to those leaks, way before they actually occur. Using Tools to Gain Architectural Observability Used well, AO doesn’t just help you find issues earlier, but helps you: Truly discover and understand your architecture and its relationships and dependencies. Prevent issues caused by architectural changes. Make systems more resilient and scalable by continually monitoring, modernizing, and strengthening your architecture. Minimize technical debt. Truly discover and understand your architecture and its relationships and dependencies. Prevent issues caused by architectural changes. Prevent Make systems more resilient and scalable by continually monitoring, modernizing, and strengthening your architecture. Minimize technical debt. I love that last one. As I pointed out in my last article , architectural debt is a foe of mine that I have been battling for over a decade. my last article Architectural observability is a new field and is starting to gain traction as something teams must have. There aren’t many tools yet built around the concept, but let’s look at how your team might use one of the first AO tools—vFunction—to gain AO. Once you’ve connected to your applications (through the OTel connector or similar), the tool analyzes your system (in this case with vFunction, it’s using AI to understand and analyze your architecture). Then you get a report on the current state of your architecture. You’ll see details such as: A visualization of the architecture across your entire app portfolio A map of services and entry points, cross services APIs, and external APIs Exclusivity of database tables (Kafka, Redis, MongoDB) and other resources Complexity scores And more… A visualization of the architecture across your entire app portfolio A map of services and entry points, cross services APIs, and external APIs Exclusivity of database tables (Kafka, Redis, MongoDB) and other resources Complexity scores And more… And you can use architectural observability to monitor your architecture not just in the present state, but dynamically as it changes. Changes to your architecture (a.k.a., architectural drift)? You’ll know right away. Add a dependency impacting resiliency? You’ll catch it before migrating. Did you create a circular flow? Did you significantly increase the complexity of your system? Find out early. Changes to your architecture (a.k.a., architectural drift)? You’ll know right away. Add a dependency impacting resiliency? You’ll catch it before migrating. Did you create a circular flow? Did you significantly increase the complexity of your system? Find out early. Incurring even more technical debt? You can’t hide from it now. more With architectural observability, you now have hard proof of how architectural debt is affecting your systems and how, by using AO, you can identify and prioritize the actual problems in your systems early and often, rather than only addressing the symptoms in production. AO makes applications more resilient, more scalable, and helps your team move faster. And AO can help proactively. Is your team moving from a monolith to a microservice? AO can give you a plan to move your architecture forward. With AO, you could analyze your monolith, understand how its domains and functionality are structured and connected, and then get actionable steps on how to modularize and move the functionality to microservices. And once you have a distributed architecture (microservices or distributed monoliths), you’ll want to make sure your architecture doesn’t drift or get more complex to the point that things start to break, you lose control, and you have to slow down your engineering velocity. AO keeps your applications in check whether monolith or microservices. Architectural Observability Gives You Better Systems My readers may recall that I have been focused on the following mission statement, which I feel can apply to any IT professional: “Focus your time on delivering features/functionality that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else.” - J. Vester “Focus your time on delivering features/functionality that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else.” - J. Vester Architectural observability adheres to my mission statement perfectly. By moving from plain vanilla observability to architectural observability , we can shift left for more reliable, more resilient, and better-performing systems. This shift affords us more time to spend on the business problems that our teams understand best. architectural observability As John F. Kennedy once said: “The best time to repair the roof is when the sun is shining.” Architectural observability should be placed on the same level (or higher) as APM. With AO, teams can truly understand their architecture, have a broader and more insightful view of their applications than with APM alone, and fix problems earlier. Have a really great day!

From Observability to Architectural Observability—Shifting Left for Resiliency

Real-World Resilient Strategies for Fintech Projects

Depending on who you asked about the outlook of the crypto industry going into 2024, you would have heard a response that ranged between cautious optimism and more of the same from 2023. The prevailing bearish macroeconomic conditions of heightened inflation and elevated interest rates tempered expectations.Even with the potential for a final Bitcoin ETF approval in the U.S. and the next Bitcoin halving, many thought the headwinds were too strong and that the explosion in popularity of AI would siphon off whatever VC money was still available.As we enter Q2 of 2024, most people are shocked at how strong and resilient the industry is proving to be. The popularity of the Bitcoin ETFs with institutional investors and the calming of the Federal Reserve’s language has spurred renewed interest in the highly volatile asset class. But what can we learn from the mistakes of the last bull run, and how can we overcome the reputational damage that crypto scandals did while navigating this new regulatory landscape? How we got here It seems like a distant memory, but 2021 was a banner year for the industry. Most crypto projects were hitting all-time highs, staking and lending protocols like the Anchor Protocoloffered over 20% returns, and charismatic founders like Do Kwon, SBF, and Alex Maschinsky were being hailed by the mainstream media as the next coming of Steve Jobs.Yet, there were warning signs that the market sentiment was about to shift and that these new wunderkinder were nothing more than grifters and charlatans preying on overhyped market sentiment. Even before the bull run in 2021, analysts were highlighting the potential pitfalls of some of the most popular projects of the last cycle. In April 2018,  former Head of Risk at MakerDAO and Research Analyst at Scaler Cyrus Younessi correctly identified the critical failure point in Terra Luna: “If Terra were to fall and break peg, then it would depend on Luna to Save Terra. But Luna would fall as investors would panic, and then Terra would continue to fall, and then they would just each keep contributing to each other’s demise.” Collapses in 2022, bear market 2023 The euphoria that was felt across the market in 2021 would come to an abrupt end in 2022 from the fall of the first of many dominoes with the collapse of Terra Luna. Terra ecosystem In one of the most remarkable collapses of this period, the Terra ecosystem’s flagship protocol, LUNA, and algorithmic stablecoin (UST) lost its peg to the U.S. dollar, causing the price to crash to zero in a matter of days. LUNA’s fall from grace was made all the more spectacular because of how arrogant its founder Do Kwon was about promoting its strength and stability. Celcius Throughout much of 2021, Celsius was considered a premium centralized lending platform. Its charismatic founder, Alex Maschinsky, aggressively promoted its more than 20% return on investment, and it became the de facto custodian for retail and institutional investors. However, the model became unsustainable as the market shifted and the underlying assets began to lose value. It was revealed that Celcius was using Ponzi economics to prop up the platform. It was borrowing new deposits to make up for the lack of funds to pay out the high returns, and the whole platform collapsed under the weight, taking with it investors' deposits. FTX No project was as popular or successful during the 2021 bull run as the centralized exchange FTX. It rose to prominence because of its founder, Sam Bankman Fried’s reputation as a dominant arbitrage trader in his first company, Alameda Research. He was an enigma that the media fawned over as he embraced the lifestyle of a humble billionaire by spouting the benefits of the philosophical lifestyle of effective altruism. FTX became the number one exchange globally, signing massive celebrity endorsements from Larry David, Tom Brady, and Steph Curry. FTX had entrenched itself so deeply into the industry by requiring projects it supported to hold their assets on the platform that when it collapsed, it impacted just about everyone in the space. The once dominant exchange was ultimately undermined by Binance’s founder CZ, after he publicly sold Binance’s entire stake in the FTX utility token FTT after it was revealed SBF had been using customers' deposits to cover Alameda’s losses and using FTT to make up the difference on the balance sheet. Shifting Regulatory Landscape 2022 plunged the crypto industry into a deep bear market that we are only starting to emerge from today. The industry had largely been left to self-regulate, with the SEC only intervening in cases of clear criminality. Yet, the experiences of 2022 left many people heavily criticizing the SEC’s approach, specifically Chairman Gary Gensler and the close relationship he had with SBF and Alameda Research’s CEO Caroline Ellison.In a change of tone, the SEC and Chairman Gensler began pursuing legal action against anyone and everyone it could in an aggressive shift to a regulation policy by enforcement. The shift impacted individual projects like Ripple, Library, and Tornado Cash. The shift also meant constant deferral delays on SEC regulatory decisions, specifically in relation to Bitcoin ETF approvals, as well as targeting good faith actors like centralized exchanges__Kraken__ and Coinbase. Kraken and Coinbase had been asking for clear regulations for years to operate within. For their troubles of trying to remain compliant, the SEC filed lawsuits against each for operating unregistered security exchanges To end 2023, the SEC went trophy hunting and emerged with the arrest of the CEO of the world’s largest crypto exchange (Binance), CZ. CZ pled guilty to anti-money laundering charges and now faces over ten years in prison, while the exchange was fined $ 4 billion. The shift in policy by the SEC represented a direct attack on the industry. Still, it ultimately did more damage by undermining these firms' ability to operate in the U.S. by creating confusion and an environment of ever-shifting regulatory goalposts. Ongoing hacks and heists Despite the SEC's new approach to the industry, heists and hacks throughout the industry remained rampant throughout the course of 2023, totaling over $ 1.7 billion. While 2024 is again proving to be a hotbed for crypto hacks and scams, the first three months totaling over $437 million. With the looming Bitcoin halving and the historical trend of it spurring the next bull run, it is relatively safe to say that this problem will get worse before it gets better. Outlook for 2024 So, with all the negative sentiment built up in the industry from the last cycle. How can we emerge and market the industry in a positive light?If we learned anything from the last cycle it's that the product needs to be centerstage rather than any charismatic leader. It is easy to get swept up in the excitement when the market moves, but we need to remember the fundamentals of DYOR. Accountability for those bad actors of the last cycle is also key. We are beginning to see how the criminal activity of those wunderkind founders was not because they were in crypto but because they were criminals. SBF and Maschinsky committed embezzlement and financial crimes because they took advantage of user funds, not because of anything inherent with the technology. They are more akin to Bernie Madoff than Satoshi Nakamoto. Explaining the difference will be key. We should also take comfort in the fact that despite Chairman Gensler’s disdain for the industry, he will be limited to his term, other chairmen and women in the SEC have dissenting opinions (Hester Pierce) to the SEC, and his legal challenges to reign in the industry often fail. Depending on who you asked about the outlook of the crypto industry going into 2024, you would have heard a response that ranged between cautious optimism and more of the same from 2023. The prevailing bearish macroeconomic conditions of heightened inflation and elevated interest rates tempered expectations. Even with the potential for a final Bitcoin ETF approval in the U.S. and the next Bitcoin halving, many thought the headwinds were too strong and that the explosion in popularity of AI would siphon off whatever VC money was still available. As we enter Q2 of 2024, most people are shocked at how strong and resilient the industry is proving to be. The popularity of the Bitcoin ETFs with institutional investors and the calming of the Federal Reserve’s language has spurred renewed interest in the highly volatile asset class. But what can we learn from the mistakes of the last bull run, and how can we overcome the reputational damage that crypto scandals did while navigating this new regulatory landscape? How we got here It seems like a distant memory, but 2021 was a banner year for the industry. Most crypto projects were hitting all-time highs, staking and lending protocols like the Anchor Protocol offered over 20% returns, and charismatic founders like Do Kwon, SBF, and Alex Maschinsky were being hailed by the mainstream media as the next coming of Steve Jobs. Yet, there were warning signs that the market sentiment was about to shift and that these new wunderkinder were nothing more than grifters and charlatans preying on overhyped market sentiment. Anchor Protocol Anchor Protocol Even before the bull run in 2021, analysts were highlighting the potential pitfalls of some of the most popular projects of the last cycle. In April 2018,  former Head of Risk at MakerDAO and Research Analyst at Scaler Cyrus Younessi correctly identified the critical failure point in Terra Luna: correctly identified correctly identified “If Terra were to fall and break peg, then it would depend on Luna to Save Terra. But Luna would fall as investors would panic, and then Terra would continue to fall, and then they would just each keep contributing to each other’s demise.” “If Terra were to fall and break peg, then it would depend on Luna to Save Terra. But Luna would fall as investors would panic, and then Terra would continue to fall, and then they would just each keep contributing to each other’s demise.” “If Terra were to fall and break peg, then it would depend on Luna to Save Terra. But Luna would fall as investors would panic, and then Terra would continue to fall, and then they would just each keep contributing to each other’s demise.” Collapses in 2022, bear market 2023 The euphoria that was felt across the market in 2021 would come to an abrupt end in 2022 from the fall of the first of many dominoes with the collapse of Terra Luna. Terra ecosystem In one of the most remarkable collapses of this period, the Terra ecosystem’s flagship protocol, LUNA, and algorithmic stablecoin (UST) lost its peg to the U.S. dollar, causing the price to crash to zero in a matter of days. LUNA’s fall from grace was made all the more spectacular because of how arrogant its founder Do Kwon was about promoting its strength and stability. price to crash to zero price to crash to zero Celcius Throughout much of 2021, Celsius was considered a premium centralized lending platform. Its charismatic founder, Alex Maschinsky, aggressively promoted its more than 20% return on investment, and it became the de facto custodian for retail and institutional investors. However, the model became unsustainable as the market shifted and the underlying assets began to lose value. It was revealed that Celcius was using Ponzi economics to prop up the platform. It was borrowing new deposits to make up for the lack of funds to pay out the high returns, and the whole platform collapsed under the weight , taking with it investors' deposits. collapsed under the weight collapsed under the weight FTX No project was as popular or successful during the 2021 bull run as the centralized exchange FTX. It rose to prominence because of its founder, Sam Bankman Fried’s reputation as a dominant arbitrage trader in his first company, Alameda Research. He was an enigma that the media fawned over as he embraced the lifestyle of a humble billionaire by spouting the benefits of the philosophical lifestyle of effective altruism. FTX became the number one exchange globally, signing massive celebrity endorsements from Larry David, Tom Brady, and Steph Curry. FTX had entrenched itself so deeply into the industry by requiring projects it supported to hold their assets on the platform that when it collapsed , it impacted just about everyone in the space. The once dominant exchange was ultimately undermined by Binance’s founder CZ, after he publicly sold Binance’s entire stake in the FTX utility token FTT after it was revealed SBF had been using customers' deposits to cover Alameda’s losses and using FTT to make up the difference on the balance sheet. when it collapsed when it collapsed Shifting Regulatory Landscape 2022 plunged the crypto industry into a deep bear market that we are only starting to emerge from today. The industry had largely been left to self-regulate, with the SEC only intervening in cases of clear criminality. Yet, the experiences of 2022 left many people heavily criticizing the SEC’s approach, specifically Chairman Gary Gensler and the close relationship he had with SBF and Alameda Research’s CEO Caroline Ellison. In a change of tone, the SEC and Chairman Gensler began pursuing legal action against anyone and everyone it could in an aggressive shift to a regulation policy by enforcement. The shift impacted individual projects like Ripple, Library, and Tornado Cash. The shift also meant constant deferral delays on SEC regulatory decisions, specifically in relation to Bitcoin ETF approvals, as well as targeting good faith actors like centralized exchanges__ Kraken __ and Coinbase . Kraken and Coinbase had been asking for clear regulations for years to operate within. For their troubles of trying to remain compliant, the SEC filed lawsuits against each for operating unregistered security exchanges Kraken Coinbase Coinbase filed lawsuits filed lawsuits operating unregistered security exchanges operating unregistered security exchanges To end 2023, the SEC went trophy hunting and emerged with the arrest of the CEO of the world’s largest crypto exchange (Binance), CZ. CZ pled guilty to anti-money laundering charges and now faces over ten years in prison, while the exchange was fined $ 4 billion. The shift in policy by the SEC represented a direct attack on the industry. Still, it ultimately did more damage by undermining these firms' ability to operate in the U.S. by creating confusion and an environment of ever-shifting regulatory goalposts. Ongoing hacks and heists Despite the SEC's new approach to the industry, heists and hacks throughout the industry remained rampant throughout the course of 2023, totaling over $ 1.7 billion. totaling over $ 1.7 billion. totaling over $ 1.7 billion. While 2024 is again proving to be a hotbed for crypto hacks and scams, the first three months totaling over $437 million . With the looming Bitcoin halving and the historical trend of it spurring the next bull run, it is relatively safe to say that this problem will get worse before it gets better. totaling over $437 million totaling over $437 million Outlook for 2024 So, with all the negative sentiment built up in the industry from the last cycle. How can we emerge and market the industry in a positive light? If we learned anything from the last cycle it's that the product needs to be centerstage rather than any charismatic leader. It is easy to get swept up in the excitement when the market moves, but we need to remember the fundamentals of DYOR. Accountability for those bad actors of the last cycle is also key. We are beginning to see how the criminal activity of those wunderkind founders was not because they were in crypto but because they were criminals. SBF and Maschinsky committed embezzlement and financial crimes because they took advantage of user funds, not because of anything inherent with the technology. They are more akin to Bernie Madoff than Satoshi Nakamoto. Explaining the difference will be key. We should also take comfort in the fact that despite Chairman Gensler’s disdain for the industry, he will be limited to his term, other chairmen and women in the SEC have dissenting opinions (Hester Pierce) to the SEC, and his legal challenges to reign in the industry often fail. dissenting opinions dissenting opinions often fail. often fail.

Resilient Strategies for Crypto Marketing Success in 2024: Overcoming Regulations and Scandals

2024 সালে ক্রিপ্টো বিপণন সাফল্যের জন্য স্থিতিস্থাপক কৌশল: প্রবিধান এবং কেলেঙ্কারি কাটিয়ে ওঠা

Belastbare Strategien für erfolgreiches Krypto-Marketing im Jahr 2024: Regulierungen und Skandale überwinden

Estrategias resilientes para el éxito del cripto marketing en 2024: superación de regulaciones y escándalos

Stratégies résilientes pour réussir le marketing cryptographique en 2024 : surmonter les réglementations et les scandales

2024 में क्रिप्टो मार्केटिंग की सफलता के लिए लचीली रणनीतियाँ: विनियमनों और घोटालों पर काबू पाना

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Estratégias resilientes para o sucesso do marketing criptográfico em 2024: superando regulamentações e escândalos

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2024 年加密货币营销成功的弹性策略：克服监管和丑闻

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 37 of 38. Line of Effort 3: Develop New Tools to Deliver Digital and Cyber Assistance Quickly and Efficiently The demand for cybersecurity and cybercrime assistance, in particular cyber defense, incident response, and skills to combat criminal misuse of cryptocurrency, is growing in scale. After cyberattacks against Ukraine, Costa Rica, and Albania, the United States and its allies shared threat intelligence; facilitated operational collaboration; enabled access to commercial cybersecurity companies’ services, including hardware, software, and embedded technical support; and funded longer term capacity building. From these and other cases, the State Department has learned the importance of regular and close coordination across the U.S. government and with international partners, as well as the importance of mobilizing private-sector technology and expertise. Modernizing authorities and mechanisms to provide technology-related foreign assistance at the speed and scale necessary is crucial. We must adapt our foreign assistance resources and authorities to support long-term U.S. leadership and foster digital solidarity. Recognizing the urgent and growing need for additional tools to advance U.S. cyber and digital foreign policy, Congress created, through the Department of State Authorization Act of 2023 and funded, through the Department of State, Foreign Operations, and Related Program Appropriations Act, 2024, the Cyberspace, Digital Connectivity, and Related Technologies Fund. This fund will provide the Department of State with authorities and dedicated funding to support strategically important cyber, digital, and technology-related foreign assistance programs. This is a significant step in advancing U.S. foreign policy. The Department will work to operationalize and implement these new authorities. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 37 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 37 of 38. here here Line of Effort 3: Develop New Tools to Deliver Digital and Cyber Assistance Quickly and Efficiently The demand for cybersecurity and cybercrime assistance, in particular cyber defense, incident response, and skills to combat criminal misuse of cryptocurrency, is growing in scale. After cyberattacks against Ukraine, Costa Rica, and Albania, the United States and its allies shared threat intelligence; facilitated operational collaboration; enabled access to commercial cybersecurity companies’ services, including hardware, software, and embedded technical support; and funded longer term capacity building. From these and other cases, the State Department has learned the importance of regular and close coordination across the U.S. government and with international partners, as well as the importance of mobilizing private-sector technology and expertise. Modernizing authorities and mechanisms to provide technology-related foreign assistance at the speed and scale necessary is crucial. We must adapt our foreign assistance resources and authorities to support long-term U.S. leadership and foster digital solidarity. Recognizing the urgent and growing need for additional tools to advance U.S. cyber and digital foreign policy, Congress created, through the Department of State Authorization Act of 2023 and funded, through the Department of State, Foreign Operations, and Related Program Appropriations Act, 2024, the Cyberspace, Digital Connectivity, and Related Technologies Fund. This fund will provide the Department of State with authorities and dedicated funding to support strategically important cyber, digital, and technology-related foreign assistance programs. This is a significant step in advancing U.S. foreign policy. The Department will work to operationalize and implement these new authorities. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State by the U.S Department of State

Building Digital Resilience: US Efforts to Develop Rapid Cyber Assistance Tools

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 36 of 38. Line of Effort 2: Augment Partner Cyber Capacity Building Efforts Cyber capacity building efforts—which usually focus on strengthening a nation’s ability to adopt and develop cyber policies and strategies or improving their technical ability to detect, respond to, and recover from cyber incidents—have a direct and positive impact on international cyber stability and the security of U.S. citizens. Assistance directed at policy- and strategy-making increases states’ credibility and engagement in international discussions. It provides them with the national-level capabilities needed to implement the norms developed under the framework for responsible state behavior in cyberspace, to conform with the standards of the Budapest Cybercrime Convention, to hold irresponsible actors accountable in cyberspace, and to develop a national-level approach to counter persistent cyber threats and build long-term resilience. Improving partner operational capabilities makes it more likely they will be able to combat transnational cybercrime threats, share useful cyber threat and incident information with the United States, and successfully partner with the United States in operations to disrupt malicious cyber activity. Over the last two decades, the Department of State has collaborated with other agencies, international partners, regional organizations, and the private sector to build cyber capacity abroad. Officials and private sector professionals from around the world participate in workshops on industrial control systems held with CISA. The United States assists efforts by the Organization of American States in areas such as cyber incident response, national cybersecurity strategy development and implementation, cybersecurity awareness, and cyber workforce development. The United States is a leading donor to Council of Europe programs designed to expand adoption of the Budapest Cybercrime Convention. The Global Forum on Cyber Expertise (GFCE), of which the United States is a founding and active member, provides a global platform to connect cyber policymakers, practitioners, and experts and to match assistance programs with recipients. Multiple agencies have supported international partners in using and adapting the NIST Cybersecurity Framework, and the Department of State has supported international participation in the development of version 2.0 of the framework. The NICE Workforce Framework for Cybersecurity (NICE Framework) has been leveraged to support talent development and management. The Department of Commerce, NIST, USAID, and the Department of State will engage international partners to promote the development of critical and emerging technology standards in areas such as best practices regarding data capture, processing, privacy, handling, and analysis; trustworthiness, verification, and assurance of AI systems and AI risk management; and content authentication and provenance, synthetic content detection, and content labeling. In addition, NIST has selected four algorithms designed to withstand cyberattacks by quantum computers and is developing standards for U.S. Government use. The Department of State will work with NIST to internationalize – including through ongoing engagements in international standards bodies – these post quantum cryptography standards so that organizations around the world can integrate them into their encryption infrastructure. They will also continue engaging international partners in developing and implementing cybersecurity best practices in areas such as Zero Trust, IoT cybersecurity, digital identity, operational technology, software security, and supply chain risk management. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 36 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 36 of 38. here here Line of Effort 2: Augment Partner Cyber Capacity Building Efforts Cyber capacity building efforts—which usually focus on strengthening a nation’s ability to adopt and develop cyber policies and strategies or improving their technical ability to detect, respond to, and recover from cyber incidents—have a direct and positive impact on international cyber stability and the security of U.S. citizens. Assistance directed at policy- and strategy-making increases states’ credibility and engagement in international discussions. It provides them with the national-level capabilities needed to implement the norms developed under the framework for responsible state behavior in cyberspace, to conform with the standards of the Budapest Cybercrime Convention, to hold irresponsible actors accountable in cyberspace, and to develop a national-level approach to counter persistent cyber threats and build long-term resilience. Improving partner operational capabilities makes it more likely they will be able to combat transnational cybercrime threats, share useful cyber threat and incident information with the United States, and successfully partner with the United States in operations to disrupt malicious cyber activity. Over the last two decades, the Department of State has collaborated with other agencies, international partners, regional organizations, and the private sector to build cyber capacity abroad. Officials and private sector professionals from around the world participate in workshops on industrial control systems held with CISA. The United States assists efforts by the Organization of American States in areas such as cyber incident response, national cybersecurity strategy development and implementation, cybersecurity awareness, and cyber workforce development. The United States is a leading donor to Council of Europe programs designed to expand adoption of the Budapest Cybercrime Convention. The Global Forum on Cyber Expertise (GFCE), of which the United States is a founding and active member, provides a global platform to connect cyber policymakers, practitioners, and experts and to match assistance programs with recipients. Multiple agencies have supported international partners in using and adapting the NIST Cybersecurity Framework, and the Department of State has supported international participation in the development of version 2.0 of the framework. The NICE Workforce Framework for Cybersecurity (NICE Framework) has been leveraged to support talent development and management. The Department of Commerce, NIST, USAID, and the Department of State will engage international partners to promote the development of critical and emerging technology standards in areas such as best practices regarding data capture, processing, privacy, handling, and analysis; trustworthiness, verification, and assurance of AI systems and AI risk management; and content authentication and provenance, synthetic content detection, and content labeling. In addition, NIST has selected four algorithms designed to withstand cyberattacks by quantum computers and is developing standards for U.S. Government use. The Department of State will work with NIST to internationalize – including through ongoing engagements in international standards bodies – these post quantum cryptography standards so that organizations around the world can integrate them into their encryption infrastructure. They will also continue engaging international partners in developing and implementing cybersecurity best practices in areas such as Zero Trust, IoT cybersecurity, digital identity, operational technology, software security, and supply chain risk management. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State by the U.S Department of State

Empowering Global Cyber Resilience: United States' Efforts in Partner Cyber Capacity Building

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 27 of 38. Line of Effort 2: Disrupt and Build Resilience to Malicious State Activity Given the interconnected nature of cyberspace, international cooperation is crucial to deny, disrupt, and counter adversary activities in and through cyberspace. The Department of State leads efforts, including facilitating international outreach, to address the rising threat of disruptive or destructive cyberattacks on the critical infrastructure of the United States and its allies and partners. This includes sharing through diplomatic channels joint cybersecurity advisories with the Cybersecurity and Infrastructure Security Agency (CISA), Federal Bureau of Investigation (FBI), and National Security Agency (NSA), and allies and partners on threats; capacity building and information sharing with new and existing partners to mitigate cyber threats and ensure the resilience of their critical infrastructure; and using bilateral, multilateral, and other fora to clarify and communicate expectations about adherence to international law and the framework for responsible behavior in cyberspace. In addition, members of the Quad have developed joint principles for the cybersecurity of critical infrastructure and NATO members have committed to ensuring the resilience of critical infrastructure, enhanced protection of critical infrastructure through training and exercises, and shared intelligence on threats. As part of its counter adversary cyber activity, the Department of State provides foreign policy guidance and uses diplomatic engagements to support the Department of Defense (DoD)’s efforts to campaign in and through cyberspace below the level of armed conflict to reinforce deterrence and frustrate adversaries. As laid out in the 2023 DoD Cyber Strategy, U.S. Cyber Command continues to defend forward to discover, expose, and protect against the sources of malicious cyber activities and to reinforce responsible state behavior by encouraging adherence to international law and internationally recognized cyberspace norms. The DoD Cyber Strategy also notes that cyber operations are most effective when used in concert with other instruments of national power, including diplomatic engagement and cyber capacity building. The Department of State, in close coordination with interagency and international partners, will continue to organize and execute sustained diplomatic pressure campaigns to raise international and public awareness of significant cyber threats and to increase the costs and risks to malicious cyber actors. For example, the United States has worked with allies, partners, and the private sector to disrupt DPRK revenue-generation efforts through cybercrime, crypto theft, and IT workers. U.S. Cyber Command, NSA, DHS, DOJ, and the FBI have exposed North Korean malware, seized malicious cyber infrastructure, seized cryptocurrency and fiat currency, and shared actionable threat intelligence with the private sector. The Department of State coordinates action with the Republic of Korea through a bilateral DPRK Cyber Working Group, including information sharing and policy coordination. Also, the United States, Japan, and the Republic of Korea coordinate efforts to counter DPRK cyber threats through a trilateral working group announced during the Camp David Summit in August 2023. The Department of State has also briefed officials around the world on threats posed by DPRK IT workers and cyber actors and deployed foreign assistance funds to build capacity to detect and defend against DPRK cyber and crypto threats. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 27 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 27 of 38. here here Line of Effort 2: Disrupt and Build Resilience to Malicious State Activity Given the interconnected nature of cyberspace, international cooperation is crucial to deny, disrupt, and counter adversary activities in and through cyberspace. The Department of State leads efforts, including facilitating international outreach, to address the rising threat of disruptive or destructive cyberattacks on the critical infrastructure of the United States and its allies and partners. This includes sharing through diplomatic channels joint cybersecurity advisories with the Cybersecurity and Infrastructure Security Agency (CISA), Federal Bureau of Investigation (FBI), and National Security Agency (NSA), and allies and partners on threats; capacity building and information sharing with new and existing partners to mitigate cyber threats and ensure the resilience of their critical infrastructure; and using bilateral, multilateral, and other fora to clarify and communicate expectations about adherence to international law and the framework for responsible behavior in cyberspace. In addition, members of the Quad have developed joint principles for the cybersecurity of critical infrastructure and NATO members have committed to ensuring the resilience of critical infrastructure, enhanced protection of critical infrastructure through training and exercises, and shared intelligence on threats. As part of its counter adversary cyber activity, the Department of State provides foreign policy guidance and uses diplomatic engagements to support the Department of Defense (DoD)’s efforts to campaign in and through cyberspace below the level of armed conflict to reinforce deterrence and frustrate adversaries. As laid out in the 2023 DoD Cyber Strategy, U.S. Cyber Command continues to defend forward to discover, expose, and protect against the sources of malicious cyber activities and to reinforce responsible state behavior by encouraging adherence to international law and internationally recognized cyberspace norms. The DoD Cyber Strategy also notes that cyber operations are most effective when used in concert with other instruments of national power, including diplomatic engagement and cyber capacity building. The Department of State, in close coordination with interagency and international partners, will continue to organize and execute sustained diplomatic pressure campaigns to raise international and public awareness of significant cyber threats and to increase the costs and risks to malicious cyber actors. For example, the United States has worked with allies, partners, and the private sector to disrupt DPRK revenue-generation efforts through cybercrime, crypto theft, and IT workers. U.S. Cyber Command, NSA, DHS, DOJ, and the FBI have exposed North Korean malware, seized malicious cyber infrastructure, seized cryptocurrency and fiat currency, and shared actionable threat intelligence with the private sector. The Department of State coordinates action with the Republic of Korea through a bilateral DPRK Cyber Working Group, including information sharing and policy coordination. Also, the United States, Japan, and the Republic of Korea coordinate efforts to counter DPRK cyber threats through a trilateral working group announced during the Camp David Summit in August 2023. The Department of State has also briefed officials around the world on threats posed by DPRK IT workers and cyber actors and deployed foreign assistance funds to build capacity to detect and defend against DPRK cyber and crypto threats. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State by the U.S Department of State

Diplomatic Efforts to Combat Cyber Threats and Maintain International Cyber Resilience 

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 14 of 38. Line of Effort 3: Enhance Security and Resilience of Undersea Cables Undersea cables carry more than 95 percent of the world’s digital traffic. As data continues to proliferate and increase exponentially, so too does demand for cables and other transmission systems. Disruption or destruction of the cables as a result of accidents, natural disasters, or malicious actions could isolate a county, threaten national security, and result in billions of dollars of damage to the economy. Choices made about which vendors to rely on for undersea cable infrastructure, maintenance, and repair operations can either drive development and innovation or lead to new forms of dependency and insecurity. As a result, the Department of State, in coordination with other agencies, will prioritize enhancing the security and resilience of undersea cables. U.S. firms and other trusted suppliers are leading producers of many network components, embedded technologies, and related services for undersea cables, and they are investing in and financing new undersea cables connecting all regions of the world. The U.S. government will continue to support U.S. and other trusted suppliers in the installation, operation, maintenance, and repair of secure infrastructure as well as to promote a regulatory environment that enables continued investments. Since 2021, the Department of State has implemented the CABLES program throughout the East Asia Pacific region, responsibly informing essential telecommunications and cables infrastructure stakeholders of the perils of choosing untrusted suppliers. The United States provided capacity building to support five countries using U.S. technology for the South-East Asia-Middle East-Western Europe 6 cable (SMW6), and separately it provided over $22 million in partnership with Australia and Japan to help fund the East Micronesia Cable being built by a Japanese firm. In October 2023, the United States announced that, working with Congress, it would provide, along with Australia, investments totaling $65 million to fund future undersea cable connectivity for Pacific Island countries in order to facilitate access to global markets and the realization of regional connectivity goals. In support of these policy objectives, the United States will continue engaging with the G7 and other multilateral groups to strengthen trusted, multi-layered global connectivity that provides data route diversity, resiliency, and redundancies. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 14 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 14 of 38. here Line of Effort 3: Enhance Security and Resilience of Undersea Cables Undersea cables carry more than 95 percent of the world’s digital traffic. As data continues to proliferate and increase exponentially, so too does demand for cables and other transmission systems. Disruption or destruction of the cables as a result of accidents, natural disasters, or malicious actions could isolate a county, threaten national security, and result in billions of dollars of damage to the economy. Choices made about which vendors to rely on for undersea cable infrastructure, maintenance, and repair operations can either drive development and innovation or lead to new forms of dependency and insecurity. As a result, the Department of State, in coordination with other agencies, will prioritize enhancing the security and resilience of undersea cables. U.S. firms and other trusted suppliers are leading producers of many network components, embedded technologies, and related services for undersea cables, and they are investing in and financing new undersea cables connecting all regions of the world. The U.S. government will continue to support U.S. and other trusted suppliers in the installation, operation, maintenance, and repair of secure infrastructure as well as to promote a regulatory environment that enables continued investments. Since 2021, the Department of State has implemented the CABLES program throughout the East Asia Pacific region, responsibly informing essential telecommunications and cables infrastructure stakeholders of the perils of choosing untrusted suppliers. The United States provided capacity building to support five countries using U.S. technology for the South-East Asia-Middle East-Western Europe 6 cable (SMW6), and separately it provided over $22 million in partnership with Australia and Japan to help fund the East Micronesia Cable being built by a Japanese firm. In October 2023, the United States announced that, working with Congress, it would provide, along with Australia, investments totaling $65 million to fund future undersea cable connectivity for Pacific Island countries in order to facilitate access to global markets and the realization of regional connectivity goals. In support of these policy objectives, the United States will continue engaging with the G7 and other multilateral groups to strengthen trusted, multi-layered global connectivity that provides data route diversity, resiliency, and redundancies. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State

Enhancing the Security and Resilience of Undersea Cables

Building Resilient Systems With Retry Patterns

Automated solutions have been around for over a decade in the cybersecurity space. However, their use has been confined to very surface-level areas—it’s time this changed. With the recent advancements in generative AI, the technology has proven to have a higher ceiling for application, including time-consuming yet vital processes like cyber discovery. AI In fact, organizations that automate cyber defense mechanisms will experience a in security breaches by 2026. So, it’s evident that automation will become the norm for many cybersecurity processes. This is especially important given the more targeted and aggressive attacks, such as advanced persistent threats (APTs), that have been launched in the past year. 66% reduction APTs make cybersecurity much murkier. If a company experiences an attack of this nature, they’re as good as gone; the sophistication of these threats can penetrate the most robust systems when mildly overlooked. One of many examples from last year is the highly secure USBs, gaining access to government data via APTs by subtly infecting and taking over every device they connected them to. malicious actors that targeted Here’s where more advanced automation comes in to keep APTs at bay. Let’s see how adopting these new practices can highly benefit your security team and business. Faster and more controlled threat intelligence turnarounds The APT protection market is set to grow by a staggering annually until 2032 when it will be worth $51.5 billion. The increasing severity of these attacks is what’s driving CISOs to spend more of their budget protecting their company’s attack surfaces — starting with proper threat intelligence and asset discovery methods. 18% Automation can speed up asset discovery, and time is of the essence when it comes to data security. A typical discovery process for a company of around 10,000 employees would take a security team a couple of days to finish. By automating this process, the timeline plummets to just one day. Instead of outsourcing this process to cybersecurity companies, in-house security teams can cut the middleman and use threat intelligence tools to automate the discovery process for them. This is possible with SaaS programs that use open source and proprietary codes to create a workflow, allowing companies to run tailored discovery as often as they see fit. The time difference between manual and automated can be a costly APT attack or a safe system. More precise and accurate discovery to bridge the skills gap You wouldn’t begin securing your systems without knowing exactly what needs to be done. It would be a waste of both time and money. This is why discovery is so crucial to threat intelligence and successful cybersecurity practices — it’s the foundation for protecting attack surfaces. And for discovery to be worthwhile, it must be done right. However, the cybersecurity talent shortage is making it harder for companies to properly protect their systems, especially when are being affected by the lack of professionals globally. Automation is helping bridge this gap and giving companies peace of mind for their foundational cybersecurity strategies. 71% of organizations By using threat intelligence SaaS tools that automate discovery, security teams can forego a discovery expert while knowing this crucial piece of the puzzle is still being handled with precision and accuracy by an AI workflow. APTs don’t let up, and minimal human error could open the gates for malicious actors to break in and set up shop. Increased human power in other specialized activities CISOs that human error is their organization’s biggest cyber vulnerability. Discovery can be a very time-consuming task, and human processes can become faulty at any point in the job. Now that threat intelligence automation is a more accessible and developed practice, security leaders shouldn’t have to place their human power in these intensive and often flawed activities. highly agree After adopting SaaS tools for discovery, security teams will rest assured their discovery needs will be handled by software that takes less time and significantly reduces errors in threat detection. What will this translate to? More time applying threat intelligence data into more robust security protocols like vulnerability prioritization and remediation, meeting data security compliance requirements, and other activities that require human input. Keeping APTs away from your company encompasses several steps in your cybersecurity practices. Automation is making it easier for security teams to spend their time on valuable activities while others run on AI workflows with less margin for errors. In this new age of AI and automation, these tools are becoming more affordable and user-friendly to address skills shortages and the increase in complex cyber attacks. Ultimately, automating certain tasks is a win-win for security teams, budgeting, and cybersecurity measures — bringing in convenience, precision, and affordability. , co-founder, Trickest Nenad Zaric

Ensuring Cyber Resilience: The Imperative of Automating the APT Discovery Process

This paper is available on arxiv under CC BY-SA 4.0 DEED license.
Authors:
(1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de;
(2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany;
(6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ;
(7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction
Materials and Methods
Data
Results
Conclusions, Open Research Section and Conflict of Interest/Competing Interests
Author Contributions, Supplementary Information, Acknowledgments and References
Additional Insight from the Conceptual Model 
AMSR2 until 2022 Excluded Due to Missing Human Land Use Data
Investigation of Resilience Loss in AMSR2’s X-Band
Results Proving Robustness
Potential Driving Forces and Sources of Bias INVESTIGATION OF RESILIENCE LOSS IN AMSR2’S X-BAND Text S7. The influence of forest loss for the years 2021 to 2022 The X-band of AMSR2 results from the observation of a shorter wavelength compared to the two C-bands. This leads to less sensitivity to aboveground biomass, which is why the results are less likely to indicate whether the ARF is loosing resilience or not. For completeness, Figures S7 and S8 show the results for the X-band. While the spatial correlation is more often increasing than not, which is comparable to the results in the C-bands, this is not the case for variance and AC1 (Figure S7). While the percentage of cells in Figure S8a) exhibiting a positive trend in all three indicators is still above what would be expected by random (12.5 %), it is far less than detected for AMSR2’s C-bands. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction Materials and Methods Data Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Potential Driving Forces and Sources of Bias Abstract and Introduction Abstract and Introduction Materials and Methods Materials and Methods Data Data Results Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Results Proving Robustness Potential Driving Forces and Sources of Bias Potential Driving Forces and Sources of Bias INVESTIGATION OF RESILIENCE LOSS IN AMSR2’S X-BAND Text S7. The influence of forest loss for the years 2021 to 2022 The X-band of AMSR2 results from the observation of a shorter wavelength compared to the two C-bands. This leads to less sensitivity to aboveground biomass, which is why the results are less likely to indicate whether the ARF is loosing resilience or not. For completeness, Figures S7 and S8 show the results for the X-band. While the spatial correlation is more often increasing than not, which is comparable to the results in the C-bands, this is not the case for variance and AC1 (Figure S7). While the percentage of cells in Figure S8 a) exhibiting a positive trend in all three indicators is still above what would be expected by random (12.5 %), it is far less than detected for AMSR2’s C-bands. a)

Loss of Amazon Rainforest Resilience: Investigation of Resilience Loss in AMSR2’s X-Band

This paper is available on arxiv under CC BY-SA 4.0 DEED license.
Authors:
(1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de;
(2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany;
(6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ;
(7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction
Materials and Methods
Data
Results
Conclusions, Open Research Section and Conflict of Interest/Competing Interests
Author Contributions, Supplementary Information, Acknowledgments and References
Additional Insight from the Conceptual Model 
AMSR2 until 2022 Excluded Due to Missing Human Land Use Data
Investigation of Resilience Loss in AMSR2’s X-Band
Results Proving Robustness
Potential Driving Forces and Sources of Bias RESULTS PROVING ROBUSTNESS Text S8. Robustness with respect to the parameterization of the STL algorithm The results in Figures 5 and 6 are based on the residual found by the STL algorithm with default parameterization. Yet, Figure S9 and S10 prove that the analysis is not influenced by this choice as the results are almost identical when the length of the seasonal smoother equals 13 (instead of 7). Text S9. Robustness with respect to the size of the sliding windows The choice of the sliding window size is difficult for short time series as that of AMSR-E’s and AMSR2’s VOD. Yet, the results are robust w.r.t it, as Figure S11 based on sliding windows of 3 years is almost identical to Figure 5 with windows of 5 years. This also holds for the summarizing Figure S12 when compared to Figure 6. It is to note that the spatially averaged time series of the three indicators are more noisy for the choice of smaller windows, which is due to the fact the the calculation of the indicators becomes more unstable when calculated on only 36 data points, corresponding to the 3 years. The right choice of the window size in an approach to resilience analysis like the one shown here is challenging, as the increase can only be determined reliably based on enough indicator time steps either. The fact that the results are robust with respect to the window size gives confidence in the choice of 5 years as default. Text S10. Robustness with respect to the measure of change In the main text, any change in indicators is measured in terms of linear trends. Yet, another option would be the Kendall’s τ as a measure of steadiness of increase. Figure S13 is the analogue of Figure 5 but with increase in time series quantified in terms of Kendall’s τ . In Figure 6, increase in the indicators is quantified in terms of linear trends. The results are almost identical when measuring the indicators’ increase by the Kendall τ rank coefficient. Text S11. Robustness with respect to the distance defining neighboring cells The definition of the spatial correlation depends on the distance taken into account when taking the average correlation with neighboring cells. Figure S15 shows the trends of the spatial correlation for the different VIs comparing different radii defining the set of neighboring cells. As expected, a greater number of neighbors spatially smooths the results, but does not influence the general spatial pattern of the trends of spatial correlation. The turquoise circle on the bottom of the map shows an exemplary circular band of neighboring cells. In the main text, all results are based on a radius of 100 km. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction Materials and Methods Data Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Potential Driving Forces and Sources of Bias Abstract and Introduction Abstract and Introduction Materials and Methods Materials and Methods Data Data Results Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Results Proving Robustness Potential Driving Forces and Sources of Bias Potential Driving Forces and Sources of Bias RESULTS PROVING ROBUSTNESS Text S8. Robustness with respect to the parameterization of the STL algorithm The results in Figures 5 and 6 are based on the residual found by the STL algorithm with default parameterization. Yet, Figure S9 and S10 prove that the analysis is not influenced by this choice as the results are almost identical when the length of the seasonal smoother equals 13 (instead of 7). Text S9. Robustness with respect to the size of the sliding windows The choice of the sliding window size is difficult for short time series as that of AMSR-E’s and AMSR2’s VOD. Yet, the results are robust w.r.t it, as Figure S11 based on sliding windows of 3 years is almost identical to Figure 5 with windows of 5 years. This also holds for the summarizing Figure S12 when compared to Figure 6. It is to note that the spatially averaged time series of the three indicators are more noisy for the choice of smaller windows, which is due to the fact the the calculation of the indicators becomes more unstable when calculated on only 36 data points, corresponding to the 3 years. The right choice of the window size in an approach to resilience analysis like the one shown here is challenging, as the increase can only be determined reliably based on enough indicator time steps either. The fact that the results are robust with respect to the window size gives confidence in the choice of 5 years as default. Text S10. Robustness with respect to the measure of change In the main text, any change in indicators is measured in terms of linear trends. Yet, another option would be the Kendall’s τ as a measure of steadiness of increase. Figure S13 is the analogue of Figure 5 but with increase in time series quantified in terms of Kendall’s τ . In Figure 6, increase in the indicators is quantified in terms of linear trends. The results are almost identical when measuring the indicators’ increase by the Kendall τ rank coefficient. Text S11. Robustness with respect to the distance defining neighboring cells The definition of the spatial correlation depends on the distance taken into account when taking the average correlation with neighboring cells. Figure S15 shows the trends of the spatial correlation for the different VIs comparing different radii defining the set of neighboring cells. As expected, a greater number of neighbors spatially smooths the results, but does not influence the general spatial pattern of the trends of spatial correlation. The turquoise circle on the bottom of the map shows an exemplary circular band of neighboring cells. In the main text, all results are based on a radius of 100 km.

Loss of Amazon Rainforest Resilience: Results Proving Robustness

This paper is available on arxiv under CC BY-SA 4.0 DEED license.
Authors:
(1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de;
(2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany;
(6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ;
(7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction
Materials and Methods
Data
Results
Conclusions, Open Research Section and Conflict of Interest/Competing Interests
Author Contributions, Supplementary Information, Acknowledgments and References
Additional Insight from the Conceptual Model 
AMSR2 until 2022 Excluded Due to Missing Human Land Use Data
Investigation of Resilience Loss in AMSR2’s X-Band
Results Proving Robustness
Potential Driving Forces and Sources of Bias POTENTIAL DRIVING FORCES AND SOURCES OF BIAS Text S12. The influence of the South American Monsoon system as a potentially bistable system In the Amazon basin, the South American monsoon system and the atmospheric moisture recycling form a complex and potentially bi-stable system. Hence the monthly precipitation itself could exhibit signs of CSD, which would then drive vegetation changes that are wrongly interpreted as the destabilization of the vegetation. In Figure S16, the signs of the trend of variance, AC1 and spatial are compared between VOD and precipitation (Funk et al., 2014). We argue that due to the high amount of cells where the values differ in sign for the VIs and precipitation (sum of diagonal cells in boxes expected to be 50 % for random assignment), the observed changes in the indicators are likely a measure of critical slowing down in the vegetation and not only a direct effect of variability in precipitation. Text S13. The influence of changes in precipitation as a potential driving force If precipitation is the main driver of ARF resilience loss, one would expect that in regions with stronger decreases in precipitation (negative trend), more critical slowing down can be observed. This comparison in displayed in Figure S17. In parts, decreased precipitation could potentially explain the destabilization detected in the western Amazon basin by both bands of AMSR-E as well as in the western and northeastern regions detected by AMSR2’s bands. However it is important to note here that the number of cells with an increasing amount of precipitation and decreasing indicators and those with decreasing amount of precipitation and increasing indicators add up to only about 50 %, which is what is to be expected for a random assignment of positive and negative trends in both. Text S14. The influence of changes in temperature Instead of decreasing precipitation (see Figure S17, an increase in temperature can also lead to vegetation destabilization through water stress by higher evapo-transpirative demand. If this was the driver of the CSD, increasing temperatures must coincide with positive trends in the indicators of CSD in Figure S18. Yet changes in temperature cannot explain the detected destabilization in the years 2002 to 2011. For the time period of AMSR2, destabilization detected in the northeast (band C1) and the east (band C2) might at least partially be accounted for rising temperatures (the percentage of cells with rising/declining temperatures and in-/decreasing indicators falls between 48.6 % and up to 59.3 %). Text S15. The potential bias induced by saturation In case of optical sensors, their saturation over high biomass regions often renders the VIs insensitive to the subtle changes due to perturbations that a resilience analysis based on CSD relies on. The argument is that with decreasing biomass, the VIs become more sensitive, which then translates into a signal of CSD as a direct artifact of the higher perception of perturbations. Even though VOD results from observations of microwaves, we want to address the question of saturation in VOD. As Figure S19 shows, we cannot exclude this as an artifact for AMSRE’s band C as well as AMSR2’s two bands, as they hardly observed any cells with increasing biomass. Interestingly, AMSR-E’s band X detected an increase in biomass as well as a loss of resilience in most of the cells (77.3 % to 78.2 %). Thus, its signal of destabilization of almost the entire ARF during the years 2002 to 2011 is not an artifact of saturation of the band. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction Materials and Methods Data Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Potential Driving Forces and Sources of Bias Abstract and Introduction Abstract and Introduction Materials and Methods Materials and Methods Data Data Results Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Results Proving Robustness Potential Driving Forces and Sources of Bias Potential Driving Forces and Sources of Bias POTENTIAL DRIVING FORCES AND SOURCES OF BIAS Text S12. The influence of the South American Monsoon system as a potentially bistable system In the Amazon basin, the South American monsoon system and the atmospheric moisture recycling form a complex and potentially bi-stable system. Hence the monthly precipitation itself could exhibit signs of CSD, which would then drive vegetation changes that are wrongly interpreted as the destabilization of the vegetation. In Figure S16, the signs of the trend of variance, AC1 and spatial are compared between VOD and precipitation (Funk et al., 2014). We argue that due to the high amount of cells where the values differ in sign for the VIs and precipitation (sum of diagonal cells in boxes expected to be 50 % for random assignment), the observed changes in the indicators are likely a measure of critical slowing down in the vegetation and not only a direct effect of variability in precipitation. Text S13. The influence of changes in precipitation as a potential driving force If precipitation is the main driver of ARF resilience loss, one would expect that in regions with stronger decreases in precipitation (negative trend), more critical slowing down can be observed. This comparison in displayed in Figure S17. In parts, decreased precipitation could potentially explain the destabilization detected in the western Amazon basin by both bands of AMSR-E as well as in the western and northeastern regions detected by AMSR2’s bands. However it is important to note here that the number of cells with an increasing amount of precipitation and decreasing indicators and those with decreasing amount of precipitation and increasing indicators add up to only about 50 %, which is what is to be expected for a random assignment of positive and negative trends in both. Text S14. The influence of changes in temperature Instead of decreasing precipitation (see Figure S17, an increase in temperature can also lead to vegetation destabilization through water stress by higher evapo-transpirative demand. If this was the driver of the CSD, increasing temperatures must coincide with positive trends in the indicators of CSD in Figure S18. Yet changes in temperature cannot explain the detected destabilization in the years 2002 to 2011. For the time period of AMSR2, destabilization detected in the northeast (band C1) and the east (band C2) might at least partially be accounted for rising temperatures (the percentage of cells with rising/declining temperatures and in-/decreasing indicators falls between 48.6 % and up to 59.3 %). Text S15. The potential bias induced by saturation In case of optical sensors, their saturation over high biomass regions often renders the VIs insensitive to the subtle changes due to perturbations that a resilience analysis based on CSD relies on. The argument is that with decreasing biomass, the VIs become more sensitive, which then translates into a signal of CSD as a direct artifact of the higher perception of perturbations. Even though VOD results from observations of microwaves, we want to address the question of saturation in VOD. As Figure S19 shows, we cannot exclude this as an artifact for AMSRE’s band C as well as AMSR2’s two bands, as they hardly observed any cells with increasing biomass. Interestingly, AMSR-E’s band X detected an increase in biomass as well as a loss of resilience in most of the cells (77.3 % to 78.2 %). Thus, its signal of destabilization of almost the entire ARF during the years 2002 to 2011 is not an artifact of saturation of the band.

Loss of Amazon Rainforest Resilience: Potential Driving Forces and Sources of Bias

This paper is available on arxiv under CC BY-SA 4.0 DEED license.
Authors:
(1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de;
(2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany;
(6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ;
(7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction
Materials and Methods
Data
Results
Conclusions, Open Research Section and Conflict of Interest/Competing Interests
Author Contributions, Supplementary Information, Acknowledgments and References
Additional Insight from the Conceptual Model 
AMSR2 until 2022 Excluded Due to Missing Human Land Use Data
Investigation of Resilience Loss in AMSR2’s X-Band
Results Proving Robustness
Potential Driving Forces and Sources of Bias ADDITIONAL INSIGHT FROM THE CONCEPTUAL MODEL Text S1. Stability in the conceptual model The interaction of vegetation and precipitation is plotted in Figure S1. In the conceputal model, vegetation is a function of absolute incoming precipitation only and is not dependent on the cell’s location. Hence, it is the same for each cell and plotted as the single black solid line. Precipitation on the other hand is a function of the vegetation of the cell, but also of the vegetation of the cell’s eastern neighbors. Hence, it differs for all cells, which are encoded by the colors as in Figure 2. For the calculation of one cell’s precipitation as a function of vegetation, its eastern neighbors are considered to be in equilibrium. Furthermore, precipitation depends on the amount of moisture coming in from the Atlantic ocean, or in other words on the control parameter B. The solid lines represent the state in the beginning of the experiment with B = 1010, and the dashed lines in the end with B = 990. Where the black and coloured lines cross at some (V∗, P∗), the corresponding value of vegetation supports the value of precipitation P∗, but also is V∗ supported by P∗. Hence, this point is considered a fixed point, or equilibrium. As the moisture inflow is reduced in the simulations from 1010 (solid lines) to 990 (dashed lines), the fixed points with high vegetation values vanish, explaining the tipping in the Figures S4 and S5 . Text S2. Precipitation decrease in the conceptual model The results of simulations with decreasing moisture inflow from the Atlantic ocean (decreasing B(t)) were presented in the main text in Figure 4. Figure S2 zooms in into cell-wise total precipitation and highlights its stochasticity as well as the almost linear decrease up to the Tipping Point of vegetation. Text S3. Deforestation scenario leads to Tipping in the conceptual model The results of simulations with decreasing moisture inflow from the Atlantic ocean (decreasingB(t)) were presented in the main text in Figure 4. Figure S3 is identical but presents the results from simulations with unchanging inflow from the Atlantic, i.e. B(t) = 1000 for all t. Instead, the amount vegetation in the east-most cell is considered the bifurcation parameter and constantly decreased from 1 to 0, thereby mimicking deforestation. Text S4. Distribution of the trends for the three resilience indicators In the conceptual model, all indicators show clear trends when approaching the Tipping Point of vegetation, as illustrated in Figure S4. There, clearly all three indicators show a strong shift towards positive values with only very few negative ones for AC1. In a spatially extended system like the ARF, one would thus expect all three indicators to increase when the corresponding grid cell loses resilience. Text S5. Distribution of the trends for the three resilience indicators In Figure 4 the time of emergence (ToE) was defined as the first time when a trend becomes significant. Yet, as more conservative approach one can define the ToE as the time when a trend becomes permanently significant, i.e. when all trends later in time are significant as well. The results with this definition are displayed in Figure S5. The uncertainty in the timing of this type of ToE differs much less for AC1 compared to the definition in the main text. This indicates that AC1 might be more prone to false alarms. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction Materials and Methods Data Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Potential Driving Forces and Sources of Bias Abstract and Introduction Abstract and Introduction Materials and Methods Materials and Methods Data Data Results Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Results Proving Robustness Potential Driving Forces and Sources of Bias Potential Driving Forces and Sources of Bias ADDITIONAL INSIGHT FROM THE CONCEPTUAL MODEL Text S1. Stability in the conceptual model The interaction of vegetation and precipitation is plotted in Figure S1. In the conceputal model, vegetation is a function of absolute incoming precipitation only and is not dependent on the cell’s location. Hence, it is the same for each cell and plotted as the single black solid line. Precipitation on the other hand is a function of the vegetation of the cell, but also of the vegetation of the cell’s eastern neighbors. Hence, it differs for all cells, which are encoded by the colors as in Figure 2. For the calculation of one cell’s precipitation as a function of vegetation, its eastern neighbors are considered to be in equilibrium. Furthermore, precipitation depends on the amount of moisture coming in from the Atlantic ocean, or in other words on the control parameter B. The solid lines represent the state in the beginning of the experiment with B = 1010, and the dashed lines in the end with B = 990. Where the black and coloured lines cross at some (V∗, P∗), the corresponding value of vegetation supports the value of precipitation P∗, but also is V∗ supported by P∗. Hence, this point is considered a fixed point, or equilibrium. As the moisture inflow is reduced in the simulations from 1010 (solid lines) to 990 (dashed lines), the fixed points with high vegetation values vanish, explaining the tipping in the Figures S4 and S5 . Text S2. Precipitation decrease in the conceptual model The results of simulations with decreasing moisture inflow from the Atlantic ocean (decreasing B(t)) were presented in the main text in Figure 4. Figure S2 zooms in into cell-wise total precipitation and highlights its stochasticity as well as the almost linear decrease up to the Tipping Point of vegetation. Text S3. Deforestation scenario leads to Tipping in the conceptual model The results of simulations with decreasing moisture inflow from the Atlantic ocean (decreasingB(t)) were presented in the main text in Figure 4. Figure S3 is identical but presents the results from simulations with unchanging inflow from the Atlantic, i.e. B(t) = 1000 for all t. Instead, the amount vegetation in the east-most cell is considered the bifurcation parameter and constantly decreased from 1 to 0, thereby mimicking deforestation. Text S4. Distribution of the trends for the three resilience indicators In the conceptual model, all indicators show clear trends when approaching the Tipping Point of vegetation, as illustrated in Figure S4. There, clearly all three indicators show a strong shift towards positive values with only very few negative ones for AC1. In a spatially extended system like the ARF, one would thus expect all three indicators to increase when the corresponding grid cell loses resilience. Text S5. Distribution of the trends for the three resilience indicators In Figure 4 the time of emergence (ToE) was defined as the first time when a trend becomes significant. Yet, as more conservative approach one can define the ToE as the time when a trend becomes permanently significant, i.e. when all trends later in time are significant as well. The results with this definition are displayed in Figure S5. The uncertainty in the timing of this type of ToE differs much less for AC1 compared to the definition in the main text. This indicates that AC1 might be more prone to false alarms.

Loss of Amazon Rainforest Resilience: Additional Insight from the Conceptual Model

Loss of Amazon Rainforest Resilience: Author Contributions

This paper is available on arxiv under CC BY-SA 4.0 DEED license.
Authors:
(1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de;
(2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany;
(5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany;
(6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ;
(7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction
Materials and Methods
Data
Results
Conclusions, Open Research Section and Conflict of Interest/Competing Interests
Author Contributions, Supplementary Information, Acknowledgments and References
Additional Insight from the Conceptual Model 
AMSR2 until 2022 Excluded Due to Missing Human Land Use Data
Investigation of Resilience Loss in AMSR2’s X-Band
Results Proving Robustness
Potential Driving Forces and Sources of Bias AMSR2 UNTIL 2022 EXCLUDED DUE TO MISSING HUMAN LAND USE DATA Text S6. The influence of forest loss for the years 2021 to 2022 Since the data sets for excluding HLU are only available until 2020, the VOD data by AMSR2 was only analyzed until 2020 even though it is available until present times. The maps in Figure S6a) and c) show the difference between the number of indicators with positive trends if analyzed until 2020 (as in the main text, Figure 6) and until 2022 for AMSR2’s band C and X, respectively. Differences mainly occur where sudden changes in VOD after 2020 can have a strong influence on the detrending method STL. Since STL acts on sliding windows over the whole data set as is (to detrend) and over the separate months (for de-seasonalizing), the influence of strong changes after 2020 increase over time. This becomes apparent in the the spatially averaged time series in b) and d), where all indicators start at the same level independent for both cases, where the analysis is performed until 2020 (solid lines) as well as until 2022 (dotted lines), but diverge afterwards. Such increasingly strong discrepancies in the period until 2020, where the data is equivalent, must stem from STL, which over time becomes more sensitive to strong changes in later time steps due to its sliding window approach. Yet, this is only the case for strong changes in the VOD after 2020, as it could result from forest loss and Human interference. Hence, we argue that the exclusion of HLU based on the years until 2020 is not sufficient to analyze data thereafter. This explanation is supported by high rates of forest loss since the end of 2020, with more than 18.000 km2 deforestation and 27.000 km2 degradation registered by the Instituto Nacional de Pesquisas Espaciais’s monitoring programs (INPE, 2023). Hence, the results for AMSR2 analyzed until 2022 cannot be relied on (see Figure S6). This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: Authors: (1) Lana L. Blaschke, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany and lana.blaschke@pik-potsdam.de; (2) Da Nian, Potsdam Institute for Climate Impact Research, Potsdam, Germany; (3) Sebastian Bathiany,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (4)  Maya Ben-Yami,  Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, and Potsdam Institute for Climate Impact Research, Potsdam, Germany; (5) Taylor Smith, Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany; (6)  Chris A. Boulton, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK ; (7) Niklas Boers, Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany, Potsdam Institute for Climate Impact Research, Potsdam, Germany, Global Systems Institute, University of Exeter, EX4 4QE Exeter, UK, and Department of Mathematics, University of Exeter, EX4 4QF Exeter, UK. Table of Links Abstract and Introduction Materials and Methods Data Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Potential Driving Forces and Sources of Bias Abstract and Introduction Abstract and Introduction Materials and Methods Materials and Methods Data Data Results Results Conclusions, Open Research Section and Conflict of Interest/Competing Interests Conclusions, Open Research Section and Conflict of Interest/Competing Interests Author Contributions, Supplementary Information, Acknowledgments and References Author Contributions, Supplementary Information, Acknowledgments and References Additional Insight from the Conceptual Model Additional Insight from the Conceptual Model AMSR2 until 2022 Excluded Due to Missing Human Land Use Data AMSR2 until 2022 Excluded Due to Missing Human Land Use Data Investigation of Resilience Loss in AMSR2’s X-Band Investigation of Resilience Loss in AMSR2’s X-Band Results Proving Robustness Results Proving Robustness Potential Driving Forces and Sources of Bias Potential Driving Forces and Sources of Bias AMSR2 UNTIL 2022 EXCLUDED DUE TO MISSING HUMAN LAND USE DATA Text S6. The influence of forest loss for the years 2021 to 2022 Since the data sets for excluding HLU are only available until 2020, the VOD data by AMSR2 was only analyzed until 2020 even though it is available until present times. The maps in Figure S6 a) and c) show the difference between the number of indicators with positive trends if analyzed until 2020 (as in the main text, Figure 6) and until 2022 for AMSR2’s band C and X, respectively. Differences mainly occur where sudden changes in VOD after 2020 can have a strong influence on the detrending method STL. Since STL acts on sliding windows over the whole data set as is (to detrend) and over the separate months (for de-seasonalizing), the influence of strong changes after 2020 increase over time. This becomes apparent in the the spatially averaged time series in b) and d) , where all indicators start at the same level independent for both cases, where the analysis is performed until 2020 (solid lines) as well as until 2022 (dotted lines), but diverge afterwards. Such increasingly strong discrepancies in the period until 2020, where the data is equivalent, must stem from STL, which over time becomes more sensitive to strong changes in later time steps due to its sliding window approach. Yet, this is only the case for strong changes in the VOD after 2020, as it could result from forest loss and Human interference. Hence, we argue that the exclusion of HLU based on the years until 2020 is not sufficient to analyze data thereafter. This explanation is supported by high rates of forest loss since the end of 2020, with more than 18.000 km2 deforestation and 27.000 km2 degradation registered by the Instituto Nacional de Pesquisas Espaciais’s monitoring programs (INPE, 2023). Hence, the results for AMSR2 analyzed until 2022 cannot be relied on (see Figure S6). a) c) b) d)

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