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As load shedding continues to challenge businesses and individuals alike, there emerges a beacon of hope and efficiency: cloud computing. This revolutionary solution is not just the future; its the present, ensuring that load shedding is but a minor glitch in our digital lives.

Load shedding has posed significant challenges to businesses, particularly in South Africa where, according to a 2019 study by the South African Department of Energy, it cost the economy approximately R59 billion, hindering economic growth, says Graeme Millar, managing director of SevenC Computing.

Millar adds, Beyond the immediate loss of productivity due to power outages, businesses also suffer from disrupted communication channels, potential data losses, compromised security systems, and a general decrease in consumer confidence. This sporadic power supply not only stalls daily operations but erodes the competitive edge of South African businesses in the global market.

Millar continues to say that while load shedding may challenge operations, with the power of cloud computing, businesses can transform these hurdles into mere inconveniences, ensuring data remains accessible and protected in the grand digital scheme of things.

At its core, cloud computing is about storing and accessing data and applications over the internet, rather than relying on a local server or personal computer. This internet-based computing method offers a variety of services including storage, management, and processing of data.

What gives the cloud its edge, especially in combatting load shedding, is its decentralised nature. Instead of data being stored in a single location, cloud services use a network of remote servers hosted on the internet.

This means that even if one server in one location faces power issues, the others can take the load, ensuring continuous data access.

While the uninterrupted access provided by cloud services is a significant advantage during load shedding, the benefits of cloud computing dont end there:

Power interruptions dont just cut off access; they can cause data corruption or loss. Cloud storage offers a safeguard against such losses. With real-time data backup, the cloud ensures that data remains intact and uncompromised even during sudden power outages.

The cloud reduces the need for businesses to invest in expensive infrastructure. Instead of maintaining their own servers (which are susceptible to local power issues), companies can leverage the power of robust cloud servers.

Cloud solutions can easily be scaled up or down based on the needs of a business. This dynamic adjustability ensures businesses only pay for what they use and can adapt swiftly to changing demands.

While cloud computing offers a solution for the present, its potential for the future is limitless.

As technology continues to evolve, the capabilities of the cloud will expand, offering even more robust solutions against challenges like load shedding. Its not just about having a backup plan; its about adopting a system that offers efficiency, protection, and scalability.

while load shedding might be a hurdle, its not insurmountable. With cloud power on our side, businesses and individuals can look forward to a future where operations remain unhindered, where data is continuously accessible and protected, and where load shedding is merely a minor inconvenience in the grand digital scheme of things.

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Loadshedding Cannot Win Against the Cloud - IT News Africa

Observability before fragility, this is how to build secure clouds.

Building contemporary software applications is a complex process. Despite the rise of low-code/no-code platforms and a whole firmament of software application development and data analytics automation channels, we still exist in a world where systems are created with instabilities, fragilities and incompatibilities.

With ERP systems very typically supporting mission-critical use cases which in some scenarios straddle and support life-critical software deployments the need to assess where the pressure and pain points are in the modern IT stack has never been more pressing.

But where do we start? Is it a question of the surface-level interactions at the presentation layer and the way we manage what users are capable of doing inside their chosen applications user interface? Is it the middle-tier networking layer and all the application programming interface (API) connections that now form neural joins between data, services, operating systems and applications themselves? Could it be the lower substrate base of our technology infrastructures and the way these are now engineered to perform in essentially cloud-native environments? Or, inevitably, is it perhaps all of these tiers and all the internal connection points within them?

Spoiler alert! No prizes for guessing that its obviously everywhere. Code fragility manifests itself in a seemingly infinite variety of forms and functional formulations from syntax structures to higher-level system semantics. Given the Sisyphean challenge ahead then, shall we at least start at the foundation level with infrastructure?

We need to build enterprise technologies based upon infrastructures that work as dynamically orchestrated entities Sumedh Thakar, Qualys

Qualys CEO Sumedh Thakar says he has looked at the use case models being showcased across his firms customer base and beyond. As an IT, security and compliance solutions company, Qualys has advocated a concerted move to Infrastructure-as-Code (or IaC) as a core capability for secured operations.

Thakar has described the need to now build enterprise technologies based uponinfrastructures that work as dynamically orchestrated entities with fine-grained engineering controls

As it sounds, IaC is a SaaS cloud method that delivers a descriptive model to define and subsequently provision a cloud infrastructure layer. Just as a physical infrastructure would include data storage capabilities, server capacities and properties, lower system level relationships and network management control tools such as load balancer an IaC layer does the same, but is defined by code, for the cloud.

Among the technologies on offer here is Qualys CloudView, an IaC-level management product designed to enable firms to assess what is now being called their level of cloud security posture management (CSPM). CEO Thakar points to his firms ability to shift left (i.e. start earlier) an enterprises approach to cloud security via a combination of integrated application services designed to insert security automation into the entire application lifecycle. Qualys says this ensures visibility into both pre-deployment application build-time and post-deployment live operational runtime environments to check for misconfiguration and more, all via a single unified dashboard.

But why does so much misconfiguration happen and can we do more to stop it? Largely, it appears to be a natural byproduct of diversity in both cloud-native and terrestrial enterprise software platform environments that may eventually migrate to cloud.

Misconfigurations happen for a range of reasons, the most blatant being insecure-by-default settings, where security or hardening is an added control rather than a default state, says Martin Jartelius, CSO at Outpost24. The next challenge is that configurations are often evaluated in test environments where insecure configurations may occur, such as use of invalid certificates or ignoring signatures and validations at the point of testing. This means that once a transition to production is made, insecurities remain just as they were tested.

Years ago, the silver bullet was called gold builds for workstations and servers today its the Infrastructure-as-Code Martin Jartelius, Outpost24

Further, notes Jartelius, new functionality is added over time and unless organizations are attentive, this may introduce new and once again default insecure options. He agrees that an IaC approach offers the benefits of reusing trusted and well-audited templates and thereby reduces the room for human errors. It does not address the root causes of misconfigurations, but it does allow consistency.

Without consistency, maintenance and keeping up to date become even harder, and keeps getting harder over time, clarifies Jartelius.

Years ago, the silver bullet was called gold builds for workstations and servers today its the Infrastructure-as-Code. It will not solve all issues, but it will provide a remedy to a degree and most importantly it will allow those proactive who utilize its full power to reap its benefits.

For those who are not versed in what they are doing, it however creates the opportunity to do a low-quality job faster, so at the end of the day just as any other tool its usefulness depends entirely on whose hands it is put.

With so many differences in syntax, format, structure, code dependencies and other delineating factors across every development environment, every application toolset and indeed every cloud platform, misconfiguration is an inevitability throughout the modern IT stack all the way to the presentation layer. But that said, we can dive into cloud security more specifically to understand whats happening here.

Misconfigurations in the cloud can cause data breaches James Hastings, eSentire

Because of the complexities described thus far, the overarching issue many organizations see when working with live cloud environments is a lack of visibility. This manifests in multiple ways such as complications when more than one cloud account or platform is leveraged, issues where the chosen technology necessitates new tooling for security monitoring purposes, or a lack of understanding of what is deployed or how its configured. This is the opinion of James Hastings in his position as senior product manager at eSentire, a cloud software and security specialist focused on managed detection and response.

Misconfigurations in the cloud, which occur due to improper settings being used when architecting and deploying to cloud platforms, can cause data breaches that have a business-wide impact. According to a recent Cyber Security Hub study on the future of cloud security, almost half of the respondents (44 percent) said their primary challenge with cloud security was a reduced ability to detect and prevent cloud misconfigurations.

This lack of visibility usually stems from improper tooling that either cant pull the needed data from a cloud account or workload, or where the tool isnt designed to scale in cloud environments, said Hastings.

These issues impact both ends of the cloud adoption model; users early in their cloud journey struggle from a lack of knowledge and experience, while cloud-native customers tend to run into issues establishing visibility and monitoring for services like serverless functions and other shared or ephemeral technologies. Outside of visibility, the eSentire team reports that cloud customers experience some common security pain points like alert fatigue and fear of (or the inability to detect) unknown threats.

Looking at the way cloud-centric IT departments are run today, can we ask whether IT security teams and developers really collaborate with each other effectively even in the so-called age of DevOps, when it should arguably be taken as a given? Or is there still a need to improve this aspect of operations?

It really depends on the teams in question, but in my experience, yes, some do and its becoming easier than ever, says Hastings. Newer cloud security tools take a more holistic approach to security. These solutions usually feature multiple modules that are all intertwined to offer native multi-signal correlation out of the box and are increasingly targeting the shift of security into the development process.

Tooling such as code analysis focuses on hardening application code before its deployed to server-based or serverless workloads; the hardening of this code reduces the attack surface of the eventual workload and also cuts down on the patching, investigation and response that might otherwise be necessary.

The previously noted CSPM-style checks found in IaC are helpful when it comes to evaluating cloud infrastructure for misconfigurations but, notes the eSentire engineering team, this process happens as a fundamental part of the automation template. So this enables organizations to create secure infrastructure from the get-go and spend less time on remediating platform misconfigurations.

The last tool that we see making a significant impact on this collaboration is the idea of integrating vulnerability assessment into a continuous integration and continuous deployment (CI/CD) pipeline, explains Hastings. Here, before any code or a container can be published, it must have a vulnerability assessment run against it. Organizations are able to set their own bar for security compliance and even go as far as blocking a build that doesnt meet their security standards.

All said and done then, doesnt where we stand now in cloud security (and system robustness as a whole) beg a wider question? Are modern IT approaches built to be secure by design, or are we missing out on embedding security into these processes from the start? It comes back to a comment and sentiment spoken many times when customers move to the cloud cloud is more secure. While the statement is true in broad terms, it really needs qualification; perhaps we should instead say cloud can be more secure, but its up to each and every organization to lock it down and make it so.

The problem with cloud security and perhaps system security in general is, its all too often bolted on and implemented as an afterthought. Hastings speaks from a position of experience and reminds us that most cloud practitioners (and certainly all cloud-native practitioners) realize this inconvenient truth.

This has ultimately spawned the idea of shifting security left i.e. starting it earlier and/or pushing a more embedded approach to security into engineering and DevOps practices, says Hastings. Doing so embeds security throughout the organizations operational fabric and means that code is written and infrastructure is created to a secure and locked-down narrative. It reduces the number of times that teams need to circle back to change code, implement patches or make other changes that likely have change control and approval processes in place.

The combination of security and development streamlines both processes, reduces the organizations risk, and enables velocity.

Cloud computings evolution has been nothing if not flaky from the start. We know that AWS CEO Adam Selipsky talks of the very early stages of cloud as having been a somewhat embryonic phase, when the virtualization planets were still aligning. Its for sure that we have spent the last decade and more shoring up security, consolidating cloud tool sprawl and looking for key avenues through which we can automate many of the management tasks that can lead to cloud fragility in the first place.

If we had the chance to do cloud all over again, we might use a different and more considered approach, but perhaps we wouldnt. This might just be a hefty symptomatic nuance of the way new technology platforms rapidly escalate and eventually germinate, oscillate, occasionally fluctuate and finally become part of our operational substrate.

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Solid air: building secure clouds - Software applications - ERP Today

With the widespread attention, and potential applications of blockchain and artificial intelligence technologies, the privacy protection techniques that arise as a direct result of integration of the two technologies is gaining notable significance. These privacy protection techniques not only protect the privacy of individuals, but they also guarantee the dependability and security of the data.

In this article, we will be talking about how the collaboration between AI and blockchain gives birth to numerous privacy protection techniques, and their application in different verticals including de-identification, data encryption, k-anonymity, and multi-tier distributed ledger methods. Furthermore, we will also try to analyze the deficiencies along with their actual cause, and offer solutions accordingly.

The blockchain network was first introduced to the world when in 2008 Nakamoto introduced Bitcoin, a cryptocurrency built on the blockchain network. Ever since its introduction, blockchain has gained a lot of popularity, especially in the past few years. The value at which Bitcoin is trading today, and it crossing the Trillion-dollar market cap mark indicates that blockchain has the potential to generate substantial revenue and profits for the industry.

Blockchain technology can be categorized primarily on the basis of the level of accessibility and control they offer, with Public, Private, and Federated being the three main types of blockchain technologies. Popular cryptocurrencies and blockchain architectures like Bitcoin and Ethereum are public blockchain offerings as they are decentralized in nature, and they allow nodes to enter or exit the network freely, and thus promotes maximum decentralization.

The following figure depicts the structure of Ethereum as it utilizes a linked list to establish connections between different blocks. The header of the block stores the hash address of the preceding block in order to establish a linkage between the two successive blocks.

The development, and implementation of the blockchain technology is followed with legitimate security and privacy concerns in various fields that cannot be neglected. For example, a data breach in the financial industry can result in heavy losses, while a breach in military or healthcare systems can be disastrous. To prevent these scenarios, protection of data, user assets, and identity information has been a major focus of the blockchain security research community, as to ensure the development of the blockchain technology, it is essential to maintain its security.

Ethereum is a decentralized blockchain platform that upholds a shared ledger of information collaboratively using multiple nodes. Each node in the Ethereum network makes use of the EVM or Ethereum Vector Machine to compile smart contracts, and facilitate the communication between nodes that occur via a P2P or peer-to-peer network. Each node on the Ethereum network is provided with unique functions, and permissions, although all the nodes can be used for gathering transactions, and engaging in block mining. Furthermore, it is worth noting that when compared to Bitcoin, Ethereum displays faster block generation speeds with a lead of nearly 15 seconds. It means that crypto miners have a better chance at acquiring rewards quicker while the interval time for verifying transactions is reduced significantly.

On the other hand, AI or Artificial Intelligence is a branch in modern science that focuses on developing machines that are capable of decision-making, and can simulate autonomous thinking comparable to a humans ability. Artificial Intelligence is a very vast branch in itself with numerous subfields including deep learning, computer vision, natural language processing, and more. NLP in particular has been a subfield that has been focussed heavily in the past few years that has resulted in the development of some top-notch LLMs like GPT and BERT. NLP is headed towards near perfection, and the final step of NLP is processing text transformations that can make computers understandable, and recent models like ChatGPT built on GPT-4 indicated that the research is headed towards the right direction.

Another subfield that is quite popular amongst AI developers is deep learning, an AI technique that works by imitating the structure of neurons. In a conventional deep learning framework, the external input information is processed layer by layer by training hierarchical network structures, and it is then passed on to a hidden layer for final representation. Deep learning frameworks can be classified into two categories: Supervised learning, and Unsupervised learning.

The above image depicts the architecture of deep learning perceptron, and as it can be seen in the image, a deep learning framework employs a multiple-level neural network architecture to learn the features in the data. The neural network consists of three types of layers including the hidden layer, the input payer, and the output layer. Each perceptron layer in the framework is connected to the next layer in order to form a deep learning framework.

Finally, we have the integration of blockchain and artificial intelligence technologies as these two technologies are being applied across different industries and domains with an increase in the concern regarding cybersecurity, data security, and privacy protection. Applications that aim to integrate blockchain and artificial intelligence manifest the integration in the following aspects.

In the current scenario, data trust systems have certain limitations that compromise the reliability of the data transmission. To challenge these limitations, blockchain technologies can be deployed to establish a dependable and secure data sharing & storage solution that offers privacy protection, and enhances data security. Some of the applications of blockchain in AI privacy protection are mentioned in the following table.

By enhancing the implementation & integration of these technologies, the protective capacity & security of current data trust systems can be boosted significantly.

Traditionally, data sharing and data storing methods have been vulnerable to security threats because they are dependent on centralized servers that makes them an easily identifiable target for attackers. The vulnerability of these methods gives rise to serious complications such as data tampering, and data leaks, and given the current security requirements, encryption methods alone are not sufficient to ensure the safety & security of the data, which is the main reason behind the emergence of privacy protection technologies based on the integration of artificial intelligence & blockchain.

Lets have a look at a blockchain-based privacy preserving federated learning scheme that aims to improve the Multi-Krum technique, and combine it with homomorphic encryption to achieve ciphertext-level model filtering and model aggregation that can verify local models while maintaining privacy protection. The Paillier homomorphic encryption technique is used in this method to encrypt model updates, and thus providing additional privacy protection. The Paillier algorithm works as depicted.

De-Identification is a method that is commonly used to anonymize personal identification information of a user in the data by separating the data from the data identifiers, and thus reducing the risk of data tracking. There exists a decentralized AI framework built on permissioned blockchain technology that uses the above mentioned approach. The AI framework essentially separates the personal identification information from non-personal information effectively, and then stores the hash values of the personal identification information in the blockchain network. The proposed AI framework can be utilized in the medical industry to share medical records & information of a patient without revealing his/her true identity. As depicted in the following image, the proposed AI framework uses two independent blockchain for data requests with one blockchain network storing the patient's information along with data access permissions whereas the second blockchain network captures audit traces of any requests or queries made by requesters. As a result, patients still have complete authority and control over their medical records & sensitive information while enabling secure & safe data sharing within multiple entities on the network.

A multi-layered distributed ledger is a data storage system with decentralization property and multiple hierarchical layers that are designed to maximize efficiency, and secure the data sharing process along with enhanced privacy protection. DeepLinQ is a blockchain-based multi-layered decentralized distributed ledger that addresses a users concern regarding data privacy & data sharing by enabling privacy-protected data privacy. DeepLinQ archives the promised data privacy by employing various techniques like on-demand querying, access control, proxy reservation, and smart contracts to leverage blockchain networks characteristics including consensus mechanism, complete decentralization, and anonymity to protect data privacy.

The K-Anonymity method is a privacy protection method that aims to target & group individuals in a dataset in a way that every group has at least K individuals with identical attribute values, and therefore protecting the identity & privacy of individual users. The K-Anonymity method has been the basis of a proposed reliable transactional model that facilitates transactions between energy nodes, and electric vehicles. In this model, the K-Anonymity method serves two functions: first, it hides the location of the EVs by constructing a unified request using K-Anonymity techniques that conceal or hide the location of the owner of the car; second, the K-Anonymity method conceals user identifiers so that attackers are not left with the option to link users to their electric vehicles.

In this section, we will be talking about comprehensive analysis and evaluation of ten privacy protection systems using the fusion of blockchain and AI technologies that have been proposed in recent years. The evaluation focuses on five major characteristics of these proposed methods including: authority management, data protection, access control, scalability and network security, and also discusses the strengths, weaknesses, and potential areas of improvement. It's the unique features resulting from the integration of AI and blockchain technologies that have paved ways for new ideas, and solutions for enhanced privacy protection. For reference, the image below shows different evaluation metrics employed to derive the analytical results for the combined application of the blockchain and AI technologies.

Access control is a security & privacy technology that is used to restrict a users access to authorized resources on the basis of pre-defined rules, set of instructions, policies, safeguarding data integrity, and system security. There exists an intelligent privacy parking management system that makes use of a Role-Based Access Control or RBAC model to manage permissions. In the framework, each user is assigned one or more roles, and are then classified according to roles that allows the system to control attribute access permissions. Users on the network can make use of their blockchain address to verify their identity, and get attribute authorization access.

Access control is one of the key fundamentals of privacy protection, restricting access based on group membership & user identity to ensure that it is only the authorized users who can access specific resources that they are allowed to access, and thus protecting the system from unwanted to forced access. To ensure effective and efficient access control, the framework needs to consider multiple factors including authorization, user authentication, and access policies.

Digital Identity Technology is an emerging approach for IoT applications that can provide safe & secure access control, and ensure data & device privacy. The method proposes to use a series of access control policies that are based on cryptographic primitives, and digital identity technology or DIT to protect the security of communications between entities such as drones, cloud servers, and Ground Station Servers (GSS). Once the registration of the entity is completed, credentials are stored in the memory. The table included below summarizes the types of defects in the framework.

Data protection is used to refer to measures including data encryption, access control, security auditing, and data backup to ensure that the data of a user is not accessed illegally, tampered with, or leaked. When it comes to data processing, technologies like data masking, anonymization, data isolation, and data encryption can be used to protect data from unauthorized access, and leakage. Furthermore, encryption technologies such as homomorphic encryption, differential privacy protection, digital signature algorithms, asymmetric encryption algorithms, and hash algorithms, can prevent unauthorized & illegal access by non-authorized users and ensure data confidentiality.

Network security is a broad field that encompasses different aspects including ensuring data confidentiality & integrity, preventing network attacks, and protecting the system from network viruses & malicious software. To ensure the safety, reliability, and security of the system, a series of secure network architectures and protocols, and security measures need to be adopted. Furthermore, analyzing and assessing various network threats and coming up with corresponding defense mechanisms and security strategies are essential to improve the reliability & security of the system.

Scalability refers to a systems ability to handle larger amounts of data or an increasing number of users. When designing a scalable system, developers must consider system performance, data storage, node management, transmission, and several other factors. Furthermore, when ensuring the scalability of a framework or a system, developers must take into account the system security to prevent data breaches, data leaks, and other security risks.

Developers have designed a system in compliance with European General Data Protection Rules or GDPR by storing privacy-related information, and artwork metadata in a distributed file system that exists off the chain. Artwork metadata and digital tokens are stored in OrbitDB, a database storage system that uses multiple nodes to store the data, and thus ensures data security & privacy. The off-chain distributed system disperses data storage, and thus improves the scalability of the system.

The amalgamation of AI and blockchain technologies has resulted in developing a system that focuses heavily on protecting the privacy, identity, and data of the users. Although AI data privacy systems still face some challenges like network security, data protection, scalability, and access control, it is crucial to consider and weigh these issues on the basis of practical considerations during the design phase comprehensively. As the technology develops and progresses further, the applications expand, the privacy protection systems built using AI & blockchain will draw more attention in the upcoming future. On the basis of research findings, technical approaches, and application scenarios, they can be classified into three categories.

The technologies belonging to the first category focus on the implementation of AI and blockchain technologies for privacy protection in the IoT industry. These methods use AI techniques to analyze high volumes of data while taking advantage of decentralized & immutable features of the blockchain network to ensure authenticity and security of the data.

The technologies falling in the second category focus on fusing AI & Blockchain technologies for enhanced privacy protection by making use of blockchains smart contract & services. These methods combine data analysis and data processing with AI and use blockchain technology alongside to reduce dependency on trusted third parties, and record transactions.

Finally, the technologies falling in the third category focus on harnessing the power of AI and blockchain technology to achieve enhanced privacy protection in large-scale data analytics. These methods aim to exploit blockchains decentralization, and immutability properties that ensure the authenticity & security of data while AI techniques ensure the accuracy of data analysis.

In this article, we have talked about how AI and Blockchain technologies can be used in sync with each other to enhance the applications of privacy protection technologies by talking about their related methodologies, and evaluating the five primary characteristics of these privacy protection technologies. Furthermore, we have also talked about the existing limitations of the current systems. There are certain challenges in the field of privacy protection technologies built upon blockchain and AI that still need to be addressed like how to strike a balance between data sharing, and privacy preservation. The research on how to effectively merge the capabilities of AI and Blockchain techniques is going on, and here are several other ways that can be used to integrate other techniques.

Edge computing aims to achieve decentralization by leveraging the power of edge & IoT devices to process private & sensitive user data. Because AI processing makes it mandatory to use substantial computing resources, using edge computing methods can enable the distribution of computational tasks to edge devices for processing instead of migrating the data to cloud services, or data servers. Since the data is processed much nearer the edge device itself, the latency time is reduced significantly, and so is the network congestion that enhances the speed & performance of the system.

Multi-chain mechanisms have the potential to resolve single-chain blockchain storage, and performance issues, therefore boosting the scalability of the system. The integration of multi-chain mechanisms facilitates distinct attributes & privacy-levels based data classification, therefore improving storage capabilities and security of privacy protection systems.

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AI and Blockchain Integration for Preserving Privacy - Unite.AI