Daily Tech Digest - February 19, 2023

2023 could be the breakthrough year for quantum computing

Despite progress on short-term applications, 2023 will not see error correction disappear. Far from it, the holy grail of quantum computing will continue to be building a machine capable of fault tolerance. 2023 may create software or hardware breakthroughs that will show how we’re closer than we think, but otherwise, this will continue to be something that is achieved far beyond 2023. Despite it being everything to some quantum companies and investors, the future corporate users of quantum computing will largely see it as too far off the time horizon to care much. The exception will be government and anyone else with a significant, long-term interest in cryptography. Despite those long time horizons, 2023 will define clearer blueprints and timelines for building fault-tolerant quantum computers for the future. Indeed, there is also an outside chance that next year will be the year when quantum rules out the possibility of short-term applications for good, and doubles down on the 7- to 10-year journey towards large-scale fault-tolerant systems.

Technical Debt is a Major Threat to Innovation

The challenge is instead of trying to keep the proverbial IT lights on during the COVID-19 era, IT teams are now being asked to innovate to advance digital business transformation initiatives, said Orlandini. A full 87% of survey respondents cited modernizing critical applications as a key success driver. As a result, many organizations are embracing platform engineering to bring more structure to their DevOps processes, he noted. The challenge, however, is striking a balance between a more centralized approach to DevOps and maintaining the ability of developers to innovate, said Orlandini. The issue, of course, is that in addition to massive technical debt, the latest generation of applications are more distributed than ever. The survey found 91% of respondents now rely on multiple public cloud providers for different workloads, with 54% of data residing on a public cloud. However, the survey also found on-premises IT environments are still relevant, with 20% planning to repatriate select public cloud workloads to an on-premises model over the next 12 months.

What’s Going Into NIST’s New Digital Identity Guidelines?

Both government and private industries have been collecting and using facial images for years. However, critics of facial recognition technology accuse it of racial, ethnic, gender and age-based biases, as it struggles to properly identify people of color and women. The algorithms in facial recognition tend to perpetuate discrimination in a technology meant to add security rather than adding risk. The updated NIST digital guidelines will directly address the struggles of facial recognition in particular, and biometrics overall. “The forthcoming draft will include biometric performance requirements designed to make sure there aren’t major discrepancies in the tech’s effectiveness across different demographic groups,” FCW reported. Rather than depend on digital photos for proof, NIST will add more options to prove identity. Lowering risk is as important to private industries as it is to federal agencies. Therefore, it would behoove enterprises to take steps to rethink their identity proofing.

The Past and Present of Serverless

As a new computing paradigm in the cloud era, Serverless architecture is a naturally distributed architecture. Its working principle is slightly changed compared with traditional architectures. In the traditional architecture, developers need to purchase virtual machine services, initialize the running environment, and install the required software (such as database software and server software). After preparing the environment, they need to upload the developed business code and start the application. Then, users can access the target application through network requests. However, if the number of application requests is too large or too small, developers or O&M personnel need to scale the relevant resources according to the actual number of requests and add corresponding policies to the load balance and reverse proxy module to ensure the scaling operation takes effect timely. At the same time, when doing these operations, it is necessary to ensure online users will not be affected. Under the Serverless architecture, the entire application release process and the working principle will change to some extent.

Why Apache Beam is the next big thing in big data processing

It’s a programming model for writing big data processing pipelines which is portable and unified. Now what does it mean exactly: First let’s understand the use cases for big data processing pipelines. Batch processing: Batch processing is a data processing technique used in big data pipelines to analyze and process large volumes of data in batches or sets. In batch processing, data is collected over a period of time, and then the entire batch of data is processed together Stream processing : Processing data as it is generated. It is a data processing technique to process data in real-time as it is generated, rather than in batches. In stream processing, data is processed continuously, as it flows through the pipeline. ... Beam offers multi-language pipelines which is basically a pipeline that is constructed using one Beam SDK language and incorporates one or more transforms from another Beam SDK language. The transforms from the other SDK language are known as cross-language transforms. 

The Use of ChatGPT in the Cyber Security Industry

ChatGPT has also been useful within cybernetic defense, by being asked to create a Web Application Firewall (WAF) rule to detect a specific type of attack, in the threat hunting scenario, where it is possible that the tool creates a machine learning model in any language, such as python, so that the tool can analyze the network traffic of a .pcap file, where the network packets were captured and thereby identify possible malicious behavior, such as a network connection with a malicious IP address that is already known and may indicate that a device is compromised, indicate an unusual increase in attempts to access the network through brute force, among other possibilities. ... This is worrying to the point of schools in NYC City blocking access to ChatGPT due to concern about the negative impacts this can generate on the students’ learning process, since in most cases, depending on the question, the answer is already provided without any effort or without having to study.

Is quantum machine learning ready for primetime?

Hopkins disagrees. “We are trying to apply [quantum ML] already,” he says, joining up with multiple clients to explore practical applications for such methods on a timescale of years and not decades, as some have ventured. ...  “You’re not going to fit that on a quantum computer with only 433 qubits,” says Hopkins – sufficient progress is being made each year to expand the possible number of quantum ML experiments that could be run. He also predicts that we will see quantum ML models become more generalisable. Schuld, too, is hopeful that the quantum ML field will directly benefit from recent and forthcoming advances on the hardware side. It’ll be at this point, she predicts, when researchers can begin testing quantum ML models on realistic problem sizes, and when we’re likely to see what she describes as a ‘smoking gun’ revealing a set of overarching principles in general quantum ML – one that reveals just how much we do and don’t know about the mysteries of applying these algorithms to complex, real-world problems.

Cyber Resilience Act: A step towards safe and secure digital products in Europe

Cybersecurity threats are global and continually evolving. They are targeting complex, interdependent systems that are hard to secure as threats can come from many places. A product that had strong security yesterday can have weak security tomorrow as new vulnerabilities and attack tactics are discovered. Even with a manufacturer appropriately mitigating risks, a product can still be compromised through supply chain attacks, the underlying digital infrastructure, an employee or many other ways. Microsoft alone analyzes 43 trillion security signals daily to better understand and protect against cyberthreats. Staying one step ahead requires speed and agility. Moreover, addressing digital threats requires a skilled cybersecurity workforce that helps organizations prepare and helps authorities ensure adequate enforcement. However, in Europe and across the world there is a shortage of skilled staff. Over 70% of businesses cannot find staff with the required digital skills. 

Microservices Architecture for Enterprise Large-Scaled Application

Microservices architecture is a good choice for complex, large-scale applications that require a high level of scalability, availability, and agility. It can also be a good fit for organizations that need to integrate with multiple third-party services or systems. However, microservices architecture is not a one-size-fits-all solution, and it may not be the best choice for all applications. It requires additional effort in terms of designing, implementing, and maintaining the services, as well as managing the communication between them. Additionally, the overhead of coordinating between services can result in increased latency and decreased performance, so it may not be the best choice for applications that require high performance or low latency. ... Microservices architecture is a good choice for organizations that require high scalability, availability, and agility, and are willing to invest in the additional effort required to design, implement, and maintain a microservices-based application.

Developing a successful cyber resilience framework

The difference between cyber security and cyber resilience is key. Cyber security focuses on protecting an organization from cyber attack. It involves things such as firewalls, VPNs, anti-malware software, and hygiene, such as patching software and firmware, and training employees about secure behavior. On the other hand, “cyber resilience focuses on what happens when cyber security measures fail, as well as when systems are disrupted by things such as human error, power outages, and weather,”. Resiliency takes into account where an organization's operations are reliant on technology, where critical data is stored, and how those areas can be affected by disruption. ... Cyber resilience includes preparation for business continuity and involves not just cyber attacks or data breaches, but other adverse conditions and challenges as well. For example, if the workforce is working remotely due to a catastrophic scenario, like the COVID-19 pandemic, but still able to perform business operations well and produce results in a cyber-secure habitat, the company is demonstrating cyber resilience.

Quote for the day:

"The art of communication is the language of leadership." -- James Humes

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