The old adage is that complexity is like an abacus: You can shift complexity around, but it never really goes away. With the movement to shift responsibility left to development teams, this also means that associated complexity is shifting to the development teams. Modern platform engineering teams provide the infrastructure (compliant Kubernetes clusters) to teams and any workload that is run on those clusters is up to the development team that owns it. Typically, development teams then focus on features and functionality. ... If you are a DevOps or platform engineer, making your internal customers—your development teams—successful is a great goal to work toward. Crucial to this is disseminating expertise. This can be in the form of automation and education. A common practice with the DevSecOps movement is to have some sort of scanning step as part of the build or deployment process, disseminating the internals as far as how the scan is performed, what happens if something is found, etc.
When organisations introduce new solutions to their technology stack, protection capabilities need to be extended to cover it. But faced with a global pandemic that no one could’ve seen coming, businesses needed to innovate fast, and their security measures failed to keep pace. This created a vulnerability lag, where systems and data have been left unprotected and open to attack. Veritas’ Vulnerability Lag Report explores how this gap between innovation and protection is affecting a variety of organisations, public and private; only three-fifths (61%) believe their organisation’s security measures have fully kept up since the implementation of COVID-led digital transformation initiatives. This means 39% are experiencing some form of security deficit. While such swift digital transformation has delivered a wealth of benefits for public sector organisations, there is a dark side to this accelerated innovation. In the rush to digitally transform, security has taken a back seat. As a result, there may be significant gaps just waiting for cyber criminals to exploit for their own gain.
Traditional software engineering practices involve designing, programming, and developing software that is largely stateless. On the other hand, data engineering practices focus on scaling stateful data systems and dealing with different levels of complexity. ... Setting up a data engineering culture is therefore crucial for companies to aim for long-term success. “At Sigmoid, these are the problems that we’re trying to tackle with our expertise in data engineering and help companies build a strong data culture,” said Mayur. With expertise in tools such as Spark, Kafka, Hive, Presto, MLflow, visualization tools, SQL, and open source technologies, the data engineering team at Sigmoid helps companies with building scalable data pipelines and data platforms. It allows customers to build data lakes, cloud data warehouses and set up DataOps and MLOps practices to operationalize the data pipelines and analytical model management. Transitioning from a software engineering environment to data engineering is a significant ‘cultural change’ for most companies.
Regardless of the task, pressure ruthlessly diminishes our judgment, decision-making, focus, and performance. Pressure moments can disrupt our thoughts, prevent us from thinking clearly, feel frustrated, and make us act in undesirable ways. The adverse impact of pressure on our cognitive skills can downgrade our performance, make us perform below our capability, commit more errors and increase the likelihood of failure. Pressure can even make us feel embarrassed and shameful when we do fail because we can act in a way that we will otherwise not act and say or do unusual things. Consider these pressure moments. Stepping out of an important client meeting and wondering “why did I make that joke. I was so stupid” or failing to share your opinion while participating in a critical decision meeting and thinking afterward, “Why didn’t I speak up? We could have made a better decision.” Pressure can either result in wrongful action or inaction. Such events make it much more difficult to deal with the pressure next time. But there are things you can do to diminish the effects of pressure on your performance.
There are several promising applications in the field of behavioral biometrics. For computer-based identity verification, there are solutions that allow identification based on keystrokes—the frequency and patterns of which prove to be individual enough to recognize identity. Due to the nature of typing, the models can also get better because they can continuously monitor and analyze keystroke data. Software developers tend to also customize confidence thresholds depending on the use case. However, in some cases, the reliability of this behavioral biometric factor is limited to the circumstances. On a different keyboard, individual patterns may differ, and physical conditions like carpal tunnel syndrome or arthritis may affect unique abilities. The lack of benchmarks makes it difficult to compare different providers’ trained algorithms in these cases, providing room for false marketing claims. Image analysis for image recognition can provide more data for behavioral research. Gait and posture biometrics are rapidly becoming useful tools, even if they do not yet match the accuracy and robustness of traditional biometric approaches.
It is alarming that the pace of DeFi’s growing influence is so fast-paced because many of the issues it presents are not addressed or solved enough in depth. People are investing in all sorts of cryptocurrency before they even educate themselves on how to manage private keys properly. Coupled with the lag in robust protective regulation, the general lack of awareness for DeFi’s threats to privacy inevitably results in large populations of users that are vulnerable to attack. Though some progress has been made at the state level to set standards for blockchain, there is a greater need for industry standardization at the international level. Additionally, the rapid expansion of blockchain technology in many industries is not met with sufficient safety protocols. As such, cybercriminals are aggressively taking action to target both users and exchanges of cryptocurrency in its under-secured state. On the flip side, there are some aspects about DeFi that are directly beneficial to protecting the privacy of users. When comparing the decentralized network that DeFi uses to a centralized one, DeFi’s “peer-to-peer” model is preferable because it prevents a “single source of failure”.
The modus operandi of these attackers parallels that of the Iranian attackers, in that it follows the same execution steps. But the researchers did not specify whether the intent of this campaign appeared to be data exfiltration. AhnLab did not respond to Information Security Media Group's request for additional information. With multiple attackers actively exploiting CVE-2021-40444, firms using Microsoft Office should immediately update their software to the latest version as a prevention measure, say researchers from EST Security, which discovered yet another campaign targeting the vulnerability. In this case, the campaign used communications that attempted to impersonate the president of North Korea's Pyongyang University of Science and Technology. "The North Korean cyberthreat organization identified as the perpetrator behind this campaign is actively introducing document-based security vulnerabilities such as PDF and DOC files to customized targeted attacks such as CVE-2020-9715 and CVE-2021-40444," the EST Security researchers say. CVE-2020-9715 is a vulnerability that allows remote attackers to execute arbitrary code on affected installations of Adobe Acrobat Reader DC.
Data mesh is a paradigm shift in managing and accessing analytical data at scale. Some of the words I highlighted here are really important, first of all, is the shift. I will justify why that's the case. Second is an analytical data solution. The word scale really matters here. What do we mean by analytical data? Analytical data is an aggregation of the data that gets generated running the business. It's the data that fuels our machine learning models. It's the data that fuels our reports, and the data that gives us an historical perspective. We can look backward and see how our business or services or products have been performing, and then be able to look forward and be able to predict, what is the next thing that a customer wants? Make recommendations and personalizations. All of those machine learning models can be fueled by analytical data. What does it look like? Today we are in this world with a great divide of data. The operational data is the data that sits in the databases of your applications, your legacy systems, microservices, and they keep the current state.
Business analysts and data scientists rely on numerous tools like PowerBI, Google Data Studio, Tableau, and SAP BI, among others, to decipher information from data and make business decisions. Coming from one of the best companies in the world, Google Data Studio, launched in 2016, is a data visualisation platform for creating reports using charts and dashboards. Tableau, on the other hand, was founded more than a decade before Google Data Studio in 2003 by Chris Stolte, Pat Hanrahan, and Christian Chabot. Tableau Software is one of the most popular visual analytics platforms with very strong business intelligence capabilities. The tool is free, and the user can log in to it by using their Google credentials. Over the years, it has become a popular tool to visualise trends in businesses, keep track of client metrics, compare time-based performance of teams, etc. It is a part of the Google Marketing Platform and downloads data from Google’s marketing tools to create reports and charts. Recently, Google announced that users can now include Google Maps in embedded reports in Google Data Studio.
Not only is the pace of system change growing; the magnitude of changes being made to complex systems today can be greater than ever. This presents a challenge to slow and overly manual data provisioning, as a substantial chunk of data might need updating or replacing based on rapid system changes. A range of practices in development have increased the rate and scale of system change. The adoption of containerization, source control, and easily reusable code libraries allow parallelized developers to rip and replace code at lightning speed. They can easily deploy new tools and technologies, developing systems that are now intricately woven webs of fast-shifting components. A test data solution today must be capable of providing consistent test data “journeys” based on the sizeable impact of these changes across interrelated system components. Data allocation must occur at the pace with which developers chop-and-change reusable and containerised components.
Quote for the day:
"One must be convinced to convince, to have enthusiasm to stimulate the others." -- Stefan Zweig