September 28, 2021

September 28, 2021

How and why automation can improve network-device security

Automating the processes of device discovery and configuration validation allows you to enforce good network security by making sure that your devices and configurations not accidentally leaving any security holes open. Stated differently, the goal of automation is to guarantee that your network policies are consistently applied across the entire network. A router that’s forgotten and left unsecured could be the avenue that bad actors exploit. Once each device on the network is discovered, the automation system downloads its configurations and checks them against the configuration rules that implement your network policies. These policies range from simple things that are not security related, like device naming standards, to essential security policies like authentication controls and access control lists. The automation system helps deploy and maintain the configurations that reflect your policies. ... A network-change and configuration-management (NCCM) system can use your network inventory to automate the backup of network-device configurations to a central repository.


How Unnecessary Complexity Gave the Service Mesh a Bad Name

The difficulty comes from avoiding “retry storms” or a “retry DDoS,” which is when a system in a degraded state triggers retries, increasing load and further decreasing performance as retries increase. A naive implementation won’t take this scenario into account as it may require integrating with a cache or other communication system to know if a retry is worth performing. A service mesh can do this by providing a bound on the total number of retries allowed throughout the system. The mesh can also report on these retries as they occur, potentially alerting you of system degradation before your users even notice. ... The design pattern of sidecar proxies is another exciting and powerful feature, even if it is sometimes oversold and over-engineered to do things users and tech aren’t quite ready for. While the community waits to see which service mesh “wins,” a reflection of the over-hyped orchestration wars before it, we will inevitably see more purpose-built meshes in the future and, likely, more end-users building their own control planes and proxies to satisfy their use cases.


How To Deal With Data Imbalance In Classification Problems?

A classification model is a technique that tries to draw conclusions or predict outcomes based on input values given for training. The input, for example, can be a historical bank or any financial sector data. The model will predict the class labels/categories for the new data and say if the customer will be valuable or not, based on demographic data such as gender, income, age, etc. Target class imbalance is the classes or the categories in the target class that are not balanced. Rao, giving an example of a marketing campaign, said, let’s say we have a classification task on hand to predict if a customer will respond positively to a campaign or not. Here, the target column — responded has two classes — yes or no. So, those are the two categories. In this case, let’s say the majority of the people responded ‘no.’ Meaning, the marketing campaign where you end up reaching out to a lot of customers, only a handful of them want to subscribe, for example, this can be you offering a credit card, a new insurance policy, etc. The one who subscribed or is interested would request more details.?


Motivational debt — it will fix itself, right?

Motivational debt is a hidden cost to product delivery. It’s the rust that is accruing on aged PBIs, the sludge at the bottom of the Sprint Backlog and the creaking of the process when needing to do something new. Technical debt is to quality what motivational debt is to process. It’s important to remember that whilst motivational debt is shouldered by the entire Scrum Team, there is an individual element of accrual to it as well. Both short-term stresses which bounce back quickly (“I didn’t get any sleep last night”) to long-term tensions which don’t (“My parents are ill) all contribute to the motivational complexities of a Scrum Team. Moving to address these actively is an ethical quandary, as individuals have different coping mechanisms, meaning efforts to help may actually exacerbate the issue. Remember that whilst some team members may be feeling down, others may be up, therefore being conscious of the overall direction of pull is vital as a Scrum Master. Holistically, it is fair to say that motivational debt is felt both individually and collectively and it is everyone’s responsibility to create an environment where it can be minimised. But how can you do this?


Waste and inefficiency in outdated government IT systems

Those responsible for addressing the government’s current levels of wasted IT expenditure may find that businesses offer positive, proactive case studies that highlight the value of embracing digital transformation. A 2020 study from Deloitte, for instance, has found that digitally mature companies – those that have embraced various aspects of digital transformation – saw net revenue growth of 45% and net profit growths of 43% compared to industry averages. The same study has found that the benefits of digital maturation are not limited to profits, but to a range of outcomes including increased efficiency, better product and service quality, and higher levels of both customer satisfaction and employee engagement. A study from McKinsey is even more strident, noting that “by digitising information-intensive processes, costs can be cut by up to 90% and turnaround times improved by several orders of magnitude.” Part of the ‘Organising for Digital Delivery Report’ includes a commitment to “investing in developing the technical fluency of senior civil service leadership.”?


Robotic process automation and intelligent automation are accelerating, study finds

Process mining is used to obtain a wide lens over business processes and workflows within a company by examining event logs across systems, including how variable they are and where there are bottlenecks. The less variable the process, the greater its potential candidacy for RPA/IA, though other factors must be considered as well. Task mining is used to understand how a user is interacting with systems and where there are opportunities for automation. Both of the above help identify automation candidates throughout an organization. IDP is a use case of IA and is growing in popularity, as there are so many document-intensive processes across organizations that impact many employees. ... Data governance, visibility of shadow deployments (and having guardrails in place for them), and security are all important to set in place ahead of RPA/IA to ensure architectural readiness. Another challenge is ensuring that the infrastructure is able to handle the increased speed and volume of transactions related to automated processes, whether it’s their own or someone they do business with.

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