Testing for weaknesses after code is written is reactive. A better approach is to anticipate weaknesses before code is written and assign mitigation controls as part of the development process. This is accomplished through security requirements. Just as functional requirements provide teams with information on the features and performance needed in a project, security requirements provide teams with required controls to mitigate risk from potential weaknesses before coding begins. Most of these weaknesses are predictable based on the regulatory requirements in scope for the application along with the language, framework, and deployment environment. By translating these into mitigation controls — actionable tasks to be implemented by product development teams, security and operations during the normal development process — teams can build more secure software and avoid much of the “find and fix” delays they currently endure. With complete security requirements and appropriate mitigation controls as part of the overall project requirements, security is built-in as the application is developed.
Both full-stack developers and software engineers must have a detailed knowledge of coding languages. But full-stack developers tend to require a broader knowledge of more advanced languages than a software engineer. This is because of the range of areas they work across, from front-end development and core application to back-end development. A full-stack developer’s responsibilities include designing user interfaces or managing how an app functions, among other e-commerce development essentials. But they’ll also work on back-end support for the app, as well as manage databases and security. With such a varied list of responsibilities, full-stack development often means overseeing a portfolio of technology, reacting to needs with agility, and switching from one area to another as and when required. A software engineer has a narrower, although no less skilled remit. As well as their essential software development, they test for and resolve programming errors, diving back into the code in order to debug and often using QA automation to speed up testing.
Test debt is exactly what it sounds like. Just like when you cannot pay your credit card bill, when you cannot test your applications, the problems that are not being found in the application continue to compound. Eliminating test debt requires first establishing a sound test automation approach. Using this an organization can create a core regression test suite for functional regression and an end-to-end test automation suite for end-to-end business process regression testing. Because these are automated tests they can be run as often as code is modified. These tests can also be run concurrently, reducing the time it takes to run these automated tests and also creating core regression test suites. According to Rao, using core functional regression tests and end-to-end regression tests are basic table stakes in an organization’s journey to higher quality. Rao explained that when getting started with test automation, it can seem like a daunting task, and a massive mountain that needs climbing. “You cannot climb it in one shot, you have to get to the base camp. And the first base camp should be like a core regression test suite, that can be achieved in a couple of weeks, because that gives them a significant relief,” he said.
Lyft built its service mesh using Envoy, ensuring that all traffic flows through Envoy sidecars. When a service is deployed, it is registered in the service mesh, becomes discoverable, and starts serving requests from the other services in the mesh. An offloaded deployment contains metadata that stops the control plane from making it discoverable. Engineers create offloaded deployments directly from their pull requests by invoking a specialised GitHub bot. Using Lyft's proxy application, they can add protobuf-encoded metadata to requests as OpenTracing baggage. This metadata is propagated across all services throughout the request's lifetime regardless of the service implementation language, request protocol or queues in between. The Envoy's HTTP filter was modified to support staging overrides and route the request to the offloaded instance based on the request's override metadata. Engineers also used Onebox environments to run integration tests via CI. As the number of microservices increased, so did the number of tests and their running time. Conversely, its efficacy diminished for the same reasons that led to Onebox's abandonment.
The DeFi sector has, to date, been based on the distributed ledger principle of “trustlessness”, whereby users replace trust in an economic relationship with an algorithm. DeFi is oversaturated with trustless applications, says Sidney Powell, CEO and co-founder of Maple Finance. This includes over-collateralised lending, whereby borrowers put up assets worth two or three times the loan value, as well as decentralised exchanges and yield aggregators, which put your money into a smart contract that searches for the best yield from other smart contracts. “I think the opportunities are in areas where there is a bit of human communication in transacting or using the protocol,” Powell says. Maple’s model, which requires no collateral when it matches lenders with institutional borrowers, requires applications to be vetted and underwritten by experienced humans rather than code. From that point on, however, it is based on transparency – lenders monitor who is borrowing, the current lending strategy and pool performance in real time.
The Google Privacy Sandbox initiative is advancing in tandem with the growth of the global data privacy software market, which researchers valued at $1.68 billion in 2021, and anticipate will reach $25.85 billion by 2029 as more organizations attempt to get to grips with international data protection laws. Google isn’t the only big tech provider attempting to innovate new solutions to combat the complexity of data protection regulations. Meta’s engineers recently shared some of the techniques the organization uses to minimize the amount of data it collects on customers, including its Anonymous Credentials Service (ACS), which enables the organization to authenticate users in a de-identified manner without processing any personally identifiable information. Likewise, just a year ago, Apple released the App Tracking Transparency (ATT) framework as part of iOS 14, which forces Apple developers to ask users to opt-in to cross-app tracking. Google Privacy Sandbox Initiative’s approach stands out because it gives users more transparency over the type of information collected on them, while giving them more granular controls to remove interest-based data at will.
Technology, deployment model, and cross-industry issues are all contributing to the evolution of data storage, according to Tong Zhang, a professor at the Rensselaer Polytechnic Institute, as well as co-founder and chief scientist for ScaleFlux. An uptick in new technologies and further acceleration in data generation growth are also moving storage technologies forward. Deployment models for compute and storage must evolve as edge, near-edge, and IoT devices change the landscape of IT infrastructure landscape, he says. “Cross-industry issues, such as data security and environmental impact / sustainability, are also major factors driving data storage changes.” Four distinct factors are currently driving the evolution in storage technology: cost, capacity, interface speeds, and density, observes Allan Buxton, director of forensics at data recovery firm Secure Data Recovery Services. Hard disk manufacturers are competing with solid-state drive (SSD) makers by decreasing access and seek times while offering higher storage capacities at a lower cost, he explains.
In terms of the dangers, if an organization becomes the victim of a client-side attack, they may not know it immediately, particularly if they’re not using an automated monitoring and inspection security solution. Sometimes it is an end-user victim (like a customer) that finds out first, when their credit card or PII has been compromised. The impact of these types of client-side attacks can be severe. If the organization has compliance or regulatory concerns, then investigations and significant fines could result. Other impacts include costs associated with attack remediation, operational delays, system infiltration, and the theft of sensitive credentials or customer data. There are long-term consequences, as well, such as reputation damage and lost customers. ... Compliance is also a major concern. Regulatory mandates like GDPR and HIPAA, as well as regulations specific to the financial sector, mean that governments are putting a lot of pressure on organizations to keep sensitive user information safe. Failing to do so can mean investigations and substantial fines.
The cloud can be thought of in three layers: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). While IaaS can be thought of as renting hardware in the cloud, PaaS and SaaS need to be thought of in a completely different way (Hardware 1.0 vs. Hardware 2.0). Migrating between services for IaaS is relatively straightforward, and a buyer is fairly well protected from vendor lock-in. Services higher up the stack, not so much. It remains to be seen if the cloud providers will actually win in the software world, but they are definitely climbing up the stack, just like the original hardware vendors did, because they want to provide stickier solutions to their customers. Let’s explore the difference between these lower-level and higher-level services from a vendor lock-in perspective. With what I call Hardware 2.0, servers, network and storage are rented in the cloud and provisioned through APIs. The switching costs of migrating virtual machines from one cloud provider equate to learning a new API for provisioning.
Autonomous artificial intelligence is defined as routines designed to allow robots, cars, planes and other devices to execute extended sequences of maneuvers without guidance from humans. The revolution in artificial intelligence (AI) has reached a stage when current solutions can reliably complete many simple, coordinated tasks. Now the goal is to extend this capability by developing algorithms that can plan ahead and build a multistep strategy for accomplishing more. Thinking strategically requires a different approach than many successful well-known applications for AI. Machine vision or speech recognition algorithms, for instance, focus on a particular moment in time and have access to all of the data that they might need. Many applications for machine learning work with training sets that cover all possible outcomes. ... Many autonomous systems are able to work quite well by simplifying the environment and limiting the options. For example, autonomous shuttle trains have operated for years in amusement parks, airports and other industrial settings.
Quote for the day:
"Leadership is about change... The best way to get people to venture into unknown terrain is to make it desirable by taking them there in their imaginations." -- Noel Tichy