Most businesses use multiple cloud services and cloud providers, a hybrid approach that can support granular security options where vital data is kept close (perhaps in a private cloud) while less sensitive applications run in a public cloud to take advantage of big tech's economies of scale. But the hybrid model also introduces new complications, as every provider will have a slightly different set of security models that cloud customers will need to understand and manage. That takes time and (often elusive) expertise. But misconfigured services are high on the list of the causes for security incidents, along with even more basic failures like poor passwords and identity controls. Little surprise that companies are evaluating tools to automate much of this. That's leading to interest in new technologies such as Cloud Security Posture Management (CSPM) tools, which can help security teams spot and fix potential security issues around misconfiguration and compliance in the cloud, so they know the same rules are being enforced across their cloud services.
If you’re thinking that becoming a member of a DevOps team sounds interesting, what are the things you need to consider? Having experience in just about any aspect of IT gives you the technical foundation to make yourself a viable candidate. Do some research. What does it take to hone your existing skills to become a successful member of a DevOps team? You’ll likely find that it takes you in a direction well within your reach. Your technical skills are just the beginning though. Your skills will contribute to the broader objective of the DevOps team. Valuable DevOps team members understand how their role fits into the bigger picture. It’s not necessary to know the details of another team member’s discipline. It is, however, important to understand how each of your roles contributes to the DevOps process. This implies that you take some time to learn about each role’s function. Becoming an invaluable DevOps team member goes one step further. DevOps engineers who possess or develop the interpersonal skills to work beyond their team in guiding others, become key players within an organization.
The price of spot instances changes over days and weeks, so you can't predict the cost at the time of purchase. The amount of money saved varies depending on the type of resource: Low-priority instances are the least expensive, but they may be unavailable or turn off abruptly depending on capacity demand in the region. But such cases are rare. For example, AWS states that the average interruption frequency across all regions and instance types doesn't exceed 10%. Spot instances are best for stateless workloads, batch operations, and other fault-tolerant or time-flexible tasks. ... Begin by examining your cloud provider's transfer fees. Then, find ways to limit the number of data transfers in your cloud architecture. For example, you may need to change your application behavior and architecture to use computing resources in the closest data location. Transfer on-premises apps that often access cloud-hosted data to the cloud. In contrast to the cloud, specific resources (such as network bandwidth) are considered free in traditional datacenters. So if you move applications from on-premises datacenters, modify your application architecture to limit the amount of data transferred.
The move highlights a general lack of international agreement about when defensive cyber attacks should be considered appropriate. There has long been a murky world of online espionage in which countries have tacitly agreed to not respond with military force, due in no small part to degrees of plausible deniability and a great difficulty in displaying concrete evidence to the public that another nation’s covert hacking teams were behind a virtual break-in. This unofficial understanding has survived in the internet age, even as allies have been caught spying on each other, so long as everyone refrained from using cyber attacks to cause physical damage. Some developments in recent years have strained that arrangement, including Russia’s repeated cyber attacks on services in Ukraine and the recent willingness of cyber criminals to hit foreign critical infrastructure and government agencies with ransomware attacks. The UK AG has expressed that there is a pressing need to establish formal rules regarding defensive cyber attacks given the demonstrated possibility of devastating incidents that could cause actual damage to civilians, and that existing non-intervention agreements could serve as a launch point.
Although many companies are experimenting with AI as a tool to assess DEI in these areas, Greenstein noted, they aren’t fully delegating those processes to AI, but rather are augmenting them with AI. Part of the reason for their caution is that in the past, AI often did more harm than good in terms of DEI in the workplace, as biased algorithms discriminated against women and non-white job candidates. “There has been a lot of news about the impact of bias in the algorithms looking to identify talent,” Greenstein said. For example, in 2018, Amazon was forced to scrap its secret AI recruiting tool after the tech giant realized it was biased against women. And a 2019 study conducted by Harvard Business Review concluded that AI-enabled recruiting algorithms introduced anti-Black bias into the process. AI bias is caused, often unconsciously, by the people who design AI models and interpret the results. If an AI is trained on biased data, it will, in turn, make biased decisions. For instance, if a company has hired mostly white, male software engineers with degrees from certain universities in the past, a recruiting algorithm might favor job candidates with similar profiles for open engineering positions.
We’ve faced an ongoing health crisis that turned into a social crisis that went to an economic crisis and, unfortunately, we’re facing humanitarian crises, such as the war in Ukraine. But the fact of the matter is, people are making decisions, different decisions than where we were three to five years ago. And I believe they’re challenging the purpose of organizations, businesses, and leadership. As we talk about sustainability and inclusivity with that combination of the foundation for growth, that’s what the priorities of people are today. You asked about today’s CFOs and sustainability, inclusivity, growth. I truly believe that history will be written about these times that we’ve been operating in. As CFOs, we’re always—Eric, as you know quite well—focused on the what: productivity, efficiency, operational stability, liquidity. But I think these times will be less about pure financials and more about a culture. And when I think about culture, IBM—let me give a little shout out to my company—has a framework. We’ve been in existence for 111 years. We have a framework around culture that’s really grounded in purpose, united in values, and demonstrated through growth behaviors.
“If you want to move beyond containers as a tool for developers and put them into production, that means you’ll also be adopting an orchestration layer like Kubernetes and the various monitoring, CI/CD, logging, and tracing tools that go with it,” Haff says. “Which is exactly what many organizations are doing.” Containers and Kubernetes tend to go hand-in-hand because without that orchestration layer, teams otherwise find that managing containers at any kind of scale in production requires untenable effort. Haff notes that 70 percent of IT leaders surveyed in the State of Enterprise Open Source 2022 report said their organizations were using Kubernetes. Speaking of open source, containerization has open source DNA – and adoption often leads to uptake of other open source technologies, too. Make sure you’re using up-to-date, reliable, and secure code. “Containerization leads to more use of open source and other public components,” Korren says. “There are a lot of useful, well-maintained code components on the Internet, but there are many that are not.”
Without a long-term strategy or clearly assigned data-custody across the digital product lifecycle, data access and management is fragmented between process owners, application owners, or development teams, becoming more unstable with every company re-organization or staff departure. Many organizations reluctantly determine that data islands, duplicate data stores, and conflicting data are inevitable. The chain reaction of resulting issues is both overwhelming and costly. It may not be possible to do a meaningful root cause analysis to resolve incidents, assess the efficiency of digital product delivery, assess the value compared with cost, or receive valuable feedback from development before deployment. Design flaws are repeated, and incorrect processes are unintentionally reinforced. The lack of end-to-end visibility results in a slow response time to development, change, and incident tickets because there is no traceability or data integrity for tracking down the root cause of problems. Add that when data ownership is transferred or unclear, frustrated teams may dodge responsibility and throw issues “over the fence” to other stakeholders through the course of the digital product’s lifecycle.
Josh Martin, product evangelist at security firm Cyolo, explains that behavioral analytics would not be possible without ML and AI. “The data collected from the detection phase will be fed into multiple AI and ML models that will allow for deeper inspection of access habits to detect patterns or outliers for specific users,” he says. He outlines a potential use case for behavioral analytics and zero trust focused on a team member working from home. This user logs in every day from their corporate Mac around 8:00 in the morning and will either log into Salesforce or O365 first thing. “Considering this is normal for the user, the AI/ML mechanisms will start to look for anything outside of this baseline,” Martin says. “So, when the user takes a vacation to a different state and uses a personal Windows laptop to access ADP around 10 o’clock at night, this would raise a flag and shut down further authentication attempts until a security analyst can investigate. In this case, it could have been a malicious entity using stolen credentials to access payroll information.” From his perspective, behavioral analytics is likely to become the new norm as AI/ML products and knowledge become more accessible to the masses.
We could say that programming is an activity that moves between the mental and the physical. We could even say it is a way to interact with the logical nature of reality. The programmer blithely skips across the mind-body divide that has so confounded thinkers. “This admitted, we may propose to execute, by means of machinery, the mechanical branch of these labours, reserving for pure intellect that which depends on the reasoning faculties.” So said Charles Babbage, originator of the concept of a digital programmable computer. Babbage was conceiving of computing in the 1800s. Babbage and his collaborator Lovelace were conceiving not of a new work, but a new medium entirely. They wrangled out of the ether a physical ground for our ideations, a way to put them to concrete test and make them available in that form to other people for consideration and elaboration. In my own life of studying philosophy, I discovered the discontent of thought form whose rubber never meets the road. In this vein, Mr. Brooks completes his thought above when he writes, “Yet the program construct, unlike the poet’s words, is real in the sense that it moves and works, producing visible outputs separate from the construct itself.”
Quote for the day:
"Great Groups need to know that the person at the top will fight like a tiger for them." -- Warren G. Bennis