August 06, 2024

Why the Network Matters to Generative AI

Applications, today, are distributed. Our core research tells us more than half (60%) of organizations operate hybrid applications; that is, with components deployed in core, cloud, and edge locations. That makes the Internet their network, and the lifeline upon which they depend for speed and, ultimately, security. Furthermore, our focused research tells us that organizations are already multi-model, on average deploying 2.9 models. And where are those models going? Just over one-third (35%) are deploying in both public cloud and on-premises. Applications that use those models, of course, are being distributed in both environments. According to Red Hat, some of those models are being used to facilitate the modernization of legacy applications. ... One is likely tempted to ask why we need such a thing. The problem is we can’t affect the Internet. Not really. For all our attempts to use QoS to prioritize traffic and carefully select the right provider, who has all the right peering points, we can’t really do much about it. For one thing, over-the-Internet connectivity doesn’t typically reach into another environment, in which there are all kinds of network challenges like overlapping IP addresses, not to mention the difficulty in standardizing security policies and monitoring network activity.

Aware of what tech debt costs them, CIOs still can’t make it an IT priority

The trick for CIOs who have significant tech debt is to sell it to organization leadership, he says. One way to frame the need to address tech debt is to tie it to IT modernization. “You can’t modernize without addressing tech debt,” Saroff says. “Talk about digital transformation.” ... “You don’t just say, ‘We’ve got an old ERP system that is out of vendor support,’ because they’ll argue, ‘It still works; it’s worked fine for years,’” he says. “Instead, you have to say, ‘We need a new ERP system because you have this new customer intimacy program, and we’ll either have to spend millions of dollars doing weird integrations between multiple databases, or we could upgrade the ERP.’” ... “A lot of it gets into even modernization as you’re building new applications and new software,” he says. “Oftentimes, if you’re interfacing with older platforms that have sources of data that aren’t modernized, it can make those projects delayed or more complicated.” As organizational leaders push CIOs to launch AI projects, an overlooked area of tech debt is data management, adds Ricardo Madan, senior vice president for global technology services at IT consulting firm TEKsystems.

Is efficiency on your cloud architect’s radar?

Remember that we can certainly measure the efficiency of each of the architecture’s components, but that only tells you half of the story. A system may have anywhere from 10 to 1,000 components. Together, they create a converged architecture, which provides several advantages in measuring and ensuring efficiency. Converged architectures facilitate centralized management by combining computing, storage, and networking resources. ... With an integrated approach, converged architectures can dynamically distribute resources based on real-time demand. This reduces idle resources and enhances utilization, leading to better efficiency. Automation tools embedded within converged architectures help automate routine tasks such as scaling, provisioning, and load balancing. These tools can adjust resource allocation in real time, ensuring optimal performance without manual intervention. Advanced monitoring tools and analytics platforms built into converged architectures provide detailed insights into resource usage, cost patterns, and performance metrics. This enables continuous optimization and proactive management of cloud resources.

ITSM concerns when integrating new AI services

The key to establishing stringent access controls lies in feeding each LLM only the information that its users should consume. This approach eliminates the concept of a generalist LLM fed with all the company’s information, thereby ensuring that access to data is properly restricted and aligned with user roles and responsibilities. ... To maintain strict control over sensitive data while leveraging the benefits of AI, organizations should adopt a hybrid approach that combines AI-as-a-Service (AIaaS) with self-hosted models. For tasks involving confidential information, such as financial analysis and risk assessment, deploying self-hosted AI models ensures data security and control. Meanwhile, utilizing AIaaS providers like AWS for less sensitive tasks, such as predictive maintenance and routine IT support, allows organizations to benefit from the scalability and advanced features offered by cloud-based AI services. This hybrid strategy ensures that sensitive data remains secure within the organization’s infrastructure while taking advantage of the innovation and efficiency provided by AIaaS for other operations.

Fighting Back Against Multi-Staged Ransomware Attacks Crippling Businesses

Ransomware has evolved from lone wolf hackers operating from basements to complex organized crime syndicates that operate just like any other professional organization. Modern ransomware gangs employ engineers that develop the malware and platform; employ help desk staff to answer technical queries; employ analysts that identify target organizations; and ironically, employ PR pros for crisis management. The ransomware ecosystem also comprises multiple groups with specific roles. For example, one group (operators) builds and maintains the malware and rents out their infrastructure and expertise (a.k.a. ransomware-as-a-service). Initial access brokers specialize in breaking into organizations and selling the acquired access, data, and credentials. Ransomware affiliates execute the attack, compromise the victim, manage negotiations, and share a portion of their profits with the operators. Even state-sponsored attackers have joined the ransomware game due to its potential to cause wide-scale disruption and because it is very lucrative.

Optimizing Software Quality: Unit Testing and Automation

Any long-term project without proper test coverage is destined to be rewritten from scratch sooner or later. Unit testing is a must-have for the majority of projects, yet there are cases when one might omit this step. For example, you are creating a project for demonstrational purposes. The timeline is very tough. Your system is a combination of hardware and software, and at the beginning of the project, it's not entirely clear what the final product will look like. ... in automation testing the test cases are executed automatically. It happens much faster than manual testing and can be carried out even during nighttime as the whole process requires minimum human interference. This approach is an absolute game changer when you need to get quick feedback. However, as with any automation, it may need substantial time and financial resources during the initial setup stage. Even so, it is totally worth using it, as it will make the whole process more efficient and the code more reliable. The first step here is to understand if the project incorporates test automation. You need to ensure that the project has a robust test automation framework in place. 

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