Tech Bytes - Daily Digest

Daily Tech Digest - July 03, 2025

Quote for the day:

"Limitations live only in our minds. But if we use our imaginations, our possibilities become limitless." --Jamie Paolinetti

The Goldilocks Theory – preparing for Q-Day ‘just right’

When it comes to quantum readiness, businesses currently have two options: Quantum key distribution (QKD) and post quantum cryptography (PQC). Of these, PQC reigns supreme. Here’s why. On the one hand, you have QKD which leverages principles of quantum physics, such as superposition, to securely distribute encryption keys. Although great in theory, it needs extensive new infrastructure, including bespoke networks and highly specialised hardware. More importantly, it also lacks authentication capabilities, severely limiting its practical utility. PQC, on the other hand, comprises classical cryptographic algorithms specifically designed to withstand quantum attacks. It can be integrated into existing digital infrastructures with minimal disruption. ... Imagine installing new quantum-safe algorithms prematurely, only to discover later they’re vulnerable, incompatible with emerging standards, or impractical at scale. This could have the opposite effect and could inadvertently increase attack surface and bring severe operational headaches, ironically becoming less secure. But delaying migration for too long also poses serious risks. Malicious actors could be already harvesting encrypted data, planning to decrypt it when quantum technology matures – so businesses protecting sensitive data such as financial records, personal details, intellectual property cannot afford indefinite delays.

Sovereign by Design: Data Control in a Borderless World

The regulatory framework for digital sovereignty is a national priority. The EU has set the pace with GDPR and GAIA-X. It prioritizes data residency and local infrastructure. China's cybersecurity law and personal information protection law enforce strict data localization. India's DPDP Act mandates local storage for sensitive data, aligning with its digital self-reliance vision through platforms such as Aadhaar. Russia's federal law No. 242-FZ requires citizen data to stay within the country for the sake of national security. Australia's privacy act focuses on data privacy, especially for health records, and Canada's PIPEDA encourages local storage for government data. Saudi Arabia's personal data protection law enforces localization for sensitive sectors, and Indonesia's personal data protection law covers all citizen-centric data. Singapore's PDPA balances privacy with global data flows, and Brazil's LGPD, mirroring the EU's GDPR, mandates the protection of privacy and fundamental rights of its citizens. ... Tech companies have little option but to comply with the growing demands of digital sovereignty. For example, Amazon Web Services has a digital sovereignty pledge, committing to "a comprehensive set of sovereignty controls and features in the cloud" without compromising performance.

Agentic AI Governance and Data Quality Management in Modern Solutions

Agentic AI governance is a framework that ensures artificial intelligence systems operate within defined ethical, legal, and technical boundaries. This governance is crucial for maintaining trust, compliance, and operational efficiency, especially in industries such as Banking, Financial Services, Insurance, and Capital Markets. In tandem with robust data quality management, Agentic AI governance can substantially enhance the reliability and effectiveness of AI-driven solutions. ... In industries such as Banking, Financial Services, Insurance, and Capital Markets, the importance of Agentic AI governance cannot be overstated. These sectors deal with vast amounts of sensitive data and require high levels of accuracy, security, and compliance. Here’s why Agentic AI governance is essential: Enhanced Trust: Proper governance fosters trust among stakeholders by ensuring AI systems are transparent, fair, and reliable. Regulatory Compliance: Adherence to legal and regulatory requirements helps avoid penalties and safeguard against legal risks. Operational Efficiency: By mitigating risks and ensuring accuracy, AI governance enhances overall operational efficiency and decision-making. Protection of Sensitive Data: Robust governance frameworks protect sensitive financial data from breaches and misuse, ensuring privacy and security.

Fundamentals of Dimensional Data Modeling

Keeping the dimensions separate from facts makes it easier for analysts to slice-and-dice and filter data to align with the relevant context underlying a business problem. Data modelers organize these facts and descriptive dimensions into separate tables within the data warehouse, aligning them with the different subject areas and business processes. ... Dimensional modeling provides a basis for meaningful analytics gathered from a data warehouse for many reasons. Its processes lead to standardizing dimensions through presenting the data blueprint intuitively. Additionally, dimensional data modeling proves to be flexible as business needs evolve. The data warehouse updates technology according to the concept of slowly changing dimensions (SCD) as business contexts emerge. ... Alignment in the design requires these processes, and data governance plays an integral role in getting there. Once the organization is on the same page about the dimensional model’s design, it chooses the best kind of implementation. Implementation choices include the star or snowflake schema around a fact. When organizations have multiple facts and dimensions, they use a cube. A dimensional model defines how technology needs to build a data warehouse architecture or one of its components using good design and implementation.

IDE Extensions Pose Hidden Risks to Software Supply Chain

The latest research, published this week by application security vendor OX Security, reveals the hidden dangers of verified IDE extensions. While IDEs provide an array of development tools and features, there are a variety of third-party extensions that offer additional capabilities and are available in both official marketplaces and external websites. ... But OX researchers realized they could add functionality to verified extensions after the fact and still maintain the checkmark icon. After analyzing traffic for Visual Studio Code, the researchers found a server request to the marketplace that determines whether the extension is verified; they discovered they could modify the values featured in the server request and maintain the verification status even after creating malicious versions of the approved extensions. ... Using this attack technique, a threat actor could inject malicious code into verified and seemingly safe extensions that would maintain their verified status. "This can result in arbitrary code execution on developers' workstations without their knowledge, as the extension appears trusted," Siman-Tov Bustan and Zadok wrote. "Therefore, relying solely on the verified symbol of extensions is inadvisable." ... "It only takes one developer to download one of these extensions," he says. "And we're not talking about lateral movement. ..."

Business Case for Agentic AI SOC Analysts

A key driver behind the business case for agentic AI in the SOC is the acute shortage of skilled security analysts. The global cybersecurity workforce gap is now estimated at 4 million professionals, but the real bottleneck for most organizations is the scarcity of experienced analysts with the expertise to triage, investigate, and respond to modern threats. One ISC2 survey report from 2024 shows that 60% of organizations worldwide reported staff shortages significantly impacting their ability to secure the organizations, with another report from the World Economic Forum showing that just 15% of organizations believe they have the right people with the right skills to properly respond to a cybersecurity incident. Existing teams are stretched thin, often forced to prioritize which alerts to investigate and which to leave unaddressed. As previously mentioned, the flood of false positives in most SOCs means that even the most experienced analysts are too distracted by noise, increasing exposure to business-impacting incidents. Given these realities, simply adding more headcount is neither feasible nor sustainable. Instead, organizations must focus on maximizing the impact of their existing skilled staff. The AI SOC Analyst addresses this by automating routine Tier 1 tasks, filtering out noise, and surfacing the alerts that truly require human judgment.

Microservice Madness: Debunking Myths and Exposing Pitfalls

Microservices will reduce dependencies, because it forces you to serialize your types into generic graph objects (read; JSON or XML or something similar). This implies that you can just transform your classes into a generic graph object at its interface edges, and accomplish the exact same thing. ... There are valid arguments for using message brokers, and there are valid arguments for decoupling dependencies. There are even valid points of scaling out horizontally by segregating functionality on to different servers. But if your argument in favor of using microservices is "because it eliminates dependencies," you're either crazy, corrupt through to the bone, or you have absolutely no idea what you're talking about (make your pick!) Because you can easily achieve the same amount of decoupling using Active Events and Slots, combined with a generic graph object, in-process, and it will execute 2 billion times faster in production than your "microservice solution" ... "Microservice Architecture" and "Service Oriented Architecture" (SOA) have probably caused more harm to our industry than the financial crisis in 2008 caused to our economy. And the funny thing is, the damage is ongoing because of people repeating mindless superstitious belief systems as if they were the truth.

Sustainability and social responsibility

Direct-to-chip liquid cooling delivers impressive efficiency but doesn’t manage the entire thermal load. That’s why hybrid systems that combine liquid and traditional air cooling are increasingly popular. These systems offer the ability to fine-tune energy use, reduce reliance on mechanical cooling, and optimize server performance. HiRef offers advanced cooling distribution units (CDUs) that integrate liquid-cooled servers with heat exchangers and support infrastructure like dry coolers and dedicated high-temperature chillers. This integration ensures seamless heat management regardless of local climate or load fluctuations. ... With liquid cooling systems capable of operating at higher temperatures, facilities can increasingly rely on external conditions for passive cooling. This shift not only reduces electricity usage, but also allows for significant operational cost savings over time. But this sustainable future also depends on regulatory compliance, particularly in light of the recently updated F-Gas Regulation, which took effect in March 2024. The EU regulation aims to reduce emissions of fluorinated greenhouse gases to net-zero by 2050 by phasing out harmful high-GWP refrigerants like HFCs. “The F-Gas regulation isn’t directly tailored to the data center sector,” explains Poletto.

Infrastructure Operators Leaving Control Systems Exposed

Threat intelligence firm Censys has scanned the internet twice a month for the last six months, looking for a representative sample composed of four widely used types of ICS devices publicly exposed to the internet. Overall exposure slightly increased from January through June, the firm said Monday. One of the devices Censys scanned for is programmable logic controllers made by an Israel-based Unitronics. The firm's Vision-series devices get used in numerous industries, including the water and wastewater sector. Researchers also counted publicly exposed devices built by Israel-based Orpak - a subsidiary of Gilbarco Veeder-Root - that run SiteOmat fuel station automation software. It also looked for devices made by Red Lion that are widely deployed for factory and process automation, as well as in oil and gas environments. It additionally probed for instances of a facilities automation software framework known as Niagara, made by Tridium. ... Report author Emily Austin, principal security researcher at Censys, said some fluctuation over time isn't unusual, given how "services on the internet are often ephemeral by nature." The greatest number of publicly exposed systems were in the United States, except for Unitronics, which are also widely used in Australia.

Healthcare CISOs must secure more than what’s regulated

Security must be embedded early and consistently throughout the development lifecycle, and that requires cross-functional alignment and leadership support. Without an understanding of how regulations translate into practical, actionable security controls, CISOs can struggle to achieve traction within fast-paced development environments. ... Security objectives should be mapped to these respective cycles—addressing tactical issues like vulnerability remediation during sprints, while using PI planning cycles to address larger technical and security debt. It’s also critical to position security as an enabler of business continuity and trust, rather than a blocker. Embedding security into existing workflows rather than bolting it on later builds goodwill and ensures more sustainable adoption. ... The key is intentional consolidation. We prioritize tools that serve multiple use cases and are extensible across both DevOps and security functions. For example, choosing solutions that can support infrastructure-as-code security scanning, cloud posture management, and application vulnerability detection within the same ecosystem. Standardizing tools across development and operations not only reduces overhead but also makes it easier to train teams, integrate workflows, and gain unified visibility into risk.

Daily Tech Digest - July 02, 2025

Quote for the day:

"Success is not the absence of failure; it's the persistence through failure." -- Aisha Tyle

How cybersecurity leaders can defend against the spur of AI-driven NHI

Many companies don’t have lifecycle management for all their machine identities and security teams may be reluctant to shut down old accounts because doing so might break critical business processes. ... Access-management systems that provide one-time-use credentials to be used exactly when they are needed are cumbersome to set up. And some systems come with default logins like “admin” that are never changed. ... AI agents are the next step in the evolution of generative AI. Unlike chatbots, which only work with company data when provided by a user or an augmented prompt, agents are typically more autonomous, and can go out and find needed information on their own. This means that they need access to enterprise systems, at a level that would allow them to carry out all their assigned tasks. “The thing I’m worried about first is misconfiguration,” says Yageo’s Taylor. If an AI agent’s permissions are set incorrectly “it opens up the door to a lot of bad things to happen.” Because of their ability to plan, reason, act, and learn AI agents can exhibit unpredictable and emergent behaviors. An AI agent that’s been instructed to accomplish a particular goal might find a way to do it in an unanticipated way, and with unanticipated consequences. This risk is magnified even further, with agentic AI systems that use multiple AI agents working together to complete bigger tasks, or even automate entire business processes.

The silent backbone of 5G & beyond: How network APIs are powering the future of connectivity

Network APIs are fueling a transformation by making telecom networks programmable and monetisable platforms that accelerate innovation, improve customer experiences, and open new revenue streams. ... Contextual intelligence is what makes these new-generation APIs so attractive. Your needs change significantly depending on whether you’re playing a cloud game, streaming a match, or participating in a remote meeting. Programmable networks can now detect these needs and adjust dynamically. Take the example of a user streaming a football match. With network APIs, a telecom operator can offer temporary bandwidth boosts just for the game’s duration. Once it ends, the network automatically reverts to the user’s standard plan—no friction, no intervention. ... Programmable networks are expected to have the greatest impact in Industry 4.0, which goes beyond consumer applications. ... 5G combined IOT and with network APIs enables industrial systems to become truly connected and intelligent. Remote monitoring of manufacturing equipment allows for real-time maintenance schedule adjustments based on machine behavior. Over a programmable, secure network, an API-triggered alert can coordinate a remote diagnostic session and even start remedial actions if a fault is found.

Quantum Computers Just Reached the Holy Grail – No Assumptions, No Limits

A breakthrough led by Daniel Lidar, a professor of engineering at USC and an expert in quantum error correction, has pushed quantum computing past a key milestone. Working with researchers from USC and Johns Hopkins, Lidar’s team demonstrated a powerful exponential speedup using two of IBM’s 127-qubit Eagle quantum processors — all operated remotely through the cloud. Their results were published in the prestigious journal Physical Review X. “There have previously been demonstrations of more modest types of speedups like a polynomial speedup, says Lidar, who is also the cofounder of Quantum Elements, Inc. “But an exponential speedup is the most dramatic type of speed up that we expect to see from quantum computers.” ... What makes a speedup “unconditional,” Lidar explains, is that it doesn’t rely on any unproven assumptions. Prior speedup claims required the assumption that there is no better classical algorithm against which to benchmark the quantum algorithm. Here, the team led by Lidar used an algorithm they modified for the quantum computer to solve a variation of “Simon’s problem,” an early example of quantum algorithms that can, in theory, solve a task exponentially faster than any classical counterpart, unconditionally.

4 things that make an AI strategy work in the short and long term

Most AI gains came from embedding tools like Microsoft Copilot, GitHub Copilot, and OpenAI APIs into existing workflows. Aviad Almagor, VP of technology innovation at tech company Trimble, also notes that more than 90% of Trimble engineers use Github Copilot. The ROI, he says, is evident in shorter development cycles, and reduced friction in HR and customer service. Moreover, Trimble has introduced AI into their transportation management system, where AI agents optimize freight procurement by dynamically matching shippers and carriers. ... While analysts often lament the difficulty of showing short-term ROI for AI projects, these four organizations disagree — at least in part. Their secret: flexible thinking and diverse metrics. They view ROI not only as dollars saved or earned, but also as time saved, satisfaction increased, and strategic flexibility gained. London says that Upwave listens for customer signals like positive feedback, contract renewals, and increased engagement with AI-generated content. Given the low cost of implementing prebuilt AI models, even modest wins yield high returns. For example, if a customer cites an AI-generated feature as a reason to renew or expand their contract, that’s taken as a strong ROI indicator. Trimble uses lifecycle metrics in engineering and operations. For instance, one customer used Trimble AI tools to reduce the time it took to perform a tunnel safety analysis from 30 minutes to just three.

How IT Leaders Can Rise to a CIO or Other C-level Position

For any IT professional who aspires to become a CIO, the key is to start thinking like a business leader, not just a technologist, says Antony Marceles, a technology consultant and founder of software staffing firm Pumex. "This means taking every opportunity to understand the why behind the technology, how it impacts revenue, operations, and customer experience," he explained in an email. The most successful tech leaders aren't necessarily great technical experts, but they possess the ability to translate tech speak into business strategy, Marceles says, adding that "Volunteering for cross-functional projects and asking to sit in on executive discussions can give you that perspective." ... CIOs rarely have solo success stories; they're built up by the teams around them, Marceles says. "Colleagues can support a future CIO by giving honest feedback, nominating them for opportunities, and looping them into strategic conversations." Networking also plays a pivotal role in career advancement, not just for exposure, but for learning how other organizations approach IT leadership, he adds. Don't underestimate the power of having an executive sponsor, someone who can speak to your capabilities when you’re not there to speak for yourself, Eidem says. "The combination of delivering value and having someone champion that value -- that's what creates real upward momentum."

SLMs vs. LLMs: Efficiency and adaptability take centre stage

SLMs are becoming central to Agentic AI systems due to their inherent efficiency and adaptability. Agentic AI systems typically involve multiple autonomous agents that collaborate on complex, multi-step tasks and interact with environments. Fine-tuning methods like Reinforcement Learning (RL) effectively imbue SLMs with task-specific knowledge and external tool-use capabilities, which are crucial for agentic operations. This enables SLMs to be efficiently deployed for real-time interactions and adaptive workflow automation, overcoming the prohibitive costs and latency often associated with larger models in agentic contexts. ... Operating entirely on-premises ensures that decisions are made instantly at the data source, eliminating network delays and safeguarding sensitive information. This enables timely interpretation of equipment alerts, detection of inventory issues, and real-time workflow adjustments, supporting faster and more secure enterprise operations. SLMs also enable real-time reasoning and decision-making through advanced fine-tuning, especially Reinforcement Learning. RL allows SLMs to learn from verifiable rewards, teaching them to reason through complex problems, choose optimal paths, and effectively use external tools.

Quantum’s quandary: racing toward reality or stuck in hyperbole?

One important reason is for researchers to demonstrate their advances and show that they are adding value. Quantum computing research requires significant expenditure, and the return on investment will be substantial if a quantum computer can solve problems previously deemed unsolvable. However, this return is not assured, nor is the timeframe for when a useful quantum computer might be achievable. To continue to receive funding and backing for what ultimately is a gamble, researchers need to show progress — to their bosses, investors, and stakeholders. ... As soon as such announcements are made, scientists and researchers scrutinize them for weaknesses and hyperbole. The benchmarks used for these tests are subject to immense debate, with many critics arguing that the computations are not practical problems or that success in one problem does not imply broader applicability. In Microsoft’s case, a lack of peer-reviewed data means there is uncertainty about whether the Majorana particle even exists beyond theory. The scientific method encourages debate and repetition, with the aim of reaching a consensus on what is true. However, in quantum computing, marketing hype and the need to demonstrate advancement take priority over the verification of claims, making it difficult to place these announcements in the context of the bigger picture.

Ethical AI for Product Owners and Product Managers

As the product and customer information steward, the PO/PM must lead the process of protecting sensitive data. The Product Backlog often contains confidential customer feedback, competitive analysis, and strategic plans that cannot be exposed. This guardrail requires establishing clear protocols for what data can be shared with AI tools. A practical first step is to lead the team in a data classification exercise, categorizing information as Public, Internal, or Restricted. Any data classified for internal use, such as direct customer quotes, must be anonymized before being used in an AI prompt. ... AI is proficient at generating text but possesses no real-world experience, empathy, or strategic insight. This guardrail involves proactively defining the unique, high-value work that AI can assist but never replace. Product leaders should clearly delineate between AI-optimal tasks, creating first drafts of technical user stories, summarizing feedback themes, or checking for consistency across Product Backlog items and PO/PM-essential areas. These human-centric responsibilities include building genuine empathy through stakeholder interviews, making difficult strategic prioritization trade-offs, negotiating scope, resolving conflicting stakeholder needs, and communicating the product vision. By modeling this partnership and using AI as an assistant to prepare for strategic work, the PO/PM reinforces that their core value lies in strategy, relationships, and empathy.

Sharded vs. Distributed: The Math Behind Resilience and High Availability

In probability theory, independent events are events whose outcomes do not affect each other. For example, when throwing four dice, the number displayed on each dice is independent of the other three dice. Similarly, the availability of each server in a six-node application-sharded cluster is independent of the others. This means that each server has an individual probability of being available or unavailable, and the failure of one server is not affected by the failure or otherwise of other servers in the cluster. In reality, there may be shared resources or shared infrastructure that links the availability of one server to another. In mathematical terms, this means that the events are dependent. However, we consider the probability of these types of failures to be low, and therefore, we do not take them into account in this analysis. ... Traditional architectures are limited by single-node failure risk. Application-level sharding compounds this problem because if any node goes down, its shard and therefore the total system becomes unavailable. In contrast, distributed databases with quorum-based consensus (like YugabyteDB) provide fault tolerance and scalability, enabling higher resilience and improved availability.

How FinTechs are turning GRC into a strategic enabler

The misconception that risk management and innovation exist in tension is one that modern FinTechs must move beyond. At its core, cybersecurity – when thoughtfully integrated – serves not as a brake but as an enabler of innovation. The key is to design governance structures that are both intelligent and adaptive (and resilient in itself). The foundation lies in aligning cybersecurity risk management with the broader business objective: enablement. This means integrating security thinking early in the innovation cycle, using standardized interfaces, expectations, and frameworks that don’t obstruct, but rather channel innovation safely. For instance, when risk statements are defined consistently across teams, decisions can be made faster and with greater confidence. Critically, it starts with the threat model. A well-defined, enterprise-level threat model is the compass that guides risk assessments and controls where they matter most. Yet many companies still operate without a clear articulation of their own threat landscape, leaving their enterprise risk strategies untethered from reality. Without this grounding, risk management becomes either overly cautious or blindly permissive, or a bit of both. We place a strong emphasis on bridging the traditional silos between GRC, IT Security, Red Teaming, and Operational teams.

Daily Tech Digest - July 01, 2025

Quote for the day:

"Listen with curiosity, speak with honesty act with integrity." -- Roy T Bennett

CIOs rethink public cloud as AI strategies mature

Regulatory and compliance concerns are a big driver toward the private cloud or on-premises solutions, says Bastien Aerni, vice president of strategy and technology adoption at GTT. Many companies are shifting their sensitive workloads to private clouds as a piece of broader multicloud and hybrid strategies to support agentic AI and other complex AI initiatives, he adds. “Most of the time, AI is touching confidential data or business-critical data,” Aerni says. “Then the thinking about the architecture and what the workload should be public vs. private, or even on-prem, is becoming a true question.” The public cloud still provides maximum scalability for AI projects, and in recent years, CIOs have been persuaded by the number of extra capabilities available there, he says. “In some of the conversations I had with CIOs, let’s say five years ago, they were mentioning, ‘There are so many features, so many tools,’” Aerni adds. ... “The paradox is clear: AI workloads are driving both massive cloud growth and selective repatriation simultaneously, because the market is expanding so rapidly it’s accommodating multiple deployment models at once,” Kirschner says. “What we are seeing is the maturation from a naive ‘everything-to-the-cloud’ strategy toward intelligent, workload-specific decisions.”

India’s DPDP law puts HR under the microscope—Here’s why that’s a good thing

At first glance, DPDP appears to mirror other data privacy frameworks like GDPR or CCPA. There’s talk of consent, purpose limitation, secure storage, and rights of the data principal (i.e., the individual). But the Indian legislation’s implications ripple far beyond IT configurations or privacy policies. “Mention data protection, and it often gets handed off to the legal or IT teams,” says Gupta. “But that misses the point. Every team that touches personal data is responsible under this law.” For HR departments, this shift is seismic. Gupta underscores how HR sits atop a “goldmine” of personal information—addresses, Aadhaar numbers, medical history, performance reviews, family details, even biometric data in some cases. And this isn't limited to employees; applicants and former workers are also in scope. ... With India housing thousands of global capability centres and outsourcing hubs, DPDP challenges multinationals to look inward. The emphasis so far has been on protecting customer data under global laws like GDPR. But now, internal data practices—especially around employees—are under the scanner. “DPDP is turning the lens inward,” says Gupta. “If your GCC in India tightens data practices, it won’t make sense to be lax elsewhere.”

3 ways developers should rethink their data stack for GenAI success

Traditional data stacks optimized for analytics, for the most part, don’t naturally support the vector search and semantic retrieval patterns that GenAI applications require. Thus, real-time GenAI data architectures need native support for embedding generation and vector storage as first-class citizens. This could mean integrating data with vector databases like Pinecone, Weaviate, or Chroma as part of the core infrastructure. It may also mean searching for multi-modal databases that can support all of your required data types out of the box without needing a bunch of separate platforms. Regardless of the underlying infrastructure, plan for needing hybrid search capabilities that combine traditional keyword search with semantic similarity, and consider how you’ll handle embedding model updates and re-indexing. ... Maintaining data relationships and ensuring consistent access patterns across these different storage systems is the real challenge when working with these various data types. While some platforms are beginning to offer enhanced vector search capabilities that can work across different data types, most organizations still need to architect solutions that coordinate multiple storage systems. The key is to design these multi-modal capabilities into your data stack early, rather than trying to bolt them on later when your GenAI applications demand richer data integration.

Cyber Hygiene Protecting Your Digital and Financial Health

Digital transformation has reshaped the commercial world, integrating technology into nearly every aspect of operations. That has brought incredible opportunities, but it has also opened doors to new threats. Cyber attacks are more frequent and sophisticated, with malevolent actors targeting everyone from individuals to major corporations and entire countries. It is no exaggeration to say that establishing, and maintaining, effective cyber hygiene has become indispensable. According to Microsoft’s 2023 Digital Defense Report, effective cyber hygiene could prevent 99% of cyber attacks. Yet cyber hygiene is not just about preventing attacks, it is also central to maintaining operational stability and resilience in the event of a cyber breach. In that event robust cyber hygiene can limit the operational, financial, and reputational impact of a cyber attack, thereby enhancing an entity’s overall risk profile. ... Even though it’s critical, data suggests that many organizations struggle to implement even basic cyber security measures effectively. For example, a 2024 survey by Extrahop, a Seattle-based cyber security services provider, found that over half of the respondents admitted to using at least one unsecured network protocol, making them susceptible to attacks.

Are Data Engineers Sleepwalking Towards AI Catastrophe?

Data engineers are already overworked. Weigel cited a study that indicated 80% of data engineering teams are already overloaded. But when you add AI and unstructured data to the mix, the workload issue becomes even more acute. Agentic AI provides a potential solution. It’s natural that overworked data engineering teams will turn to AI for help. There’s a bevy of providers building copilots and swarms of AI agents that, ostensibly, can build, deploy, monitor, and fix data pipelines when they break. We are already seeing agentic AI have real impacts on data engineering teams, as well as the downstream data analysts who ultimately are the ones requesting the data in the first place. ... Once human data engineers are out of the loop, bad things can start happening, Weigel said. They potentially face a situation where the volume of data requests–which originally were served by human data engineers but now are being served by AI agents–is beyond their capability to keep up. ... “We’re now back in the dark ages, where we were 10 years ago [when we wondered] why we need data warehouses,” he said. “I know that if person A, B, and C ask a question, and previously they wrote their own queries, they got different results. Right now, we ask the same agent the same question, and because they’re non-deterministic, they will actually create different queries every time you ask it.

How cybercriminals are weaponizing AI and what CISOs should do about it

Security teams are using AI to keep up with the pace of AI-powered cybercrime, scanning large volumes of data to surface threats earlier. AI helps scan massive amounts of threat data, surface patterns, and prioritize investigations. For example, analysts used AI to uncover a threat actor’s alternate Telegram channels, saving significant manual effort. Another use case: linking sockpuppet accounts. By analyzing slang, emojis, and writing styles, AI can help uncover connections between fake personas, even when their names and avatars are different. AI also flags when a new tactic starts gaining traction on forums or social media. ... As more defenders turn to AI to make sense of vast amounts of threat data, it’s easy to assume that LLMs can handle everything on their own. But interpreting chatter from the underground is not something AI can do well without help. “This diffuse environment, rich in vernacular and slang, poses a hurdle for LLMs that are typically trained on more generic or public internet data,” Ian Gray, VP of Cyber Threat Intelligence at Flashpoint, told Help Net Security. The problem goes deeper than just slang. Threat actors often communicate across multiple niche platforms, each with its own shorthand and tone.

How To Keep AI From Making Us Stupid

The allure of AI is undeniable. It drafts emails, summarizes lengthy reports, generates code snippets, and even whips up images faster than you can say “neural network.” This unprecedented convenience, however, carries a subtle but potent risk. A study from MIT has highlighted concerns that overuse of AI tools might be degrading our thinking capabilities. That degradation is the digital equivalent of using a GPS so much that you forget how to read a map. Suddenly, your internal compass points vaguely toward convenience and not much else. When we offload critical cognitive tasks entirely to AI, our muscles for those tasks can begin to atrophy, leading to cognitive offloading. ... Treat AI-generated content like a highly caffeinated first draft — full of energy but possibly a little messy and prone to making things up. Your job isn’t to simply hit “generate” and walk away, unless you enjoy explaining AI hallucinations or factual inaccuracies to your boss. Or worse, your audience. Always, always, aggressively edit, proofread, and, most critically, fact-check every single output. ... The real risk isn’t AI taking over our jobs; it’s us letting AI take over our brains. To maintain your analytical edge, continuously challenge yourself. Practice skills that AI complements but doesn’t replace, such as critical thinking, complex problem-solving, nuanced synthesis, ethical judgment, and genuine human creativity.

Governance meets innovation: Protiviti’s strategy for secure, scalable growth in BFSI and beyond

In today’s BFSI landscape, technology alone is no longer a differentiator. True competitive advantage lies in the orchestration of innovation with governance. The deployment of AI in underwriting, the migration of customer data to the cloud, or the use of IoT in insurance all bring immense opportunity—but also profound risks. Without strong guardrails, these initiatives can expose firms to cyber threats, data sovereignty violations, and regulatory scrutiny. Innovation without governance is a gamble; governance without innovation is a graveyard. ... In cloud transformation projects, for instance, we work with clients to proactively assess data localisation risks, cloud governance maturity, and third-party exposures, ensuring resilience is designed from day one. As AI adoption scales across financial services, we bring deep expertise in Responsible AI governance. From ethical frameworks and model explainability to regulatory alignment with India’s DPDP Act and the EU AI Act, our solutions ensure that automated systems remain transparent, auditable, and trustworthy. Our AI risk models integrate regulatory logic into system design, bridging the gap between innovation and accountability.

Cybercriminals take malicious AI to the next level

Cybercriminals are tailoring AI models for specific fraud schemes, including generating phishing emails tailored by sector or language, as well as writing fake job posts, invoices, or verification prompts. “Some vendors even market these tools with tiered pricing, API access, and private key licensing, mirroring the [legitimate] SaaS economy,” Flashpoint researchers found. “This specialization leads to potentially greater success rates and automated complex attack stages,” Flashpoint’s Gray tells CSO. ... Cybercrime vendors are also lowering the barrier for creating synthetic video and voice, with deepfake as a service (DaaS) offerings ... “This ‘prompt engineering as a service’ (PEaaS) lowers the barrier for entry, allowing a wider range of actors to leverage sophisticated AI capabilities through pre-packaged malicious prompts,” Gray warns. “Together, these trends create an adaptive threat: tailored models become more potent when refined with illicit data, PEaaS expands the reach of threat actors, and the continuous refinement ensures constant evolution against defenses,” he says. ... Enterprises need to balance automation with expert analysis, separating hype from reality, and continuously adapt to the rapidly evolving threat landscape. “Defenders should start by viewing AI as an augmentation of human expertise, not a replacement,” Flashpoint’s Gray says.

“DevOps is Dead? Long Live DevOps-Powered Platforms”

If DevOps and platform engineering needed a common enemy — or ally — to bond over, AI provided it. A panel featuring Nvidia, Google, Rootly and Thoughtworks explained how large language models are automating “the last mile” of toil, from incident response bots that reason over Grafana dashboards to code-gen pipelines that spit out compliant Terraform. ... The logic is straightforward: You can’t automate what you can’t see. For DevOps practitioners, high-fidelity telemetry is now table stakes — whether you’re feeding an agentic AI, debugging an ephemeral sandbox, or proving compliance to auditors. Expect platform blueprints to ship with observability baked in, not bolted on. Look at the badges behind every coffee urn and you’ll spot familiar DevOps and DevSecOps logos — GitHub Actions, Mezmo, Teleport, Cortex, Sedai, Tailscale. Many of these vendors cut their teeth in CI/CD, IaC, or shift-left security long before “platform engineering” was a LinkedIn hashtag. ... So why the funeral garb? My guess: A tongue-in-cheek jab at hype cycles. Just as “DevOps is dead” clickbait pushed us to sharpen our message, the sash was a reminder that real value — not buzzwords — keeps a movement alive. Judging by the hallway traffic and workshop queues, platform engineering is passing that test.

Daily Tech Digest - June 30, 2025

Quote for the day:

"Sheep are always looking for a new shepherd when the terrain gets rocky." -- Karen Marie Moning

The first step in modernization: Ditching technical debt

At a high level, determining when it’s time to modernize is about quantifying cost, risk, and complexity. In dollar terms, it may seem as simple as comparing the expense of maintaining legacy systems versus investing in new architecture. But the true calculation includes hidden costs, like the developer hours lost to patching outdated systems, and the opportunity cost of not being able to adapt quickly to business needs. True modernization is not a lift-and-shift — it’s a full-stack transformation. That means breaking apart monolithic applications into scalable microservices, rewriting outdated application code into modern languages, and replacing rigid relational data models with flexible, cloud-native platforms that support real-time data access, global scalability, and developer agility. Many organizations have partnered with MongoDB to achieve this kind of transformation. ... But modernization projects are usually a balancing act, and replacing everything at once can be a gargantuan task. Choosing how to tackle the problem comes down to priorities, determining where pain points exist and where the biggest impacts to the business will be. The cost of doing nothing will outrank the cost of doing something.

Is Your CISO Ready to Flee?

“A well-funded CISO with an under-resourced security team won’t be effective. The focus should be on building organizational capability, not just boosting top salaries.” While Deepwatch CISO Chad Cragle believes any CISO just in the role for the money has “already lost sight of what really matters,” he agrees that “without the right team, tools, or board access, burnout is inevitable.” Real impact, he contends, “only happens when security is valued and you’re empowered to lead.” Perhaps that stands as evidence that SMBs that want to retain their talent or attract others should treat the CISO holistically. “True professional fulfillment and long-term happiness in the CISO role stems from the opportunities for leadership, personal and professional growth, and, most importantly, the success of the cybersecurity program itself,” says Black Duck CISO Bruce Jenkins. “When cyber leaders prioritize the development and execution of a comprehensive, efficient, and effective program that delivers demonstrable value to the business, appropriate compensation typically follows as a natural consequence.” Concerns around budget constraints is that all CISOs at this point (private AND public sector) have been through zero-based budget reviews several times. If the CISO feels unsafe and unable to execute, they will be incentivized to find a safer seat with an org more prepared to invest in security programs.

AI is learning to lie, scheme, and threaten its creators

For now, this deceptive behavior only emerges when researchers deliberately stress-test the models with extreme scenarios. But as Michael Chen from evaluation organization METR warned, "It's an open question whether future, more capable models will have a tendency towards honesty or deception." The concerning behavior goes far beyond typical AI "hallucinations" or simple mistakes. Hobbhahn insisted that despite constant pressure-testing by users, "what we're observing is a real phenomenon. We're not making anything up." Users report that models are "lying to them and making up evidence," according to Apollo Research's co-founder. "This is not just hallucinations. There's a very strategic kind of deception." The challenge is compounded by limited research resources. While companies like Anthropic and OpenAI do engage external firms like Apollo to study their systems, researchers say more transparency is needed. As Chen noted, greater access "for AI safety research would enable better understanding and mitigation of deception." ... "Right now, capabilities are moving faster than understanding and safety," Hobbhahn acknowledged, "but we're still in a position where we could turn it around." Researchers are exploring various approaches to address these challenges.

The network is indeed trying to become the computer

Think of the scale-up networks such as the NVLink ports and NVLink Switch fabrics that are part and parcel of an GPU accelerated server node – or, these days, a rackscale system like the DGX NVL72 and its OEM and ODM clones. These memory sharing networks are vital for ever-embiggening AI training and inference workloads. As their parameter counts and token throughput requirements both rise, they need ever-larger memory domains to do their work. Throw in a mixture of expert models and the need for larger, fatter and faster scale-up networks, as they are now called, is obvious even to an AI model with only 7 billion parameters. ... Then there is the scale-out network, which is used to link nodes in distributed systems to each other to share work in a less tightly coupled way than the scale-up network affords. This is the normal networking we are familiar with in distributed HPC systems, which is normally Ethernet or InfiniBand and sometimes proprietary networks like those from Cray, SGI, Fujitsu, NEC, and others from days gone by. On top of this, we have the normal north-south networking stack that allows people to connect to systems and the east-west networks that allow distributed corporate systems running databases, web infrastructure, and other front-office systems to communicate with each other.

What Can We Learn From History’s Most Bizarre Software Bugs?

“It’s never just one thing that causes failure in complex systems.” In risk management, this is known as the Swiss cheese model, where flaws that occur in one layer aren’t as dangerous as deeper flaws overlapping through multiple layers. And as the Boeing crash proves, “When all of them align, that’s what made it so deadly.” It is difficult to test for every scenario. After all, the more inputs you have, the more possible outputs — and “this is all assuming that your system is deterministic.” Today’s codebases are massive, with many different contributors and entire stacks of infrastructure. “From writing a piece of code locally to running it on a production server, there are a thousand things that could go wrong.” ... It was obviously a communication failure, “because NASA’s navigation team assumed everything was in metric.” But you also need to check the communication that’s happening between the two systems. “If two systems interact, make sure they agree on formats, units, and overall assumptions!” But there’s another even more important lesson to be learned. “The data had shown inconsistencies weeks before the failure,” Bajić says. “NASA had seen small navigation errors, but they weren’t fully investigated.”

Europe’s AI strategy: Smart caution or missed opportunity?

Companies in Europe are spending less on AI, cloud platforms, and data infrastructure. In high-tech sectors, productivity growth in the U.S. has far outpaced Europe. The report argues that AI could help close the gap, but only if it is used to redesign how businesses operate. Using AI to automate old processes is not enough. ... Feinberg also notes that many European companies assumed AI apps would be easier to build than traditional software, only to discover they are just as complex, if not more so. This mismatch between expectations and reality has slowed down internal projects. And the problem isn’t unique to Europe. As Oliver Rochford, CEO of Aunoo AI, points out, “AI project failure rates are generally high across the board.” He cites surveys from IBM, Gartner, and others showing that anywhere from 30 to 84 percent of AI projects fail or fall short of expectations. “The most common root causes for AI project failures are also not purely technical, but organizational, misaligned objectives, poor data governance, lack of workforce engagement, and underdeveloped change management processes. Apparently Europe has no monopoly on those.”

A Developer’s Guide to Building Scalable AI: Workflows vs Agents

Sometimes, using an agent is like replacing a microwave with a sous chef — more flexible, but also more expensive, harder to manage, and occasionally makes decisions you didn’t ask for. ... Workflows are orchestrated. You write the logic: maybe retrieve context with a vector store, call a toolchain, then use the LLM to summarize the results. Each step is explicit. It’s like a recipe. If it breaks, you know exactly where it happened — and probably how to fix it. This is what most “RAG pipelines” or prompt chains are. Controlled. Testable. Cost-predictable. The beauty? You can debug them the same way you debug any other software. Stack traces, logs, fallback logic. If the vector search fails, you catch it. If the model response is weird, you reroute it. ... Agents, on the other hand, are built around loops. The LLM gets a goal and starts reasoning about how to achieve it. It picks tools, takes actions, evaluates outcomes, and decides what to do next — all inside a recursive decision-making loop. ... You can’t just set a breakpoint and inspect the stack. The “stack” is inside the model’s context window, and the “variables” are fuzzy thoughts shaped by your prompts. When something goes wrong — and it will — you don’t get a nice red error message.

Leveraging Credentials As Unique Identifiers: A Pragmatic Approach To NHI Inventories

Most teams struggle with defining NHIs. The canonical definition is simply "anything that is not a human," which is necessarily a wide set of concerns. NHIs manifest differently across cloud providers, container orchestrators, legacy systems, and edge deployments. A Kubernetes service account tied to a pod has distinct characteristics compared to an Azure managed identity or a Windows service account. Every team has historically managed these as separate concerns. This patchwork approach makes it nearly impossible to create a consistent policy, let alone automate governance across environments. ... Most commonly, this takes the form of secrets, which look like API keys, certificates, or tokens. These are all inherently unique and can act as cryptographic fingerprints across distributed systems. When used in this way, secrets used for authentication become traceable artifacts tied directly to the systems that generated them. This allows for a level of attribution and auditing that's difficult to achieve with traditional service accounts. For example, a short-lived token can be directly linked to a specific CI job, Git commit, or workload, allowing teams to answer not just what is acting, but why, where, and on whose behalf.

How Is AI Really Impacting Jobs In 2025?

Pessimists warn of potential mass unemployment leading to societal collapse. Optimists predict a new age of augmented working, making us more productive and freeing us to focus on creativity and human interactions. There are plenty of big-picture forecasts. One widely-cited WEF prediction claims AI will eliminate 92 million jobs while creating 170 million new, different opportunities. That doesn’t sound too bad. But what if you’ve worked for 30 years in one of the jobs that’s about to vanish and have no idea how to do any of the new ones? Today, we’re seeing headlines about jobs being lost to AI with increasing frequency. And, from my point of view, not much information about what’s being done to prepare society for this potentially colossal change. ... An exacerbating factor is that many of the roles that are threatened are entry-level, such as junior coders or designers, or low-skill, including call center workers and data entry clerks. This means there’s a danger that AI-driven redundancy will disproportionately hit economically disadvantaged groups. There’s little evidence so far that governments are prioritizing their response. There have been few clearly articulated strategies to manage the displacement of jobs or to protect vulnerable workers.

AGI vs. AAI: Grassroots Ingenuity and Frugal Innovation Will Shape the Future

One way to think of AAI is as intelligence that ships. Vernacular chatbots, offline crop-disease detectors, speech-to-text tools for courtrooms: examples of similar applications and products, tailored and designed for specific sectors, are growing fast. ... If the search for AGI is reminiscent of a cash-rich unicorn aiming for growth at all costs, then AAI is more scrappy. Like a bootstrapped startup that requires immediate profitability, it prizes tangible impact over long-term ambitions to take over the world. The aspirations—and perhaps the algorithms themselves—may be more modest. Still, the context makes them potentially transformative: if reliable and widely adopted, such systems could reach millions of users who have until now been on the margins of the digital economy. ... All this points to a potentially unexpected scenario, one in which the lessons of AI flow not along the usual contours of global geopolitics and economic power—but percolate rather upward, from the laboratories and pilot programs of the Global South toward the boardrooms and research campuses of the North. This doesn’t mean that the quest for AGI is necessarily misguided. It’s possible that AI may yet end up redefining intelligence.

Daily Tech Digest - June 29, 2025

Quote for the day:

“Great minds discuss ideas; average minds discuss events; small minds discuss people.” -- Eleanor Roosevelt

Who Owns End-of-Life Data?

Enterprises have never been more focused on data. What happens at the end of that data's life? Who is responsible when it's no longer needed? Environmental concerns are mounting as well. A Nature study warns that AI alone could generate up to 5 million metric tons of e-waste by 2030. A study from researchers at Cambridge University and the Chinese Academy of Sciences said top reason enterprises dispose of e-waste rather than recycling computers is the cost. E-waste can contain metals, including copper, gold, silver aluminum and rare earth elements, but proper handling is expensive. Data security is a concern as well as breach proofing doesn't get better than destroying equipment. ... End-of-life data management may sit squarely in the realm of IT, but it increasingly pulls in compliance, risk and ESG teams, the report said. Driven by rising global regulations and escalating concerns over data leaks and breaches, C-level involvement at every stage signals that end-of-life data decisions are being treated as strategically vital - not simply handed off. Consistent IT participation also suggests organizations are well-positioned to select and deploy solutions that work with their existing tech stack. That said, shared responsibility doesn't guarantee seamless execution. Multiple stakeholders can lead to gaps unless underpinned by strong, well-communicated policies, the report said.

How AI is Disrupting the Data Center Software Stack

Over the years, there have been many major shifts in IT infrastructure – from the mainframe to the minicomputer to distributed Windows boxes to virtualization, the cloud, containers, and now AI and GenAI workloads. Each time, the software stack seems to get torn apart. What can we expect with GenAI? ... Galabov expects severe disruption in the years ahead on a couple of fronts. Take coding, for example. In the past, anyone wanting a new industry-specific application for their business might pay five figures for development, even if they went to a low-cost region like Turkey. For homegrown software development, the price tag would be much higher. Now, an LLM can be used to develop such an application for you. GenAI tools have been designed explicitly to enhance and automate several elements of the software development process. ... Many enterprises will be forced to face the reality that their systems are fundamentally legacy platforms that are unable to keep pace with modern AI demands. Their only course is to commit to modernization efforts. Their speed and degree of investment are likely to determine their relevance and competitive positioning in a rapidly evolving market. Kleyman believes that the most immediate pressure will fall on data-intensive, analytics-driven platforms such as CRM and business intelligence (BI).

AI Improves at Improving Itself Using an Evolutionary Trick

The best SWE-bench agent was not as good as the best agent designed by expert humans, which currently scores about 70 percent, but it was generated automatically, and maybe with enough time and computation an agent could evolve beyond human expertise. The study is a “big step forward” as a proof of concept for recursive self-improvement, said Zhengyao Jiang, a cofounder of Weco AI, a platform that automates code improvement. Jiang, who was not involved in the study, said the approach could made further progress if it modified the underlying LLM, or even the chip architecture. DGMs can theoretically score agents simultaneously on coding benchmarks and also specific applications, such as drug design, so they’d get better at getting better at designing drugs. Zhang said she’d like to combine a DGM with AlphaEvolve. ... One concern with both evolutionary search and self-improving systems—and especially their combination, as in DGM—is safety. Agents might become uninterpretable or misaligned with human directives. So Zhang and her collaborators added guardrails. They kept the DGMs in sandboxes without access to the Internet or an operating system, and they logged and reviewed all code changes. They suggest that in the future, they could even reward AI for making itself more interpretable and aligned.

Data center costs surge up to 18% as enterprises face two-year capacity drought

Smart enterprises are adapting with creative strategies. CBRE’s Magazine emphasizes “aggressive and long-term planning,” suggesting enterprises extend capacity forecasts to five or 10 years, and initiate discussions with providers much earlier than before. Geographic diversification has become essential. While major hubs price out enterprises, smaller markets such as São Paulo saw pricing drops of as much as 20.8%, while prices in Santiago fell 13.7% due to shifting supply dynamics. Magazine recommended “flexibility in location as key, exploring less-constrained Tier 2 or Tier 3 markets or diversifying workloads across multiple regions.” For Gogia, “Tier-2 markets like Des Moines, Columbus, and Richmond are now more than overflow zones, they’re strategic growth anchors.” Three shifts have elevated these markets: maturing fiber grids, direct renewable power access, and hyperscaler-led cluster formation. “AI workloads, especially training and archival, can absorb 10-20ms latency variance if offset by 30-40% cost savings and assured uptime,” said Gogia. “Des Moines and Richmond offer better interconnection diversity today than some saturated Tier-1 hubs.” Contract flexibility is also crucial. Rather than traditional long-term leases, enterprises are negotiating shorter agreements with renewal options and exploring revenue-sharing arrangements tied to business performance.

Fintech’s AI Obsession Is Useless Without Culture, Clarity and Control

what does responsible AI actually mean in a fintech context? According to PwC’s 2024 Responsible AI Survey, it encompasses practices that ensure fairness, transparency, accountability and governance throughout the AI lifecycle. It’s not just about reducing model bias — it’s about embedding human oversight, securing data, ensuring explainability and aligning outputs with brand and compliance standards. In financial services, these aren’t "nice-to-haves" — they’re essential for scaling AI safely and effectively. Financial marketing is governed by strict regulations and AI-generated content can create brand and legal risks. ... To move AI adoption forward responsibly, start small. Low-risk, high-reward use cases let teams build confidence and earn trust from compliance and legal stakeholders. Deloitte’s 2024 AI outlook recommends beginning with internal applications that use non-critical data — avoiding sensitive inputs like PII — and maintaining human oversight throughout. ... As BCG highlights, AI leaders devote 70% of their effort to people and process — not just technology. Create a cross-functional AI working group with stakeholders from compliance, legal, IT and data science. This group should define what data AI tools can access, how outputs are reviewed and how risks are assessed.

Is Microsoft’s new Mu for you?

Mu uses a transformer encoder-decoder design, which means it splits the work into two parts. The encoder takes your words and turns them into a compressed form. The decoder takes that form and produces the correct command or answer. This design is more efficient than older models, especially for tasks such as changing settings. Mu has 32 encoder layers and 12 decoder layers, a setup chosen to fit the NPU’s memory and speed limits. The model utilizes rotary positional embeddings to maintain word order, dual-layer normalization to maintain stability, and grouped-query attention to use memory more efficiently. ... Mu is truly groundbreaking because it is the first SLM built to let users control system settings using natural language, running entirely on a mainstream shipping device. Apple’s iPhones, iPads, and Macs all have a Neural Engine NPU and run on-device AI for features like Siri and Apple Intelligence. But Apple does not have a small language model as deeply integrated with system settings as Mu. Siri and Apple Intelligence can change some settings, but not with the same range or flexibility. ... By processing data directly on the device, Mu keeps personal information private and responds instantly. This shift also makes it easier to comply with privacy laws in places like Europe and the US since no data leaves your computer.

Is It a Good Time to Be a Software Engineer?

AI may be rewriting the rules of software development, but it hasn’t erased the thrill of being a programmer. If anything, the machines have revitalised the joy of coding. New tools make it possible to code in natural language, ship prototypes in hours, and bypass tedious setup work. From solo developers to students, the process may feel more immediate or rewarding. Yet, this sense of optimism exists alongside an undercurrent of anxiety. As large language models (LLMs) begin to automate vast swathes of development, some have begun to wonder if software engineering is still a career worth betting on. ... Meanwhile, Logan Thorneloe, a software engineer at Google, sees this as a golden era for developers. “Right now is the absolute best time to be a software engineer,” he wrote on LinkedIn. He points out “development velocity” as the reason. Thorneleo believes AI is accelerating workflows, shrinking prototype cycles from months to days, and giving developers unprecedented speed. Companies that adapt to this shift will win, not by eliminating engineers, but by empowering them. More than speed, there’s also a rediscovered sense of fun. Programmers who once wrestled with broken documentation and endless boilerplate are rediscovering the creative satisfaction that first drew them to the field.

Dumping mainframes for cloud can be a costly mistake

Despite industry hype, mainframes are not going anywhere. They quietly support the backbone of our largest banks, governments, and insurance companies. Their reliability, security, and capacity for massive transactions give mainframes an advantage that most public cloud platforms simply can’t match for certain workloads. ... At the core of this conversation is culture. An innovative IT organization doesn’t pursue technology for its own sake. Instead, it encourages teams to be open-minded, pragmatic, and collaborative. Mainframe engineers have a seat at the architecture table alongside cloud architects, data scientists, and developers. When there’s mutual respect, great ideas flourish. When legacy teams are sidelined, valuable institutional knowledge and operational stability are jeopardized. A cloud-first mantra must be replaced by a philosophy of “we choose the right tool for the job.” The financial institution in our opening story learned this the hard way. They had to overcome their bias and reconnect with their mainframe experts to avoid further costly missteps. It’s time to retire the “legacy versus modern” conflict and recognize that any technology’s true value lies in how effectively it serves business goals. Mainframes are part of a hybrid future, evolving alongside the cloud rather than being replaced by it.

Why Modern Data Archiving Is Key to a Scalable Data Strategy

Organizations are quickly learning they can’t simply throw all data, new and old, at an AI strategy; instead, it needs to be accurate, accessible, and, of course, cost-effective. Without these requirements in place, it’s far from certain AI-powered tools can deliver the kind of insight and reliability businesses need. As part of the various data management processes involved, archiving has taken on a new level of importance. ... For organizations that need to migrate data, for example, archiving is used to identify which essential datasets, while enabling users to offload inactive data in the most cost-effective way. This kind of win-win can also be applied to cloud resources, where moving data to the most appropriate service can potentially deliver significant savings. Again, this contrasts with tiering systems and NAS gateways, which rely on global file systems to provide cloud-based access to local files. The challenge here is that access is dependent on the gateway remaining available throughout the data lifecycle because, without it, data recall can be interrupted or cease entirely. ... It then becomes practical to strike a much better balance across the typical enterprise storage technology stack, including long-term data preservation and compliance, where data doesn’t need to be accessed so often, but where reliability and security are crucial.

The Impact of Regular Training and Timely Security Policy Changes on Dev Teams

Constructive refresher training drives continuous improvement by reinforcing existing knowledge while introducing new concepts like AI-powered code generation, automated debugging and cross-browser testing in manageable increments. Teams that implement consistent training programs see significant productivity benefits as developers spend less time struggling with unfamiliar tools and more time automating tasks to focus on delivering higher value. ... Security policies that remain static as teams grow create dangerous blind spots, compromising both the team’s performance and the organization’s security posture. Outdated policies fail to address emerging threats like malware infections and often become irrelevant to the team’s current workflow, leading to workarounds and system vulnerabilities. ... Proactive security integration into development workflows represents a fundamental shift from reactive security measures to preventative strategies. This approach enables growing teams to identify and address security concerns early in the development process, reducing the cost and complexity of remediation. Cultivating a security-first culture becomes increasingly important as teams grow. This involves embedding security considerations into various stages of the development life cycle. Early risk identification in cloud infrastructure reduces costly breaches and improves overall team productivity.

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