Daily Tech Digest by Kannan Subbiah: software quality

Showing posts with label software quality. Show all posts

Daily Tech Digest - May 28, 2026

Quote for the day:

“Knowledge is knowing what to say. Wisdom is knowing whether to say it or not.” -- Vala Afshar

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The death of network perimeter security is rewriting trust

The traditional model of defending a corporate network by securing a fixed physical perimeter is no longer viable. Because modern employees work from scattered locations and rely on various cloud applications, organizations can no longer trust a user based simply on their office location. Instead, digital defense must center on identity, making verification an ongoing process that evaluates who a person is, what device they are using, and their specific context. Personal computers, laptops, and smartphones have become the main targets for external threats, especially as attackers employ artificial intelligence to craft sophisticated phishing and credential theft schemes aimed at exploiting human behavior. Compounding this challenge, the widespread use of unapproved consumer software and unsecured home networks creates invisible vulnerabilities that standard network tools fail to see. To counter these widespread risks, businesses are moving away from separate, disconnected security products and are adopting integrated, unified platforms that continuously check access permissions. This practical transition requires an operational shift where protection follows the individual everywhere rather than remaining tied to a physical building. Ultimately, achieving safety depends on implementing adaptive, intelligent systems that safeguard sensitive information while supporting the day-to-day flexibility of a distributed workforce.

Converging File and Object Storage for AI-Scale Data Architectures

Enterprise data infrastructure has traditionally been split into two separate systems: file storage and object storage. File storage uses a hierarchical folder layout that works well for traditional software applications and the interactive workspaces used by artificial intelligence agents. Object storage, by contrast, relies on a flat address space that excels at holding immense data repositories and raw training sets quite economically. Historically, attempting to connect these two systems meant relying on complex translation utilities or constantly copying data back and forth. That approach created severe performance bottlenecks, added latency, and wasted space on duplicate information, which ultimately slowed down artificial intelligence workflows. To resolve this friction, newer storage developments focus on the native convergence of these two methods. By combining both frameworks within a single shared global namespace, data can be written as a regular file and read immediately as a standard object without any translation delays or background copying. This unified setup allows processing clusters and graphics cards to ingest data at true network speeds without encountering software friction. Ultimately, bringing these protocols together creates a stable data foundation that simplifies storage operations, lowers hardware expenses, and satisfies the heavy requirements of modern artificial intelligence models.

The AI Premium: Why Cutting-Edge Tech Can Cost More Than the Human It Replaces

While many organizations expect artificial intelligence to reduce corporate spending by automating roles, evidence suggests that sophisticated technology frequently costs more than the human professionals it replaces. This financial discrepancy arises because initial estimates overlook full operational costs, which include rigorous data preparation, legacy system integration, strict compliance protocols, and ongoing software maintenance. Furthermore, advanced and intricate AI models consume enormous amounts of computing power, generating high processing and data costs that can quickly overwhelm corporate technology budgets. In complex fields like law, finance, and medicine, these automated tools are also prone to factual errors and lack human common sense. As a direct result, businesses must pay for experienced human specialists to thoroughly review and correct the machine's outputs, an administrative overhead that can completely erase any intended financial savings. Studies show that a large majority of organizations attempting to cut costs through automation fail to achieve a clear financial benefit. Ultimately, the article notes that companies should avoid broad, indiscriminate replacements of specialized personnel. Instead, management teams should evaluate expenses on a separate task level basis, deploying automation only for routine, predictable duties where the economic advantages are proven, while reserving highly complex work for human staff.

From Logs to Tests: A Practical Guide to Production-Driven QA Coverage in Regulated Environments

In this article, QA professional Tanvi Mittal explains how software teams can use production logs to identify and fix hidden gaps in their automated testing. She points out that roughly sixty percent of production failures trace back to real transaction paths that completely lack test coverage. In complex setups like financial platforms, standard test suites often miss these paths because they only verify how the system was originally expected to work, rather than how it actually behaves after years of quick patches and adjustments. To safely use this production data without violating strict privacy regulations, organizations must implement a careful data sanitization pipeline. Instead of just blacking out numbers, the process uses synthetic substitution, which keeps the structural relationships between fields intact while completely removing sensitive customer information. Once the data is safe to use, teams can group log files by similar behaviors, cross-reference them against current test suites, and rank the unmapped paths based on practical factors like past failures, daily usage volume, and recent code changes. This method lets engineering teams prioritize high-risk gaps and quickly build new test stubs. Ultimately, this practice turns routine logs into clear, factual proof for auditors, showing exactly why certain tests are prioritized while keeping the entire process compliant and secure.

The End of the Digital Age

The perspective shared in the Communications of the ACM opinion piece suggests that the traditional digital era, defined by classical binary code and the predictable scaling of silicon chips, is reaching its natural conclusion. For decades, society relied on the steady doubling of computer power to drive progress, but physical boundaries have made it increasingly difficult to shrink components any further. This plateau is shifting the focus of computer science away from simply making chips smaller and faster. Instead, the field is moving toward entirely new architectures, such as systems that mimic the human brain or leverage quantum mechanics to process information. Furthermore, the nature of technology itself is transforming from a deterministic tool that does exactly what it is told into probabilistic systems that learn from patterns. This means the classic definition of software engineering, which is rooted in writing explicit lines of code, is sharing the stage with systems that adapt and generate outputs based on probability. This transition marks a deeper evolution from a period focused on connecting devices and accumulating data to one centered on managing autonomous systems. Ultimately, the article views this shift not as a failure of technology, but as an invitation to redefine our relationship with computing.

Why Cyber Insurance and Cyber Assurance Matter More When Considered Together

In this Cyber Defense Magazine article, the author highlights a significant gap in corporate risk management: the traditional separation of cyber insurance and cyber assurance. While cyber insurance functions as a financial safety net to offset the losses from unpredictable network breaches, it often relies on static, outdated questionnaires during underwriting. Conversely, cyber assurance focuses on continuously verifying that an organization’s security controls are operational and effective. Keeping these two practices isolated creates clear inefficiencies, leaving insurance providers with inaccurate risk profiles and forcing businesses to accept misaligned premiums. The article argues that marrying these disciplines creates a more dynamic framework built on clear evidence. By feeding continuous assurance data directly into insurance evaluations, companies can demonstrate their actual security setup over time rather than relying on a single annual snapshot. This integration allows insurers to make highly accurate underwriting decisions and establish fairer coverage terms. For businesses, this collaborative approach turns daily security management from an abstract expense into a concrete asset that directly lowers operational and financial risk. Ultimately, treating insurance and assurance as deeply connected elements helps organizations move past simple compliance, building real digital trust and a much stronger defense against rapidly evolving online threats and vulnerabilities.

Mastering Red-Teaming for Generative AI

The article outlines the critical role of red-teaming in identifying and mitigating safety risks associated with generative artificial intelligence. While traditional security testing often concentrates on model-level flaws like offensive outputs, biases, or prompt injections, modern systems require a significantly broader evaluation strategy. The text highlights that generative AI applications are deeply connected to larger digital networks, meaning they can inadvertently expose or exploit existing ecosystem vulnerabilities such as weak authentication, unprotected endpoints, and insecure application programming interfaces. Furthermore, operational risks like training data leakage, human overreliance on automated answers, employee misuse, and highly tailored social engineering campaigns introduce substantial safety concerns. To address these multi-layered threats effectively, organizations must update their testing methods. This shift involves merging network security knowledge with artificial intelligence engineering, testing applications within their actual live deployment environments, and structuring audits around recognized industry safety frameworks. Ultimately, the article underscores that automated testing tools are insufficient on their own; human intuition and specialized professional expertise remain essential for identifying deep-seated flaws, nuanced cultural biases, and complex system plugin vulnerabilities. Because thorough security assessments require diverse technical perspectives, outsourcing these rigorous stress tests to professional teams is presented as a practical way to protect corporate infrastructure.

Microsoft Extends Rust-Influenced Memory-Safety Push to C#

According to a report by David Ramel, Microsoft is incorporating design principles inspired by the Rust programming language to enhance memory safety features within C#. While C# is fundamentally safe by default, developers occasionally use the unsafe keyword for performance tuning, raw memory access, and native interoperability. To minimize the security risks associated with these edge cases, Microsoft plans to overhaul the language's unsafe code model beginning with C# 16. The proposed changes will require unsafe operations to be explicitly isolated within specific inner blocks and documented through clearer contracts enforced by the compiler. Instead of generating simple warnings, the compiler will produce errors for contract violations, ensuring that memory obligations are intentionally managed or passed along to calling methods rather than remaining implied. This initiative reflects a broader multiyear effort by Microsoft to systematically mitigate memory safety vulnerabilities, which historically accounted for roughly seventy percent of their tracked security flaws. By implementing these strict boundary models similar to Rust, the engineering team aims to make raw memory manipulations significantly easier to audit and reason about across complex software projects without altering the primary managed nature of C#. Although this update does not address separate issues like thread safety, it provides a structured framework for managing unsafe code.

The Unpredictable Power Of Leadership Amplification

In this article, the author explains how a leader's words, actions, and even silence are deeply magnified across an organization, a phenomenon termed the leadership amplification effect. When a leader falls silent, it creates an unintended gap that employees often fill with anxiety, rumors, and their own worst fears, especially during challenging periods of organizational change. This communication breakdown frequently stems from managers who lean toward extreme goal orientation, sharing only bare facts while omitting regular praise or timely updates. On the other end of the spectrum are leaders who focus purely on pleasing people, which can shield workplace relationships but ultimately sacrifices clear direction. True leadership effectiveness requires navigating the delicate balance between these two opposing styles. Drawing on human evolutionary history, the author notes that cooperation relies heavily on our innate ability to see the world through the eyes of others. Rather than overvaluing either the company goals or individual employees in isolation, successful managers must protect the core relationship between their people and the shared goals. This balance is never static and requires a daily adjustment of perspective rooted in empathy, ensuring that every deliberate comment or absence of feedback is handled with care.

The Credential Crisis: How Stolen Credentials Defeat Modern Security

The article discusses the severe and growing challenge of stolen credentials, which allow attackers to log in as legitimate users rather than hacking through traditional network boundaries. Because compromised logins grant immediate trust to an intruder, malicious activity easily blends into regular network patterns, making initial detection highly difficult. The rise of automated phishing and malicious information stealing software has worsened this problem by accelerating how quickly passwords, biometrics, and session tokens are stolen. To combat this issue, security experts argue that organizations must look past mere boundary defenses and focus heavily on checking identities constantly. If an attacker succeeds in gaining entry, the strategy must immediately shift toward containing the blast radius and slowing the intruder down. This is best accomplished by assuming no account is permanently safe and using continuous behavioral monitoring, which watches user actions throughout a session to spot unusual changes in normal patterns. Furthermore, the growing use of independent AI tools introduces even greater risks, as stolen access keys can give automated systems the power to cause widespread damage at incredible speeds. Ultimately, protecting networks requires an ongoing commitment to constantly verifying users and cutting off suspect sessions rather than relying on a single, initial login approval.

Daily Tech Digest - January 27, 2026

Quote for the day:

"Supreme leaders determine where generations are going and develop outstanding leaders they pass the baton to." -- Anyaele Sam Chiyson

Why code quality should be a C-suite concern

At first, speed feels like progress. Then the hidden costs begin to surface: escalating maintenance effort, rising incident frequency, delayed roadmaps and growing organizational tension. The expense of poor code slowly eats into return on investment — not always in ways that show up neatly on a spreadsheet, but always in ways that become painfully visible in daily operations. ... During the planning phase, rushed architectural decisions often lead to tightly coupled, monolithic systems that are expensive and risky to change. During development, shortcuts accumulate into what we call technical debt: duplicated logic, brittle integrations and outdated dependencies that appear harmless at first but quietly erode system stability over time. Like financial debt, technical debt compounds. ... Architecture always comes first. I advocate for modular growth — whether through a well- structured modular monolith that can later evolve into microservices, or through service-oriented architectures with clear domain boundaries. Platforms such as Kubernetes enable independent scaling of components, but only when the underlying architecture is cleanly segmented. Language and framework choices matter more than most leaders realize. ... The technologies we select, the boundaries we define and the failure modes we anticipate all place invisible limits on how far an organization can grow. From what I’ve seen, you simply cannot scale a product on a foundation that was never designed to evolve.

How to regulate social media for teens (and make it stick)

Noting that age assurance proposals have broad support from parents and educators, Allen says “the question is not whether children deserve safeguarding (they do) but whether prohibition is an effective tool for achieving it.” “History suggests that bans succeed or fail not on the basis of intention, but on whether they align with demand, supply, moral legitimacy and enforcement capacity. Prohibition does not remove human desire; it reallocates who fulfils it. Whether that reallocation reduces harm or increases it depends on how well policy engages with the underlying economics and psychology of behaviour.” ... “There is little evidence that young people themselves view social media as morally repugnant. On the contrary, it is where friendships are maintained, identities are explored and social status is negotiated. That does not mean it is harmless. It means it is meaningful.” “This creates a problem for prohibition. Where demand remains strong, supply will be found.” Here, Allen’s argument falters somewhat, in that it follows the logic that says bans push kids onto less regulated and more dangerous platforms. I.e., “the risk is not simply that prohibition fails. It is that it succeeds in changing who supplies children’s social connectivity.” The difference is that, while a basket of plums and some ingenuity are all you need to produce alcohol, social media platforms have their value in the collective. Like Star Trek’s Borg, they are more powerful the more people they assimilate.

The era of agentic AI demands a data constitution, not better prompts

If a data pipeline drifts today, an agent doesn't just report the wrong number. It takes the wrong action. It provisions the wrong server type. It recommends a horror movie to a user watching cartoons. It hallucinates a customer service answer based on corrupted vector embeddings. ... In traditional SQL databases, a null value is just a null value. In a vector database, a null value or a schema mismatch can warp the semantic meaning of the entire embedding. Consider a scenario where metadata drifts. Suppose your pipeline ingests video metadata, but a race condition causes the "genre" tag to slip. Your metadata might tag a video as "live sports," but the embedding was generated from a "news clip." When an agent queries the database for "touchdown highlights," it retrieves the news clip because the vector similarity search is operating on a corrupted signal. The agent then serves that clip to millions of users. At scale, you cannot rely on downstream monitoring to catch this. By the time an anomaly alarm goes off, the agent has already made thousands of bad decisions. Quality controls must shift to the absolute "left" of the pipeline. ... Engineers generally hate guardrails. They view strict schemas and data contracts as bureaucratic hurdles that slow down deployment velocity. When introducing a data constitution, leaders often face pushback. Teams feel they are returning to the "waterfall" era of rigid database administration.

QA engineers must think like adversaries

Test engineers are now expected to understand pipelines, cloud-native architectures, and even prompt engineering for AI tools. The mindset has become more preventive than detective. AI has become part of QA’s toolkit, helping predict weak spots and optimise testing. At the same time, QA must validate the integrity and fairness of AI systems — making it both a user and a guardian of AI. ... With DevOps, QA became embedded into the pipeline — automated test execution, environment provisioning, and feedback loops are all part of CI/CD now. With SecOps, we’re adding security scans and penetration checks earlier, creating a DevTestSecOps model. QA is no longer a separate stage. It’s a mindset that exists throughout the lifecycle — from requirements to observability in production. ... Regression testing has become AI-augmented and data-driven. Instead of re-running all test cases, systems now prioritise based on change impact analysis. The SDET role is also evolving — they now bridge coding, observability, and automation frameworks, often owning quality gates within CI/CD. ... Security checks are now embedded as automated gates within pipelines. Performance testing, too, is moving earlier — with synthetic monitoring and API-level load simulations. In effect, security and speed can coexist, provided teams integrate validation rather than treat it as an afterthought.

The biggest AI bottleneck isn’t GPUs. It’s data resilience

The risks of poor data resilience will be magnified as agentic AI enters the mainstream. Whereas generative AI applications respond to a prompt with an answer in the same manner as a search engine, agentic systems are woven into production workflows, with models calling each other, exchanging data, triggering actions and propagating decisions across networks. Erroneous data can be amplified or corrupted as it moves between agents, like the party game “telephone.” ... Experts cite numerous reasons data protection gets short shrift in many organizations. A key one is an overly intense focus on compliance at the expense of operational excellence. That’s the difference between meeting a set of formal cybersecurity metrics and being able to survive real-world disruption. Compliance guidelines specify policies, controls and audits, while resilience is about operational survivability, such as maintaining data integrity, recovering full business operations, replaying or rolling back actions and containing the blast radius when systems fail or are attacked. ... “Resilience and compliance-oriented security are handled by different teams within enterprises, leading to a lack of coordination,” said Forrester’s Ellis. “There is a disconnect between how prepared people think they are and how prepared they actually are.” ... Missing or corrupted data can lead models to make decisions or recommendations that appear plausible but are far off the mark.

When open science meets real-world cybersecurity

If there is no collaboration, usually the product that emerges is a great scientific specimen with very risky implementations. The risk is usually caught by normal cyber processes and reduced accordingly; however, scientists who see the value in IT/cyber collaboration usually also end up with a great scientific specimen. There is also managed risk in the implementation with almost no measurable negative impacts or costs. We’ve seen that if collaboration is planned into the project very early on, cybersecurity can provide value. ... Cybersecurity researchers often are confused and look for issues on the internet where they stumble onto the laboratory IT footprint and make claims that we are leaking non-public information. We clearly label and denote information that is releasable to the public, but it always seems there are folks who are quicker to report than to read the dissemination labels. ... Encryption at rest (EIR) is really a control to prevent data loss when the storage medium is no longer in your control. So, when the data has been reviewed for public release, we don’t spend the extra time, effort, and money to apply a control to data stores that provide no value to either the implementation or to a cyber control. ... You can imagine there are many custom IT and OT parts that run that machine. The replacement of components is not on a typical IT replacement schedule. This can present longer than ideal technology refresh cycles. The risk here is that integrating modern cyber technology into an older IT/OT technology stack has its challenges.

4 issues holding back CISOs’ security agendas

CISOs should aim to have team members know when and how to make prioritization calls for their own areas of work, “so that every single team is focusing on the most important stuff,” Khawaja says. “To do that, you need to create clear mechanisms and instructions for how you do decision-support,” he explains. “There should be criteria or factors that says it’s high, medium, low priority for anything delivered by the security team, because then any team member can look at any request that comes to them and they can confidently and effectively prioritize it.” ... According to Lee, the CISOs who keep pace with their organization’s AI strategy take a holistic approach, rather than work deployment to deployment. They establish a risk profile for specific data, so security doesn’t spend much time evaluating AI deployments that use low-risk data and can prioritize work on AI use cases that need medium- or high-risk data. They also assign security staffers to individual departments to stay on top of AI needs, and they train security teams on the skills needed to evaluate and secure AI initiatives. ... the challenge for CISOs not being about hiring for technical skills or even soft skills, but what he called “middle skills,” such as risk management and change management. These skills he sees becoming more crucial for aligning security to the business, getting users to adopt security protocols, and ultimately improving the organization’s security posture. “If you don’t have [those middle skills], there’s only so far the security team can go,” he says.

Rethinking data center strategy for AI at scale

Traditional data centers were engineered for predictable, transactional workloads. Your typical enterprise rack ran at 8kW, cooled with forced air, powered through 12-volt systems. This worked fine for databases, web applications, and cloud storage. Yet, AI workloads are pushing rack densities past 120kW. That's not an incremental change—it's a complete reimagining of what a data center needs to be. At these densities, air cooling becomes physically impossible. ... Walk into a typical data center today. The HVAC system has its own monitoring dashboard. Power distribution runs through a separate SCADA system. Compute performance lives in yet another tool. Network telemetry? Different stack entirely. Each subsystem operates in isolation, reporting intermittently through proprietary interfaces that don't talk to each other. Operators see dashboards, not decisions. ... Cooling systems can respond instantly to thermal changes, and power orchestration becomes adaptive rather than provisioned for theoretical peaks. AI clusters can scale based not just on demand, but in coordination with available power, cooling capacity, and network bandwidth. ... Real-time visibility, unified data architectures, and adaptive control will define performance, efficiency, and competitiveness in AI-ready data centers. The organizations that thrive in the AI era won't necessarily be those with the most data centers or the biggest chips; they'll be the ones that treat infrastructure as an intelligent, responsive system capable of sensing, adapting, and optimizing in real time.

Microsoft handed over BitLocker keys to law enforcement, raising enterprise data control concerns

The US Federal Bureau of Investigation approached Microsoft with a search warrant in early 2025, seeking keys to unlock encrypted data stored on three laptops in a case of alleged fraud involving the COVID unemployment assistance program in Guam. As the keys were stored on a Microsoft server, Microsoft adhered to the legal order and handed over the encryption keys ... While the encryption of BitLocker is robust, enterprises need to be mindful of who has custody of the keys, as this case illustrates. ... Enterprises using BitLocker should treat the recovery keys as highly sensitive, and avoid default cloud backup unless there is a clear business requirement and the associated risks are well understood and mitigated. ... CISOs should also ensure that when devices are repurposed, decommissioned, or moved across jurisdictions, keys should be regenerated as part of the workflow to ensure old keys cannot be used. ... If recovery keys are stored with a cloud provider, that provider may be compelled, at least in its home jurisdiction, to hand them over under lawful order, even if the data subject or company is elsewhere without notifying the company. This becomes even more critical from the point of view of a pharma company, semiconductor firm, defence contractor, or critical-infrastructure operator, as it exposes them to risks such as exposure of trade secrets in cross‑border investigations.

Moore’s law: the famous rule of computing has reached the end of the road, so what comes next?

For half a century, computing advanced in a reassuring, predictable way. Transistors – devices used to switch electrical signals on a computer chip – became smaller. Consequently, computer chips became faster, and society quietly assimilated the gains almost without noticing. ... Instead of one general-purpose processor trying to do everything, modern systems combine different kinds of processors. Traditional processing units or CPUs handle control and decision-making. Graphics processors, are powerful processing units that were originally designed to handle the demands of graphics for computer games and other tasks. AI accelerators (specialised hardware that speeds up AI tasks) focus on large numbers of simple calculations carried out in parallel. Performance now depends on how well these components work together, rather than on how fast any one of them is. Alongside these developments, researchers are exploring more experimental technologies, including quantum processors (which harness the power of quantum science) and photonic processors, which use light instead of electricity. ... For users, life after Moore’s Law does not mean that computers stop improving. It means that improvements arrive in more uneven and task-specific ways. Some applications, such as AI-powered tools, diagnostics, navigation, complex modelling, may see noticeable gains, while general-purpose performance increases more slowly.

Daily Tech Digest - December 07, 2025

Quote for the day:

"Definiteness of purpose is the starting point of all achievement." -- W. Clement Stone

Balancing AI innovation and cost: The new FinOps mandate

Yet as AI moves from pilot to production, an uncomfortable truth is emerging: AI is expensive. Not because of reckless spending, but because the economics of AI are unlike anything technology leaders have managed before. Most CIOs and CTOs underestimate the financial complexity of scaling AI. Models that double in size can consume ten times the compute. Exponential should be your watchword. Inference workloads run continuously, consuming GPU cycles long after training ends, which creates a higher ongoing cost compared to traditional IT projects. ... The irony is that even as AI drives operational efficiency, its own operating costs are becoming one of the biggest drags on IT budgets. IDC’s research shows that, without tighter alignment between line of business, finance, and platform engineering, enterprises risk turning AI from an innovation catalyst into a financial liability. ... AI workloads cut across infrastructure, application development, data governance, and business operations. Many AI workloads will run in a hybrid environment, meaning cost impacts for on-premises as well as cloud and SaaS are expected. Managing this multicloud and hybrid landscape demands a unified operating model that connects technical telemetry with financial insight. The new FinOps leader will need fluency in both IT engineering and economics — a rare but rapidly growing skill set that will define next-generation IT leadership.

Local clouds shape Europe’s AI future

The new “sovereign” offerings from US-based cloud providers like Microsoft, AWS, and Google represent a significant step forward. They are building cloud regions within the EU, promising that customer data will remain local, be overseen by European citizens, and comply with EU laws. They’ve hired local staff, established European governance, and crafted agreements to meet strict EU regulations. The goal is to reassure customers and satisfy regulators. For European organizations facing tough questions, these steps often feel inadequate. Regardless of how localized the infrastructure is, most global cloud giants still have their headquarters in the United States, subject to US law and potential political pressure. There is always a lingering, albeit theoretical, risk that the US government might assert legal or administrative rights over data stored in Europe. ... As more European organizations pursue digital transformation and AI-driven growth, the evidence is mounting: The new sovereign cloud solutions launched by the global tech giants aren’t winning over the market’s most sensitive or risk-averse customers. Those who require freedom from foreign jurisdiction and total assurance that their data is shielded from all external interference are voting with their budgets for the homegrown players. ... In the months and years ahead, I predict that Europe’s own clouds—backed by strong local partnerships and deep familiarity with regulatory nuance—will serve as the true engine for the region’s AI ambitions.

When Innovation and Risks Collide: Hexnode and Asia’s Cybersecurity Paradox

“If you look at the way most cyberattacks happen today—take ransomware, for example—they often begin with one compromised account. From there, attackers try to move laterally across the network, hunting for high-value data or systems. By segmenting the network and requiring re-authentication at each step, ZT essentially blocks that free movement. It’s a “verify first, then grant access” philosophy, and it dramatically reduces the attacker’s options,” Pavithran explained. Unfortunately, way too many organisations still view Zero Trust as a tool rather than a strategic framework. Others believe it requires ripping out existing infrastructure. In reality, however, Zero Trust can be implemented incrementally and is both adaptable and scalable. It integrates technologies such as multifactor authentication, microsegmentation, and identity and access management into a cohesive architecture. Crucially, Zero Trust is not a one-off project. It is a continuous process of monitoring, verification, and fine-tuning. As threats evolve, so too must policies and controls. “Zero Trust isn’t a box you check and move on from,” Pavithran emphasised. “It’s a continuous, evolving process. Threats evolve, technologies evolve, and so do business needs. That means policies and controls need to be constantly reviewed and fine-tuned. It’s about continuous monitoring and ongoing vigilance—making sure that every access request, every single time, is both appropriate and secure.”

CIOs take note: talent will walk without real training and leadership

“Attracting and retaining talent is a problem, so things are outsourced,” says the CIO of a small healthcare company with an IT team of three. “You offload the responsibility and free up internal resources at the risk of losing know-how in the company. But at the moment, we have no other choice. We can’t offer the salaries of a large private group, and IT talent changes jobs every two years, so keeping people motivated is difficult. We hire a candidate, go through the training, and see them grow only to see them leave. But our sector is highly specialized and the necessary skills are rare.” ... CIOs also recognize the importance of following people closely, empowering them, and giving them a precise and relevant role that enhances motivation. It’s also essential to collaborate with the HR function to develop tools for welfare and well-being. According to the Gi Group study, the factors that IT candidates in Italy consider a priority when choosing an employer are, in descending order, salary, a hybrid job offer, work-life balance, the possibility of covering roles that don’t involve high stress levels, and opportunities for career advancement and professional growth. But there’s another aspect that helps solve the age-old issue of talent management. CIOs need to recognize more of the role of their leadership. At the moment, Italian IT directors place it at the bottom of their key qualities.

Rethinking the CIO-CISO Dynamic in the Age of AI

Today's CIOs are perpetual jugglers, balancing budgets and helping spur technology innovation at speed while making sure IT goals are aligned with business priorities, especially when it comes to navigating mandates from boards and senior leaders to streamline and drive efficiency through the latest AI solutions. ... "The most common concern with having the CISO report into legal is that legal is not technically inclined," she said. "This is actually a positive as cybersecurity has become more of a business-enabling function over a technological one. It also requires the CISO to translate tech-speak into language that is understandable by non-tech leaders in the organization and incorporate business and strategic drivers." As organizations undergo digital transformation and incorporate AI into their tech stacks, more are creating alternate C-suite roles such as "Chief Digital Officer" and "Chief AI Officer." ... When it comes to AI systems, the CISO's organization may be better positioned to lead enterprise-wide transformation, Sacolick said. AI systems are nondeterministic - they can produce different outputs and follow different computational paths even when given the exact same input - and this type of technology may be better suited for CISOs. CIOs have operated in the world of deterministic IT systems, where code, infrastructure systems, testing frameworks and automation provide predictable and consistent outputs, while CISOs are immersed in a world of ever-changing, unpredictable threats.

The AI reckoning: How boards can evolve

AI-savvy boards will be able to help their companies navigate these risks and opportunities. According to a 2025 MIT study, organizations with digitally and AI-savvy boards outperform their peers by 10.9 percentage points in return on equity, while those without are 3.8 percent below their industry average.5 What boards should do, however, is the bigger question—and the focus of this article. The intensity of the board’s role will depend on the extent to which AI is likely to affect the business and its competitive dynamics and the resulting risks and opportunities. Those competitive dynamics should shape the company’s AI posture and the board’s governance stance. ... What matters is that the board aligns on the business’s aspirational strategy using a clear view of the opportunities and risks so that it can tailor the governance approach. As the business gains greater experience with AI, the board can modify its posture. ... Directors should focus on determining whether management has the entrepreneurial experience, technological know-how, and transformational leadership experience to run an AI-driven business. The board’s role is particularly important in scrutinizing the sustainability of these ventures—including required skills, implications on the traditional business, and energy consumption—while having a clear view of the range of risks to address, such as data privacy, cybersecurity, the global regulatory environment, and intellectual property (IP).

Do Tariffs Solicit Cyber Attention? Escalating Risk in a Fractured Supply Chain

Offensive cyber operations are a fourth possibility largely serving to achieve the tactical and strategic objectives of decisionmakers, or in the case of tariff imposition, retaliation. Depending on its goals, a government may use the cyber domain to steal sensitive information such as amount and duration of a potential tariff or try to ascertain the short- and long-term intent of the tariff-imposing government. A second option may be a more aggressive response, executing disruptive operations to signal its dissatisfaction over tariff rates. ... It’s tempting to think of tariffs as purely a policy lever, and a way to increase revenue or ratchet up pressure on foreign governments. But in today’s interconnected world, trade policy and cybersecurity policy are deeply intertwined. When they aren’t aligned, companies risk becoming collateral damage in the larger geopolitical space, where hostile actors jockey to not only steal data for profit, but also look to steal secrets, compromise infrastructure, and undermine trust. This offers adversaries new ways to facilitate cyber intrusion to accomplish all of these objectives, requiring organizations to up their efforts in countering these threats via a variety of established practices. These include rigorous third-party vetting; continuous monitoring of third-party access through updates, remote connections, and network interfaces; implementing zero trust architecture; and designing incident response playbooks specifically around supply-chain breaches, counterfeit-hardware incidents, and firmware-level intrusions.

Resilience: How Leaders Build Organizations That Bend, Not Break

Resilient leaders don’t aim to restore what was; they reinvent what’s next. Leadership today is less about stability and more about elasticity—the ability to stretch, adapt, and rebound without breaking. ... Resilient cultures don’t eliminate risk—they absorb it. Leaders who privilege learning over blame and transparency over perfection create teams that can think clearly under pressure. In my companies, we’ve operationalized this with short, ritualized cadences—weekly priorities, daily huddles, and tight AARs that focus on behavior, not ego. The goal is never to defend a plan; it’s to upgrade it. ... “Resilience is mostly about adaptation rather than risk mitigation.” The distinction matters. Risk mitigation reduces downside. Adaptation converts disruption into forward motion. The organizations that redefine their categories after shocks aren’t the ones that avoid volatility; they’re the ones that metabolize it. ... In uncertainty, people don’t expect perfection—they expect presence. Transparent leadership doesn’t eliminate volatility, but it changes how teams experience it. Silence erodes trust faster than any market correction; people fill gaps with assumptions that are worse than reality. ... Treat resilience as design, not reaction. Build cultures that absorb shock, operating systems that learn fast, and communication habits that anchor trust. In an era where strategy half-life keeps shrinking, these are the leaders—and organizations—that won’t just survive volatility.

AI-Powered Quality Engineering: How Generative Models Are Rewriting Test Strategies

Despite significant investments in automation, many organizations still struggle with the same bottlenecks. Test suites often collapse due to minor UI changes. Maintenance cycles grow longer each quarter. Even mature teams rarely achieve effective coverage that truly exceeds 70-80%. Regression cycles stretch for days or weeks, slowing down release velocity and diluting confidence across engineering teams. It isn’t just productivity that suffers; it’s trust. These problems reduce teams’ confidence in releasing immediately and diminish automation ROI in addition to slowing down delivery. Traditional test automation has reached its limits because it automates execution, not understanding. And this is exactly where Generative AI changes the conversation. ... Synthetic data that mirrors production variability can be produced without waiting for dependent systems. Scripts no longer break every time a button shifts. As AI self-heal selectors and locators without human assistance, tests start to regenerate themselves. While predictive signals identify defects early through examining past data and patterns, natural-language inputs streamline test descriptions. ... GenAI isn’t magic, though. When generative models are fed ambiguous input, they can produce brittle or incorrect test cases. Ingesting production logs without adequate anonymization introduces privacy and compliance risks. Risks to data privacy and compliance must be considered while using production traces.

The Great Cloud Exodus: Why European Companies Are Massively Returning to Their Own Infrastructure

Many European managers and policymakers live under the assumption that when they choose "Region Western Europe" (often physically located in datacenters around Amsterdam or Eemshaven), their data is safely shielded from American interference. "The data is in our country, isn't it?" is the oft-heard defense. This is, legally speaking, a dangerous illusion. American legislation doesn't look at the ground on which the server stands, but at who holds the keys to the front door. ... The legal criterion is not the location of the server, but the control ("possession, custody, or control") that the American parent company has over the data. Since Microsoft Corporation in Redmond, Washington, has full control over subsidiary Microsoft Netherlands BV, data in the datacenter in the Wieringermeer legally falls under the direct scope of an American subpoena. ... Additionally, Microsoft applies "consistent global pricing," meaning European customers often see additional increases to align Euro prices with the strong US dollar. This makes budgeting a nightmare of foreign exchange risks. AWS shows a similar pattern. The complexity of the AWS bill is now notorious; an entire industry of "FinOps" consultants has emerged to help companies understand their invoice. ... or organizations seeking ultimate control and data sovereignty, purchasing own hardware and placing it in a Dutch datacenter is the best option. This approach combines the advantages of on-premise with the infrastructure of a professional datacenter.

Daily Tech Digest - November 01, 2025

Quote for the day:

"Definiteness of purpose is the starting point of all achievement." -- W. Clement Stone

How to Fix Decades of Technical Debt

Technical debt drains companies of time, money and even customers. It arises whenever speed is prioritized over quality in software development, often driven by the pressure to accelerate time to market. In such cases, immediate delivery takes precedence, while long-term sustainability is compromised. The Twitter Fail Whale incident between 2007 and 2012 is testimony to the adage: "Haste makes waste." ... Gartner says companies that learn to manage technical debt will achieve at least 50% faster service delivery times to the business. But organizations that fail to do this properly can expect higher operating expenses, reduced performance and a longer time to market. ... Experts say the blame for technical debt should not be put squarely on the IT department. There are other reasons, and other forms of debt that hold back innovation. In his blog post, Masoud Bahrami, independent software consultant and architect, prefers to use terms such as "system debt" and "business debt," arguing that technical debt does not necessarily stem from outdated code, as many people assume. "Calling it technical makes it sound like only developers are responsible. So calling it purely technical is misleading. Some people prefer terms like design debt, organizational debt or software obligations. Each emphasizes a different aspect, but at its core, it's about unaddressed compromises that make future work more expensive and risky," he said.

Modernizing Collaboration Tools: The Digital Backbone of Resilience

Resilience is not only about planning and governance—it depends on the tools that enable real-time communication and decision-making. Disruptions test not only continuity strategies but also the technology that supports them. If incident management platforms are inaccessible, workforce scheduling collapses, or communication channels fail, even well-prepared organizations may falter. ... Crisis response depends on speed. When platforms are not integrated, departments must pass information manually or through multiple channels. Each delay multiplies risks. For example, IT may detect ransomware but cannot quickly communicate containment status to executives. Without updates, communications teams may delay customer notifications, and legal teams may miss regulatory deadlines. In crises, minutes matter. ... Integration across functions is another essential requirement. Incident management platforms should not operate in silos but instead bring together IT alerts, HR notifications, supply chain updates, and corporate communications. When these inputs are consolidated into a centralized dashboard, the resilience council and crisis management teams can view the same data in real time. This eliminates the risk of misaligned responses, where one department may act on incomplete information while another is waiting for updates. A truly integrated platform creates a single source of truth for decision-making under pressure.

AI-powered bug hunting shakes up bounty industry — for better or worse

Security researchers turning to AI is creating a “firehose of noise, false positives, and duplicates,” according to Ollmann. “The future of security testing isn’t about managing a crowd of bug hunters finding duplicate and low-quality bugs; it’s about accessing on demand the best experts to find and fix exploitable vulnerabilities — as part of a continuous, programmatic, offensive security program,” Ollmann says. Trevor Horwitz, CISO at UK-based investment research platform TrustNet, adds: “The best results still come from people who know how to guide the tools. AI brings speed and scale, but human judgment is what turns output into impact.” ... As common vulnerability types like cross-site scripting (XSS) and SQL injection become easier to mitigate, organizations are shifting their focus and rewards toward findings that expose deeper systemic risk, including identity, access, and business logic flaws, according to HackerOne. HackerOne’s latest annual benchmark report shows that improper access control and insecure direct object reference (IDOR) vulnerabilities increased between 18% and 29% year over year, highlighting where both attackers and defenders are now concentrating their efforts. “The challenge for organizations in 2025 will be balancing speed, transparency, and trust: measuring crowdsourced offensive testing while maintaining responsible disclosure, fair payouts, and AI-augmented vulnerability report validation,” HackerOne’s Hazen concludes.

Achieving critical key performance indicators (KPIs) in data center operations

KPIs like PUE, uptime, and utilization once sufficed. But in today’s interconnected data center environments, they are no longer enough. Legacy DCIM systems measure what they can see – but not what matters. Their metrics are static, siloed, and reactive, failing to reflect the complex interplay between IT, facilities, sustainability, and service delivery. ... Organizations embracing UIIM and AI tools are witnessing measurable improvements in operational maturity: Manual audits are replaced by automated compliance checks; Capacity planning evolves from static spreadsheets to predictive, data-driven modeling; Service disruptions are mitigated by foresight, not firefighting. These are not theoretical gains. For example, a major international bank operating over 50 global data centers successfully transitioned from fragmented legacy DCIM tools to Rit Tech’s XpedITe platform. By unifying management across three continents, the bank reduced implementation timelines by up to three times, lowered energy and operational costs, and significantly improved regulatory readiness – all through centralized, real-time oversight. ... Enduring digital infrastructure thinks ahead – it anticipates demand, automates risk mitigation, and scales with confidence. For organizations navigating complex regulatory landscapes, emerging energy mandates, and AI-scale workloads, the choice is stark: evolve to intelligent infrastructure management, or accept the escalating cost of reactive operations.

Accelerating Zero Trust With AI: A Strategic Imperative for IT Leaders

Zero trust requires stringent access controls and continuous verification of identities and devices. Manually managing these policies in a dynamic IT environment is not only cumbersome but also prone to error. AI can automate policy enforcement, ensuring that access controls are consistently applied across the organization. ... Effective identity and access management is at the core of zero trust. AI can enhance IAM by providing continuous authentication and adaptive access controls. “AI-driven access control systems can dynamically set each user's access level through risk assessment in real-time,” according to the CSA report. Traditional IAM solutions often rely on static credentials, such as passwords, which can be easily compromised. ... AI provides advanced analytics capabilities that can transform raw data into actionable insights. In a zero-trust framework, these insights are invaluable for making informed security decisions. AI can correlate data from various sources — such as network logs, endpoint data and threat intelligence feeds — to provide a holistic view of an organization’s security posture. ... One of the most significant advantages of AI in a zero-trust context is its predictive capabilities. The CSA report notes that by analyzing historical data and identifying patterns, AI can predict potential security incidents before they occur. This proactive approach enables organizations to address vulnerabilities and threats in their early stages, reducing the likelihood of successful attacks.

Zombie Projects Rise Again to Undermine Security

"Unlike a human being, software doesn’t give up in frustration, or try to modify its approach, when it repeatedly fails at the same task," she wrote. Automation "is great when those renewals succeed, but it also means that forgotten clients and devices can continue requesting renewals unsuccessfully for months, or even years." To solve the problem, the organization has adopted rate limiting and will pause account-hostname pairs, immediately rejecting any requests for a renewal. ... Automation is key to tackling the issue of zombie services, devices, and code. Scanning the package manifests in software, for example, is not enough, because nearly two-thirds of vulnerabilities are transitive — they occur in software package imported by another software package. Scanning manifests only catches about 77% of dependencies, says Black Duck's McGuire. "Focus on components that are both outdated and contain high [or] critical-risk vulnerabilities — de-prioritize everything else," he says. "Institute a strict and regular update cadence for open source components — you need to treat the maintenance of a third-party library with the same rigor you treat your own code." AI poses an even more complex set of problems, says Tenable's Avni. For one, AI services span across a variety of endpoints. Some are software-as-a-service (SaaS), some are integrated into applications, and others are AI agents running on endpoints.

Are room-temperature superconductors finally within reach?

Predicting superconductivity -- especially in materials that could operate at higher temperatures -- has remained an unsolved challenge. Existing theories have long been considered accurate only for low-temperature superconductors, explained Zi-Kui Liu, a professor of materials science and engineering at Penn State. ... For decades, scientists have relied on the Bardeen-Cooper-Schrieffer (BCS) theory to describe how conventional superconductors function at extremely low temperatures. According to this theory, electrons move without resistance because of interactions with vibrations in the atomic lattice, called phonons. These interactions allow electrons to pair up into what are known as Cooper pairs, which move in sync through the material, avoiding atomic collisions and preventing energy loss as heat. ... The breakthrough centers on a concept called zentropy theory. This approach merges principles from statistical mechanics, which studies the collective behavior of many particles, with quantum physics and modern computational modeling. Zentropy theory links a material's electronic structure to how its properties change with temperature, revealing when it transitions from a superconducting to a non-superconducting state. To apply the theory, scientists must understand how a material behaves at absolute zero (zero Kelvin), the coldest temperature possible, where all atomic motion ceases.

Beyond Accidental Quality: Finding Hidden Bugs with Generative Testing

Automated tests are the cornerstone of modern software development. They ensure that every time we build new functionalities, we do not break existing features our users rely on. Traditionally, we tackle this with example-based tests. We list specific scenarios (or test cases) that verify the expected behaviour. In a banking application, we might write a test to assert that transferring $100 to a friend’s bank account changes their balance from $180 to $280. However, example-based tests have a critical flaw. The quality of our software depends on the examples in our test suites. This leaves out a class of scenarios that the authors of the test did not envision – the "unknown unknowns". Generative testing is a more robust method of testing software. It shifts our focus from enumerating examples to verifying the fundamental invariant properties of our system. ... generative tests try to break the property with randomized inputs. The goal is to ensure that invariants of the system are not violated for a wide variety of inputs. Essentially, it is a three step process:Given a property (aka invariant); Generate varying inputs; To find the smallest input for which the property does not hold. As opposed to traditional test cases, inputs that trigger a bug are not written in the test – they are found by the test engine. That is crucial because finding counter examples to code written by us is not easy or an accurate process. Some bugs simply hide in plain sight – even in basic arithmetic operations like addition.

Learning from the AWS outage: Actions and resources

Drawing on lessons from this and previous incidents, here are three essential steps every organization should take. First, review your architecture and deploy real redundancy. Leverage multiple availability zones within your primary cloud provider and seriously consider multiregion and even multicloud resilience for your most critical workloads. If your business cannot tolerate extended downtime, these investments are no longer optional. Second, review and update your incident response and disaster recovery plans. Theoretical processes aren’t enough. Regularly test and simulate outages at the technical and business process levels. Ensure that playbooks are accurate, roles and responsibilities are clear, and every team knows how to execute under stress. Fast, coordinated responses can make the difference between a brief disruption and a full-scale catastrophe. Third, understand your cloud contracts and SLAs and negotiate better terms if possible. Speak with your providers about custom agreements if your scale can justify them. Document outages carefully and file claims promptly. More importantly, factor the actual risks—not just the “guaranteed” uptime—into your business and customer SLAs. Cloud outages are no longer rare. As enterprises deepen their reliance on the cloud, the risks rise. The most resilient businesses will treat each outage as a crucial learning opportunity to strengthen both technical defenses and contractual agreements before the next problem occurs.

When AI Is the Reason for Mass Layoffs, How Must CIOs Respond?

CIOs may be tempted to try and protect their teams from future layoffs -- and this is a noble goal -- but Dontha and others warn that this focus is the wrong approach to the biggest question of working in the AI age. "Protecting people from AI isn't the answer; preparing them for AI is," Dontha said. "The CIO's job is to redeploy human talent toward high-value work, not preserve yesterday's org chart." ... When a company describes its layoffs as part of a redistribution of resources into AI, it shines a spotlight on its future AI performance. CIOs were already feeling the pressure to find productivity gains and cost savings through AI tools, but the stakes are now higher -- and very public. ... It's not just CIOs at the companies affected that may be feeling this pressure. Several industry experts described these layoffs as signposts for other organizations: That AI strategy needs an overhaul, and that there is a new operational model to test, with fewer layers, faster cycles, and more automation in the middle. While they could be interpreted as warning signs, Turner-Williams stressed that this isn't a time to panic. Instead, CIOs should use this as an opportunity to get proactive. ... On the opposite side, Linthicum advised leaders to resist the push to find quick wins. He observed that, for all the expectations and excitement around AI's impact, ROI is still quite elusive when it comes to AI projects.

Daily Tech Digest - October 26, 2025

Quote for the day:

"Everywhere is within walking distance if you have the time." -- Steven Wright

AI policy without proof is just politics

History shows us that regulation without verification rarely works. Imagine if Wall Street firms were allowed to audit their own books, or if pharmaceutical companies could approve their own drugs. The risks would be obvious and unacceptable. Yet, in AI today, much of the information policymakers see about model performance and safety comes straight from the companies developing those systems, leaving regulators dependent on the very firms they are meant to oversee. Self-reporting, intentionally or not, creates structural blind spots. Developers have incentives to highlight strengths and minimize weaknesses, and even honest disclosures can leave out important context. ... The first requirement is independence. Oversight must be based on information that does not come solely from the companies themselves: data that can be inspected, verified and trusted as neutral. Without that independence, even well-intentioned disclosures risk being selective or incomplete. The second requirement is continuity. AI systems evolve quickly, and their performance often shifts once they are deployed in the wild. Benchmarks conducted at launch can’t capture how models change over time, or how they behave across different languages, domains and user needs. ... AI policy is at a crossroads. The U.S. has set bold goals, but without reliable evaluation, those goals risk becoming little more than rhetoric. Rules set the direction. Proof provides the trust.

5 ways ambitious IT pros can future-proof their tech careers in an age of AI

Successful IT chiefs are expected to be the expert resources for pioneering technology developments. In fact, Daly said the CIOs of the future will demonstrate how AI can fulfill some executive roles and responsibilities. ... David Walmsley, chief digital and technology officer at jewelry specialist Pandora, said up-and-coming IT stars take responsibilities and opportunities. The disconnected technology organization of old, which relied on outsourcing arrangements for project delivery, has been replaced by a department that works closely with the business to achieve its objectives. "The days of technology leaders leaning back and saying, 'Well, which of my external providers do I blame now?' are long gone," he said. "Everyone can see that technology is a strategic lever for growing the business and helping it succeed in its mission." ... The critical skill for next-generation leaders lies not in chasing every new platform or coding language, but in cultivating the human capacities that allow you to adapt. "Those capabilities include curiosity, critical thinking, collaboration, and an understanding of human behavior," he said. "At LIS, we emphasize interdisciplinary learning precisely because technology never exists in isolation; it is always entangled with psychology, economics, ethics, and culture."

Biometrics increase integrity from age checks to agents, but not when compelled

Biometrics are anchoring trust for established but growing use cases like national IDs even as new use cases are taking off. But surveillance concerns inevitably come with increases in the collection of personal data, particularly when the collection is compelled or involuntary. ... Tech industry group the CCIA took aim at Texas’ app store level age checks, arguing the plan is bound to fail in several ways, including data privacy breaches. One of those alleged likely failures is the accuracy of facial age estimation, but the supporting stat from NIST is outdated, and the new figure significantly better. Automated license-place reader-maker Flock and Amazon’s Ring have partnered to share data, allowing law enforcement agencies that use Flock’s investigative platforms to request footage from homeowners. ... The growth of online interactions with credentials that are anchored with biometrics continues unabated, in the form of national ID systems, agentic AI, age checks and identity verification. Juniper Research forecasts digital identity will be an $80 billion global market by 2030, with growth driven by new regulations and credentials. ... Age checks could catalyze digital ID adoption Luciditi CPO Dan Johnson says on the Biometric Update Podcast. He makes the case for the advantages of adding age assurance to apps by integrating a component, rather than building a standalone branded app.

Weak Data Infrastructure Keeps Most GenAI Projects From Delivering ROI

Kolbeck sees companies investing billions while overlooking adequate storage to support their AI infrastructure as one of the major mistakes corporations make. He said that oversight leads to three key failure factors — festering silos, lack of performance, and uptime dilemmas. The most critical resource for AI is data training. When companies store data across multiple silos, data scientists lack access to essential details. “Storage systems must be able to scale and provide unified access to enable an AI data lake, a centralized and efficient storage for the entire company,” he observed. ... “Early AI projects may work well, but as soon as these projects grow in size [as in more GPUs], these arrays tip over, and that’s when mission-critical workflows grind to a halt,” he said. Kolbeck explained the difference between scale-out architecture versus a scale-up approach as a better option for handling the massive and unpredictable data demands of modern AI and ML. He cited his company’s experience in making that transition. ... “Developing and training AI technology is still a very experimental process and requires the infrastructure — including storage — to adapt quickly when data scientists develop new ideas,” Kolbeck noted. Real-time performance analytics are critical. Storage administrators need to be able to precisely identify how applications, such as training or other pipeline phases, impact the storage.

When your AI browser becomes your enemy: The Comet security disaster

Your regular Chrome or Firefox browser is basically a bouncer at a club. It shows you what's on the webpage, maybe runs some animations, but it doesn't really "understand" what it's reading. If a malicious website wants to mess with you, it has to work pretty hard — exploit some technical bug, trick you into downloading something nasty or convince you to hand over your password. AI browsers like Comet threw that bouncer out and hired an eager intern instead. This intern doesn't just look at web pages — it reads them, understands them and acts on what it reads. Sounds great, right? Except this intern can't tell when someone's giving them fake orders. ... They can actually do stuff: Regular browsers mostly just show you things. AI browsers can click buttons, fill out forms, switch between your tabs, even jump between different websites. ... They remember everything: Unlike regular browsers that forget each page when you leave, AI browsers keep track of everything you've done across your whole session. ... You trust them too much: We naturally assume our AI assistants are looking out for us. That blind trust means we're less likely to notice when something's wrong. Hackers get more time to do their dirty work because we're not watching our AI assistant as carefully as we should. They break the rules on purpose: Normal web security works by keeping websites in their own little boxes — Facebook can't mess with your Gmail, Amazon can't see your bank account.

Rewriting the Rules of Software Quality: Why Agentic QA is the Future CIOs Must Champion

From continuous deployment to AI-powered applications, software systems are more dynamic, distributed and adaptive than ever. In this changing environment, static testing frameworks are crumbling. What worked yesterday is increasingly not going to work today, and tomorrow’s risks cannot be addressed using yesterday’s checklists. This is where agentic QA steps in, heralding a transformative approach that integrates autonomous, intelligent agents throughout the entire software lifecycle. ... What distinguishes this model isn’t just its intelligence — it’s its adaptability. In a world where AI models are themselves part of the application stack, QA must account for nondeterminism. Agentic systems are uniquely equipped to do this. When AI-driven components exhibit variable behavior based on internal learning states, traditional test-case comparisons fail for evident reasons. Agentic QA, on the other hand, thrives in uncertainty. It detects anomalies, learns from edge cases, and refines its approach continuously. ... However, it is essential to note that as AI takes over repetitive and complex validations, it enables QA professionals to step up and evolve into curators of quality. Their role is freed up to become more strategic: Defining testing intent, ensuring AI alignment with business goals, interpreting nuanced behaviors, and upholding ethical standards. This shift calls for a cultural transformation.

AI-Powered Ransomware Is the Emerging Threat That Could Bring Down Your Organization

AI fundamentally transforms every phase of ransomware operations through several key capabilities. Enhanced reconnaissance allows malware to autonomously scan security perimeters, identify vulnerabilities, and select precise exploitation tools. This eliminates the need for human operators during initial phases, enabling attacks to spread rapidly across IT environments. Adaptive encryption techniques represent another revolutionary advancement. AI-powered ransomware can analyze system resources and data types to modify encryption algorithms dynamically, making decryption more complex. The malware can prioritize high-value targets by analyzing document content using Natural Language Processing before encryption, ensuring maximum strategic impact. Evasive tactics powered by machine learning enable ransomware to continuously modify its code and behavior patterns. This polymorphic capability makes signature-based detection methods ineffective, as the malware presents different fingerprints with each execution. AI also enables malware to track user presence and activate during off-hours to maximize damage while minimizing detection opportunities. The financial consequences of AI-powered ransomware attacks far exceed traditional threats. ... Small businesses face particularly severe consequences, with 60% of attacked companies closing permanently within six months.

When a Provider's Lights Go Out, How Can CIOs Keep Operations Going?

This may seem obvious, but a thousand companies still lost digital functionality on Monday. Why weren't they better prepared? One answer is that while redundancy isn't new, it also isn't very sexy. In a field full of innovation and growth, redundancy is about slowing down, checking your work, and taking the safest route. It's not surprising if some companies are more excited about investing in new AI capabilities than implementing failsafe protocols. Nor is it necessarily wrong. ... "It is important to invest where failure creates real risk, not just minor inconvenience, or noise," he added. This will look different for companies of different sizes, but particularly for companies within different sectors. Some industries, such as healthcare or finance, require a higher level of redundancy across the board simply because the stakes are greater; lack of access to patient records or financial information could have severe repercussions in terms of safety and public trust, which are far beyond inconvenience or frustration. ... But this isn't as simple as tracing third-party contracts, counting how often one name appears, and shifting some operations away from too-dominant providers. If an organization has partnered predominantly with one provider, it's probably for good reason. As Hitchens explained, working with a single provider can accelerate innovation and simplify management, offering visibility, native integrations and unified tooling.

Three Ways Secure Modern Networks Unlock the True Power of AI

AI is network-bound. As always-on models demand up to 100 times more compute, storage, and bandwidth, traditional networks risk becoming bottlenecks both on capacity, and latency. For AI tasks that happen instantly, like self-driving cars or automated stock trading, even tiny delays can cause problems. Modern network infrastructure needs to be more than just fast. It also needs to be safe from cyberattacks and strong enough to handle more AI growth in the future. To realize AI’s full potential, businesses must build purpose-built “AI superhighways”, secure networks designed to scale seamlessly, handling distributed AI workloads across core, cloud, and edge environments. ... The value organizations expect from AI, be it automating workflows, unlocking predictive insights, or powering new digital experiences, depends on more than just compute power or clever algorithms. Furthermore, the demand for real-time machine data from business operations to train AI models is increasing the need for more detailed and extensive networks. This, in turn, accelerates the integration of IT and OT, and expands the adoption of the Internet of Things (IoT) ... The sensitivity of AI data flows is raising the bar for security and compliance. The risks of sticking with outdated infrastructure are stark. 95% of technology leaders say a resilient network is critical to their operations, and 77% have experienced major outages due to congestion, cyberattacks, or misconfigurations.

"It’s not about security, it’s about control" – How EU governments want to encrypt their own comms, but break our private chats

In the wake of ever-larger and frequent cyberattacks – think of the Salt Typhoon in the US – encryption has become crucial to shield everyone's security, whether that's ID theft, scams, or national security risks. Even the FBI urged all Americans to turn to encrypted chats. ... Law enforcement, however, often sees this layer of protection as an obstacle to their investigations, pushing for "lawful access" to encrypted data as a way to combat hideous crimes like terrorism or child abuse. That's exactly where legislation proposals like Chat Control and ProtectEU in the European bloc, or the Online Safety Act in the UK, come from. Yet, people working with encryption know that these solutions are flawed. On a technical level, experts all agree that an encryption backdoor cannot guarantee the same level of online security and privacy we have now. Is then time to redefine what we mean when we talk about privacy? This is what's probably needed, according to Rocket.Chat's Strategic Advisor, Christian Calcagni. "We need to have a new definition of private communication, and that's a big debate. Encryption or no encryption, what could be the way?" Calcagni is, nonetheless, very critical of the current push to break encryption. He told me: "Why should the government know what I think or what I'm sharing on a personal level? We shouldn't focus only on encryption or not encryption, but on what that means for our privacy, our intimacy."