Daily Tech Digest by Kannan Subbiah: Business Intelligence

Showing posts with label Business Intelligence. Show all posts

Daily Tech Digest - September 18, 2026

Quote for the day:

“An investment in knowledge pays the best interest.” -- Benjamin Franklin

🎧 Listen to the audio debrief on YouTube

Duration: 24 mins • Perfect for listening on the go.

Brevo supply-chain attack injected ClickFix scripts on customer sites

Brevo, a popular digital marketing and customer management platform, recently experienced a security breach affecting its website and tools embedded on customer sites. On September 14, attackers used a compromised Cloudflare API key, which had been mistakenly left inside the company's application code, to alter the platform's web traffic. For about five and a half hours, the attackers injected malicious scripts into Brevo's web forms and chat tools. When visitors loaded a website using these tools, they saw a fake verification screen urging them to run a harmful command, a technique known as a ClickFix attack. Additionally, if the visitor was logged into a WordPress site as an administrator, the script secretly attempted to install a hidden backdoor plugin called Web Media Optimizer. Security researchers estimate this incident may have affected up to one hundred thousand websites. Once Brevo identified the issue, the company quickly removed the unauthorized access, deleted the harmful files, and confirmed that core systems like email delivery and customer data remained secure. Website administrators who were logged in during the attack window are advised to carefully check their plugin lists for any unauthorized additions and update their passwords to ensure their systems remain completely safe.

Abandoned IoT apps keep sending sensitive data to broken servers

A recent study by the University of Massachusetts Amherst highlights the significant security risks posed by abandoned Internet of Things (IoT) companion apps. These apps, used to control smart devices like thermostats and cameras, often remain on users' phones long after developers stop updating them. The researchers analyzed over 61,500 abandoned Android IoT apps and found that a staggering number contained software dependencies linked to known vulnerabilities. Many of these apps were still being downloaded by millions of users, despite not receiving an update in over two years. Furthermore, these apps often bundle old software libraries and hard-coded web addresses, many of which no longer function or belong to entirely different owners. This creates a dangerous scenario where sensitive data, gathered through permissions like camera and location access, is sent to broken or potentially malicious endpoints. While the study found similar rates of known vulnerabilities in both abandoned and actively maintained apps, the real issue lies in the destination of the data. Over 40% of the data sinks in abandoned apps were associated with unreachable or vulnerable endpoints, compared to less than 1% in active apps. This research underscores the need for users to regularly review and uninstall abandoned IoT apps to minimize their security exposure.

Is your low code security keeping up with business speed?

Low code development platforms have transformed how organizations build applications, often leading to a misconception that they are as unstructured as vibe coding — the practice of relying entirely on artificial intelligence to generate software from casual prompts. However, while low code environments provide more structure and included guardrails than AI generated code, they still present significant security challenges that teams cannot ignore. Because these platforms empower everyday users to assemble functional applications quickly using visual interfaces, they introduce risks related to improper data handling, misconfigured permissions, and poor access controls. Included security features within low code platforms offer a baseline of protection, ensuring that development is not merely a chaotic environment, but they are not a complete safety net. To maintain a secure environment, IT departments must establish clear governance policies and conduct regular audits of user created applications. Without proper oversight, everyday builders might unintentionally expose sensitive company information or create software vulnerabilities that external attackers could exploit. Ultimately, organizations must strike a careful balance between enabling rapid, accessible software creation and maintaining strict security standards across the board. Relying solely on a platform's default protections is a risky approach; continuous monitoring and proactive management remain essential to keeping your business data truly safe.

Prioritise on the best governance, not the best model

The article from FutureCISO highlights that by mid-2026, the deployment of AI agents in Asia Pacific enterprises has significantly outpaced governance capabilities. Research shows that active AI agents have nearly tripled in a year, while the time to create them has halved. Gartner predicts that 40% of enterprise applications will feature embedded task-specific AI agents by the end of 2026. However, this rapid adoption has led to a rise in "shadow AI," with security incidents doubling year over year, according to IBM. The core issue is a lack of visibility; many organizations do not know what AI agents they have deployed. Lavy Stokhamer from Standard Chartered emphasizes that organizations need the same accountability and visibility for AI agents as they do for human employees, applications, and privileged accounts. A real-time inventory is crucial to understanding what each agent is authorized to do, the data it can access, and who is accountable. This comprehensive inventory of agent identities and permissions is fundamentally the "organizational chart for a digital workforce." Without knowing what digital actors exist and their authority, it is impossible to govern, secure, or manage risk at scale, leading to significant challenges in trust, resilience, and economics.

Malicious JavaScript Evaded VirusTotal in Seven of Eight E-Commerce Storefront Attacks

A recent cybersecurity investigation has revealed that traditional malware scanners are struggling to detect sophisticated e-commerce storefront attacks. Security researchers identified four distinct malicious JavaScript operations actively targeting online retailers. Across these campaigns, they found eight unique payloads designed to run quietly in a shopper's browser. Remarkably, when these payloads were tested against standard security tools, seven of the eight completely evaded detection by VirusTotal, and none were flagged as malicious by URLScan. These attacks succeed because they do not break the website. A modern storefront can look and function perfectly normally while the hidden script secretly siphons affiliate revenue, hijacks clicks, manipulates analytics, or opens a backdoor for remote access. To avoid detection, the malicious code uses clever evasion tactics, such as waiting for specific mobile devices, operating only during certain hours, or staying dormant until particular product buttons load on the page. Because these scripts only execute under exact conditions, traditional signature-based scanners often miss them during routine checks. This incident underscores a critical shift in e-commerce security. Relying solely on standard vendor trust or basic scans is no longer enough. Protecting online storefronts now requires advanced, behavior-based monitoring to catch these elusive threats in live traffic.

Rethinking Disaster Recovery Planning Using Optimized Sequencing

This article from Disaster Recovery Journal focuses on how organizations can improve their IT disaster recovery plans by optimizing their recovery sequences. When a widespread system outage occurs, simply restoring applications one by one based on a static list isn't always effective. Systems rely on each other—for example, an essential business app might need its database and identity services to be brought back online first. The author argues that companies need to look at multiple factors when deciding what to restore first. These include technical dependencies, recovery time objectives, and the potential impact on revenue and critical services. Because tech environments are always changing, with new applications and integrations being added, a fixed recovery sequence can quickly become outdated. To handle this, organizations can adopt recovery optimization. This approach uses existing data on dependencies and business priorities to compute the best recovery sequence for a specific situation. It allows teams to adjust their strategy based on current needs, whether that means prioritizing strict recovery timelines or protecting revenue. Ultimately, using an explainable, data-driven method helps teams make better decisions during a crisis and improves the value of their disaster recovery exercises.

Zombie Workloads Haunt Data Center Efficiency Efforts

Zombie workloads, such as unused applications or abandoned storage volumes, are creating notable challenges in data center efficiency. According to recent findings from the International Data Center Authority, up to 13% of US cloud usage is attributed to these idle workloads. The issue stems from scenarios like incomplete post-merger integrations and employees leaving apps active. The problem is becoming more critical with the rise of AI and GPUs, as the cost of idle time rises steeply compared to traditional CPU workloads. To address this, organizations are relying on Cloud FinOps tools and observability tools that find inactive resources. While features like scale-to-zero in serverless architectures offer some relief, they bring challenges like cold starts. The complexities of AI workloads also make hunting for zombies difficult, because they introduce issues like abandoned GPUs and mid-flight pipeline crashes. Effective management is built upon having sound policies. Clear guidelines, automated reminders, and routine scans are important in curbing zombie workloads. The cost of failing to decommission these idle assets has severe implications.

A Framework for Taming Unstructured Data at Scale

The provided article from CDO Magazine discusses the critical need for a framework to manage unstructured data, which constitutes 80% to 90% of corporate information. This "dark matter" includes emails, PDFs, and Teams messages, often lacking visibility and posing significant risks. The author, Lana DeMaria, highlights two main drivers for this urgency: the rise of "shadow AI," where employees might unknowingly feed sensitive data into public models, and the evolution of ransomware into "double extortion" tactics that target valuable unstructured data. Traditional governance methods, such as manual classification and reliance on regular expressions, fail because they are not scalable and treat governance as a one-time event rather than a continuous process. To address these challenges, the article proposes a cyclical, automated framework centered on three layers: Discovery (indexing data in place), Classification (using AI for semantic analysis), and Continuous Compliance (automating lifecycle management, including defensible deletion). By leveraging AI, organizations can better understand their data, manage risks, and ensure that governance scales effectively. Ultimately, implementing this framework allows leaders to turn unstructured data from a liability into a strategic asset for the enterprise.

The Standard BI Playbook Wasn't Built for the Physical Economy

The standard business intelligence approach often fails when applied to the physical economy, which includes industrial distribution, manufacturing, and marine transportation. These sectors do not suffer from a lack of information but rather struggle with making that information accessible across the organization. Traditional advice assumes data is already organized in a central location, but industrial companies typically rely on fragmented legacy systems, isolated applications, and numerous manual spreadsheets. To make any meaningful progress, companies must first do the practical work of gathering this scattered data into one unified platform. Furthermore, the typical strategy assumes teams are eager for new reports and have dedicated analysts ready to use them. In reality, operational teams are deep domain experts who are often overwhelmed by manual reporting tasks and naturally skeptical of new tools. They need immediate, reliable answers to handle their daily operations, not long-term analytical deep dives. Success in this environment should not be measured by how many reports are created, but by how many hours of manual work are eliminated. By focusing on centralizing information, sharing knowledge across departments, and automating tedious processes, industrial organizations can give employees their time back and significantly improve how they operate on a daily basis.

You Can’t Patch Cybersecurity Burnout: Joe Marshall’s Human Incident Response Framework

The provided article details Joe Marshall's Human Incident Response Framework, introduced during his CYBR.SEC.CON. 2026 keynote. Inspired by his grueling experience fighting the VPNFilter botnet in 2018, Marshall argues the cybersecurity industry expertly manages technical incident response but fails to support the human defenders. His framework provides a playbook to address occupational stress by first differentiating "burnout" into four specific injuries: actual burnout (workload exhaustion), secondary traumatic stress, vicarious trauma, and moral injury. Because they stem from different causes, they require distinct responses beyond just taking time off. The framework challenges the notion that stress merely comes from long hours, highlighting six exposure factors like content type and secrecy that make different cybersecurity roles uniquely taxing. It adapts military and emergency medicine concepts, classifying human strain into four zones: Ready, Reacting, Injured, and Crisis. Crucially, it replaces passive "open-door policies" with structured peer check-ins designed to establish baselines and recognize when a colleague is struggling. While offering practical tools like a 43-page Field Guide and a two-page Playbook, Marshall stresses the framework is a detection aid, not a clinical replacement. It aims to give the industry a shared vocabulary to recognize human distress and properly escalate issues without turning support into surveillance.

Daily Tech Digest - November 14, 2025

Quote for the day:

"The only way to achieve the impossible is to believe it is possible." -- Charles Kingsleigh

When will browser agents do real work?

Vision-based agents treat the browser as a visual canvas. They look at screenshots, interpret them using , and output low-level actions like “click (210,260)” or “type “Peter Pan”.” This mimics how a human would use a computer—reading visible text, locating buttons visually, and clicking where needed. ... DOM-based agents, by contrast, operate directly on the Document Object Model (DOM), the structured tree that defines every webpage. Instead of interpreting pixels, they reason over textual representations of the page: element tags, attributes, ARIA roles, and labels. ... Running a browser agent once successfully doesn’t mean it can repeat the task reliably. The next frontier is learning from exploration: transforming first-time behaviors into reusable automations. A promising strategy starting to be deployed more and more is to let agents explore workflows visually, then encode those paths into structured representations like or code. ... With new large language models excelling at writing and editing code, these agents can self-generate and improve their own scripts, creating a cycle of self-optimization. Over time, the system becomes similar to a skilled worker: slower on the first task, but exponentially faster on repeat executions. This hybrid, self-improving approach—combining vision, structure, and code synthesis—is what makes browser automation increasingly robust.

Security Degradation in AI-Generated Code: A Threat Vector CISOs Can’t Ignore

LLMs have been a boon for developers since OpenAI’s was publicly released in November 2022, followed by other AI models. Developers were quick to utilize the tools, which significantly increased productivity for overtaxed development teams. However, that productivity boost came with security concerns, such as AI models trained on flawed code from internal or publicly available repositories. Those models introduced vulnerabilities that sometimes spread throughout the entire software ecosystem. One way to address the problem was by using LLMs to make iterative improvements to code-level security during the development process, under the assumption that LLMs, given the job of correcting mistakes, would amend them. The study, however, turns that assumption on its head. Although previous studies (and extensive real-world experience, including our own data) have demonstrated that an LLM can introduce vulnerabilities in the code it generates, this study went a step further, finding that iterative refinement of code can introduce new errors. ... The security degradation introduced in the feedback loop raises troubling questions for developers, tool designers and . The answer to those questions, the authors write, involves human intervention. Developers, for instance, must maintain control of the development process, viewing AI as a collaborative assistant rather than an autonomous tool.

Are We in the Quantum Decade?

It would be prohibitively expensive even for a Fortune 100 company to own, operate and maintain its own . It would require a quantum ecosystem that includes government, academia and industry entities to make it accessible to an enterprise. In most cases, the push and funding could come from the government or through cooperation among nations. Historically, new computing technology was rented and used as a service. Compute resources financed by government were booked in advance. Processing occurred in batches using resource-sharing techniques such as . Equivalent models are expected for quantum processing. ... The era of looms large, but enterprises and IT teams should be thinking about it today. Infrastructure needs to be deployed and algorithms need to be written for executing business use cases. "For several years to come, CIOs may not have much to do with quantum computing. But they need to know what it is, what it can do and how much it costs," said Lawrence Gasman, president of . "Quantum networks and cybersecurity will become necessary for secure communications by 2030 or even earlier." Quantum computing will not replace classical computing, but data center providers need to be thinking about how they will integrate the two architectures using interconnects like co-packaged optics.

When Data Gravity Meets Disaster Recovery

Data starts to pull everything else toward it: apps, analytics, integrations, even people and processes, the more it aggregates in one place. That environment becomes a tightly woven web of dependencies, over time. While it may be fine for day-to-day operations, it becomes a nightmare when something breaks. At that point, DR turns into a delicate task of relocating an entire ecosystem, not just a matter of simply copying files. You have to think about relationships, which systems rely on which datasets, how permissions are mapped, and how applications expect to find what they need. Of course, the bigger that web gets, the heavier the “gravitational field.” Moving petabytes of interconnected data across regions or clouds isn’t fast or easy. It takes time, bandwidth, and planning, and every extra gigabyte adds friction – in other words, the more gravity your data has, the harder it is to recover from disaster quickly. ... To push back against gravity, organizations are rethinking their architectures. Instead of forcing all data into one environment, they’re distributing it intelligently, keeping mission-critical workloads close to where they’re created, while replicating copies to nearby or complementary environments for protection. Hybrid and have become the go-to solution for this. They blend the best of both worlds: the low-latency performance of local infrastructure with the flexibility and geographic reach of cloud storage.

What’s Driving the EU’s AI Act Shake-Up?

The move to revise the AI Act follows sustained lobbying from US tech giants. In October, the Computer and Communications Industry Association (CCIA), whose members include , launched a campaign pushing for simplification not only of the AI Act but of the EU’s entire digital rulebook. Meanwhile, EU officials have reportedly engaged with the Trump administration on these issues. ... The potential delay reflects pressure from national authorities. Denmark and Germany have both pushed for a one-year extension. A spokesperson from Germany’s Federal Ministry for Digital Transformation and Government Modernization said that a delay “would allow sufficient time for the practical application of European standards by AI providers, with standards still currently being elaborated.” ... Another major reform under consideration is expanding and centralizing oversight powers within the Commission’s AI Office. Currently responsible for general-purpose AI models (GPAI), the office would gain new authority to oversee all AI systems based on GPAI and conduct conformity assessments for certain high-risk systems. The Commission would also gain new authority to perform conformity assessments for certain high-risk systems and supervise online services deemed to pose “systemic risk” under the . This would shift more power to Brussels and expand the mandate of the Commission’s AI Office beyond its current role supervising GPAI.

BITS & BYTES : The Foundational Lens for Enterprise Transformation

BITS serves as high-level strategic governance—ensuring balanced maturity assessments across business alignment, information-centric decision-making, technology enablement, and security resilience—while leveraging BDAT’s detailed sub-domains (layers and components) for tactical implementation and operational oversight. This allows organizations to maintain BDAT’s precision in decomposing complex IT landscapes (e.g., mapping specific data architectures or application portfolios) within BITS’s overarching pillars, fostering adaptive governance that scales from atomic “bits” of change to enterprise-wide transformations ... If BITS defines what must be managed, BYTES (Balanced Yearly Transformation to Enhance Services) define how change must be processed. BYTES is more than a set of principles; it is a derivative of the core architectural lifecycle: Plan (Balanced Yearly), Design& Build (Transformation Enhancing) , and Run (Services). Each component of BYTES directly maps to the mandatory stages of a continuous transformation framework, enabling architects to manage change at its source. ... The BITS & BYTES framework is not intended to replace existing architecture frameworks (e.g., ). Instead, it acts as a meta-framework—a simplified, high-level matrix that accommodates and contextualizes the applicability of all existing models.

Unlocking GenAI and Cloud Effectiveness With Intelligent Archiving

Unlike tiering, which functions like a permanent librarian selectively fetching individual files from deep storage, true archiving is a one-time event that moves files based on defined policies, such as last access or modification date. Once archived, files are stored on a long-term platform and remain accessible without reliance on any intermediary system or application. In this context, one of the main challenges is that most enterprise data is unstructured, including everything from images and videos to emails and social media content. Collectively, these vast and diverse data lakes present a formidable management challenge, and without rigorous control, organizations risk falling victim to the classic “garbage in, garbage out” problem. ... Modern archiving technologies that connect directly to both primary and archive storage platforms eliminate the need for a middleman, drastically improving migration speed, accuracy, and long-term data accessibility. This means organizations can migrate only what’s necessary, ensuring high-value data is cloud-ready while offloading cold data to cost-efficient archival platforms. This not only reduces cloud storage costs but also supports the adoption of cloud-native formats, enabling greater scalability and performance for active workloads. ... For modern enterprises, where more than 60% of enterprise data is typically inactive and often goes untouched for years, organizations are still consuming high-performance (and high-cost) storage.

Why 60% of BI Initiatives Fail (and How Enterprises Can Avoid It)

Many fail because goals and outcomes aren’t clearly defined. While enterprises may be confident that they understand BI gaps, often their goals are vague, lacking proper detailing and no internal consensus. ... Poor project management practices, vague processes, and changing responsibilities create even more confusion. In many failed BI projects, BI is viewed as “just another IT initiative,” whereas it should be treated as part of a business transformation program. Without active sponsorship and accountability, the technology may be delivered, but its adoption and impact suffer. ... Agile and iterative methods are often preferred since they are effective for BI. Whereas, the is not recommended for BI projects since it lacks the necessary agility to adapt to changing requirements, iterative data exploration, and continuous business feedback. Under the waterfall approach, the users are engaged only in the beginning of the project and during the end, which leaves gaps for development or data analysis incase of issues. ... A system is only as good as the users who use it; research has shown that 55% of users lack confidence in BI tools due to insufficient training. Enterprises often expend considerable resources on deployment, but neglect enablement. If employees can’t find how to navigate dashboards, understand the data quality, data visualizations, or use insights to make daily decisions, the adoption rates suffer.

Authentication in the age of AI spoofing

Unlike traditional malware, which may find its way into networks through a compromised software update or downloads, utilize machine learning to analyze how employees authenticate themselves to access networks, including when they log in, from which devices, typing patterns and even mouse movements. The AI learns to mimic legitimate behavior while collecting login credentials and is ultimately deployed to evade basic detection. ... Beyond the statistics, AI’s effectiveness is driven by its exponentially improving abilities to — replicating writing style, voice cadence, facial expressions or speech with subtle nuance and adding realistic context by scanning social media and other publicly available references. The data is striking and reflects the crucial need for a multi-layer approach to help sidestep the exponentially escalating ability for AI to trick humans. ... Cryptographic protection complements biometric authentication, which verifies “Is this the right person?” at the device level, while passkeys are used to verify “Is this the right website or service?” at the network level. Multi-modal biometrics, such as plus or biometrics plus , further strengthen this approach. As AI-powered attacks make credential theft and impersonation attacks more sophisticated, the only sustainable line of defense is a form of authentication that cannot be tricked or must be cryptographically verified.

Why your security strategy is failing before it even starts

The biggest mistake I see among organizations is initiating cybersecurity efforts with technology rather than prioritizing risk and business alignment. Cybersecurity is often mischaracterized as a technical issue, when in reality it’s a business risk management function. Failure to establish this connection early often results in fragmented decision-making and limited executive engagement. Effective cybersecurity strategies should be embedded into business objectives from the outset. This requires identifying the business’s critical assets, assessing potential threats and motivations, and evaluating the impact of assets becoming compromised. Too often, CISOs jump straight into acquiring cybersecurity tools without addressing these questions. ... First, the threat landscape shifted dramatically. Cybersecurity attacks today target . In food manufacturing, those systems run production lines, refrigeration, and safety processes. A cyber incident in these areas extends beyond data loss, it can disrupt production and even compromise food safety, introducing a far more complex level of risk. Second, it became evident to me that cybersecurity cannot operate in isolation. It must support and enable business operations and growth. Today, my approach is risk-based and aligned with our business prioritizes, while still built on . We focus on resilience, not just compliance, and OT security is a core pillar of that strategy.

Daily Tech Digest - October 12, 2025

Quote for the day:

"Trust because you are willing to accept the risk, not because it's safe or certain." -- Anonymous

AI and Data Governance: The Power Duo Reshaping Business Intelligence

Fortunately, the relationship between isn’t one-sided. By leveraging , , and , AI enables organizations to manage more effectively. AI models can identify inaccuracies or inconsistencies, flag , and automatically correct missing or , minimizing the risk of generating misleading results from poor-quality datasets. It can track organizational data in real time, ensuring accurate classification of sensitive information, enforcing access controls, and proactively identifying policy violations before they escalate. This approach enables organizations to move away from manual auditing and adopt automated, self-correcting governance workflows. ... To leverage the full potential of the relationship between AI and governance, organizations must establish a continuous feedback loop between their governance frameworks and AI systems. AI shouldn’t function independently; it must be constantly updated and aligned with governance policies to maintain accuracy, transparency, and compliance. One of the best ways to achieve this is by using intelligent data platforms such as Semarchy’s master data management (MDM) and . These solutions unify and control AI data from a trusted, single source of truth, ensuring consistency across business functions.

Building cyber resilience in a volatile world

show just how fragile the ecosystem can be, given that when one link breaks, the shockwaves ripple across agencies and sectors. That’s why the shift away from outmoded ideas of “prevention” by building walls around environments to a new kind of resilience is so stark. For example, is no longer optional; it’s the baseline. Verification must be constant, and assumptions about “safe” internal networks belong in the past. Meanwhile, AI governance and have jumped from academic conversations to immediate government standards. Institutional muscle is being flexed too. ... The transformation ahead is as much cultural as technical. Agencies must shift from being static defenders to , and need to be ready to adapt, recover, and press on even as attacks intensify. Cybersecurity is not just another line item in the IT budget, but rather the backbone of national resilience. The ability to keep delivering services, protect citizen trust, and safeguard critical infrastructure is now inseparable from how well agencies manage cyber risk. Resilience is not built by chance. It’s built through strategy, investment, and relentless partnership. It means turning frameworks into live capability, leveraging industry expertise, and embedding a mindset that sees cyber not as a constraint but as a foundation for confidence and continuity.

Fighting Disinformation Demands Confronting Social and Economic Drivers

Moving beyond security theater requires embracing as a foundational methodology for . This means shifting from “how do we stop misinformation?” to “what material and symbolic interests does information serve, and how do power relations shape what counts as legitimate knowledge?” This approach demands examining not just false information, but the entire apparatus through which beliefs become hegemonic, others verboten. Ideological critique offers three analytical tools absent from current information integrity policy research. First, it provides established scholarly techniques for examining how seemingly neutral technical systems encode worldviews and serve specific class interests. , , and all embed assumptions about authority, truth, and social order that more often than not favor existing power arrangements. Second, it offers frameworks for understanding how dominant groups maintain : the ability to shape not just what people think, but how they think. Third, it provides tools for analyzing how groups develop , alternative meaning-making systems and their ‘hidden transcripts’. Adopting these techniques can craft better policy responses to disinformation.

Cloud Infrastructure Isn't Dead, It's Just Becoming Invisible

Let's be honest: most cloud platforms are more alike than different. Storage, compute, and networking are commoditized. are standard. Reliability and scalability is expected. Most agree that the cloud itself is no longer a differentiator, it's a utility. That's why the value is moving up the stack. Engineers don't need more , they need better ways to work with it. They want file systems that feel local, even when they're remote. They want zero-copy collaboration and speed. And they want all of that without worrying about provisioning, syncing, or latency. Today, cloud users are shifting their expectations toward solutions that utilize standard infrastructure such as and , yet abstract away the complexity. The appeal is in performance and usability improvements that make infrastructure feel invisible. ... What makes this shift important is that it's rooted in practical need. When you're working with terabytes or petabytes of high-resolution video, training a model on noisy real-world data, or collaborating across time zones on a shared dataset, traditional cloud workflows break down. Downloading files locally isn't scalable, and copying data between environments wastes time and resources. Latency is a momentum killer. This is where shines. It doesn't just abstract the cloud, it makes it better suited to the way developers actually build and collaborate today.

The great misalignment in business transformation

It’s easy to point the finger at artificial intelligence (AI) for today’s disruption in the tech workforce. After all, AI is changing how coding, analysis and even project management are done. Entire categories of tasks are being automated. Advocates argue that workers will inevitably be replaced, while critics frame it as the next wave of . Recent surveys have shown that employee optimism is fading. ... The problem is compounded by the emphasis on being “more artistic” or “more technical.” Both approaches miss the mark. Neither artistry for its own sake nor hyper-technical detail guarantees relevance if business problems remain unsolved. The technology industry has always experienced cycles of boom and bust. From the dot-com bubble to the recent AI surge, waves of hiring and layoffs are nothing new. What is new, however, is the growing realization that some jobs may not need to come back at all. ... Analysis without insight devolves into repetitive reporting, adding noise rather than clarity. Creativity without business grounding drifts into theatre, producing workshops and “innovation sessions” that inspire but fail to deliver results. Both are missing the target. Worse still, companies have proven they can operate without many of these roles altogether. The lesson is clear: being more artistic or more technical is not the answer.

The Architecture Repository: Turning Enterprise Architecture into a Strategic Asset

While the provides the context — a spectrum from generic to organization-specific models — the Architecture Repository provides the structure to store, manage, and evolve those models. ... At the heart of the repository lies the . This is the blueprint for how architectural content is structured, related, and interpreted. It defines the vocabulary, relationships, and rules that govern the creation and classification of artifacts. The metamodel ensures consistency across the repository. Whether you’re modeling business processes, application components, or data flows, the metamodel provides a common language and structure. It’s the foundation for traceability, reuse, and integration. In practice, the metamodel is tailored to the organization’s needs. It reflects the enterprise’s modeling standards, governance policies, and stakeholder requirements. It’s not just a technical artifact — it’s a strategic enabler of clarity and coherence. ... Architecture must respond to real needs. The Architecture Requirements Repository captures all authorized requirements — business drivers, stakeholder concerns, and regulatory mandates — that guide architectural development. ... Architecture is not just about models — it’s about solutions. The presents the architectural representation of Solution Building Blocks (SBBs) that support the Architecture Landscape.

Cyberpsychology’s Influence on Modern Computing

Psychological research on decision making and cognitive processes has been fundamental to understanding perceptions and behavior in the areas of cybersecurity and cyberprivacy. Much of this work focuses on and , which inform the actions of both users and attackers. ... Both cognition and affect play a role in these phenomena. Specifically, under conditions of diminished information processing—such as in the case of cognitive demands or affective experiences such as a positive mood state—people are less likely to make decisions based on strongly held beliefs. For example, a consumer’s positive emotional state, such as happiness with the Internet, mediates the negative effects of information-collection concerns on their willingness to disclose personal information. Interestingly, cybersecurity experts are as vulnerable to and as those who are not cybersecurity experts. A deep understanding of the perceptual, cognitive, and emotional mechanisms that result in lapses of judgment or even behavior incongruent with one’s intellectual understanding is vital to minimizing such threats. In addition to cognitive and emotional states, have provided insight into human behavior vis á vis technology. The “big five” personality theory, also known as the five-factor model, is a widely accepted framework that has been applied to a broad range of cyber-related behaviors, including cybersecurity.

The Cybersecurity Skills Gap and the Role of Diversity

Cybersecurity is often presented as a technically demanding field, she points out. “This further discourages some women from first entering the industry. For those who have, it’s then about being able to continue growing their careers when they may feel challenged by perceived technical demands,” says Pinkard. And today, cybersecurity is not a purely technical subject. Demand for technical skills will always exist, but the job has changed, says Amanda Finch, CEO, The Chartered Institute for Information Security. ... While the low number of women in cybersecurity is concerning, it’s also important to consider how other types of diversity can help fill the skills gap in the workforce. Inclusion and opportunity is “100% about more than just bringing in more women”: “It's about the different life perspective,” says Pinkard. Those “lived perspectives” are driven by areas such as , and physical ability diversity, she says. ... Too many companies still treat diversity as a compliance exercise, says Mullins. “When it was no longer a legal requirement in the US, many simply stopped. Others will say, ‘we want more women’, but won’t update their and complain that only men apply to their roles. Or they say ‘we want neurodiverse talent’, but resist implementing more flexible working policies to facilitate them.”

Data quality is no longer optional

AI systems can only be as good as the data that feeds them. When information is incomplete, inconsistent or trapped in silos, the insights and predictions those systems produce become unreliable. The risk is not just missed opportunities but strategic missteps that erode customer trust and competitive positioning. ... Companies with a strong digital foundation are already ahead in AI adoption, and those without risk drowning in information while starving their AI models of the clean, reliable inputs they need. But before any organisation can realise AI’s full potential, it must first build a resilient data foundation, and the enterprises that place data quality at the heart of their digital strategy are already seeing measurable gains. By investing in robust governance, integrating AI with data management and removing silos across departments, they create connected teams and more agile operations. ... Raising data quality is not a one-off exercise; it requires a cultural shift that calls for collaboration across IT, operations and business units. Leaders must set clear standards for how data is captured, cleaned and maintained, and champion the idea that every employee is a steward of data integrity. The long-term challenge is to design data architectures that can support scale and complexity and embrace distributed paradigms that support interoperability. These architectures do more than maintain order.

Shadow AI in Your Systems: How to Detect and Control It

"Shadow AI" is when people in an organization use AI tools like generative models, , , or third-party LLM services without getting permission from IT or cybersecurity. This is the next step in the evolution of "shadow IT," but the stakes are higher because models can read sensitive text, make API calls on their own, and do automated tasks across systems. Industry definitions and primers say that shadow AI happens when employees use AI apps without official supervision, which can lead to data leaks, privacy issues, and compliance problems. ... Agents that automate web interactions usually need credentials, API keys, or tokens to do things for employees. Agents can get into systems directly if keys are poorly managed or embedded in scripts. ... Queries are outbound traffic to known AI provider endpoints, nonstandard hostname patterns, or unusual POST bodies. Modern proxy and firewall logs often show ULRs and headers that show which model vendors are being used. Check your web gateway and proxy logs for spikes in API calls and endpoints that you don't know about. ... Agents often do a lot of navigations, clicks, and form submissions in a short amount of time, which is different from how people do it. Look for patterns in how people navigate, intervals that are always the same, or pages that are crawled in tight loops.

Daily Tech Digest - April 04, 2025

Quote for the day:

“Going into business for yourself, becoming an entrepreneur, is the modern-day equivalent of pioneering on the old frontier.” -- Paula Nelson

Hyperlight Wasm points to the future of serverless

WebAssembly support significantly expands the range of supported languages for Hyperlight, ensuring that compiled languages as well as interpreted ones like JavaScript can be run on a micro VM. Your image does get more complex here, as you need to bundle an additional runtime in the Hyperlight image, along with writing code that loads both runtime and application as part of the launch process. ... There’s a lot of work going on in the WebAssembly community to define a specification for a component model. This is intended to be a way to share binaries and libraries, allowing code to interoperate easily. The Hyperlight Wasm tool offers the option of compiling a development branch with support for WebAssembly Components, though it’s not quite ready for prime time. In practice, this will likely be the basis for any final build of the platform, as the specification is being driven by the main WebAssembly platforms. One point that Microsoft makes is that Wasm isn’t only language-independent, it’s architecture-independent, working against a minimal virtual machine. So, code written and developed on an x64 architecture system will run on Arm64 and vice versa, ensuring portability and allowing service providers to move applications to any spare capacity, no matter the host virtual machine.

Beyond SIEM: Embracing unified XDR for smarter security

Implementing SIEM solutions can have challenges and has to be managed proactively. Configuring the SIEM system can be very complex where any error can lead to false positives or missed threats. Integrating SIEM tools with existing security tools and systems is not easy. The implementation and maintenance processes are also resource-intensive and require significant time and manpower. Alert fatigue can be set with traditional SIEM platforms where numerous alerts are generated making it rather difficult to identify the genuine ones. ... For industries with stringent compliance requirements, such as finance and healthcare, SIEM remains a necessity due to its log retention, compliance reporting, and event correlation capabilities. Microsoft Sentinel’s AI-driven analytics help security teams fine-tune alerts, reducing false positives and increasing threat detection accuracy. Microsoft Defender XDR platform offers, Unified visibility across attack surfaces, CTEM Exposure management solution, CIS framework assessment, Zero Trust, EASM, AI-driven automated response to threats, Integrated security across all Microsoft 365 and third-party platforms, Office, Email, Data, CASB, Endpoint, Identity, and Reduced complexity by eliminating the need for custom configurations.

Compliance Without Chaos: Build Resilient Digital Operations

A unified platform makes service ownership a no-brainer by directly connecting critical services to the right responders so there’s no scrambling when things go sideways. Teams can set up services quickly and at scale, making it easier to get a real-time pulse on system health and see just how far the damage spreads when something breaks. Instead of chasing down data across a dozen monitoring tools, everything is centralized in one place for easy analysis. ... With all data centralized in a unified platform, the classification and reporting of incidents is far easier with accessible and detailed incident logs that provide a clear audit trail. Sophisticated platforms also integrate with IT service management (ITSM) and IT operations (ITOps) tools to simplify the reporting of incidents based on predefined criteria. ... Every incident, both real and simulated, should be viewed as a learning opportunity. Aggregating data from disparate tools into a single location gives teams a full picture of how their organization’s operations have been affected and supplies a narrative for reporting. Teams can then uncover patterns across tools, teams and time to drive continuous learning in post-incident reviews. Coupled with regular, automated testing of disaster recovery runbooks, teams can build greater confidence in their system’s resilience.

How Organizations Can Benefit From Intelligent Data Infra

The first is getting your enterprise data AI-ready. Predictive AI has been around for a long time. But teams still spend a significant amount of time identifying and cleaning data, which involves handling ETL pipelines, transformations and loading data into data lakes. This is the most expensive step. The same process applies to unstructured data in generative AI. But organizations still need to identify the files and object streams that need to be a part of the training datasets. Organizations need to securely bring them together and load them into feature stores. That's our approach to data management. ... There's a lot of intelligence tied to files and objects. Without that, they will continue to be seen as simple storage entities. With embedded intelligence, you get detection capabilities that let you see what's inside a file and when it was last modified. For instance, if you create embeddings from a PDF file and vectorize them, imagine doing the same for millions of files, which is typical in AI training. This consumes significant computing resources. You don't want to spend compute resources while recreating embeddings on a million files every time there is a modification to the files. Metadata allows us to track changes and only reprocess the files that have been modified. This differential approach optimizes compute cycles.

Tariff war throws building of data centers into disarray

The potentially biggest variable affecting data center strategy is timing. Depending on the size of an enterprise data center and its purpose, it could take as little as six months to build, or as much as three years. Planning for a location is daunting when ever-changing tariffs and retaliatory tariffs could send costs soaring. Another critical element is knowing when those tariffs will take effect, a data point that has also been changing. Some enterprises are trying to sidestep the tariff issues by purchasing components in bulk, in enough quantities to potentially last a few years. ... “It’s not only space, available energy, cooling, and water resources, but it’s also a question of proximity to where the services are going to be used,” Nguyen said. Finding data center personnel, Nguyen said, is becoming less of an issue, thanks to the efficiencies gained through automation. “The level of automation available means that although personnel costs can be a bit more [in different countries], the efficiencies used means that [hiring people] won’t be the drag that it used to be,” he said. Given the vast amount of uncertainty, enterprise IT leaders wrestling with data center plans have some difficult decisions to make, mostly because they will have to guess where the tariff wars will be many months or years in the future, a virtually impossible task.

The Modern Data Architecture: Unlocking Your Data's Full Potential

If the Data Cloud is your engine, the CDP is your steering wheel—directing that power where it needs to go, precisely when it needs to get there. True real-time CDPs have the ability to transform raw data into immediate action across your entire technology ecosystem, with an event-based architecture that responds to customer signals in milliseconds rather than minutes. This ensures you can dynamically personalize experiences as they unfold—whether during a website visit, mobile app session, or contact center interaction–all while honoring consent. ... As AI capabilities evolve, this Intelligence Layer becomes increasingly autonomous—not just providing recommendations but taking appropriate actions based on pre-defined business rules and learning from outcomes to continuously improve its performance. ... The Modern Data Architecture serves as the foundation for truly intelligent customer experiences by making AI implementations both powerful and practical. By providing clean, unified data at scale, these architectures enable AI systems to generate more accurate predictions, more relevant recommendations, and more natural conversational experiences. Rather than creating isolated AI use cases, forward-thinking organizations are embedding intelligence throughout the customer journey.

Why AI therapists could further isolate vulnerable patients instead of easing suffering

While chatbots can be programmed to provide some personalised advice, they may not be able to adapt as effectively as a human therapist can. Human therapists tailor their approach to the unique needs and experiences of each person. Chatbots rely on algorithms to interpret user input, but miscommunication can happen due to nuances in language or context. For example, chatbots may struggle to recognise or appropriately respond to cultural differences, which are an important aspect of therapy. A lack of cultural competence in a chatbot could alienate and even harm users from different backgrounds. So while chatbot therapists can be a helpful supplement to traditional therapy, they are not a complete replacement, especially when it comes to more serious mental health needs. ... The talking cure in psychotherapy is a process of fostering human potential for greater self-awareness and personal growth. These apps will never be able to replace the therapeutic relationship developed as part of human psychotherapy. Rather, there’s a risk that these apps could limit users’ connections with other humans, potentially exacerbating the suffering of those with mental health issues – the opposite of what psychotherapy intends to achieve.

Breaking Barriers in Conversational BI/AI with a Semantic Layer

The push for conversational BI was met with adoption inertia. Two major challenges have hindered its potential—the accuracy of the data insights and the speed at which the interface could provide the answers that were sought. This can be attributed to the inherent complexity of data architecture, which involves fragmented data in disparate systems with varying definitions, formats, and contexts. Without a unified structure, even the most advanced AI models risk delivering contextually irrelevant, inconsistent, or inaccurate results. Moreover, traditional data pipelines are not designed for instantaneous query resolution and resolving data from multiple tables, which delays responses. ... Large language models (LLMs) like GPT excel at interpreting natural language but lack the domain-specific knowledge of a data set. A semantic layer can resolve this challenge by acting as an intermediary between raw data and the conversational interface. It unifies data into a consistent, context-aware model that is comprehensible to both humans and machines. Retrieval-augmented generation (RAG) techniques are employed to combine the generative power of LLMs with the retrieval capabilities of structured data systems.

The rise of AI PCs: How businesses are reshaping their tech to keep up

Companies are discovering that if they want to take full advantage of AI and run models locally, they need to upgrade their employees' laptops. This realization has introduced a hardware revolution, with the desire to update tech shifting from an afterthought to a priority and attracting significant investment from companies. ... running models locally gives organizations more control over their information and reduces reliance on third-party services. That setup is crucial for companies in financial services, healthcare, and other industries where privacy is a big concern or a regulatory requirement. "For them, on-device AI computer, it's not a nice to have; it's a need to have for fiduciary and HIPAA reasons, respectively," said Mike Bechtel, managing director and the chief futurist at Deloitte Consulting LLP. Another advantage is that local running reduces lag and creates a smoother user experience, which is especially valuable for optimizing business applications. ... As more companies get in on the action and AI-capable computers become ubiquitous, the premium price of AI PCs will continue to drop. Furthermore, Flower said the potential gains in performance offset any price differences. "In those high-value professions, the productivity gain is so significant that whatever small premium you're paying for that AI-enhanced device, the payback will be nearly immediate," said Flower.

Many CIOs operate within a culture of fear

The culture of fear often stems from a few roots, including a lack of accountability from employees who don’t understand their roles, and mistrust of coworkers and management, says Alex Yarotsky, CTO at Hubstaff, vendor of a time tracking and workforce management tool. In both cases, company leadership is to blame. Good leaders create a positive culture laid out in a set of rules and guidelines for employees to follow, and then model those actions themselves, Yarotsky says. “Any case of misunderstanding or miscommunication is always on the management because the management is the force in the company that sets the rules and drives the culture,” he adds. ... Such a culture often starts at the top, says Jack Allen, CEO and chief Salesforce architect at ITequality, a Salesforce consulting firm. Allen experienced this scenario in the early days of building a career, suggesting the problems may be bigger than the survey respondents indicate. “If the leader is unwilling to admit mistakes or punishes mistakes in an unfair way, then the next layer of leadership will be afraid to admit mistakes as well,” Allen says. ... Cultivating a culture of fear leads to several problems, including an inability to learn from mistakes, Mort says. “Organizations that do the best are those that value learning and highlight incidents as valuable learning events,” he says.

Daily Tech Digest - April 29, 2024

The end of vendor-backed open source?

Considering what’s happened over the last few years, I hope we are witnessing the end of single-vendor-backed open-source projects. Many of these companies see open source as a vehicle to drive the adoption of their software and then switch to a prohibitive license once the software starts to reach massive adoption through a cloud provider. This has happened more times in recent years than I care to count. There are only a few companies that can reliably provide open-source software without hurting their own bottom line. These are the software giants like AWS, Google, and Microsoft. They can comfortably contribute to open-source software and collaborate with companies who specialize in some of the more niche use cases. They may not have been able to cover these niches on their own, but with the help of the community they can. And at the end of the day, even the smaller companies who provide managed offerings need somewhere to host them, right? I see a bright future for Valkey, the open-source fork of Redis. It’s starting off right in the Linux Foundation, where companies contribute freely without fear of a single company dictating the direction of a project.

The Rise of Augmented Analytics: Combining AI with BI for Enhanced Data Insights

Traditionally, data analysis has been the domain of highly skilled data scientists and BI experts. Augmented analytics changes this dynamic by empowering non-technical users to participate actively in the insight-generation process. Augmented analytics automates complex tasks and presents information in a user-friendly format so that employees across departments can easily access, explore, and derive insights from their organization’s data. This will both streamline and simplify business processes – no more worrying about how many business accounts you should have, whether or not your financing option offers optimal interest rates, or if adopting new standards of technologies is worth it in the long run. The relevant teams can easily use data to find the right information without waiting for an expert to analyze it. ... One of the most significant challenges faced by businesses is the shortage of data science expertise. Augmented analytics helps alleviate this bottleneck by reducing the reliance on specialized data professionals.

The 4 Types Of Generative AI Transforming Our World

Generative Adversarial Networks (GANs) emerged in 2014 and quickly became one of the most effective models for generating synthetic content, both text and images. The basic principle involves pitting two different algorithms against each other. One is known as the ‘generator,’ and the other is known as the ‘discriminator,’ and both are given the task of getting better and better at out-foxing each other. ... Neural Radiance Fields (NeRFs) are the newest technology covered here, only emerging onto the scene in 2020. Unlike the other generative technologies, they are specifically used to create representations of 3D objects using deep learning. This means creating an aspect of an image that can't be seen by the ‘camera’ ... One of the latest advancements in the field of generative AI is the development of hybrid models, which combine various techniques to create innovative content generation systems. These models draw on the strengths of different approaches, such as blending the adversarial training of Generative Adversarial Networks (GANs) with the iterative denoising of diffusion models to produce more refined and realistic outputs.

Closing the cybersecurity skills gap with upskilling programs

The power of upskilling is undeniable but putting it into practice with employees is the real challenge. The top reason organizations struggle to implement successful upskilling programs has not changed in the last three years of this study: lack of time. Despite clear barriers, organizations can unlock upskilling engagement with a culture of continuous learning. The first step is to identify existing skills in order to see the gaps. Leaders need to stay engaged in this process: only 33% of executives completely understand the skills their IT teams need and 68% of technologists say leadership at their organization is not aware of a tech skills gap. When it comes to discovering what drives employees to upskill, a new #1 motivation to participate emerged this year: stronger job security and improved confidence. This is yet another proof point in upskilling contributing to higher employee engagement that can drive performance and innovation. With 78% of organizations abandoning projects partway through because of not having enough employees with the right tech skills, there is no time to waste in closing the gap.

Are Enterprises Overconfident About Cybersecurity Readiness?

Comparing statistics for global companies and their overall state of readiness, between the years 2022-2023 and 2023-2024, shows certain surprising changes. The number of Mature companies dropped to 3% this year from 15% in the previous year. The number of Progressive companies dropped by 4% this year; however, the number of Formative companies increased by 13%. In addition, more Beginners were included in the index this year. Raymond Janse van Rensburg, VP, specialists and solutions engineering, APJC, Cisco, said this should not be considered as a static comparison, as the benchmark was done with “a lot more capabilities” this year. "Digitization continues to accelerate around us. We see that there is pressure on organizations to transform. But with that, the attack surface is changing, and there is a lot of technology advancement that is taking place as well," said van Rensburg. "We need to keep pace with change, with the transformation of the organizations, and also to keep pace with change for cybersecurity and the capabilities we bring in across the industry to provide the necessary protections."

Risk orchestration: Addressing the Misconceptions – Hub Platform vs True Orchestration Performance

A true risk orchestration platform synchronises workflows and data and enables customisable journeys. Waterfalling reduces friction and saves costs, ensuring appropriate checks are only applied as necessary. Genuine customers are fast-tracked, while others are routed for additional due diligence. The technology unites complex systems and processes to create a single view of customer risk, a unified risk score and automated decision making. ... In true risk orchestration, all of these processes are seamlessly synchronised to deliver a single view of customer risk. Informed decision making is made simpler, smoother, more accurate and efficient. The impact of this is felt not only in customer satisfaction, which can be vastly enhanced by the speed at which individuals are authenticated and served, but in the overall effectiveness of compliance processes too, in terms of how much time and resource is spent. To that end, there’s a genuine bottom-line benefit to be realised through effective risk orchestration of your compliance processes.

If Software Quality Is Everybody’s Responsibility, So Is Failure

Quality failures tend to stem from broader organizational and leadership failures. Scapegoating testers for systemic issues is counterproductive. It obscures the real problems and stands in the way of meaningful solutions to quality failings. ... To build ownership, organizations need to shift testing upstream, integrating it earlier into requirements planning, design reviews, and development processes. It also requires modernizing the testing practice itself, utilizing the full range of innovation available: from test automation, shift-left testing, and service virtualization, to risk-based test case generation, modeling, and generative AI. With a shared understanding of who owns quality, governance policies can better balance competing demands around cost, schedule, capabilities, and release readiness. Testing insights will inform smarter tradeoffs, avoiding quality failures and the finger-pointing that today follows them. This future state reduces the likelihood of failures — but also acknowledges that some failures will still occur despite best efforts. In these cases, organizations must have a governance model to transparently identify root causes across teams, learn from them, and prevent recurrence.

Navigating personal liability: post data-breach recommendations for CISOs

The single most important guidance that you should follow is to involve counsel promptly and frequently. Upon your first knowledge of a claimed data breach, you should promptly determine who your organization’s legal counsel is for such matters and contact those lawyers. ... It can be tempting for CSOs and CISOs to take the reins in data breach incidents, given their technical expertise or sense of personal responsibilities. However, this can lead to unintended legal complications. In the aftermath of a data breach, it’s critical to let your organization’s legal counsel guide decision-making processes. They can ensure that the response to the data breach complies with applicable laws and that both communication and remediation efforts are handled appropriately to minimize potential liability. ... Although it’s rare to face personal liability or criminal charges, there can be situations where it could be a real or feared risk. Independent legal advice can provide guidance tailored to your specific situation, to identify where your interests may be different from those of your organization, to allay your concerns, all of which can be protected under attorney-client privilege.

Change Healthcare Admits to Making Ransom Payment to Protect Patient Data, Discloses That Hacker Broke in Days Before Attack

Even though a ransom payment was made, the attack was nevertheless devastating to the processing of health insurance claims and payment information across the country. The UnitedHealth Group update indicates that 99% of pharmacies are now back to normal ability to process claims, and that its internal payment processing capacity is back to 86% of normal. 80% of the function of its major platforms and products has also been restored and the group expects a full recovery in a matter of weeks. It also says that medical claims are back to near-normal processing levels, but that it is working directly with some smaller providers that remain hampered by the attack fallout and is seeking to set up alternate methods of submission for them. The ransom payment had been widely reported prior to the company’s admission, due to an otherwise inexplicable Bitcoin transaction (equaling about $22 million) traced to a wallet known to be associated with AlphV. The RaaS provider also opted to pull what appeared to be an “exit scam” after this payment rolled in, leading to the bag-holding affiliate taking to dark web forums to accuse the group of theft.

Top 10 barriers to strategic IT success

The State of the CIO Study found that staff and skills shortages was the No. 1 challenge cited by CIOs when asked what most often forces them to redirect resources away from strategic tasks, with 59% saying as much. “Talent — or the lack of it — is a huge, huge issue, and when we look at the demographics, we don’t see that changing in the future,” says Chirajeet (CJ) Sengupta, managing partner at Everest Group, a global research firm. Sengupta acknowledges that layoffs at the tech giants in the past year or so eased the talent crunch a bit — but only for other big companies who could offer highly-competitive salaries to those laid-off workers. And while CIOs are working to train internal candidates within IT and the business units for tough-to-fill tech jobs or are using contractors to fill in staffing gaps, Sengupta says those practices create talent challenges, too. Upskilling takes time, and contracted workers aren’t usually as close to the business as employees. Now there’s news that the competition for tech workers may heat up even more this year.

Quote for the day:

"'To do great things is difficult; but to command great things is more difficult.'' -- Friedrich Nietzsche