Daily Tech Digest by Kannan Subbiah: storage

Showing posts with label storage. Show all posts

Daily Tech Digest - April 27, 2026

Quote for the day:

"Security is not a product, but a process. It is a mindset that assumes the 'impossible' will happen, and builds the walls before the water starts rising." -- Inspired by Bruce Schneier

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Your AI strategy is all wrong

In this Computerworld article, argues that the prevailing corporate strategy of using artificial intelligence to slash headcount is fundamentally flawed. While mass layoffs provide immediate cost savings, Elgan cites research from the suggesting that organizations should instead prioritize "knowledge ecosystems" built on human-AI collaboration. The core issue is that AI excels at rapid data processing and complex task execution, but it lacks the critical judgment, ethical reasoning, and contextual understanding inherent to human experts. Furthermore, an over-reliance on automated tools risks a "skills atrophy paradox," where employees lose the ability to perform independently. To avoid these pitfalls, Elgan suggests that leaders must redesign workflows around strategic handoffs rather than total replacements. This involves shifting employee training toward metacognition—learning how to effectively integrate personal expertise with AI outputs—and creating new roles focused on AI specialization. Ultimately, companies that treat AI as a tool to augment collective intelligence will achieve compounding, long-term advantages over those that merely optimize for short-term productivity gains. By keeping humans in authorship of decisions, businesses ensure they remain legally defensible and ethically grounded while leveraging the unprecedented speed and analytical power that modern AI provides.

The New Software Economics: Earn the Right to Invest Again, in 90-day Cycles

"" by Leonard Greski explores the evolving financial landscape of technology, emphasizing how the shift to subscription-based infrastructure and cloud computing has moved IT spending from balance sheets to income statements. This transition complicates traditional software capitalization practices, such as , which often conflict with the modern reality of continuous delivery. To address these challenges, Greski proposes a breakthrough framework called "earning the right to invest again." This model shifts focus from rigid accounting treatments to accountability for value generation through 90-day investment cycles. The process involves shipping a "thin slice" of functionality within 30 to 60 days, immediately monetizing that slice through revenue increases or measurable cost reductions, and then using that evidence to fund the next tranche of development. By treating application development as a series of bounded pilots rather than fixed-scope projects, organizations can better manage uncertainty and align spending with actual end-user value. Greski concludes by recommending strategic actions for modern executives, such as prioritizing value streams over projects, pre-writing AI policies, and integrating into senior leadership, to ensure technology investments remain agile, evidence-based, and fiscally responsible in a rapidly changing digital economy.

Deepfake threats exploiting the trust inside corporate systems

The article "Deepfake threats exploiting the trust inside corporate systems" by Anthony Kimery on Biometric Update explores a dangerous evolution in cybercrime, as detailed in a new playbook by AI security firm Reality Defender. Deepfake technology has transitioned from isolated fraud schemes into sophisticated attacks that infiltrate internal corporate workflows, specifically targeting the "trust boundaries" businesses rely on for daily operations. This shift poses a severe risk to sensitive processes such as password resets, access recovery, internal meetings, and executive communications. Because traditional security models often equate seeing or hearing a person with identity assurance, can now bypass standard technical controls by mimicking trusted colleagues or leadership. Once these digital imitations enter internal approval chains or customer service interactions, they can cause significant damage before traditional systems recognize the breach. emphasizes that organizations must transition from ad hoc reactions to a structured strategy involving real-time detection, procedural response, and operational containment. The fundamental issue is that modern deepfakes have effectively broken the assumption that sensory verification is foolproof. To mitigate this risk, the article suggests that early visibility and forensic accountability are more critical than absolute certainty, urging organizations to establish clear protocols for handling suspicious media.

Why Integration Tech Debt Holds Back SaaS Growth

The article "Why Integration Tech Debt Holds Back SaaS Growth" by Adam DuVander explains how a specific form of technical debt——acts as a silent anchor for . While typical technical debt involves internal code quality, integration debt arises from the rapid, often "quick-and-dirty" connections made between a platform and the third-party apps its customers use. To achieve early market traction, many SaaS providers build fragile, custom integrations that lack scalability and robust error handling. Over time, these brittle connections require constant maintenance, pulling engineering resources away from core product innovation. This creates a "growth paradox" where the very integrations intended to attract new users eventually prevent the company from scaling effectively or entering enterprise markets that demand high reliability. DuVander argues that to sustain long-term growth, companies must transition from these bespoke, hard-coded integrations to a more strategic, platform-led approach. By investing in a unified integration architecture or using specialized tools to handle third-party connectivity, SaaS providers can reduce maintenance overhead, improve system reliability, and free their developers to focus on delivering unique value, thereby "paying down" the debt that stifles competitive agility.

Why GCCs Must Move to Product-Led Models to Stay Relevant

In the article "Why GCCs Must Move to Product-Led Models to Stay Relevant," the author argues that Global Capability Centers (GCCs) are at a critical crossroads. Historically established as cost-arbitrage hubs focused on back-office operations and service delivery, GCCs are now facing pressure to evolve into value-driven entities. To maintain their strategic importance within parent organizations, they must transition from a project-centric approach to a . This shift requires integrating engineering excellence with business outcomes, moving beyond merely executing tasks to owning end-to-end product lifecycles. A product-led GCC prioritizes user-centric design, agile methodologies, and cross-functional teams that include product managers, designers, and engineers. By fostering a culture of innovation and data-driven decision-making, these centers can accelerate speed-to-market and enhance customer experiences. Furthermore, the article highlights that a product mindset helps attract top-tier talent who seek ownership and impact rather than repetitive support roles. Ultimately, for GCCs to survive the era of digital transformation and AI, they must shed their identity as "cost centers" and emerge as "innovation engines" that proactively contribute to the global enterprise's growth, scalability, and long-term competitive advantage.

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

In the article "Cold Data, Hot Problem," Brian Henderson discusses how the surge of generative AI is fundamentally altering enterprise storage strategies. Traditionally, organizations categorized data into "hot" (frequently accessed) and "cold" (archived), with the latter relegated to low-cost, slow-access tiers. However, the rise of Large Language Models (LLMs) has turned this "cold" data into a "hot" asset, as historical archives are now vital for training models and providing context through Retrieval-Augmented Generation (RAG). This shift creates a significant bottleneck: traditional archival storage cannot provide the high-throughput, low-latency access required for modern AI workloads. To solve this, Henderson argues that enterprises must modernize their data architecture by adopting high-performance "all-flash" object storage and unified data platforms. These solutions bridge the gap between performance and scale, allowing companies to leverage their entire data estate without the latency penalties of legacy silos. By integrating advanced data management and FinOps principles, organizations can ensure that their storage infrastructure is not just a passive repository, but a dynamic engine for AI innovation. Ultimately, the article emphasizes that surviving the AI era requires treating all data as potentially active, ensuring it is discoverable, accessible, and ready for immediate computational use.

Context decay, orchestration drift, and the rise of silent failures in AI systems

In "Context Decay, Orchestration Drift, and the Rise of Silent Failures in AI Systems," Sayali Patil explores the "reliability gap" in enterprise AI—a dangerous disconnect where systems appear operationally healthy but are behaviorally broken. Unlike traditional software, where failures trigger clear error codes, AI failures are often "silent," meaning the system remains functional while producing confidently incorrect or stale results. Patil identifies four critical failure patterns: context degradation, where models reason over incomplete or outdated data; orchestration drift, where complex agentic sequences diverge under real-world pressure; silent partial failure, where subtle performance drops erode user trust before reaching alert thresholds; and the automation blast radius, where a single early misinterpretation propagates across an entire business workflow. To combat these risks, the article argues that traditional infrastructure monitoring (uptime and latency) is insufficient. Instead, organizations must adopt "" and intent-based testing frameworks. By shifting the focus from "is the service up?" to "is the service behaving correctly?", enterprises can build disciplined infrastructure capable of withstanding production stress. This transition requires shared accountability across teams to ensure that AI deployments remain reliable, evidence-based, and fiscally responsible in an increasingly automated digital economy.

AI is reshaping DevSecOps to bring security closer to the code

The integration of artificial intelligence into DevSecOps is fundamentally transforming the software development lifecycle by shifting security from a reactive, post-deployment validation to a continuous, proactive enforcement mechanism. According to industry experts cited in the article, AI is reshaping three primary areas: secure coding, issue detection, and automated remediation. By embedding third-party security tooling directly into coding assistants, organizations can now provide real-time policy guidance, secrets detection, and dependency validation as code is written. This "" approach ensures that security is no longer an afterthought but a foundational component of the generation workflow. Furthermore, AI-driven automation helps bridge the persistent gap between development and security teams by providing contextual fixes and reducing the manual burden of triaging vulnerabilities. Beyond mere tooling, this evolution demands a strategic shift in skills, requiring developers to become more security-conscious while security professionals transition into architectural oversight roles. Ultimately, AI-enhanced DevSecOps enables enterprises to maintain a rapid pace of innovation without compromising the integrity of the software supply chain. By leveraging intelligent agents to monitor and enforce guardrails throughout the development pipeline, businesses can more effectively mitigate risks in an increasingly complex and fast-paced digital landscape.

Unpacking the SECURE Data Act

The article "Unpacking the SECURE Data Act" by Eric Null, featured on Tech Policy Press, critically analyzes the House Republicans' newly proposed federal privacy bill, the Securing and Establishing Consumer Uniform Rights and Enforcement (SECURE) Data Act. Null argues that the legislation represents a significant step backward for American privacy protections. Rather than establishing a robust national standard, the bill mirrors industry-friendly state laws, such as Kentucky’s, but often excludes even their basic safeguards, like impact assessments or protections for smart TV and neural data. A primary concern highlighted is the bill's strong , which would override more protective state laws, effectively turning federal law into a "ceiling" rather than a "floor." Furthermore, the Act contains broad exemptions that allow companies to bypass compliance through simple privacy policies, terms of service contracts, or by labeling data collection as "internal research" to train AI systems. Null contends that the bill’s data minimization standards are essentially the status quo, providing a "free pass" for companies to continue invasive data practices as long as they are disclosed. Ultimately, the article warns that the SECURE Data Act prioritizes industry interests over meaningful consumer rights, leaving individuals vulnerable in an increasingly AI-driven digital economy.

Why legacy data centre networks are no longer fit for purpose

The article "Why legacy data centre networks are no longer fit for purpose" highlights the critical disconnect between traditional infrastructure and the explosive demands of modern computing, particularly driven by artificial intelligence and high-performance workloads. Legacy networks, often built on rigid, three-tier architectures, struggle with the "east-west" traffic patterns prevalent in today’s virtualized environments. These older systems frequently suffer from high latency, limited scalability, and significant energy inefficiencies, making them a liability as power costs and sustainability regulations intensify. The shift toward AI-ready data centers necessitates a transition to and software-defined networking, which provide the high-bandwidth, low-latency fabrics required for parallel processing. Furthermore, legacy hardware often lacks the integrated security and real-time observability needed to defend against sophisticated cyber threats. The piece emphasizes that staying competitive in 2026 requires more than just incremental updates; it demands a fundamental modernization of the network fabric to ensure agility and reliability. By moving away from siloed, hardware-centric models toward modular and automated infrastructure, organizations can achieve the density and flexibility required for future growth. Ultimately, the article argues that failing to replace these aging systems risks operational bottlenecks and financial strain in an increasingly cloud-native world.

Daily Tech Digest - March 18, 2026

Quote for the day:

"Leadership cannot really be taught. It can only be learned." -- Harold S. Geneen

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Why hardware + software development fails

In the CIO article "Why hardware + software development fails," Chris Wardman explores the chronic pitfalls that lead complex technical projects to stall or collapse. He argues that failure often stems from a fundamental misunderstanding of the ""—the reality that code is never truly finished and requires continuous refinement. Key contributors to failure include unrealistic timelines that force engineers to cut critical corners and the "mythical man-month" fallacy, where adding more personnel to a slipping project only increases communication overhead and further delays. Additionally, Wardman identifies the premature focus on building a final product rather than first resolving technical unknowns, which account for roughly 80% of total effort. Draconian IT policies and the misuse of simplified frameworks also stifle innovation by creating friction and capping system capabilities. Finally, the author points to inadequate testing strategies that fail to distinguish between hardware, software, and physical environmental issues. To succeed, organizations must foster empowered leadership, set realistic expectations, and prioritize solving core uncertainties before moving to production. By mastering these fundamentals, companies can transform the inherent difficulties of hardware-software integration into a competitive advantage, delivering reliable, value-driven products to the market.

New font-rendering trick hides malicious commands from AI tools

The BleepingComputer article details a sophisticated "," dubbed "" by researchers at LayerX, which exploits the disconnect between how AI assistants and human browsers interpret web content. By utilizing custom font files and CSS styling, attackers can perform character remapping through glyph substitution. This allows them to display a clear, malicious command to a human user while presenting the underlying HTML to an AI scanner as entirely benign or unreadable text. Consequently, when a user asks an AI assistant—such as , Gemini, or Copilot—to verify the safety of a command (like a reverse shell payload), the AI analyzes only the hidden, safe DOM elements and mistakenly provides a reassuring response. Despite the high success rate across multiple popular AI platforms, most vendors initially dismissed the vulnerability as "out of scope" due to its reliance on social engineering, though Microsoft has since addressed the issue. The research underscores a critical blind spot in modern automated security tools that rely strictly on text-based analysis rather than visual rendering. To combat this, experts recommend that LLM developers incorporate visual-aware parsing or optical character recognition to bridge the gap between machine processing and human perception, ensuring that security safeguards cannot be bypassed through creative font manipulation.

More Attackers Are Logging In, Not Breaking In

In the Dark Reading article "More Attackers Are Logging In, Not Breaking In," Jai Vijayan highlights a critical shift in cybercrime where attackers increasingly favor legitimate credentials over technical exploits to infiltrate enterprise networks. Data from Recorded Future reveals that credential theft surged in late 2025, with nearly two billion credentials indexed from malware combo lists. This rapid escalation is fueled by the industrialization of infostealer malware, malware-as-a-service ecosystems, and AI-enhanced social engineering. Most alarmingly, roughly 31% of stolen credentials now include active session cookies, which allow threat actors to bypass multi-factor authentication entirely through session hijacking. Attackers are specifically targeting high-value entry points like , Azure Active Directory, and corporate VPNs to gain stealthy, broad access while avoiding traditional security alarms. Because identity has become the primary attack surface, experts argue that perimeter-centric defenses are no longer sufficient. Organizations are urged to move beyond basic MFA toward continuous identity monitoring, phishing-resistant FIDO2 standards, and behavioral-based conditional access policies. By treating identity as a "Tier-0" asset, businesses can better defend against a landscape where criminals simply log in using valid, stolen data rather than making noise by breaking through technical barriers.

From SAST to “Shift Everywhere”: Rethinking Code Security in 2026

The article "From SAST to 'Shift Everywhere': Rethinking Code Security in 2026" on DZone explores the necessary evolution of software security in response to modern development challenges. It argues that traditional static analysis (SAST) is no longer adequate on its own, advocating instead for a "shift everywhere" approach that integrates security testing throughout the entire software development lifecycle (SDLC). The author emphasizes that true security is not achieved through isolated scans but through continuous risk management, robust architecture, and comprehensive threat modeling. In an era of cloud-native systems and AI-assisted coding, vulnerabilities can spread rapidly across large dependency graphs, making early design decisions more impactful than ever. The text notes that "secure code" is a relative concept defined by an organization's specific threat model and maturity level rather than an absolute state. Key strategies for improvement include fostering developer security literacy, gaining executive commitment, and utilizing AI-driven tools to prioritize findings and reduce alert fatigue. Ultimately, the article suggests that security must become a core property of software systems, evolving into a more analytical and context-driven discipline to effectively combat sophisticated global threats and manage the risks inherent in open-source components.

CISOs rethink their data protection strategi/es

In the contemporary digital landscape, Chief Information Security Officers (CISOs) are fundamentally re-evaluating their data protection strategies, primarily driven by the rapid proliferation of artificial intelligence. According to recent research, the integration of has necessitated a shift in how organizations manage sensitive information, with approximately 90% of firms expanding their privacy programs to address these new complexities. Beyond AI, security leaders are grappling with exponential increases in data volume, expanding attack surfaces, and heightening regulatory pressures that demand greater operational resilience. To combat "data sprawl," CISOs are moving away from traditional perimeter-based defenses toward more sophisticated models that emphasize granular data classification, tagging, and the monitoring of lateral data movement. This evolution involves rethinking legacy tools like Data Loss Prevention (DLP) systems, which often struggle to secure modern, AI-driven environments. Consequently, modern strategies prioritize collaborative risk assessments with executive peers to align security spending with tangible business impact. By adopting automation, exploring passwordless environments, and co-innovating with vendors, CISOs aim to build proactive guardrails that protect data regardless of how it is accessed or used. This strategic pivot reflects a broader transition from reactive compliance to a dynamic, intelligence-driven framework essential for navigating today’s volatile threat landscape.

Storage wars: Is this the end for hard drives in the data center?

The debate over the future of hard disk drives (HDDs) in data centers has intensified, as highlighted by Pure Storage executive Shawn Rosemarin’s bold prediction that HDDs will be obsolete by 2028. This potential shift is primarily driven by the escalating costs and limited availability of electricity, as data centers currently consume approximately three percent of global power. Proponents of an all-flash future argue that solid-state drives (SSDs) offer superior energy efficiency—reducing power consumption by up to ninety percent—while providing the high density and performance required for modern AI and machine learning workloads. Conversely, industry giants like Seagate and Western Digital maintain that HDDs remain the indispensable backbone of the storage ecosystem, currently holding about ninety percent of enterprise data. They contend that the structural cost-per-terabyte advantage of magnetic storage is insurmountable for mass-capacity needs, particularly as AI-driven data growth surges. While flash technology continues to capture performance-sensitive tiers, HDD manufacturers report that their capacity is already sold out through 2026, suggesting that the "end" of spinning disk may be premature. Ultimately, the industry appears to be moving toward a multi-tiered architecture where both technologies coexist to balance performance, power sustainability, and economic scale.

Update your databases now to avoid data debt

The InfoWorld article "Update your databases now to avoid data debt" warns that 2026 will be a pivotal year for database management due to several major end-of-life (EOL) milestones. Popular systems such as MySQL 8.0, PostgreSQL 14, Redis 7.2 and 7.4, and MongoDB 6.0 are all facing EOL status throughout the year, forcing organizations to confront the looming risks of "data debt." While many IT teams historically follow the "if it isn't broken, don't fix it" philosophy, delaying these critical upgrades eventually leads to increased long-term costs, security vulnerabilities, and system instability. Conversely, rushing complex migrations without proper preparation can introduce significant operational failures. To navigate these challenges, the author emphasizes a disciplined planning approach that starts with a comprehensive inventory of all database instances across test, development, and production environments. Migrations should ideally begin with lower-risk test instances to ensure resilience before moving to mission-critical production deployments. A successful transition also requires benchmarking current performance to measure the impact of any changes accurately. Ultimately, gaining organizational buy-in involves highlighting the performance and ease-of-use benefits of modern versions rather than merely focusing on deadlines. By prioritizing proactive updates today, businesses can effectively avoid the technical debt that threatens future scalability.

Data Sovereignty Isn’t a Policy Problem, It’s a Battlefield

Samuel Bocetta’s article, "Data Sovereignty Isn’t a Policy Problem, It’s a Battlefield," argues that has evolved from a simple compliance checklist into a high-stakes geopolitical contest. Bocetta asserts that datasets now carry significant political weight, as their physical and digital locations dictate who can access, subpoena, or monetize information. While governments and cloud providers understand this dynamic, many enterprises view sovereignty merely through the lens of regional settings or slow-moving regulations. However, the reality is that data moves too quickly for traditional laws to maintain control, creating a widening gap where power shifts to those controlling underlying infrastructure rather than legal frameworks. Cloud providers, often perceived as neutral, are active participants in this struggle, where physical location does not guarantee political independence. The article warns that enterprises often fail by treating sovereignty reactively or delegating it as a minor technical detail. Instead, it must be recognized as a core strategic issue impacting risk and procurement. As the digital landscape fragments into competing spheres of influence, businesses must prioritize architectural flexibility and dynamic governance. Ultimately, surviving this battlefield requires moving beyond static compliance to embrace a proactive, defensive posture that anticipates constant shifts in the global data landscape.

A chief AI officer is no longer enough - why your business needs a 'magician' too

As organizations grapple with how to best leverage generative artificial intelligence, a significant debate is emerging over whether to appoint a dedicated Chief AI Officer (CAIO) or pursue alternative leadership structures. While industry data suggests that approximately 60% of companies have already installed a CAIO to oversee governance and security, some leaders argue for a more integrated approach. For instance, the insurance firm Howden has pioneered the role of Director of AI Productivity, a specialist who bridges the gap between technical IT infrastructure and data science teams. This specific role focuses on three primary objectives: ensuring seamless cross-departmental collaboration, maximizing the value of enterprise-grade tools like Microsoft Copilot and ChatGPT, and driving competitive advantage. By appointing a dedicated productivity lead to manage broad tool adoption and user training, senior data leaders are freed to focus on high-value, proprietary machine learning models that differentiate the business. Ultimately, the article suggests that while a CAIO provides high-level oversight, a productivity-focused director acts as a magician who translates complex AI capabilities into tangible daily efficiency gains for employees, ensuring that expensive technology licenses are fully exploited rather than being underutilized by a confused workforce across the global enterprise.

Scientists Harness 19th-Century Optics To Advance Quantum Encryption

Researchers at the University of Warsaw’s Faculty of Physics have developed a groundbreaking quantum key distribution (QKD) system by reviving a 19th-century optical phenomenon known as the Talbot effect. Traditionally, QKD relies on qubits, the simplest units of quantum information, but this method often struggles with the high-bandwidth demands of modern digital communication. To address this, the team implemented high-dimensional encoding using time-bin superpositions of photons, where light pulses exist in multiple states simultaneously. By applying the temporal Talbot effect—where light pulses "self-reconstruct" after traveling through a dispersive medium like optical fiber—the researchers created a setup that is significantly simpler and more cost-effective than current alternatives. Unlike standard systems that require complex networks of interferometers and multiple detectors, this innovative approach utilizes commercially available components and a single photon detector to register multi-pulse superpositions. Although the method currently faces higher measurement error rates, its efficiency is superior because every photon detection event contributes to the cryptographic key. Successfully tested in urban fiber networks for both two-dimensional and four-dimensional encoding, this advancement, supported by rigorous international security analysis, marks a vital step toward making high-capacity, secure quantum communication commercially viable and technically accessible.

Daily Tech Digest - March 17, 2026

Quote for the day:

"Make heroes out of the employees who personify what you want to see in the organization." -- Anita Roddick

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How organizations can make a successful transition to Post-Quantum Cryptography (PQC)

In the article "How Organizations Can Make a Successful Transition to Post-Quantum Cryptography (PQC)," the author outlines a strategic framework for businesses to defend against the impending "Harvest Now, Decrypt Later" (HNDL) threat. This tactic involves malicious actors exfiltrating sensitive data today to decrypt it once powerful quantum computers become viable. To counter this, organizations must first establish a top-down strategy that prioritizes a hybrid cryptographic approach. By combining classical, proven algorithms like ECDH with new NIST-standardized PQC algorithms such as ML-KEM, companies create a safety net against unforeseen vulnerabilities in emerging standards. A critical foundational step is the creation of a comprehensive "Crypto-Bill of Materials" (CBOM) to inventory all cryptographic assets and prioritize "crown jewels" like financial transactions and intellectual property. Furthermore, enterprises should codify these requirements into their procurement policies to prevent the accumulation of further cryptographic debt during new software acquisitions. Finally, the article stresses the importance of assigning clear, cross-functional ownership to ensure accountability across IT, legal, and supply chain departments. By treating the PQC transition as a long-term strategic initiative rather than a simple technical patch, CIOs can ensure their organizations remain resilient and protect the long-term integrity of their most vital data.

Who’s in the data-center space race?

In the article "Who’s in the data-center space race?" on Network World, Maria Korolov explores the ambitious frontier of orbital computing and the major players vying for celestial dominance. Tech giants like SpaceX and Google lead the charge, with Elon Musk’s SpaceX proposing a massive constellation of one million satellites for xAI workloads, while Google’s Project Suncatcher aims to deploy solar-powered tensor processing units in orbit. These initiatives seek to capitalize on abundant solar energy and the natural cooling of space, bypassing terrestrial power constraints and environmental hurdles. Startups like Lonestar are even targeting lunar data storage, while European and Chinese consortiums plan to establish extensive AI training networks by 2030. Despite the promise of high-speed optical downlinks and lower latency, significant obstacles remain, including the extreme costs of orbital launches and the necessity of radiation-hardening sensitive silicon chips. Experts predict that economic feasibility hinges on reducing launch prices to under $200 per kilogram, a milestone expected by the mid-2030s. Ultimately, this space race represents a transformative shift in infrastructure, moving beyond terrestrial limitations to build a decentralized, planet-scale intelligence backbone that could redefine global connectivity and artificial intelligence processing.

When Code Becomes Cheap, Engineering Becomes Governance

In the article "When Code Becomes Cheap, Engineering Becomes Governance" on DevOps.com, Alan Shimel discusses how generative AI is fundamentally recalibrating the software development lifecycle by making the production of code almost instantaneous and effectively "cheap." As AI agents handle the manual labor of writing syntax, the traditional bottleneck of code authorship is vanishing, creating a significant paradox: while output volume explodes, risks associated with security, technical debt, and architectural coherence multiply. Consequently, the core discipline of software engineering is transitioning from a focus on creation to a focus on governance. Engineering teams must now prioritize the curation, verification, and oversight of automated output to prevent unmanageable complexity. This new paradigm demands that developers act as strategic supervisors or "building inspectors," implementing rigorous policy enforcement and guardrails to ensure system integrity. Shimel argues that in an era of abundant code, human expertise is most valuable for high-level decision-making and risk management. Ultimately, success depends on an organization's ability to evolve its culture, treating governance as the essential backbone of sustainable, secure software delivery. This evolution ensures that while machines generate syntax, humans remain responsible for the stability and comprehensibility of the overall system.

Trump cyber plan leaves identity gap unresolved

On March 6, 2026, the Trump Administration unveiled its "Cyber Strategy for America," an aggressive framework emphasizing offensive deterrence, deregulation, and the rapid adoption of AI-powered security measures. While the seven-page document outlines six core pillars—including shaping adversary behavior and hardening critical infrastructure—experts at Biometric Update highlight a significant "identity gap" within the overarching plan. Although the strategy explicitly prioritizes emerging technologies like blockchain, post-quantum cryptography, and autonomous agentic AI, it notably fails to establish a centralized national digital identity strategy or a unified identity assurance framework. This omission is particularly striking as identity fraud and synthetic personas increasingly fuel transnational cybercrime, financial scams, and voter suppression fears. Critics argue that treating digital identity as an afterthought rather than a front-line defense leaves both government and the private sector navigating a fragmented regulatory environment. Interestingly, this lack of focus contrasts with concurrent reports from the Treasury Department, which position digital identity as a critical security layer for modern digital assets. Ultimately, while the strategy successfully shifts the national posture toward risk imposition and technological dominance, it remains an incomplete doctrine by leaving the foundational challenge of identity verification unresolved in an era of sophisticated AI-generated deception.

Practical DevOps leadership Without the Drama

In the article "Practical DevOps Leadership Without the Drama" on the DevOps Oasis blog, the author argues that effective leadership in a technical environment is less about "mystical" management and more about grounded problem-solving and unblocking teams. The piece outlines several pragmatic pillars to maintain a high-performing, low-stress culture. First, it emphasizes starting every initiative by clearly defining the problem to avoid "hobby projects" and align with DORA metrics. Second, it champions visibility through flow, risk, and ownership tracking, suggesting that "red is a color, not a career-limiting event" to surface issues early. Third, leadership involves setting standards that remove repetitive decisions rather than autonomy, using tools like Kubernetes baselines to make the "safe path the easy path." The article also stresses that incident leadership requires a calm, structured routine where coordination is prioritized over individual heroics. Finally, it highlights the importance of a systematic approach to feedback, intentional hiring for systems thinking, and the courage to use guardrails—such as policy-as-code—to prevent predictable operational pain. Ultimately, the post serves as a playbook for building resilient teams that ship quality code without sacrificing sleep or psychological safety.

Rocketlane CEO: AI requires a structural reset of professional SaaS

In the Techzine article, Rocketlane CEO Srikrishnan Ganesan argues that the rise of artificial intelligence necessitates a fundamental "structural reset" of the professional SaaS industry. He contends that simply layering AI features onto existing platforms is a superficial approach that fails to capture the technology's true potential. Instead, the next generation of SaaS must transition from being mere "systems of record" to "systems of action" where AI agents actively execute tasks—such as automated documentation, data transformation, and project management—rather than just tracking them. This shift is particularly impactful for professional services and customer onboarding, where traditional hourly billing models are becoming obsolete in favor of value-based outcomes and fixed fees. Ganesan emphasizes that by delegating routine configurations to AI, human teams can evolve into "orchestrators" focused on high-level strategy and ROI. This transformation enables vendors to offer more scalable, "white-glove" experiences while significantly reducing delivery costs. Ultimately, the article suggests that organizations re-architecting their service models around autonomous capabilities will define the next operating model, while those clinging to legacy, labor-intensive frameworks risk being outpaced by AI-native competitors that redefine the speed of service delivery.

Cryptojackers Lurk in Open Source Clouds

The article "Cryptojackers Lurk in Open Source Clouds" from CACM News explores the growing threat of host-based cryptojacking, where attackers infiltrate Linux cloud environments to surreptitiously mine cryptocurrency. Unlike traditional PC-based malware, cloud-level cryptojacking is highly lucrative because a single entry point can grant access to millions of processors. Attackers typically evade detection by "throttling" their resource usage to blend into background kernel noise and utilizing techniques like program-identification randomization to bypass standard monitoring. This structural complexity often obscures accountability, enabling malicious code to persist even through manual scans. To combat these sophisticated vulnerabilities, researchers introduced CryptoGuard, an open-source framework that leverages deep learning to integrate detection and automated remediation. By tracking specific time-series patterns in kernel-space system calls rather than relying on easily obfuscated process IDs, CryptoGuard can pinpoint scheduler tampering and execute periodic automated erasures to thwart reinfection. This represents a vital shift toward proactive defense, moving beyond simple alerting to real-time, scale-ready intervention. Ultimately, the article argues that restoring visibility in dynamic cloud infrastructures requires such automated, high-fidelity solutions to empower security teams against innovatively hidden cyber threats that continue to exploit vast, under-monitored computational resources.

A million hard drives go offline daily: The massive data waste problem

The article "A million hard drives go offline daily: the massive data waste problem" on Data Center Dynamics highlights a critical yet often overlooked sustainability crisis within the global technology industry. Each year, tens of millions of hard disk drives reach the end of their functional lifespan, yet a staggering number are shredded rather than repurposed. This practice, often driven by rigid security compliance standards like NIST 800-88, leads to an environmental "tsunami" of e-waste, with an estimated one million drives being destroyed every single day. The destruction of these devices not only creates massive amounts of physical waste but also results in the permanent loss of precious, non-renewable raw materials such as neodymium, gold, and copper, valued at hundreds of millions of dollars annually. To combat this, the piece advocates for a shift toward a circular economy model, emphasizing secure data sanitization—software-based wiping—over physical destruction. By adopting "delete, don't destroy" policies and utilizing robotic disassembly for component recovery, the industry could significantly reduce its carbon footprint. Ultimately, the article calls for a collaborative effort between tech giants, regulators, and data center operators to prioritize resource recovery and sustainable innovation to protect the planet’s future.

Green IT Meets Database Engineering: How Better Data Architecture Reduces Energy Footprint

In the article "Green IT Meets Database Engineering," Craig S. Mullins explores the critical intersection of database administration and environmental sustainability, arguing that efficient data architecture is essential for reducing an organization's energy footprint. As data centers consume a significant portion of global electricity, DBAs must transition toward "carbon-aware" engineering by addressing "data sprawl"—the accumulation of unused tables and redundant records that inflate storage and cooling demands. The author emphasizes that fundamental practices like proper schema normalization, appropriate data typing, and rigorous index discipline are not just performance boosters but key drivers for energy conservation. Efficient SQL coding further reduces CPU cycles and I/O operations, directly cutting power usage. Furthermore, the shift toward cloud-native environments requires precise "right-sizing" to prevent energy waste from overprovisioned resources. By integrating these green principles into the architectural lifecycle, database engineers can align cost-effectiveness with corporate social responsibility. Ultimately, the piece posits that sustainable data management is rooted in disciplined engineering, where every optimized query and trimmed dataset contributes to a more ecologically responsible digital ecosystem without sacrificing growth or technical excellence.

What Africa’s shared data centres can teach the rest of EMEA

In the article "What Africa’s shared data centres can teach the rest of EMEA" on Data Centre Review, Ryan Holmes explores how African nations are leapfrogging traditional IT evolution by bypassing legacy infrastructure in favor of local, shared colocation platforms. As demand for AI-driven workloads and real-time processing surges, organizations across the continent are prioritizing proximity to minimize latency and ensure data sovereignty. This shift mirrors earlier technological breakthroughs like mobile money, allowing emerging markets to avoid the high costs and risks associated with self-managed enterprise servers or offshore hyperscale dependency. The author highlights that shared data centers offer a pragmatic solution for governments and businesses to meet strict residency regulations while maintaining high operational resilience. Furthermore, the absence of major hyperscalers in many African regions has fostered a robust ecosystem of professionally managed, carrier-neutral facilities that provide a cost-effective, opex-based alternative to capital-intensive builds. Ultimately, Africa’s move toward localized, resilient, and collaborative infrastructure provides a vital blueprint for the rest of EMEA, demonstrating that digital independence and performance are best achieved through partnership and strategic proximity rather than isolated ownership or total reliance on global giants.

Daily Tech Digest - December 25, 2025

Quote for the day:

"When I dare to be powerful - to use my strength in the service of my vision, then it becomes less and less important whether I am afraid." -- Audre Lorde

Declaring Quantum Christmas Advantage: How Quantum Computing Could Optimize The Holidays

If logistics is about moving stuff, gaming is about moving minds. And quantum computing’s influence here is more playful, at least for now. At the intersection of quantum and gaming, researchers are experimenting with quantum-inspired procedural content generation. Essentially, this is using hybrid quantum-classical approaches to generate game worlds, rules and narratives that are bigger and more complex than traditional methods allow. ... The holiday shopping season — part retail frenzy, part seasonal ritual and part absolute bottom-line need for business survival — is another area where quantum computing’s optimization chops could shine in a future-looking Christmas playbook. Retailers are beginning to explore how quantum optimization could help with workforce scheduling, inventory planning, dynamic pricing, and promotion planning, all classic holiday headaches for brick-and-mortar and online merchants alike, according to a D-Wave report. ... Finally, an esoteric — but perhaps way more festive — application of quantum tech would be using it for holiday analytics and personalization. Imagine real-time gift-recommendation engines that use quantum-accelerated models to process massive datasets instantly, teasing out patterns and preferences that help retailers suggest the perfect present for even the hardest-to-buy-for relative.

How Today’s Attackers Exploit the Growing Application Security Gap

Zero-day vulnerabilities in applications are quite common these days, even in well-supported and mature technologies. But most zero-days aren’t that fancy. Attackers regularly exploit some common errors developers make. A good resource to learn from about this is the OWASP Top 10, which was recently updated to cover the latest application security gaps. The main issue on the list is broken access controls, which happens when the application doesn’t properly enforce who can access what. In reality, this translates into bad actors being able to view or manipulate data and functionality they shouldn’t have access to. Next on the list are security misconfigurations. These are simple to tune, but given the vast number of environments, services, and cloud platforms most applications span, they are difficult to maintain at scale. A common example are exposed admin interfaces, which opens the door to credential-related attacks, particularly brute-forcing. Software supply chain failures add another layer of risk. Modern applications rely heavily on open-source libraries, APIs, packages, container images, and CI/CD components. Any of these can introduce vulnerabilities or malicious code into production. A single compromised dependency can impact thousands of downstream applications. For application developers and enthusiasts, it is highly recommended to study the entries in the OWASP Top 10, along with related OWASP lists such as the API Security Top 10 and emerging AI security guidance.

Data governance key to AI security

Cybersecurity was once built to respond. Today, the response alone is no longer enough. We believe security must be predictive, adaptive, and intelligent. This belief led to the creation of the Digital Vaccine, an evolution of Managed Security Services (MSSP) designed for an AI-first, quantum-ready world. "Much like a biological vaccine, Digital Vaccine continuously identifies new and unknown attack patterns, learns from every attempted breach, and builds defence mechanisms before damage occurs," he explained. The urgency is real, according to the experts, because post-quantum risks will soon render many of today's encryption methods ineffective, exposing sensitive data that was once considered secure. At the same time, AI-powered cyber threats are becoming autonomous, faster, and more targeted-operating at machine speed and scale. ... Almost every AI is built on data. "It is transforming data into knowledge. Once it is learned, we cannot remove it. So what is being fed into the data and LLModels? No governance policies exist as of today," pointed out Krishnadas. Cybersecurity was once built to respond. Today, the response alone is no longer enough. We believe security must be predictive, adaptive, and intelligent. This belief led to the creation of the Digital Vaccine, an evolution of Managed Security Services (MSSP) designed for an AI-first, quantum-ready world.

How the AI era is driving the resurgence in disaggregated storage

As AI workloads surge and accelerated computing takes the center stage, data center architectures and storage systems must keep pace with the increasing demand for memory and compute. Yet, the fast and ever-evolving high-performance computing (HPC) and AI systems have different requirements for the various IT infrastructure hardware components. While they require Central Processing Unit (CPU) and Graphic Processing Unit (GPU) nodes to be refreshed every couple of years to keep up with the AI workload demands, storage solutions like high-capacity HDDs come with longer warranties (up to five years), are therefore built to last several years longer, and don’t need to be refreshed as often. Based on this, more and more organizations are moving storage out of the server and embracing disaggregated infrastructures to avoid wasting resources. ... In the AI era and ZB age, IT leaders need more from their storage systems. They are looking for scalable, low-risk solutions that can evolve with them, delivering an optimized cost per Terabyte ($/TB), better energy-efficiency per TB (kW/TB), improved storage density, high-quality, and trust to perform at scale. Disaggregated storage can be a solution that offers precisely this flexibility of demand-driven scaling to meet the individual requirements of data center workloads and business needs. ... With disaggregated storage, enterprises can embrace AI and HPC while no longer being tethered to HCI architectures.

OpenAI admits prompt injection is here to stay as enterprises lag on defenses

OpenAI, the company deploying one of the most widely used AI agents, confirmed publicly that agent mode “expands the security threat surface” and that even sophisticated defenses can’t offer deterministic guarantees. For enterprises already running AI in production, this isn’t a revelation. It’s validation — and a signal that the gap between how AI is deployed and how it’s defended is no longer theoretical. None of this surprises anyone running AI in production. What concerns security leaders is the gap between this reality and enterprise readiness. ... OpenAI pushed significant responsibility back to enterprises and the users they support. It’s a long-standing pattern that security teams should recognize from cloud shared responsibility models. The company recommends explicitly using logged-out mode when the agent doesn't need access to authenticated sites. It advises carefully reviewing confirmation requests before the agent takes consequential actions like sending emails or completing purchases. And it warns against broad instructions. "Avoid overly broad prompts like 'review my emails and take whatever action is needed,'" OpenAI wrote. "Wide latitude makes it easier for hidden or malicious content to influence the agent, even when safeguards are in place." The implications are clear regarding agentic autonomy and its potential threats. The more independence you give an AI agent, the more attack surface you create.

The 3-Phase Framework for Turning a Cyberattack Into a Strategic Advantage

Typically, a lot of companies will panic and then look for a scapegoat when faced with a crisis. Maersk opted to realize that the root cause of the problem was not just a virus. Leaders accepted that they were bang average in terms of how they handled cybersecurity. The company also accepted that what happened may have been due to a cultural problem internally that needed to be fixed. While malware was a cause of issues, they also understood that their culture played a part, as security was seen as something that IT dealt with and not a core business thing. ... Maersk succeeded in strengthening customer trust and communication as it turned what could have been a defeat into a competitive advantage. Rather than trying to sugarcoat, they were very transparent and quickly informed customers of what was happening in the journey to recovery. Instead of telling customers, “we failed you,” they opted for a stance of “we are being tested, and we are in this together.” ... After a data disaster, your aim should not just be to recover, but you must also aim to build an “antifragile” organization that can come out stronger after a major challenge. An important step is to ensure that you fully internalize the lessons. When Maersk had to act, it did not just fix the problem. Instead, it embedded a new security system into its future planning. Accountability was added to all teams. Resilience should not just be something you aim for or use in a one-time project.

Leadership And The Simple Magic Of Getting Lost

There’s a part of the brain called the hippocampus that’s deeply tied to memory and spatial reasoning. It’s what helps us build internal maps of the world. It helps us recognize patterns, landmarks, distance and direction. It lights up when we have to figure things out for ourselves. When we follow turn-by-turn directions all the time, something subtle shifts. We’re not really navigating anymore. We’re just ... complying. It's efficient, yes. But also quieter, mentally. There’s growing concern among neuroscientists that when we outsource too much of this kind of thinking, we may be weakening one of the core systems tied to memory and long-term brain health. The research is still unfolding. Nothing is fully settled. But there’s enough there that it’s worth paying attention. Because the brain, like the body, works on a simple principle: Use it or lose it. ... This is why, every once in a while, I’ll let myself get a little lost on purpose. Not dangerously. Not recklessly. Just less optimized. I’ll take a different road. Walk through a neighborhood I don’t know. Let the uncertainty stretch a little. Let my brain build the map instead of borrowing one. This is the same skill we build in children when we’re teaching them how to find their way, but inside companies, it shows up as orientation. When you’re facing something unfamiliar—a new market, a hard strategic turn, a problem no one has quite named yet—your job isn’t to hand your team a route. It’s to give them landmarks: Here’s what we know. Here’s what can’t change.

Gen AI Paradox: Turning Legacy Code Into an Asset

Legacy modernization for decades was unglamorous and often postponed until the pain of technical debt surpassed the risks of migration. There is $2.41 trillion in technical debt in the United States alone. Seventy percent of workloads still run on-premises, and 70% of legacy IT software for Fortune 500 companies was developed over 20 years ago. ... It's not just about wishful thinking but is also driven by internal organizational dynamics. When we launched AWS Transform, after processing over a billion lines of code, we estimated it saved customers about 800,000 hours of manual work. But for a CIO, the true measure often relates to capacity. We observe organizations saving up to 80% in manual effort. This doesn't only mean cost reductions, but also avoiding the need to increase headcount for maintenance. For instance, I spoke with a technology leader managing a smaller team of about 200 people. His dilemma was: "Do I invest in building new functions, or do I maintain and modernize?" He told his team he wouldn't add a single person for modernization. They have to use tools to accomplish it. Using these tools, he completed a .NET transformation of 800,000 lines of code in two weeks, a project he estimated would typically take six months. The justification for the CIO is simple: save time and redirect 20% to 30% of the budget previously spent on tech debt toward innovation.

5 stages to observability maturity

The first requirement is coherence. Companies must move away from fragmented tooling and build unified telemetry pipelines capable of capturing logs, metrics, traces, and model signals in a consistent way. For many, this means embracing open standards such as OpenTelemetry and consolidating data sources so AI systems have a complete picture of the environment. ... The second requirement is business alignment. Enterprises that successfully evolve from monitoring to observability, and from observability to autonomous operations, do so because they learn to articulate the relationship between technical signals and business outcomes. Leaders want to understand not just the number of errors thrown by a microservice, but customers affected, the revenue at stake, or the SLA exposure if the issue persists. ... A third element is AI governance. As Nigam says, AI models change character over time, so observability must extend into the AI layer, providing real-time visibility into model behavior and early signs of instability. Companies that rely more heavily on AI must also accept a new operational responsibility to ensure the AI itself remains reliable, auditable, and secure. Finally, organizations must learn to construct guardrails for automation. Casanova and Woodside both say the shift to autonomous operations isn’t an overnight leap but a progressive widening of the boundary between what humans review and what machines handle automatically.

In the race to be AI-first, discipline matters more than speed

In an environment defined by uncertainty, economic volatility, cyber threats, supply-chain shocks, Srivastava believes resilience must be architected deliberately into the IT ecosystem. “We create an ecosystem that is so frugal that even if there are funding cuts or crisis situations, operations continue to run,” he explains. The objective is simple and uncompromising, the business must not stop. Digital initiatives may slow down, but the organisation itself should remain operational, regardless of external disruption. This focus on frugality is not about austerity. It is about discipline. “Resilience is not built when times are good,” Srivastava says. “It’s built when you assume disruption is inevitable.” ... Despite the complexity of modern IT stacks, Srivastava is unequivocal about where the real difficulty lies. “Technology is the easiest piece to crack,” he says. “Digital transformation is one of the most abused terms in the industry. Digital is easy. Transformation is hard.” Enterprises, he notes, are usually successful at acquiring tools, platforms, and licenses. “Everything that money can buy…tools, people, licenses…falls into place,” he says. What money cannot buy, however, is where transformation often breaks down to mindset shifts, adoption, ownership, and behavioural change. This challenge is particularly acute in manufacturing.

Daily Tech Digest - December 23, 2025

Quote for the day:

"What seems to us as bitter trials are often blessings in disguise." -- Oscar Wilde

The CIO Playbook: Reimagining Transformation in a Shifting Economy

The CIO has travelled from managing mainframes to managing meaning and purpose-driven transformation. And as AI becomes the nervous system of the enterprise, technology’s centre of gravity has shifted decisively to the boardroom. The basement may be gone, but its persona remains — a reminder that every evolution begins with resistance and is ultimately tamed by the quiet persistence of those who keep the systems running and the vision alive. Those who embraced progressive technology and blended business with innovation became leaders; the rest faded into also-rans. At the end of the day, the concern isn’t technology — it’s transformation capacity and the enterprise’s appetite to take risks, embrace change, and stay relevant. Organisations that lack this mindset will fail to evolve from traditional enterprises into intelligent, interactive digital ecosystems built for the AI age. The question remains: how do you paint the plane while flying it — and keep repainting it as customer needs, markets, and technologies shift mid-air? In this GenAI-driven era, the enterprise must think like software: in continuous integration, continuous delivery, and continuous learning. This isn’t about upgrading systems; it’s about rewiring strategy, culture, and leadership to respond in real time. We are at a defining inflection point. The time is now to connect the dots — to build an experience delivery matrix that not only works for your organisation but evolves with your customer.

Flexibility or Captivity? The Data Storage Decision Shaping Your AI Future

Enterprises today must walk a tightrope: on one side, harness the performance, trust, and synergies of long-standing storage vendor relationships; on the other, avoid entanglements that limit their ability to extract maximum value from their data, especially as AI makes rapid reuse of massive unstructured data sets a strategic necessity. ... Financial barriers also play a role. Opaque or punitive egress fees charged by many cloud providers can make it prohibitively expensive to move large volumes of data out of their environments. At the same time, workflows that depend on a vendor’s APIs, caching mechanisms, or specific interfaces can make even technically feasible migrations risky and disruptive. ... Budget and performance pressures add another layer of urgency. You can save tremendously by offloading cold data to lower-cost storage tiers. Yet if retrieving that data requires rehydration, metadata reconciliation, or funneling requests through proprietary gateways, the savings are quickly offset. Finally, the rapid evolution of technology means enterprises need flexibility to adopt new tools and services. Being locked into a single vendor makes it harder to pivot as the landscape changes. ... Longstanding vendor relationships often provide stability, support, and volume pricing discounts. Abandoning these partnerships entirely in the pursuit of perfect flexibility could undermine those benefits. The more pragmatic approach is to partner deeply while insisting on open standards and negotiating agreements that preserve data mobility.

Agentic AI already hinting at cybersecurity’s pending identity crisis

First, many of these efforts are effectively shadow IT, where a line of business (LOB) executive has authorized the proof of concept to see what these agents can do. In these cases, IT or cyber teams haven’t likely been involved, and so security hasn’t been a top priority for the POC. Second, many executives — including third-party business partners handling supply chain, distribution, or manufacturing — have historically cut corners for POCs because they are traditionally confined to sandboxes isolated from the enterprise’s live environments. But agentic systems don’t work that way. To test their capabilities, they typically need to be released into the general environment. The proper way to proceed is for every agent in your environment — whether IT authorized, LOB launched, or that of a third party — to be tracked and controlled by PKI identities from agentic authentication vendors. ... “Traditional authentication frameworks assume static identities and predictable request patterns. Autonomous agents create a new category of risk because they initiate actions independently, escalate behavior based on memory, and form new communication pathways on their own. The threat surface becomes dynamic, not static,” Khan says. “When agents update their own internal state, learn from prior interactions, or modify their role within a workflow, their identity from a security perspective changes over time. Most organizations are not prepared for agents whose capabilities and behavior evolve after authentication.”

Expanding Zero Trust to Critical Infrastructure: Meeting Evolving Threats and NERC CIP

StandardsPrevious compliance requirements have emphasized a perimeter defense model, leaving blind spots for any threats that happen to breach the perimeter. Zero Trust initiatives solve this by making accesses inside the perimeter visible and subjecting them to strong, identity-based policies. This proactive, Zero Trust-driven model naturally fulfills CIP-015-1 requirements, reducing or eliminating false positives compared to threat detection methods. In fact, an organization with a mature Zero Trust posture should be able to operate normally, even if the network is compromised. This resilience is possible when critical assets—such as controls in electrical substations or business software in the data center—are properly shielded from the shared network. Zero Trust enforces access based on verified identity, role, and context. Every connection is authenticated, authorized, encrypted, and logged. ... In short, Zero Trust’s identity-centric enforcement ensures that unauthorized network activity is detected and blocked. Even if a hacker has network access, they won’t be able to leverage that access to exfiltrate data or attack other hosts. A Zero Trust-protected organization can operate normally, even if the network is compromised. ... Zero Trust doesn’t replace your perimeter but instead reinforces it. Rather than replacing existing network firewalls, a Zero Trust can overlay existing security architectures, providing a comprehensive layer of defense through identity-based control and traffic visibility.

Top 5 enterprise tech priorities for 2026

The first is that the top priority, cited by 211 of the enterprises, is to “deploy the hardware, software, data, and network tools needed to optimize AI project value.” ... “You can’t totally immunize yourself against a massive cloud or Internet problem,” say planners. Most cloud outages, they note, resolve in a maximum of a few hours, so you can let some applications ride things out. When you know the “what,” you can look at the “how.” Is multi-cloud the best approach, or can you build out some capacity in the data center? ... “We have too many things to buy and to manage,” one planner said. “Too many sources, too many technologies.” Nobody thinks they can do some massive fork-lift restructuring (there’s no budget), but they do believe that current projects can be aligned to a long-term simplification strategy. This, interestingly, is seen by over a hundred of the group as reducing the number of vendors. They think that “lock-in” is a small price to pay for greater efficiency and reduction in operations complexity, integration, and fault isolation. ... The biggest problem, these enterprises say, is that governance has tended to be applied to projects at the planning level, meaning that absent major projects, governance tended to limp along based on aging reviews. Enterprises note that, like AI, orderly expansions in how applications and data are used can introduce governance issues, just like changes in laws and regulations.

Why flaky tests are increasing, and what you can do about it

One of the most persistent challenges is the lack of visibility into where flakiness originates. As build complexity rises, false positives or flaky tests often rise in tandem. In many organizations, CI remains a black box stitched together from multiple tools as artifact size increases. Failures may stem from unstable test code, misconfigured runners, dependency conflicts or resource contention, yet teams often lack the observability needed to pinpoint causes with confidence. Without clear visibility, debugging becomes guesswork and recurring failures become accepted as part of the process rather than issues to be resolved. The encouraging news is that high-performing teams are addressing this pattern directly. ... Better tooling alone will not solve the problem. organizations need to adopt a mindset that treats CI like production infrastructure. That means defining performance and reliability targets for test suites, setting alerts when flakiness rises above a threshold and reviewing pipeline health alongside feature metrics. It also means creating clear ownership over CI configuration and test stability so that flaky behaviour is not allowed to accumulate unchecked. ... Flaky tests may feel like a quality issue, but they are also a performance problem and a cultural one. They shape how developers perceive the reliability of their tools. They influence how quickly teams can ship. Most importantly, they determine whether CI/CD remains a source of confidence or becomes a source of drag.

Stop letting ‘urgent’ derail delivery. Manage interruptions proactively

As engineers and managers, we all have been interrupted by those unplanned, time-sensitive requests (or tasks) that arrive outside normal planning cadences. An “urgent” Slack, a last-minute requirement or an exec ask is enough to nuke your standard agile rituals. Apart from randomizing your sprint, it causes thrash for existing projects and leads to developer burnout. ... Existing team-level mechanisms like mid-sprint checkpoints provide teams the opportunity to “course correct”; however, many external randomizations arrive with an immediacy. ... Even well-triaged items can spiral into open-ended investigations and implementations that the team cannot afford. How do we manage that? Time-box it. Just a simple “we’ll execute for two days, then regroup” goes a long way in avoiding rabbit-holes. The randomization is for the team to manage, not for an individual. Teams should plan for handoffs as a normal part of supporting randomizations. Handoffs prevent bottlenecks, reduce burnout and keep the rest of the team moving. ... In cases where there are disagreements on priority, teams should not delay asking for leadership help. ... Without making it a heavy lift, teams should capture and periodically review health metrics. For our team, % unplanned work, interrupts per sprint, mean time to triage and periodic sentiment survey helped a lot. Teams should review these within their existing mechanisms (ex., sprint retrospectives) for trend analysis and adjustments.

How does Agentic AI enhance operational security

With Agentic AI, the deployment of automated security protocols becomes more contextual and responsive to immediate threats. The implementation of Agentic AI in cybersecurity environments involves continuous monitoring and assessment, ensuring that NHIs and their secrets remain fortified against evolving threats. ... Various industries have begun to recognize the strategic importance of integrating Agentic AI and NHI management into their security frameworks. Financial services, healthcare, travel, DevOps, and Security Operations Centers (SOC) have benefited from these technologies, especially those heavily reliant on cloud environments. In financial services, for instance, securing hybrid cloud environments is paramount to protecting sensitive client data. Healthcare institutions, with their vast troves of personal health information, have seen significant improvements in data protection through the use of these advanced cybersecurity measures. ... Agentic AI is reshaping how decisions are made in cybersecurity by offering algorithmic insights that enhance human judgment. Incorporating Agentic AI into cybersecurity operations provides the data-driven insights necessary for informed decision-making. Agentic AI’s capacity to process vast amounts of data at lightning speed means it can discern subtle signs of an impending threat long before a human analyst might notice. By providing detailed reports and forecasts, it offers decision-makers a 360-degree view of their security.

AI-fuelled cyber onslaught to hit critical systems by 2026

"Historically, operational technology cyber security incidents were the domain of nation states, or sometimes the act of a disgruntled insider. But recently, we've seen year-on-year rises in operational technology ransomware from criminal groups as well and with hacktivists: All major threat actor categories have bridged the IT-OT gap. With that comes a shift from highly targeted, strategic campaigns to the types of opportunistic attacks CISA describes. These are the predators targeting the slowest gazelles, so to speak," said Dankaart. ... Australian policymakers are expected to revise cybersecurity legislation and regulations for critical sectors. Morris added that organisations are looking at overseas case studies to reduce fraud and infrastructure-level attacks. ... "The scam ecosystem will continue to be exposed globally, raising new awareness of the many aspects of these crimes, including payment processors, geographic distribution of call centres and connected financial crimes. ... "The solution will be to find the 'Goldilocks Spot' of high automation and human accountability, where AI aggregates related tasks, alerts and presents them as a single decision point for a human to make. Humans then make one accountable, auditable policy decision rather than hundreds to thousands of potentially inconsistent individual choices; maintaining human oversight while still leveraging AI's capacity for comprehensive, consistent work."

Rising Tides: When Cybersecurity Becomes Personal – Inside the Work of an OSINT Investigator

The upside of all the technology and access we have is also what creates so much risk in the multitude of dangerous situations that Miller has seen and helped people out of in the most efficient and least disruptive ways possible. But, we as a cyber community have to help, but building ethics and integrity into our products so they can be used less maliciously in human cases; not simply data cases. ... When everything complicated is failing, go back to basics, and teach them over and over again, until the audience moves forward. I’ve spent a decade doing this and still share the same basic principles and safety measures. Technology changes, so do people, but sometimes the things they need the most are to to be seen, heard and understood. This job is a lot of emotional support and working through the things where the client gets hung up making a decision, or moving forward. ... The amount of energy and time devoted to cases has to have a balance. I say no to more cases than I say yes, simply because I don’t have the resources or time to do them. ... As the world changes, you have to adapt and shift your tactics, delivery, and capabilities to help more people. While people like to tussle over politics, I remind them, everything is political. It’s no different in community care, mutual aid, or non-profit work. If systems cannot or won’t support communities, you have a responsibility to help build parallel systems of care that can. This means not leaving anyone behind, not sacrificing one group over another.