Daily Tech Digest by Kannan Subbiah: chaos engineering

Showing posts with label chaos engineering. Show all posts

Daily Tech Digest - May 10, 2026

Quote for the day:

"Disengagement is a failure of biology — not motivation. Our brains are hardwired to avoid anything we think will fail. Change the environment. The biology follows." -- Gordon Tredgold

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Intent-based chaos testing is designed for when AI behaves confidently — and wrongly

The VentureBeat article by Sayali Patil addresses a critical reliability gap in autonomous AI systems, where agents often perform with high confidence but produce fundamentally incorrect outcomes. Traditional observability metrics like uptime and latency fail to capture these silent failures because the systems appear operationally healthy while being behaviorally compromised. To combat this, Patil introduces intent-based chaos testing, a framework focused on measuring deviation from intended behavioral boundaries rather than simple success or failure. Central to this approach is the intent deviation score, which quantifies how far an agent's actions drift from its baseline purpose. The testing methodology follows a rigorous four-phase structure: starting with single tool degradation to test adaptation, followed by context poisoning to challenge data integrity and escalation logic. The third phase examines multi-agent interference to surface emergent conflicts from overlapping autonomous entities, while the final phase utilizes composite failures to simulate the complex entropy of actual production environments. By intentionally injecting chaos into behavioral logic rather than just infrastructure, enterprise architects can identify dangerous blast radii before deployment. This paradigm shift ensures that AI agents remain aligned with human intent even when facing real-world unpredictability, ultimately transforming how organizations validate the trustworthiness and safety of their sophisticated, agentic AI infrastructure.

Unlocking Cloud Modernization: Strategies Every CIO Needs for Agility, Security, and Scale

The article "Unlocking Cloud Modernization: Strategies Every CIO Needs for Agility, Security, and Scale" emphasizes that in 2026, cloud modernization has transitioned from a secondary long-term goal to a critical business priority. As enterprises accelerate their adoption of artificial intelligence and data automation, traditional IT infrastructures often struggle to provide the necessary speed, scalability, and operational resilience. To address these mounting limitations, CIOs are urged to implement strategic transformation roadmaps that reshape legacy environments into agile, secure, and AI-ready ecosystems. Key strategies highlighted include adopting hybrid and multi-cloud architectures to avoid vendor lock-in, incrementally modernizing legacy applications through containerization, and strengthening security via Zero Trust models. Furthermore, the article stresses the importance of automating complex operations using Infrastructure as Code and optimizing expenditures through FinOps practices. Effective modernization not only reduces technical debt and infrastructure complexity but also significantly enhances innovation cycles. By prioritizing business-aligned strategies and building AI-supporting architectures, organizations can better respond to market shifts and deliver superior digital experiences to customers. Ultimately, a phased approach allows leaders to balance innovation with stability, ensuring that modernization supports long-term digital growth while maintaining robust governance across increasingly distributed and multi-faceted cloud environments.

The CIO succession gap nobody admits

In the insightful article "The CIO succession gap nobody admits," Scott Smeester explores a critical leadership crisis where many seasoned CIOs find themselves unable to leave their roles because they lack a viable internal successor. This "succession gap" primarily stems from the "architect trap," where CIOs promote deputies based on technical brilliance and operational reliability rather than the requisite executive leadership skills. Consequently, these trusted deputies often excel at managing complex platforms but struggle with broader P&L ownership, boardroom politics, and high-stakes financial negotiations. To bridge this divide, Smeester proposes three proactive design choices for modern IT leadership. First, CIOs should grant deputies authority over specific decision domains, such as vendor escalations, to build genuine professional judgment. Second, they must stop shielding high-potential talent from conflict, allowing them to defend budgets and strategies against peer executives. Finally, the board must be introduced to these deputies early through substantive presentations to build credibility long before a vacancy occurs. Failing to address this gap results in stalled digital transformations, expensive external hires, and the loss of talented staff who feel overlooked. Ultimately, a true succession plan is not just a list of names but a deliberate developmental pipeline that prepares future leaders to step into the boardroom with confidence and authority.

Cyber Regulation Made Us More Auditable. Did It Make Us More Defensible?

In his article, Thian Chin explores the critical disconnect between cybersecurity auditability and actual defensibility, arguing that while decades of regulation and frameworks like ISO 27001 have successfully "raised the floor" for organizational governance, they have failed to guarantee operational resilience. Chin highlights a systemic issue where the industry prioritizes documenting the existence of controls over verifying their effectiveness against real-world adversaries. Evidence from threat-led testing programs like the Bank of England’s CBEST reveals that even heavily supervised financial institutions often succumb to foundational hygiene failures, such as unpatched systems and weak identity management, despite being certified as compliant. This gap persists because traditional assurance models reward countable artifacts rather than actual security outcomes, leading to "audit fatigue" and a false sense of safety. To address this, Chin advocates for a transition toward outcome-based and threat-informed regulatory architectures, such as the UK’s Cyber Assessment Framework (CAF) and the EU’s DORA. These modern approaches treat certification merely as a baseline rather than the ultimate proof of security. Ultimately, the article challenges practitioners and regulators to stop confusing the documentation of a control with the successful defense of a system, insisting that future cyber regulation must demand rigorous evidence that security measures can withstand genuine adversarial pressure.

TCLBANKER Banking Trojan Targets Financial Platforms via WhatsApp and Outlook Worms

TCLBANKER is a sophisticated Brazilian banking trojan recently identified by Elastic Security Labs, representing a significant evolution of the Maverick and SORVEPOTEL malware families. Targeting approximately 59 financial, fintech, and cryptocurrency platforms, the malware is primarily distributed via trojanized MSI installers disguised as legitimate Logitech software through DLL side-loading techniques. At its core, the threat employs a multi-modular architecture featuring a full-featured banking trojan and a self-propagating worm component. The banking module monitors browser activities using UI Automation to detect financial sessions, while the worm leverages hijacked WhatsApp Web sessions and Microsoft Outlook accounts to spread malicious payloads to thousands of contacts. This distribution model is particularly effective as it originates from trusted accounts, bypassing traditional email gateways and reputation-based security defenses. Furthermore, TCLBANKER exhibits advanced anti-analysis techniques, including environment-gated decryption that ensures the payload only executes on systems matching specific Brazilian locale fingerprints. If analysis tools or debuggers are detected, the malware fails to decrypt, effectively shielding its operations from security researchers. By utilizing real-time social engineering through WPF-based full-screen overlays and WebSocket-driven command loops, the operators can manipulate victims and facilitate fraudulent transactions while remaining hidden. This maturation of Brazilian crimeware highlights a growing trend of adopting sophisticated techniques once reserved for advanced persistent threats.

The Best Risk Mitigation Strategy in Data? A Single Source of Truth

Jeremy Arendt’s article on O’Reilly Radar posits that establishing a "Single Source of Truth" (SSOT) serves as the preeminent strategy for mitigating modern organizational data risks. In today’s increasingly complex digital landscape, information is frequently scattered across disparate systems, creating isolated data silos that foster inconsistency, internal friction, and "multiple versions of reality." Arendt argues that these silos introduce significant operational and strategic hazards, as different departments often rely on conflicting metrics to drive their decision-making processes. By implementing an SSOT, organizations can ensure that every stakeholder accesses a unified, high-fidelity dataset, effectively eliminating discrepancies that undermine executive trust. This centralization is not merely a storage solution; it is a fundamental governance framework that simplifies regulatory compliance, enhances cybersecurity, and guarantees long-term data integrity. Furthermore, a single source of truth serves as a critical prerequisite for successful artificial intelligence and machine learning initiatives, providing the reliable, high-quality data foundation necessary for accurate model training and deployment. Ultimately, this architectural approach reduces technical debt and operational overhead while fostering a corporate culture of transparency. By prioritizing a consolidated data platform, companies can shield themselves from the financial and reputational dangers of misinformation, ensuring their strategic maneuvers are grounded in verified facts rather than fragmented interpretations.

Boards Are Falling Short on Cybersecurity

The article "Boards Are Falling Short on Cybersecurity" examines why corporate boards, despite increased investment and focus, are struggling to effectively govern and mitigate cyber risks. According to the research, which includes interviews with over 75 directors, three primary factors drive this deficiency. First, there is a pervasive lack of cybersecurity expertise among board members; a study revealed that only a tiny fraction of directors on cybersecurity committees possess formal training or relevant practical experience. Second, while boards are enthusiastic about artificial intelligence, their conversations typically prioritize strategic gains like operational efficiency while neglecting the significant security vulnerabilities AI introduces, such as automated malware generation. Third, boards often conflate regulatory compliance with actual security, spending excessive time on box checking and dashboards that offer marginal value in protecting against sophisticated threats. To address these gaps, the authors suggest that boards must shift from a reactive to a proactive stance, integrating cybersecurity into the very foundation of product development and brand strategy. By treating security as a core business driver rather than a back-office bureaucratic hurdle, organizations can better protect their reputations and operational integrity in an era where cybercrime losses continue to escalate sharply year over year. Finally, the authors emphasize that FBI data reveals a surge in losses, underscoring the need for improved oversight.

Giving Up Should Never Be An Option: Why Persistence Is The Ultimate Key To Success

The article "Giving Up Should Never Be An Option: Why Persistence Is The Ultimate Key To Success" centers on a transformative personal narrative that illustrates the critical role of endurance in achieving professional milestones. The author recounts a grueling experience as a door-to-door salesperson, facing six consecutive days of rejection and failure amidst harsh, snowy conditions. Rather than yielding to the urge to quit, the author approached the seventh day with renewed focus and a meticulously planned strategy. After knocking on nearly one hundred doors without success, the final attempt of the evening resulted in a breakthrough sale that fundamentally shifted their career trajectory. This pivotal moment proved that persistence, rather than raw talent alone, acts as the ultimate catalyst for progress. The experience served as a foundational training ground, eventually leading to rapid promotions, increased confidence, and significant corporate benefits. By reflecting on this "seventh day," the author argues that many individuals abandon their goals when they are mere inches away from a breakthrough. The core message serves as a powerful mantra for modern business leaders: success becomes an inevitability when one commits unwavering belief and effort to their objectives, especially when circumstances are at their absolute worst.

Anthropic's Claude Mythos: how can security leaders prepare?

Anthropic’s release of the Claude Mythos Preview System Card has signaled a transformative shift in the cybersecurity landscape, compelling security leaders to rethink their defensive strategies. This advanced AI model demonstrates a sophisticated ability to autonomously identify software vulnerabilities and develop exploit chains, significantly lowering the barrier for cyberattacks. According to the article, the cost of weaponizing exploits has plummeted to mere dollars, while the timeline from discovery to exploitation has collapsed from days to hours. To prepare for this accelerated threat environment, Melissa Bischoping argues that security professionals must prioritize wall-to-wall visibility across all cloud, on-premise, and remote endpoints. The piece emphasizes that manual remediation workflows are no longer sufficient; instead, organizations should adopt real-time threat exposure management and maintain continuous, SBOM-grade inventories to keep pace with AI-driven discovery cycles. Furthermore, the summary underscores that while Mythos enhances offensive capabilities, traditional hygiene—specifically the "Essential Eight" controls like multi-factor authentication and rigorous patching—remains effective against even the most powerful frontier models if implemented with precision. Ultimately, the article serves as a call to action for leaders to close the exposure-to-remediation loop before adversaries can leverage AI to exploit emerging zero-day vulnerabilities, shifting from predictive models to real-time verification and rapid response.

How the evolution of blockchain is changing our ideas about trust

The article "How the evolution of blockchain is changing our ideas about trust" by Viraj Nair explores the transformation of trust mechanisms from the 2008 financial crisis to the modern era. Initially, Satoshi Nakamoto’s Bitcoin white paper introduced a radical alternative to failing central institutions by engineering trust through a "proof of work" consensus model, which favored decentralized network validation over delegated institutional authority. However, this first generation was energy-intensive, leading to a second evolution: "proof of stake." Popularized by Ethereum’s 2022 transition, this model drastically reduced energy consumption but shifted influence toward asset ownership. A third phase, "proof of authority," has since emerged, utilizing pre-approved, reputable validators to prioritize speed and accountability for real-world applications like supply chains and government transactions in Brazil and the UAE. Far from eliminating the need for trust, blockchain technology has reconfigured it into a more nuanced framework. While it began as a way to bypass traditional intermediaries, its current trajectory suggests a hybrid future where trust is distributed across a collaborative ecosystem of banks, technology firms, and governments. Ultimately, the evolution of blockchain demonstrates that while the methods of verification change, the fundamental necessity of trust remains, now bolstered by unprecedented traceability and auditability.

Daily Tech Digest - March 22, 2026

Quote for the day:

“Success does not consist in never making mistakes but in never making the same one a second time.” -- George Bernard Shaw

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Data Readiness as a Product

In "Data Readiness as a Product," argues that preparing data for AI agents is not a one-time project but a continuous product capability requiring dedicated ownership, strict SLAs, and rigorous quality gates. He highlights that most AI failures are operational, rooted in "" and a fundamental "" where literal-minded agents misinterpret contextually noisy information. A critical distinction is made between static "Knowledge" (best handled via ) and dynamic "State" (requiring real-time APIs); confusing the two often leads to costly, inaccurate outputs. Deudney advocates for "" to resolve systemic ownership conflicts and stresses the importance of codifying specific tie-breaking rules, as agents cannot inherently recognize when they are guessing between conflicting sources. Robust metadata—including provenance, versioning, and time-to-live (TTL) tags—is presented as essential for maintaining an auditable, trustworthy system. Ultimately, the piece asserts that because data quality directly dictates agent behavior, organizations must prioritize resolving their underlying data architecture before deployment. By treating data readiness as a living, evolving product rather than a static foundation, businesses can avoid the "zombie data" and semantic ambiguities that typically derail complex automation efforts.

The inference lattice: One option for how the AI factory model will evolve

The article "The Inference Lattice: One option for how the AI factory model will evolve" explores the necessary architectural shift in data centers as they transition from general-purpose facilities into specialized "AI factories." Currently, the industry relies on a centralized model dominated by massive training clusters; however, the author argues that the future of AI scalability lies in the "Inference Lattice." This concept envisions a distributed, interconnected network of smaller, highly efficient inference nodes that move computation closer to the end-user and data sources. By deconstructing monolithic data center designs into a more fluid and resilient lattice, providers can better manage the extreme power demands and heat densities associated with next-generation GPUs. The piece highlights that while training remains computationally intensive, the vast majority of future AI workloads will be dedicated to inference. To support this, the lattice model offers a way to scale horizontally, reducing latency and improving cost-effectiveness. Ultimately, the article suggests that the evolution of the AI factory will be defined by this move toward decentralized, purpose-built infrastructure that prioritizes the continuous, real-time delivery of "intelligence" over the raw batch processing of the past.

App Modernization in Regulated Industries: Audit Trails, Approvals, and Release Control

Application modernization within regulated sectors like healthcare and finance transcends mere aesthetic updates, prioritizing robust audit trails, orderly approvals, and verifiable release controls. As legacy systems often persist due to familiar manual compliance habits, modernizing these platforms requires a shift from feature-focused development to mapping "regulatory promises." This ensures that record retention, separation of duties, and data access remain provable throughout the transition. Effective modernization replaces fragmented manual processes with integrated digital narratives that capture the "who, what, when, and why" of every action in searchable, tamper-proof logs. Furthermore, the article emphasizes that approval workflows should be risk-stratified—automating low-risk updates while maintaining rigorous sign-offs for high-impact changes—to prevent compliance from becoming a bottleneck. By treating logging and release management as foundational components rather than afterthoughts, organizations can achieve greater agility without compromising safety or regulatory standing. Ultimately, a successful modernization strategy builds a transparent, connected ecosystem where every software version is linked to its specific approvals and intent. This holistic approach allows regulated firms to ship updates confidently, maintain continuous audit readiness, and eliminate the frantic scramble typically associated with formal inspections and technical oversight.

Agentic Architecture Maturity Model (AAMM) How AI Agents Are Redefining Architectural Intelligence

The "Agentic Architecture Maturity Model (AAMM): How AI Agents Are Redefining Architectural Intelligence" article explores a transformative framework designed to modernize enterprise architecture through the integration of autonomous AI agents. The AAMM identifies five levels of maturity, progressing from unmanaged, tribal knowledge to a state of autonomous architecture intelligence where AI systems continuously simulate and optimize the organizational landscape. By moving through stages of formal documentation and structured traceability, enterprises can reach level four, where AI agents actively participate in design reviews and governance, and level five, where they orchestrate complex architectural decisions autonomously. The article highlights critical structural gaps that hinder this evolution, such as documentation drift and the "impact analysis bottleneck," emphasizing that traditional manual governance cannot scale with modern delivery speeds. To bridge these gaps, the author advocates for leveraging emerging technologies like , graph-native enterprise architecture platforms, and architecture-as-code. Ultimately, the AAMM serves as a strategic roadmap for leaders to transition architecture from a passive record-keeping function into a high-leverage, intelligent capability that drives faster transformations, reduces technical debt, and ensures long-term organizational resilience in an increasingly complex digital era.

The Gap Between Buying Security and Actually Having It

The TechSpective article explores the critical discrepancy between investing in tools and achieving genuine protection, often termed the "capability gap." Despite eighty percent of organizations increasing their security budgets for 2026, research from Kroll indicates that a staggering seventy-two percent still face misalignment between security priorities and actual business operations. This disconnect stems from a "know-what-you-have" problem, where organizations purchase high-end technology but fail to configure it according to best practices or account for "security drift" as environments evolve. While executives often favor new technology investments for their optics in board presentations, they frequently deprioritize essential validation activities like red and purple teaming. Consequently, while many firms believe they can respond to incidents within twenty-four hours, actual attacker breakout times are often under thirty minutes. The article highlights that high-maturity organizations—comprising only ten percent of those surveyed—distinguish themselves not by higher spending, but by allocating significant resources toward testing and confirming that their existing controls actually work. Ultimately, the piece warns that without bridging the gap between deployment and validation, especially as AI accelerates emerging threats, the multi-million dollar potential of security tools remains largely unfulfilled and organizations remain vulnerable.

The AI Dilemma: Leadership in the Age of Intelligent Threats

The article "The AI Dilemma: Leadership in the Age of Intelligent Threats" highlights the critical shift of artificial intelligence from an experimental tool to a central executive priority by 2026. While AI offers transformative benefits for cybersecurity, such as automated security operations centers and accelerated threat detection, it simultaneously empowers adversaries through deepfake-enabled fraud, adaptive malware, and automated vulnerability scanning. This "double-edged sword" necessitates a leadership evolution that matches machine speed with governance maturity. Internally, the rise of "vibe coding" and unsanctioned "shadow AI" usage creates significant risks, requiring organizations to implement structured oversight and clear data-sharing practices. To navigate this landscape, leaders must adopt a "" model, ensuring that machine pattern recognition is always augmented by human context and ethical judgment. Strategic imperatives include embracing AI for defense responsibly, enhancing continuous monitoring through zero-trust architectures, and updating corporate policies to address AI-specific threats. Ultimately, the article argues that while the future of cybersecurity may resemble an AI-versus-AI contest, organizational success will depend on balancing rapid innovation with disciplined governance. Human oversight remains the foundational element for maintaining security and resilience in an increasingly automated and intelligent threat environment.

Why Agentic AI Demands Intent-Based Chaos Engineering

The DZone article "Why Agentic AI Demands Intent-Based Chaos Engineering" explores the evolution of system resilience in the era of autonomous software. Traditional chaos engineering, which relies on static fault injection like latency or server shutdowns, proves inadequate for AI-driven environments where failures often manifest as subtle quality degradations rather than visible outages. To address this, the author introduces Intent-Based Chaos Engineering, a framework where failure magnitude is derived from environmental risk and business sensitivity. This approach evaluates three critical dimensions: intent parameters (such as SLA thresholds and business criticality), topology data (mapping service dependencies), and a sensitivity index (measuring how components influence inference quality). As AI systems transition toward agentic autonomy—where agents independently trigger remediation, scale infrastructure, and rebalance traffic—the risk of minor disturbances spiraling into systemic instability through automated decision loops increases significantly. By shifting from reactive experimentation to a closed-loop, predictive modeling system, Intent-Based Chaos provides the calibrated stress needed to validate these autonomous agents. Ultimately, this methodology ensures that as AI systems become more complex and independent, their resilience remains grounded in controlled, goal-oriented experimentation, protecting enterprise-scale operations from the unpredictable nature of silent AI degradation.

Cloud at 20: Cost, complexity, and control

As cloud computing reaches its twentieth anniversary, the initial promise of seamless, cost-effective IT has evolved into a sobering landscape of managed complexity. Originally envisioned as a way to reduce overhead through simple pay-as-you-go models, the reality for modern enterprises involves spiraling costs that often eclipse the traditional infrastructure they were meant to replace. This financial strain is compounded by "cloud sprawl," where thousands of workloads across multiple regions create a lack of transparency and unpredictable billing. Beyond economics, the technical promise of outsourcing security and operations has shifted into a new paradigm of operational difficulty. Instead of eliminating IT headaches, the cloud has introduced a "multicloud reality" requiring specialized skills to manage intricate permissions, encryption keys, and interoperability issues across diverse platforms. Consequently, the next era of cloud computing will focus less on the fantasy of total outsourcing and more on rigorous FinOps discipline, continuous security investment, and the strategic orchestration of complex environments. Ultimately, the journey has transformed from a sprint toward simplicity into a marathon of governance, where the goal is no longer to eliminate complexity but to master it through automation and expert oversight.

Digital Banking Experience: A Good Fit for Techfin Firms

The appointment of , former digital banking head at State Bank of India, as CEO of underscores a significant leadership shift within the financial services sector. As digital banking platforms like evolve into multifaceted ecosystems encompassing payments, lending, and commerce, the executives behind them are increasingly sought after by TechFin firms. These leaders possess a unique blend of product strategy, platform governance, and regulatory expertise, which is essential for companies providing critical financial infrastructure. TechFin organizations, such as Perfios, are transitioning from being mere tool providers to becoming embedded operational layers for banks and insurers. Their focus areas—including financial data aggregation, credit decisioning, and fraud intelligence—require a deep understanding of how to operationalize technology at scale within strictly regulated environments. Furthermore, the integration of artificial intelligence is revolutionizing these services by enhancing the speed and quality of financial decision-making. This convergence of banking and technology reflects a broader trend where technology leadership is no longer just about execution but about driving digital business growth and ecosystem partnerships. Consequently, the demand for CEOs who can navigate the intersection of traditional finance and enterprise software continues to rise.

AI Governance Moves From Boardrooms To Business Strategy

The Inc42 report, "AI Governance Moves from Boardrooms to Business Strategy," explores a fundamental shift in how Indian enterprises and startups perceive artificial intelligence oversight. Historically treated as a passive compliance matter for boardrooms, has now transitioned into a pivotal pillar of core business strategy. This evolution is fueled by the realization that trust, transparency, and accountability serve as critical "moats" for companies looking to scale AI beyond initial pilot phases into high-impact, enterprise-wide workflows. The report highlights how robust governance frameworks are being integrated directly into operational roadmaps to mitigate risks such as algorithmic bias and data privacy breaches while simultaneously driving long-term ROI. As India transitions into an AI-first economy, the discourse is moving toward the "monetization depth" of AI, where reliable and explainable models are essential for customer retention and market differentiation. By embedding safety and ethical considerations from the outset, businesses are not only complying with emerging national guidelines but are also positioning themselves as resilient leaders in a globally competitive landscape. Ultimately, the report emphasizes that mature AI governance is no longer a professional development goal but a strategic prerequisite for sustainable growth in the modern corporate ecosystem.

Daily Tech Digest - February 08, 2026

Quote for the day:

"The litmus test for our success as Leaders is not how many people we are leading, but how many we are transforming into leaders" -- Kayode Fayemi

Why agentic AI and unified commerce will define ecommerce in 2026

Agentic AI and unified commerce are set to shape ecommerce in 2026 because the foundations are now in place: consumers are increasingly comfortable using AI tools, and retailers are under pressure to operate seamlessly across channels. ... When inventory, orders, pricing, and customer context live in disconnected systems, both humans and AI struggle to deliver consistent experiences. When those systems are unified, retailers can enable more reliable automation, better availability promises, and more resilient fulfillment, especially at peak. ... Unified commerce platforms matter because they provide a single operational framework for inventory, orders, pricing, and customer context. That coordination is increasingly critical as more interactions become automated or AI-assisted. ... The shift toward “agentic” happens when AI can safely take actions, like resolving a customer service step, updating a product feed, or proposing a replenishment recommendation, based on reliable data and explicit rules. That’s why unified commerce matters: it reduces the risk of automation acting on partial truth. Because ROI varies dramatically by category, maturity, and data quality, it’s safer to avoid generic percentage claims. The defensible message is: companies that pair AI with clean operational data and clear governance will unlock automation faster and with fewer reputational risks. ... Ultimately, success in 2026 will not be defined by how many AI features a retailer deploys, but by how well their systems can interpret context, act reliably, and scale under pressure.

EU's Digital Sovereignty Depends On Investment In Open-Source And Talent

We argue that Europe must think differently and invest where it matters, leveraging its strengths, and open technologies are the place to look. While Europe does not have the tech giants of the US and China, it possesses a huge pool of innovation and human capital, as well as a small army of capable and efficient technology service providers, start-ups, and SMEs. ... Recent data shows that while Europe accounts for a substantial share of global open source developers, its contribution to open source-derived infrastructure remains fragmented across countries, with development being concentrated in a small number of countries. ... Europe may not have a Silicon Valley, but it has something better: a robust open source workforce. We are beginning to recognize this through fora such as the recent European Open Source Awards, which celebrated European citizens and residents working on things ranging from the Linux kernel and open office suites to open hardware and software preservation. ... Europe has a chance of succeeding. Historically, Europe has done a good job in making open source and open standards a matter of public policy. For example, the European Commission's DG DIGIT has an open source software strategy which is being renewed this year, and Europe possesses three European Standards Organizations, including CEN, CENELEC, and ETSI. While China has an open source software strategy, Europe is arguably leading the US in harnessing the potential of open technologies as a matter of public and industrial policy, and it has a strong foundation for catching up to China.

Is artificial general intelligence already here? A new case that today's LLMs meet key tests

Approaching the AGI question from different disciplinary perspectives—philosophy, machine learning, linguistics, and cognitive science—the four scholars converged on a controversial conclusion: by reasonable standards, current large language models (LLMs) already constitute AGI. Their argument addresses three key questions: What is general intelligence? Why does this conclusion provoke such strong reactions? And what does it mean for ... "There is a common misconception that AGI must be perfect—knowing everything, solving every problem—but no individual human can do that," explains Chen, who is lead author. "The debate often conflates general intelligence with superintelligence. The real question is whether LLMs display the flexible, general competence characteristic of human thought. Our conclusion: insofar as individual humans possess general intelligence, current LLMs do too." ... "This is an emotionally charged topic because it challenges human exceptionalism and our standing as being uniquely intelligent," says Belkin. "Copernicus displaced humans from the center of the universe, Darwin displaced humans from a privileged place in nature; now we are contending with the prospect that there are more kinds of minds than we had previously entertained." ... "We're developing AI systems that can dramatically impact the world without being mediated through a human and this raises a host of challenging ethical, societal, and psychological questions," explains Danks.

Biometrics deployments at scale need transparency to help businesses, gain trust

As adoption invites scrutiny, more biometrics evaluations, completed assessments and testing options come available. Communication is part of the same issue, with major projects like EES, U.S. immigration and protest enforcement, and more pedestrian applications like access control and mDLs all taking off. ... Biometric physical access control is growing everywhere, but with some key sectorial and regional differences, Goode Intelligence Chief Analyst Alan Goode explains in a preview of his firm’s latest market research report on the latest episode of the Biometric Update Podcast. Imprivata could soon be on the market, with PE owner Thoma Bravo working with JPMorgan and Evercore to begin exploring its options. ... A panel at the “Identity, Authentication, and the Road Ahead 2026” event looked at NIST’s work on a playbook to help businesses implement mDLs. Representatives from the NCCoE, Better Identity Coalition, PNC Bank and AAMVA discussed the emerging situation, in which digital verifiable credentials are available, but people don’t know how to use them. ... DHS S&T found 5 of 16 selfie biometrics providers met the performance goals of its Remote Identity Validation Rally, Shufti and Paravision among them. RIVR’s first phase showed that demographically similar imposters still pose a significant problem for many face biometrics developers.

The Invisible Labor Force Powering AI

A low-cost labor force is essential to how today’s AI models function. Human workers are needed at every stage of AI production for tasks like creating and annotating data, reinforcing models, and moderating content. “Today’s frontier models are not self-made. They’re socio-technical systems whose quality and safety hinge on human labor,” said Mark Graham, a professor at the University of Oxford Internet Institute and a director of the Fairwork project, which evaluates digital labor platforms. In his book Feeding the Machine: the Hidden Human Labor Powering AI (Bloomsbury, 2024), Graham and his co-authors illustrate that this global workforce is essential to making these systems usable. “Without an ongoing, large human-in-the-loop layer, current capabilities would be far more brittle and misaligned, especially on safety-critical or culturally sensitive tasks,” Graham said. ... The industry’s reliance on a distributed, gig-work model goes back years. Hung points to the creation of the ImageNet database around 2007 as the moment that set the referential data practices and work organization for modern AI training. ... However, cost is not the only factor. Graham noted that cost arbitrage plays a role, but it is not the whole explanation. AI labs, he said, need extreme scale and elasticity, meaning millions of small, episodic tasks that can be staffed up or down at short notice, as well as broad linguistic and cultural coverage that no single in-house team can reproduce.

Code smells for AI agents: Q&A with Eno Reyes of Factory

In order to build a good agent, you have to have one that's model agnostic. It needs to be deployable in any environment, any OS, any IDE. A lot of the tools out there force you to make a hard trade off that we felt wasn't necessary. You either have to vendor lock yourself to one LLM or ask everyone at your company to switch IDEs. To build like a true model agnostic, vendor agnostic coding agent, you put in a bunch of time and effort to figure out all the harness engineering that's necessary to make that succeed, which we think is a fairly different skillset from building models. And so that's why we think companies like us actually are able to build agents that outperform on most evaluations from our lab. ... All LLMs have context limits so you have to manage that as the agent progresses through tasks that may take as long as eight to ten hours of continuous work. There are things like how you choose to instruct or inject environment information. It's how you handle tool calls. The sum of all of these things requires attention to detail. There really is no individual secret. Which is also why we think companies like us can actually do this. It's the sum of hundreds of little optimizations. The industrial process of building these harnesses is what we think is interesting or differentiated. ... Of course end-to-end and unit tests. There are auto formatters that you can bring in, SaaS static application security testers and scanners: your sneaks of the world.

Software-Defined Vehicles Transform Auto Industry With Four-Stage Maturity Framework For Engineers

More refined software architectures in both edge and cloud enable the interpretation of real-time data for predictive maintenance, adaptive user interfaces, and autonomous driving functions, while cloud-based AI virtualized development systems enable continuous learning and updates. Electrification has only further accelerated this evolution as it opened the door for tech players from other industries to enter the automotive market. This represents an unstoppable trend as customers now expect the same seamless digital experiences they enjoy on other devices. ... Legacy vehicle systems rely on dozens of electronic control units (ECUs), each managing isolated functions, such as powertrain or infotainment systems. SDVs consolidate these functions into centralized compute domains connected by high-speed networks. This architecture provides hardware and software abstraction, enabling OTA updates, seamless cross-domain feature integration, and real-time data sharing, are essential for continuous innovation. ... Processing sensor data at the edge – directly within the vehicle – enables highly personalized experiences for drivers and passengers. It also supports predictive maintenance, allowing vehicles to anticipate mechanical issues before they occur and proactively schedule service to minimize downtime and improve reliability. Equally important are abstraction layers that decouple software applications from underlying hardware.

Cybersecurity and Privacy Risks in Brain-Computer Interfaces and Neurotechnology

Neuromorphic computing is developing faster than predicted by replicating the human brain's neural architecture for efficient, low-power AI computation. As highlighted in talks around brain-inspired chips and meshing, these systems are blurring distinctions between biological and silicon-based computation. In the meanwhile, bidirectional communication is made possible by BCIs, such as those being developed by businesses and research facilities, which can read brain activity for feedback or control and possibly write signals back to affect cognition. ... Neural data is essentially personal. Breaches could expose memories, emotions, or subconscious biases. Adversaries may reverse-engineer intentions for coercion, fraud, or espionage as AI decodes brain scans for "mind captioning" or talent uploading. ... Compromised BCIs blur cyber-physical boundaries farther than OT-IT convergence already has. A malevolent actor might damage medical implants, alter augmented reality overlays, or weaponize neurotech in national security scenarios. ... Implantable devices rely on worldwide supply chains prone to tampering. Neuromorphic hardware, while efficient, provides additional attack surfaces if not designed with zero-trust principles. Using AI to process neural signals can introduce biases, which may result in unfair treatment in brain-augmented systems

Designing for Failure: Chaos Engineering Principles in System Design

To design for failure, we must understand how the system behaves when failure inevitably happens. What is the cost? What is the impact? How do we mitigate it? How do we still maintain over 99% uptime? This requires treating failure as a default state, not an exception. ... The first step is defining steady-state behavior. Without this, there is no baseline to measure against. ... Chaos experiments are most valuable in production. This is where real traffic patterns, real user behavior, and real data shapes exist. That said, experiments must be controlled. ... Chaos Engineering is not a one-off exercise. Systems evolve. Dependencies change. Teams rotate. Experiments should be automated, repeatable, and run continuously, either as scheduled jobs or integrated into CI/CD pipelines. Over time, experiments can be expanded to test higher-impact scenarios. ... Additional considerations include health checks, failover timing, and data consistency. Strong consistency simplifies reasoning but reduces availability. Eventual consistency improves availability but introduces complexity and potential inconsistency windows. ... Network failures are unavoidable in distributed systems. Latency spikes, packets get dropped, DNS fails, and sometimes the network splits entirely. Many system outages are not caused by servers crashing, but by slow or unreliable communication between otherwise healthy components. This is where several of the classic fallacies of distributed computing show up, especially the assumption that the network is reliable and has zero latency.

Why SMBs Need Strong Data Governance Practices

Good data governance for small businesses is about building trust, control and scalability into your data from day one. Governance should be built into the data foundation, not bolted on later. Small businesses move fast, and governance works best when it’s native to how data is managed. That means choosing platforms that apply security, access controls and compliance consistently across all data, without requiring manual oversight or specialized teams. Additionally, clear visibility and control over what data exists and who can access it is essential. Even at a smaller scale, businesses handle sensitive information ranging from customer and financial data to operational insights. ... Governance also future proofs the business. Regulations are becoming more complex, customer expectations for data protection are rising, and AI systems must have high-quality, well-governed data to perform reliably. Small businesses that treat governance as a foundation are better positioned to adopt AI and safely expand into new use cases, markets and regulatory environments without needing to rearchitect later. At the same time, strong data governance improves day-to-day efficiency. When data is well governed, teams can spend more time acting on insights and less time questioning data quality, managing access manually or duplicating work. ... From a cybersecurity perspective, governance provides the controls and visibility needed to reduce attack surfaces and detect misuse.

Daily Tech Digest - December 26, 2025

Quote for the day:

“Rarely have I seen a situation where doing less than the other guy is a good strategy.” -- Jimmy Spithill

Is Your Enterprise Architecture Ready for AI?

The old model of building, deploying, and governing apps is being reshaped into a composable enterprise blueprint. By abstracting complexity through visual models and machine intelligence, businesses are creating systems that are faster to adapt yet demand stronger governance, interoperability, and security. What emerges is not just acceleration but transformation at the foundation. ... With AI copilots spitting out code at scale, the traditional software development life cycle faces an existential test. Developers may not fully understand every line of AI-generated code, making manual reviews insufficient. The solution: automate aggressively. ... This new era also demands AI observability in SDLC, tracking provenance, explainability, and liability. Provenance shows the chain of prompts and responses. Explainability clarifies decisions. Bias and drift monitoring ensure AI systems don’t quietly shift into harmful or unreliable patterns. Without these, enterprises risk blind trust in black-box code. ... The destination for enterprises is clear: AI-native enterprise architecture and composable enterprise blueprint strategies, where every capability is exposed as an API and orchestrated by LCNC and AI. The road, however, is slowed by legacy monoliths in industries like banking and healthcare. These systems won’t vanish overnight. Instead, strategies like wrapping monoliths with APIs and gradually replacing components will define the journey.

After LLMs and agents, the next AI frontier: video language models

World models — which some refer to as video language models — are the new frontier in AI, following in the footsteps of the iconic ChatGPT and more recently, AI agents. Current AI tech largely affects digital outcomes, but world models will allow AI to improve physical outcomes. World models are designed to help robots understand the physical world around them, allowing them to track, identify and memorize objects. On top of that, just like humans planning their future, world models allow robots to determine what comes next — and plan their actions accordingly. ... Beyond robotics, world models simulate real-world scenarios. They could be used to improve safety features for autonomous cars or simulate a factory floor to train employees. World models pair human experiences with AI in the real world, said Deepak Seth, director analyst at Gartner. “This human experience and what we see around us, what’s going on around us, is part of that world model, which language models are currently lacking,” Seth said. ... World models are one of several tools that will be used to deploy robots in the real world, and they will continue to improve, said Kenny Siebert, AI research engineer at Standard Bots. But the models suffer from similar problems — the hallucinations and degradation — that affect the likes of ChatGPT and video-generators. Moving hallucinations into the physical world could cause harm, so researchers are trying to solve those kinds of issues.

Hub & Spoke: The Operating System for AI-Enabled Enterprise Architecture

Today most enterprises still run on heroics, emails, slide decks, and 200-person conference calls. Even when a good repository and healthy collaboration culture exist, nothing “sticks” without a mechanism that relentlessly harvests reality, unifies understanding, and broadcasts the right truth to the right person at the right moment. That mechanism is a new application of hub-and-spoke – not just for data integration, but for architecture governance itself. We call it simply Hub & Spoke. ... At the centre runs a continuous cycle of three actions: Harvest – Ingest everything that matters: scanner output, CI/CD metadata, application inventories, risk registers, process models, meeting outcomes, human feedback, and (increasingly) agentic AI crawls; Unify – Connect the dots. Establish relationships, resolve duplicates, detect patterns and anti-patterns, and maintain one coherent model of the enterprise; and Broadcast – Push the right view, in the right language, through the right channel, at the right time. A CIO sees strategic heatmaps; a developer receives contextual architecture guardrails inside the IDE; a regulator gets a compliance report on demand. ... To fully leverage the H.U.B. actions, we apply them to five fundamental capabilities that drive any organisation, encapsulated in S.P.O.K.E.: Stakeholders – who cares and who decides; Processes – sequences that deliver value; Outcome – the why (always placed in the centre of the model); Knowledge – codified artefacts (models, policies, decisions, blueprints); and Enterprise Assets – systems, data, infrastructure, contracts

Orchestrating value: The new discipline of continuous digital transformation

The most important principle for any CIO today is deceptively simple: every transformation must begin with value and be engineered for agility. In a volatile and fast-moving environment, success depends not on how much technology you deploy, but on how effectively you align it to outcomes that matter. Every initiative should begin with clarity of purpose. What is the value hypothesis? What problem are we solving? Who owns the outcome, and when will impact be visible? ... Architecture then becomes the critical enabler. Agility must be built into the design, through modular platforms, adaptable processes, and feedback-driven operating models that allow business change, talent movement, and technological evolution to coexist seamlessly. Measurement turns agility from theory into discipline. Continuous value reviews, architectural checkpoints, and strategy resets ensure transformation remains evidence-led rather than aspirational. Every initiative must answer three questions: Why value? Why now? Why this architecture? In a world defined by velocity and volatility, transformation isn’t about doing more – it’s about doing what matters, faster, smarter, and with enduring value. ... Today’s CIOs also demand composable, interoperable platforms that integrate seamlessly into existing ecosystems, avoiding vendor lock-in while accelerating scale through APIs, microservices, and modular architectures. Partners must bring both agility and discipline – speed balanced with governance.

Why Integration Debt Threatens Enterprise AI and Modernization

AI agents rely on fast, trusted data exchanges across applications. However, point-to-point connectors often break under new query loads. Matt McLarty of MuleSoft states that integration challenges slow digital transformation. Integration Debt surfaces here as latent System Friction that derails AI pilots. Furthermore, developers spend 39% of their time writing custom glue code. Consequently, innovation budgets shrink while maintenance backlogs grow. Such opportunity cost defines Integration Debt in real dollars and morale. Disconnected integrations throttle AI benefits and drain talent. In contrast, scale introduces additional complexity exposed next. ... Effective governance establishes shared schemas, versioning, and certification for every API. Nevertheless, shadow IT and citizen developers complicate enforcement. Therefore, leading CIOs create integration review boards with quarterly scorecards. Accenture and Deloitte embed such controls in Modernization playbooks to prevent relapse. Additionally, companies publish portal dashboards that display live Integration Debt metrics to executives. ... The evidence is clear: disconnected architectures tax innovation, security, and profits. Ramsey Theory Group reminds leaders that random complexity often concentrates risk in surprising places. Similarly, unchecked System Friction erodes developer morale and board confidence. However, organizations that quantify debt, enforce governance, and adopt reusable APIs accelerate Modernization success.

The Widening AI Value Gap: Strategic Imperatives for Business Leaders

AI value creation in business settings extends far beyond narrow efficiency gains or cost reductions. Contemporary frameworks increasingly distinguish between three fundamental pathways through which AI generates economic returns: deploying efficiency-enhancing tools, reshaping existing workflows, and inventing entirely new business models ... Reshaping represents a more ambitious approach, targeting core business workflows for end-to-end transformation. Rather than automating existing steps in isolation, reshaping asks: How would we design this workflow from scratch if AI capabilities were available from the outset? This might involve redesigning marketing campaign development to leverage AI-driven personalization at scale, restructuring supply chain management around predictive demand algorithms, or reimagining customer service through intelligent agent orchestration. ... Value measurement frameworks must capture both tangible and strategic dimensions. Tangible metrics include revenue increases (projected at 14.2% for future-built companies in areas where AI applies by 2028), cost reductions (9.6% for leaders), and measurable improvements in key performance indicators such as time-to-hire, customer satisfaction scores, and defect rates ... The strategic implications extend beyond near-term financial performance. Organizations trailing in AI maturity face deteriorating competitive positions as digital-native competitors and AI-advanced incumbents reshape industry economics.

4 mandates for CIOs to bridge the AI trust gap

As a CIO, you must recognize that low trust in public AI eventually seeps into the enterprise. If your customers or employees see AI being used unethically in media scenarios through misinformation and bias, or in personal scenarios like cybercrime, their skepticism will bleed into your enterprise-grade CRM or HR systems. The recommendation is to build on the existing trust in the workplace. Use the enterprise as a model for responsible deployment. Document and communicate your AI internal usage policies with exceptional clarity, and allow this transparency to be your market differentiator. Show your customers and partners the standards you hold your internal AI to, and then extrapolate those standards to your external products. ... For CIOs in highly regulated industries such as finance and healthcare, the mandate is to not just maintain but elevate the current level of rigor. The existing regulatory compliance is the baseline, not the ceiling, and the market will punish the first major breach or bias incident, undoing years of consumer confidence. ... We must stop telling end users AI is trustworthy and start showing them through tangible experience. Trust is a feature that must be designed from the start, not something patched in later. The first step is to involve the customer. Implement co-design programs where the end-users and customers, not just product managers, are involved in the design and testing phases of new AI applications.

The Enterprise “Anti-Cloud” Thesis: Repatriation of AI Workloads to On-Premises Infrastructure

Today, a new inflection point has arrived: the dawn of artificial intelligence and large-scale model training. Running in parallel is an observable and rapidly growing trend in which companies are repatriating AI workloads from the public cloud to on-premises environments. This “anti-cloud” thesis represents a readjustment, rather than a backlash, mirroring other historical shifts in leadership in which prescience reordered entire industries. As Gartner has remarked, “By 2025, 60% of organizations will use sovereignty requirements as a primary factor in selecting cloud providers.” ... Navigating this transition requires fundamentally different abilities, integrating deep technical fluency with disciplined strategic thinking. AI infrastructure differs sharply from other traditional cloud workloads in that it is compute-intensive, highly resource-intensive, latency-sensitive, and tightly connected with data governance. ... The repatriation of AI workloads brings several challenges: lack of AI infrastructure talent, high upfront GPU procurement costs, operational overhead, security risks, and sustainability concerns. Leaders must manage hardware supply chain volatility, model reliability, and energy efficiency. Lacking disciplined governance, repatriation creates a high risk of cost overruns and fragmentation. The central challenge is to balance innovation with control, calling for transparency of plans and scenario modeling.

The Fragile Edge: Chaos Engineering For Reliable IoT

Chaos engineering is mostly used in cloud environments because it works very well there. However, it is more difficult to apply to IoT and edge computing systems. IoT devices are physical, often located in remote places and sometimes perform critical tasks. This makes managing them even more challenging. Restarting cloud servers using scripts is usually simple. But rebooting medical devices like pacemakers, industrial robots or warehouse sensors is much more complex and can be dangerous. Resetting edge devices also takes longer because system failures often have immediate physical outcomes. Chaos engineering in IoT systems has both benefits and challenges. Engineers need to design methods to test failures safely without harming devices. The testing process aims to detect equipment breakdowns while developing systems that function during actual operational conditions. The proven cloud software methods of chaos engineering enable organisations to meet the requirements of edge devices. ... The implementation of chaos engineering for IoT systems requires both strategic planning and innovative solutions. Engineers should perform system vulnerability tests, which ensure operational safety and reliability for real world deployment. The risk assessment process needs tested and accurate methods to protect both system devices and their users from harm. ... Organisations need to maintain ethical standards when they use chaos engineering to safeguard their IoT systems. Engineers who want to perform IoT chaos testing need to follow established safety protocols.

Can Agentic AI operate independently within secure parameters

Context-aware security, enabled by Agentic AI, is essential for effective NHI management. This approach goes beyond traditional methods by understanding the context within which NHIs operate. It evaluates the ownership, permissions, and usage patterns, offering invaluable insights into potential vulnerabilities. By employing context-aware security, organizations can surpass the limitations of point solutions, such as secret scanners, which provide only surface-level protection. ... With the proliferation of digital identities, organizations must adopt a comprehensive approach that incorporates both technological advancements and strategic oversight. Agentic AI, with its ability to operate independently, aligns perfectly with this need, offering a robust framework that supports the secure management of machine identities across various industries. Given the increasing complexities of digital, organizations must continuously evolve their cybersecurity strategies. ... For enterprises navigating complex regulatory environments, predictive insights from AI models can forecast potential compliance issues, allowing preemptive action. When regulations evolve, this foresight is invaluable in maintaining adherence without resource-intensive overhauls of existing processes. ... Investing in AI-driven strategies ensures that organizations can withstand disruptions, safeguarding both operational functions and reputation.

Daily Tech Digest - December 24, 2025

Quote for the day:

"The only person you are destined to become is the person you decide to be." -- Ralph Waldo Emerson

When is an AI agent not really an agent?

If you believe today’s marketing, everything is an “AI agent.” A basic workflow worker? An agent. A single large language model (LLM) behind a thin UI wrapper? An agent. A smarter chatbot with a few tools integrated? Definitely an agent. The issue isn’t that these systems are useless. Many are valuable. The problem is that calling almost anything an agent blurs an important architectural and risk distinction. ... If a vendor knows its system is mainly a deterministic workflow plus LLM calls but markets it as an , buyers are misled not just about branding but also about the system’s actual behavior and risk. That type of misrepresentation creates very real consequences. Executives may assume they are buying capabilities that can operate with minimal human oversight when, in reality, they are procuring brittle systems that will require substantial supervision and rework. Boards may approve investments on the belief that they are leaping ahead in AI maturity, when they are really just building another layer of . Risk, compliance, and security teams may under-specify controls because they misunderstand what the system can and cannot do. ... demand evidence instead of demos. Polished demos are easy to fake, but architecture diagrams, evaluation methods, , and documented limitations are harder to counterfeit. If a vendor can’t clearly explain how their agents reason, plan, act, and recover, that should raise suspicion.

Five identity-driven shifts reshaping enterprise security in 2026

Organizations that continue to treat identity as a static access problem will fall behind attackers who exploit AI-powered automation, credential abuse, and . The enterprises that succeed will be those that re-architect identity security as a continuous, data-aware control plane, one built to govern humans, machines, and AI with the same rigor, visibility, and accountability. ... Unlike traditional shadow IT, is both more powerful and more dangerous. Employees can deploy advanced models trained on sensitive company data, and these tools often store or transmit privileged credentials, API keys, and service tokens without oversight. Even sanctioned AI tools become risky when improperly configured or connected to internal workflows. ... With AI-driven automation, sophisticated playbooks previously reserved for top-tier nation-states become accessible to countries, and non-state actors, with far fewer resources. This levels the playing field and expands the number of threat actors capable of meaningful, identity-focused cyber aggression. In 2026, expect more geopolitical disruptions driven by identity warfare, synthetic information, and AI-enabled critical infrastructure targeting. ... Machine identities have become the primary source of privilege misuse, and their growth shows no sign of slowing. As AI-driven automation accelerates and IoT ecosystems proliferate, organizations will hit a governance tipping point.2026 will force security teams to confront a tough reality. Identity-first security can’t stop with humans.

Implementing NIS2 — without getting bogged down in red tape

NIS2 essentially requires three things: concrete security measures; processes and guidelines for managing these measures; and robust evidence that they work in practice. ... Therefore, two levels are crucial for NIS2: the technical measures and the evidence that they are effective. This is precisely where the transformation of recent years becomes apparent. Previously, concepts, measures, and specifications for software and IT infrastructures were predominantly documented in text form. ... The second area that NIS2 and the new for digital services are enshrining in law is in the company’s own code and supply chain. This requires regular vulnerability scans, procedures for assessment and prioritization, timely remediation of critical vulnerabilities, and regulated vulnerability handling and — where necessary — coordinated vulnerability disclosure. Cloud and SaaS providers also face additional supply chain obligations ... The third area where NIS2 quickly becomes a paper tiger is the combination of monitoring, incident response, and the new reporting requirements. The directive sets clear deadlines: early warning within 24 hours, a structured report after 72 hours, and a final report no later than one month. ... NIS2 forces companies to explicitly define their security measures, processes, and documentation. This is inconvenient — especially for organizations that have previously operated largely on an ad-hoc basis.

Rethinking Anomaly Detection for Resilient Enterprise IT

Being armed with this knowledge is only the first step, though. The next challenge is detecting anomalies consistently and accurately in complex environments. This task is becoming increasingly difficult as IT environments undergo continuous digital transformation, shift towards hybrid-cloud setups, and rely on legacy systems that are well past their prime. These challenges introduce dynamic data, pushing IT leaders to rethink their processes. ... By incorporating seasonal patterns, user behavior, and workload types, filter out the noise and highlight genuine deviations. Another factor to integrate is the overall context of a situation. Metrics rarely operate in isolation. During planned deployment, it would be anticipated for a spike in network latency. This same spike would be seen completely differently if it were to occur during steady operations. By combining telemetry with contextual signals, anomaly detection systems can separate the expected from the unexpected. ... Anomaly detection is meant to strengthen operations and improve overall resilience. However, it is not capable of delivering on this promise when teams are constantly swimming through the seas of generated alerts. By contextually and comprehensively adopting new approaches to the variety of anomalies, systems can identify root causes, uniformly correct systemic failures created from multiple metrics points, and mitigate the risk of outages.

Bridging the Gap: Engineering Resilience in Hybrid Environments (DR, Failover, and Chaos)

Resilience in a hybrid environment isn't just about preventing failure; it’s about enduring it. It requires moving beyond hope as a strategy and embracing a tripartite approach: Robust Disaster Recovery (DR), automated Failover, and proactive . ... Disaster Recovery is your insurance policy for catastrophic events. It is the process of regaining access to data and infrastructure after a significant outage—a hurricane hitting your primary data center, a massive ransomware attack, or a prolonged regional cloud failure. ... While DR handles catastrophes, Failover handles the everyday hiccups. Failover is the (ideally automatic) process of switching to a redundant or standby system upon the failure of the primary system, mostly automatic. Failover mechanisms in a hybrid environment ensure immediate operational continuity by automatically switching workloads from a failed primary system (on-premises or cloud) to a redundant secondary system with minimal downtime. This requires coordinating recovery across cloud and on-premises platforms. ... Chaos engineering is a proactive discipline used to stress-test systems by intentionally introducing controlled failures to identify weaknesses and build resilience. In hybrid environments—which combine on-premises infrastructure with cloud resources—this practice is essential for navigating the added complexity and ensuring continuous reliability across diverse platforms.

Should CIOs rethink the IT roadmap?

As technology consultancy states: “You don’t need bigger plans — you need faster moves.” This is a fitting mantra for IT roadmap development today. CIOs should ask themselves where the most likely business and technology plan disrupters are going to come from. ... Understandably, CIOs can only develop future-facing technology roadmaps with what they see at a present point in time. However, they do have the ability to improve the quality of their roadmaps by reviewing and revising these plans more often. ... CIOs should revisit IT roadmaps quarterly at a minimum. If roadmaps must be altered, CIOs should communicate to their CEOs, boards, and C-level peers what’s happening and why. In this way, no one will be surprised when adjustments must be made. As CIOs get more engaged with lines of business, they can also show how technology changes are going to affect company operations and finances before these changes happen ... Equally important is emphasizing that a seismic change in technology roadmap direction could impact budgets. For instance, if AI-driven security threats begin to impact company AI and general systems, IT will need AI-ready tools and skills to defend and to mitigate these threats. ... Now is the time for CIOs to transform the IT roadmap into a more malleable and responsive document that can accommodate the disruptive changes in business and technology that companies are likely to experience.

Why shadow IT is a growing security concern for data centre teams

It is essential to recognise that employees use shadow IT to get their work done efficiently, not to deliberately create security risks. This should be front of mind for any IT teams and data centre consultants involved in infrastructure design and security provision. Finding blame or taking an approach that blocks everything does not work. A more effective way to address shadow IT use is to invest for the long term in a culture which promotes IT as a partner to workplace productivity, not something which is a hindrance. Ideally, this demands buy-in from senior management. Although it falls to IT teams to provide people with the tools for their jobs, providing choice, listening to employees’ requests and offering prompt solutions, will encourage the transparency so much needed for IT to analyse usage patterns, identify potential issues and address minor issues before they grow into costly problems. Importantly, this goes a long way towards embracing new technologies and avoiding employees turning to shadow IT that they find and use without approval. ... While IT teams are focused on gaining visibility and control over the software, hardware and services gainfully used by their organisations, they also need to be careful not to stifle innovation. It is here that data centre operators can share ideas on ways to best achieve this balance, as there is never going to be one model that suits every business.

From Digitalization to Intelligence: How AI Is Redefining Enterprise Workflows

In the AI economy, digitalization plays another important role—turning paper documents into data suitable for LLM engines. This will become increasingly important as more sites restrict crawlers or require licensing, which reduces the usable pool of data. A 2024 report from the nonprofit watchdog Epoch AI projected that large language models (LLMs) could run out of fresh, human-generated training data as soon as 2026. Companies that rely purely on publicly available crawl data for continuous scaling likely will encounter diminishing returns. To avoid the looming publicly accessed data shortage, enterprises will need to use their digitized documents and corporate data to fine‐tune models for domain specific tasks rather than rely only on generic web data. Intelligent capture technologies can now recognize document types, extract key entities, and validate information automatically. Once digitized, this data flows directly into enterprise systems where AI models can uncover insights or predict outcomes. ... Automation isn’t just about doing more with less; it’s about learning from every action. Each scan, transaction, or decision strengthens the feedback loop that powers enterprise AI systems. The organizations recognizing this shift early will outpace competitors that still treat data capture as a back-office function. The winners will be those that turn the last mile of digitalization into the first mile of intelligence.

Boardrooms demand tougher AI returns & stronger data

Budget scrutiny is increasing as wider economic conditions remain uncertain and as organisations review early generative AI experiments. "AI investment is no longer about FOMO. Boards and CFOs want answers about what's working, where it's paying off, and why it matters now. 2026 will be a year of focus. Flashy experiments and perpetual pilots will lose funding. Projects that deliver measurable outcomes will move to the center of the roadmap," said McKee, CEO, . ... "For years people have predicted that AI will hollow out data teams, yet the closer you get to real deployments, the harder that story is to believe. Once agents take over the repetitive work of querying, cleaning, documenting, and validating data, the cost of generating an insight will begin falling toward zero. And when the cost of something useful drops, demand rises. We've seen this pattern with steam engines, banking, spreadsheets, and cloud compute, and data will follow the same curve," said Keyser. Keyser said easier access to data and analysis is likely to change behaviours in business units that have not traditionally engaged with central data groups. He expects a rise in AI-literate staff across operational functions and a larger need for oversight. ... The organizations that adopt agents will discover something counterintuitive. They won't end up with fewer data workers, but more. This is applied to analytics. When insight becomes easier, curiosity will expand and decision-making will accelerate.

The Blind Spots Created by Shadow AI Are Bigger Than You Think

If you think it’s the same as the old “shadow IT” problem with different branding, you’re wrong. Shadow AI is faster, harder to detect, and far more entangled with your intellectual property and data flows than any consumer SaaS tool ever was. ... Shadow AI is not malicious in nature; in fact, the intent is almost always to improve productivity or convenience. Unfortunately, the impact is a major increase in unplanned data exposure, untracked model interactions, and blind spots across your attack surface. ... Most AI tools don’t clearly explain how long they keep your data. Some retrain on what you enter, others store prompts forever for debugging, and a few had almost no limits at all. That means your sensitive info could be copied, stored, reused for training, or even show up later to people it shouldn’t. Ask , whose internal code found its way into a public model’s responses after an engineer uploaded it. They banned AI instantly. Hardly the most strategic solution, and definitely not the last time you’ll see this happen. ... Shadow AI bypasses Identity controls, DLP controls, SASE boundaries, Cloud logging, and Sanctioned inference gateways. All that “AI data exhaust” ends up scattered across a slew of unsanctioned tools and locations. Your exposure assessments are, by default, incomplete because you can’t protect what you can’t see. ... Shadow AI has changed from an occasional or unusual instance case to everyday behavior happening across all departments.