Daily Tech Digest by Kannan Subbiah: 2025

Daily Tech Digest - December 10, 2025

Quote for the day:

"Develop success from failures. Discouragement and failure are two of the surest stepping stones to success." -- Dale Carnegie

Design in the age of AI: How small businesses are building big brands faster

Instead of hiring separate agencies for naming, logo design, and web development, small businesses are turning to unified AI platforms that handle the full early-stage design stack. Tools like Design.com merge naming, logo creation, and website generation into a single workflow — turning an entrepreneur’s first sketch into a polished brand system within minutes. ... Behind the surge in AI design tools lies a broader ecosystem shift. Companies like Canva and Wix made design accessible; the current wave — led by AI-native platforms like Design.com — is more personal and adaptive. Unlike templated platforms, these tools understand context. A restaurant founder and a SaaS startup will get not just different visuals, but different copy tones, typography systems, and user flows — automatically. “What we’re seeing,” Lynch explains, “isn’t just growth in one product category. It’s a movement toward connected creativity — where every part of the brand experience learns from every other.” ... Imagine naming a company and watching an AI instantly generate a logo, color palette, and homepage layout that all reflect the same personality. As your audience grows, the same system helps you update your visual identity or tone to match new goals — while preserving your original DNA.

Henkel CISO on the messy truth of monitoring factories built across decades

On the factory floor, it is common to find a solitary engineering workstation that holds the only up-to-date copies of critical logic files, proprietary configuration tools, and project backups. If that specific computer suffers a hardware failure or is compromised by ransomware, the maintenance team loses the ability to diagnose errors or recover the production line. ... If the internet connection is severed, or if the third-party cloud provider suffers an outage, the equipment on the floor stops working. This architecture fails because it prioritizes connectivity over local autonomy, creating a fragile ecosystem where a disruption in a remote cloud environment creates a “digital brick” out of physical machinery. ... An attacker does not need sophisticated “zero-day” exploits to compromise a fifteen-year-old human-machine interface, they often just need publicly known vulnerabilities that will never be fixed by the vendor. By compromising a peripheral camera or an outdated visualization node, they gain a persistence mechanism that security teams rarely monitor, allowing them to map the operational technology network and prepare for a disruptive attack on the critical control systems at their leisure. ... A critical question for CISOs to ask is: “Can you provide a continuously updated Software Bill of Materials for your firmware, and what is your specific process for mitigating vulnerabilities in embedded third-party libraries?”

AI churn has IT rebuilding tech stacks every 90 days

Even without full production status, the fact that so many organizations are rebuilding components of their agent tech stacks every few months demonstrates not only the speed of change in the AI landscape but also a lack of faith in agentic results, Northcutt claims. Changes in the agent tech stack range from something as simple as updating the underlying AI model’s version, to moving from a closed-source to an open-source model or changing the database where agent data is stored, he notes. In many cases, replacing one component in the stack sets off a cascade of changes downstream, he adds. ... While the speed of AI evolution can drive frequent rebuilds, part of the problem lies in the way AI models are tweaked, she says. “The deeper issue is that many agent systems rely on behaviors that sit inside the model rather than on clear rules,” Hashem explains. “When the model updates, the behavior drifts. When teams set clear steps and checks for the agent, the stack can evolve without constant breakage.” ... “What works now may become suboptimal later on,” he says. “If organizations don’t actively keep up to date and refresh their stack, they risk falling behind in performance, security, and reliability.” Constant rebuilds don’t have to create chaos, however, Balabanskyy adds. CIOs should take a layered approach to their agent stacks, he recommends, with robust version control, continuous monitoring, and a modular deployment approach.

Why Losing One Security Engineer Can Break Your Defences

When tools are hard to manage – or if you need to bundle numerous tools from different vendors together – tribal knowledge builds up in one engineer’s head. It’s unrealistic to expect them to document it. Gartner recently said that organizations use an average of 45 cybersecurity tools and called for security leaders to optimize their toolsets. And in that context, losing the one person who understands how these systems actually work is not just inconvenient: it's a structural risk. And the impact this has is seen in the data from the State of AI in Security & Development report; using numerous vendors for security tools correlates with more incidents, more time spent prioritising alerts and slower remediation. In short, a security engineer has too much on their plate, and most security tools aren’t making their job any easier. ... “Organisations tend to be all looking for the same blend of technical cloud, integration, SecOps, IAM experience but with extensive knowledge in each pillar,” says James Walsh, National Lead for Cyber, Data & Cloud UK&I at Hays. “Everyone wants the unicorn security engineer whose experience spans all of this, but it comes at too high a price for lots of organisations,” he adds. Walsh notes that hiring is often driven by teams below the CISO — such as Heads of SecOps — which can create inconsistent expectations of what a ‘fully competent’ engineer should look like.

Overload Protection: The Missing Pillar of Platform Engineering

Some limits exist to protect systems. Others enforce fairness between customers or align with contractual tiers. Regardless of the reason, these limits must be enforced predictably and transparently. ... In data-intensive environments, bottlenecks often appear in storage, compute, or queueing layers. One unbounded query or runaway job can starve others, impacting entire regions or tenants. Without a unified overload protection layer, every team becomes a potential failure domain. ... Enterprise customers often face challenges when quota systems evolve organically. Quotas are published inconsistently, counted incorrectly, or are not visible to the right teams. Both external customers and internal services need predictable limits. A centralized Quota Service solves this. It defines clear APIs for tracking and enforcing usage across tenants, resources, and time intervals. ... When overload protection is not owned by the platform, teams reinvent it repeatedly. Each implementation behaves differently, often under pressure. The result is a fragile ecosystem where: Limits are enforced inconsistently, for example, some endpoints apply resource limits, while others run requests without enforcing any limits, leading to unpredictable behavior and downstream problems; Failures cascade unpredictably, for example, a runaway data pipeline job can saturate a shared database, delaying or failing unrelated jobs and triggering retries and alerts across teams

Is your DR plan just wishful thinking? Prove your resilience with chaos engineering

At its core, it’s about building confidence in your system’s resilience. The process starts with understanding your system's steady state, which is its normal, measurable, and healthy output. You can't know the true impact of a failure without first defining what "good" looks like. This understanding allows you to form a clear, testable hypothesis: a statement of belief that your system's steady state will persist even when a specific, turbulent condition is introduced. To test this hypothesis, you then execute a controlled action, which is a precise and targeted failure injected into the system. This isn't random mischief; it's a specific simulation of real-world failures, such as consuming all CPU on a host (resource exhaustion), adding network latency (network failure), or terminating a virtual machine (state failure). While this action is running, automated probes act as your scientific instruments, continuously monitoring the system's state to measure the effect. ... Beyond simply proving system availability, chaos engineering builds trust in your reliability metrics, ensuring that you meet your SLOs even when services become unavailable. An SLO is a specific, acceptable target level of your service's performance measured over a specified period that reflects the user's experience. SLOs aren't just internal goals; they are the bedrock of customer trust and the foundation of your contractual service level agreements (SLAs).

The data center of the future: high voltage, liquid cooled, up to 4 MW per rack

Developments such as microfluidic cooling could have a profound impact on how racks and accompanying infrastructure will be built towards the future. Also, it is not all about the type of cooling, but also about the way chips communicate with each other and communicate internally. What will the impact of an all-photonics network be on cooling, for example? The first couple of stages building that type of end-to-end connection have been completed. The interesting parts for the discussion we have here are next on the roadmap for all-photonics networks: using photonics connections between and inside silicon on boards. ... However, there are many moving parts to take into account. It will need a more dynamic approach to selling space in data centers, which is usually based on the amount of watts a customer wants. Irrespective of the actual load, the data center reserves that for the customer. If data centers need to be more dynamic, so do the contracts. ... The data center of the future will be characterized by high-density computing, liquid cooling, sustainable power sources, and a more integrated role in the grid ecosystem. As technology continues to advance, data centers will become more efficient, flexible, and environmentally responsible. That may sound like an oxymoron to many people nowadays, but it’s the only way to get to the densities we need moving forward.

Vietnam integrating biometrics into daily life in digital transformation drive

Vietnam is rapidly integrating biometrics and digital identity into everyday life, rolling out identity‑based systems across public transport, air travel and banking as part of an ambitious national digital transformation drive. New deployments in Hanoi’s metro, airports nationwide and the financial sector show how VNeID and biometric verification increasingly constitute Vietnam’s infrastructure. ... Officials argue the initiative strengthens Hanoi’s ambitions as a smart city and improves interoperability across transport modes. It also introduces a unified digital identity layer for public transit, which no other Vietnamese city can yet boast. Passenger data, operations and transactions are now centralized on a single platform, enabling targeted subsidies based on usage patterns rather than flat‑rate models. The Hanoi Metro app, available on major app stores, supports tap‑and‑go access and discounted fares for verified digital identities. ... The new rules require banks to conduct face‑to‑face identity checks and verify biometric data, such as facial information, before issuing cards to individual customers. The same requirement applies to the legal representatives of corporate clients, with limited exceptions, reports Vietnam Plus. ... Foreigners without electronic identity credentials, as well as Vietnamese nationals with undetermined citizenship status, will undergo in‑person biometric collection using data from the National Population Database.

Why 2025 broke the manager role — and what it means for leadership ahead

Managers did far more than supervise. “They became mentors, skill-builders, culture carriers and the first line of emotional support,” Tyagi said. They coached diverse teams, supported women and marginalised groups entering new roles, and navigated talent crunches by building internal pipelines. They adopted learning apps, facilitated experience-sharing sessions and absorbed the emotional load of stretched teams. ... Sustaining morale amid continual uncertainty was the most difficult task, Tyagi said. Workloads were redistributed constantly. Managers had to reassure employees while balancing performance expectations with wellbeing. Chopra saw the same tensions. Recognition and feedback remained inconsistent. Gallup research showed a gap between managers’ belief that they offered regular feedback and employees’ experience that they rarely received it. Remote work deepened disconnection. “Creating team cohesion, trust and belonging when people are dispersed remains difficult,” she said. ... Empathy dominated the management skill-set in 2025. Transparency, communication and emotional intelligence were indispensable as uncertainty persisted. Coaching and talent development grew central, especially in organisations investing in women, new hires and marginalised communities. Chopra pointed to several non-negotiables: emotional intelligence, tech literacy, outcome-focused leadership, psychological safety, coaching and ethical awareness in technology use.

The Missing Link in AI Scaling: Knowledge-First, Not Data-First

Organizations today need to ensure data readiness to avoid failures in model performance, system trust, and strategic alignment. To succeed, CIOs must shift from a “data-first” to a “knowledge-first” approach in order to capitalize on the true benefits of AI. ... Domain-specific reasoning capabilities provide context and meaning to data, which is crucial for professional and reliable advice. A semantic layer across silos creates unified views of all data, enabling comprehensive insights that are otherwise impossible to achieve. Another benefit is its ability to support AI governance and explainability by ensuring that AI systems are not “black boxes,” but are transparent and trustworthy. Lastly, it acts as an agentic AI backbone by orchestrating a workforce of AI agents that can execute complex tasks with reliability and context. ... Shifting to a knowledge-first architecture is not just an option, but a necessity, and is a direct challenge to the conventional data-first mindset. For decades, enterprises have focused on accumulating vast lakes of data, believing that more data inherently leads to better insights. However, this approach created fragmented, context-poor data silos. This “digital quicksand” is the root of the “Semantic Challenge” because data is siloed and heterogeneous. ... A knowledge-first approach fundamentally changes the goal from simply storing data to building an interconnected, enterprise-wide, knowledge graph. This architecture is built on the principle of “things, not strings”.

Daily Tech Digest - December 08, 2025

Quote for the day:

"You don't build business, you build people, and then people build the business." -- Zig Ziglar

CIOs shift from ‘cloud-first’ to ‘cloud-smart’

The is being influenced by better on-prem technology, longer hardware cycles, ultra-high margins with , and the typical hype cycles of the industry, according to . All favor hybrid infrastructure approaches. However, “AI has added another major wrinkle with siloed data and compute,” he adds. “Many organizations aren’t interested in or able to build high-performance , and need to use the cloud. But if they’ve been conservative or cost-averse, their data may be in the on-prem component of their hybrid infrastructure.” These variables have led to complexity or unanticipated costs, either through migration or data egress charges, McElroy says. ... IT has parsed out what should be in a private cloud and what goes into a public cloud. “Training and fine-tuning large models requires strong control over customer and telemetry data,” explains. “So we increasingly favor hybrid architectures where inference and data processing happen within secure, private environments, while orchestration and non-sensitive services stay in the public cloud.” starts with data classification and workload profiling. Anything with customer-identifiable information, diagnostic traces, and model feedback loops are processed within regionally compliant private clouds, he says. ... “Many organizations are wrestling with cloud costs they know instinctively are too high, but there are few incentives to take on the risky work of repatriation when a CFO doesn’t know what savings they’re missing out on,” he says.

Harmonizing EU's Expanding Cybersecurity Regulations

Aligning , and is difficult, since each framework approaches risks differently, which creates overlapping obligations for reporting, controls and vendor oversight, leading to areas that require careful interpretation. Given these overlapping requirements, organizations should establish an integrated governance model that consolidates risk management to report workflows and third-party oversight across all relevant EU frameworks. Strengthening internal coordination - especially between legal, compliance, cybersecurity and executive teams - helps ensure consistent interpretation of obligations and reduces fragmentation in implementation. ... Developers must build safeguards into AI systems, including adversarial testing, robust access controls and monitoring for unexpected behavior. Transparent development practices and collaboration with cybersecurity teams help prevent AI models from being exploited for malicious purposes. ... A trust-based ecosystem depends on transparency, consistent governance and strong cybersecurity practices across all stakeholders. Key elements still missing include harmonized standards, comprehensive regulatory guidance, and mechanisms to verify compliance and foster confidence among users and businesses. ... Ethical frameworks guide responsible decision-making by balancing societal impact, individual right and technological innovation. Organizations can apply them through policies, AI oversight and risk assessments that incorporate principles from , , and into everyday operations and strategic planning.

Invisible IT is becoming the next workplace priority

defines as support that runs in the background and prevents problems before employees notice them. The report highlights two areas that bring this approach to life. The first is . Eighty three percent of leaders say this approach is essential, but only 21 percent have achieved it. With that monitor telemetry data across devices, support teams can detect early signs of failure and trigger automated fixes. If a fix requires human involvement, the repair can happen before the user experiences downtime. This reduces disruptions and shifts support teams away from repetitive tasks that slow down operations. The second area is hyper personalization. Many organizations personalize support by role or seniority, but the study argues this does not reflect how people work. AI systems can now create personas based on individual usage patterns. This lets support teams tailor responses and rollouts to real conditions rather than assumptions. ... Although interest in invisible IT is high, most companies are still using manual processes. Sixty five percent detect issues only when users contact support. Fifty five percent resolve them through manual interventions. Hyper personalization is also limited, with 51 percent of organizations offering standard support for all employees. Barriers are widespread. Fifty one percent cite fragmented systems as their top challenge. Another 47 percent point to cost concerns or uncertain return on investment. Limited AI capabilities and skills gaps also slow progress, along with slow upgrade cycles and a lack of time for planning.

Why AI coding agents aren’t production-ready: Brittle context windows, broken refactors, missing operational awareness

AI agents have demonstrated a critical lack of awareness regarding OS machine, command-line and environment installations. This deficiency can lead to frustrating experiences, such as the agent attempting to execute Linux commands on PowerShell, which can consistently result in ‘unrecognized command’ errors. Furthermore, agents frequently exhibit inconsistent ‘wait tolerance’ on reading command outputs, prematurely declaring an inability to read results before a command has even finished, especially on slower machines. ... Working with AI coding agents often presents a longstanding challenge of , or incorrect or incomplete pieces of information (such as small code snippets) within a larger set of changesexpected to be fixed by a developer with trivial-to-low effort. However, what becomes particularly problematic is when incorrect behavior is repeated within a single thread, forcing users to either start a new thread and re-provide all context, or intervene manually to “unblock” the agent. ... Agents may not consistently leverage the latest , instead generating more verbose and harder-to-maintain implementations. ... Despite the allure of autonomous coding, the reality of AI agents in enterprise development often demands constant human vigilance. Instances like an agent attempting to execute Linux commands on PowerShell, false-positive safety flags or introduce inaccuracies due to domain-specific reasons highlight critical gaps; developers simply cannot step away.

Offensive security takes center stage in the AI era

Now a growing percentage of see as a must-have and, as such, are building up offensive capabilities and integrating them into their security processes to ensure the information revealed during offensive exercises leads to improvements in their overall security posture. ... sees several buckets of activities involved in offensive security, starting with at the bottom end of the maturity scale, and then moving up to and , to threat hunting and , such as . “Then there’s the concept of where the organization looks at an attack scenario and what were the defenses that should have alerted but didn’t and how to rectify those,” he says. ... Many CISOs also have had team members with specific offensive security skills for many years. In fact, the Offensive Security Certified Professional (OSCP), the Offensive Security Experienced Penetration Tester (OSEP), and the Offensive Security Certified Expert (OSCE) certifications from are all credentials that have been in demand for years. ... Another factor that keeps CISOs from incorporating more offensive security into their strategies is concern about exposing vulnerabilities they don’t have the ability to address, Mellen adds. “They can’t unknow that they have those vulnerabilities if they’re not able to do something about them, although the hackers are going to find them whether or not you identify them,” he says.

Securing AI for Cyber Resilience: Building Trustworthy and Secure AI Systems

Attackers increasingly target the - poisoning training data, manipulating models, or exploiting vulnerabilities during deployment and operations. When an AI system or model is compromised, it can quietly skew decisions. This poses significant risks for autonomous systems or analytics engines. Thus, it is important that we embed security and resilience into our AI systems, ensuring robust protection from design to deployment and operations. ... Visibility is key. You can’t protect what you can’t see. Without visibility into data flows, model behavior and system interactions, threats can remain undetected until it is too late. Continuous validation and monitoring help surface anomalies and early, enabling timely interventions. Explainability is just as pivotal. Detecting an anomaly is one thing, but understanding why it happened drives true resilience. Explainability clarifies the reasoning behind AI systems and their decisions, helps verify threats, traces manipulations, makes AI systems auditable, and strengthens trust. Assurance must be continuous. ... Attackers are exploiting AI-specific security weaknesses, such as , , and adversarial manipulations. As AI adoption accelerates, its threats will follow in equal sophistication and scale. The rapid proliferation of AI systems across industries not only drives innovation but also expands the attack surface, drawing the attention from both state-sponsored and criminal actors.

From silos to strategy: What the era of cloud 'coopetition' means for CIOs

This week, historic competitors announced the launch of a cross-cloud interconnect service, effectively tearing down the digital iron curtain that once separated their ecosystems. With expected to join this framework in 2026, the cloud industry is pivoting toward "coopetition"-- a strategic truce driven by the modern enterprise's embrace of multi-cloud. ... One of the primary drivers accelerating AWS and Google's cross-cloud interconnect service is AI. The potential of enterprise AI has been hampered by data silos, with fragmented pockets of information trapped in different systems, which then prevents the training of comprehensive models. found that integration challenges are a leading cause of stalled AI initiatives, with nearly 95% of 1,050 IT leaders surveyed citing connectivity issues as a major hurdle. A cross-cloud partnership is a critical tool for dismantling these barriers -- one that could even eliminate the challenge of data silos, according to . ... However, coopetition is not a silver bullet. It also introduces new friction points where the complexity of managing multiple environments can outweigh the benefits if not addressed properly. Peterson warned that there may not be sufficient value when workloads are "highly dependent and intertwined, requiring low-latency communication across different providers".

Simplicity, speed & scalability are the key pillars of our AI strategy: Siddharth Sureka, Motilal Oswal Financial Services

AI is here to stay, and will transform all industries. Naturally, the sector tends to be on the leading edge of this journey, following closely behind pure technology companies. However, rather than viewing this purely through a technology lens, we approached it from an end-to-end organisational transformation lens. ... The first pillar is simplicity. To reach tier two, three, and four cities, we must make the financial experience intuitive. Simplicity is driven by personalisation, which means how we curate the information delivered to clients and ensure their digital journey is frictionless. The second pillar is speed. We are in the business of providing the right insights at the speed of the market. As an event occurs, we must be able to serve our clients with immediate insights. A prime example of this is our ‘’ product. As news arrives, the system measures the sentiment and analyses how it may impact the market, and then serves that insight directly to the client instantly. The third vertical is scalability. Once we have achieved simplicity and speed, our focus is to scale this architecture to reach the deeper pockets of the country. This scalability is essential for the financial inclusion journey we are embarked upon, ensuring that investors in tier three and four cities can take full advantage of the markets. ... In software engineering, you are delivering a . However, when you move into the domain of AI, the outcomes become or probabilistic in nature. As leaders, we must understand the use cases we are working on and, crucially, the ‘cost of getting it wrong’.

Observability at the Edge: A Quiet Shift in Reliability Thinking

Most organizations still don’t really know what’s happening inside their own digital systems. A survey found that 84% of companies struggle with , the basic ability to understand if their systems are working as they should. The reasons are familiar: monitoring tools are expensive, their architectures clumsy, and when scaled across thousands of locations, the complexity often overwhelms the promise. The cost of that opacity is not abstract. Every minute of downtime is lost revenue. Every unnoticed glitch is a frustrated customer. And every delay in diagnosis erodes trust. In this sense, observability is not just a matter for engineers; it’s central to how modern businesses function. ... When systems fail, the speed of diagnosis becomes critical. In fact, organizations can lose an average of $1 million per hour during unplanned downtime, a striking testament to the high cost of delays. The standard approach, engineers combing through logs, traces, and deployment histories, often slows response when time is most precious. ... What stands out is not only the design of these solutions but their uptake elsewhere. The edge observability model first proven in retail has been mirrored in other industries, including banking. The approach has been picked up by financial services firms seeking to sharpen digital performance. And the reflects a broader shift toward embedding AI into reliability practices. Industry peers have described the work as “innovative, cost-effective, and cloud-native.”

2026 DevOps Predictions - Part 1

In 2026, software teams will begin challenging the rising complexity of their own development environments, shifting from simply executing work to questioning why that work exists in the first place. After years of accumulating tools, rituals, and dependencies, developers will increasingly pause to ask whether a feature, deadline, or workflow actually warrants the effort. ... Death of agile as we used to know it: Agile methodologies have dominated software development for the past 20+ years. However, most organizations still "do agile" rather than be agile: they have adopted the agile practices and rituals that foster team collaboration and have become somewhat faster in both executing and reacting to changes. Meanwhile, AI agents have entered the stage. The speed of getting things done is multiplying and a single developer can sometimes replace a whole team. This means on one hand that the traditional human-centered agile practices become less relevant and on the other hand, that agile may become easier to scale. The death of Agile as we used to know it is a positive thing: now we become agile rather than keep doing agile. ... The momentum is shifting from "" to what's becoming known as "": instead of placing specialized responsibilities on developers, organizations are building development platforms that present opinionated paths and implement best practices by default. That change in momentum is bound to accelerate in 2026.

Daily Tech Digest - December 07, 2025

Quote for the day:

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

Balancing AI innovation and cost: The new FinOps mandate

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

Local clouds shape Europe’s AI future

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

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

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

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

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

Rethinking the CIO-CISO Dynamic in the Age of AI

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

The AI reckoning: How boards can evolve

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

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

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

Resilience: How Leaders Build Organizations That Bend, Not Break

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

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

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

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

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

Daily Tech Digest - December 06, 2025

Quote for the day:

"The distance between insanity and genius is measured only by success." -- Bruce Feirstein

AI denial is becoming an enterprise risk: Why dismissing “slop” obscures real capability gains

After all, by any objective measure AI is wildly more capable than the vast majority of computer scientists predicted only five years ago and it is still improving at a surprising pace. The impressive leap demonstrated by Gemini 3 is only the latest example. At the same time, McKinsey recently reported that 20% of organizations already derive tangible value from genAI. ... So why is the public buying into the narrative that AI is faltering, that the output is “slop,” and that the AI boom lacks authentic use cases? Personally, I believe it’s because we’ve fallen into a collective state of AI denial, latching onto the narratives we want to hear in the face of strong evidence to the contrary. Denial is the first stage of grief and thus a reasonable reaction to the very disturbing prospect that we humans may soon lose cognitive supremacy here on planet earth. In other words, the overblown AI bubble narrative is a societal defense mechanism. ... It’s likely that AI will soon be able to read our emotions faster and more accurately than any human, tracking subtle cues in our micro-expressions, vocal patterns, posture, gaze and even breathing. And as we integrate AI assistants into our phones, glasses and other wearable devices, these systems will monitor our emotional reactions throughout our day, building predictive models of our behaviors. Without strict regulation, which is increasingly unlikely, these predictive models could be used to target us with individually optimized influence that maximizes persuasion.

A smarter way for large language models to think about hard problems

“The computational cost of inference has quickly become a major bottleneck for frontier model providers, and they are actively trying to find ways to improve computational efficiency per user queries. For instance, the recent GPT-5.1 release highlights the efficacy of the ‘adaptive reasoning’ approach our paper proposes. By endowing the models with the ability to know what they don’t know, we can enable them to spend more compute on the hardest problems and most promising solution paths, and use far fewer tokens on easy ones. That makes reasoning both more reliable and far more efficient,” says Navid Azizan, the Alfred H. and Jean M. Hayes ... Typical inference-time scaling approaches assign a fixed amount of computation for the LLM to break the problem down and reason about the steps. Instead, the researchers’ method, known as instance-adaptive scaling, dynamically adjusts the number of potential solutions or reasoning steps based on how likely they are to succeed, as the model wrestles with the problem. “This is how humans solve problems. We come up with some partial solutions and then decide, should I go further with any of these, or stop and revise, or even go back to my previous step and continue solving the problem from there?” Wang explains. ... “The beauty of our approach is that this adaptation happens on the fly, as the problem is being solved, rather than happening all at once at the beginning of the process,” says Greenewald.

Extending Server Lifespan: Cost-Saving Strategies for Data Centers

Predicting server lifespan is complicated, as servers don’t typically wear out at a consistent rate. In fact, many of the components inside servers, like CPUs, don’t really wear out at all, so long as they’re not subject to unusual conditions, like excess heat. But certain server parts, such as hard disks, will eventually fail because they contain mechanical components that wear down over time. ... A challenge is that cooler server rooms often lead to higher data center energy costs, and possibly greater water usage, due to the increased load on cooling systems. But if you invest in cooling optimization measures, it may be possible to keep your server room cool without compromising on sustainability goals. ... Excess or highly fluctuating electrical currents can fry server components. Insufficient currents may also cause problems, as can frequent power outages. Thus, making smart investments in power management technologies for data centers is an important step toward keeping servers running longer. The more stable and reliable your power system, the longer you can expect your servers to last. ... The greater the percentage of available CPU and memory a server uses on a regular basis, the more likely it is to wear out due to the increased load placed on system components. That’s why it’s important to avoid placing workloads on servers that continuously max out their resource consumption.

Sleepless in Security: What’s Actually Keeping CISOs Up at Night

While mastering the fundamentals keeps your organization secure day to day, CISOs face another, more existential challenge. The interconnected nature of the modern software ecosystem — built atop stacks of open-source components and complex layers of interdependent libraries — is always a drumbeat risk in the background. It’s a threat that often flies under the radar until it’s too late. ... While CISOs can’t rewrite the entire software ecosystem, what they can do is bring the same discipline to third-party and open-source risk management that they apply to internal controls. That starts with visibility, especially when it comes to third-party libraries and packages. By maintaining an accurate and continuously updated inventory of all components and their dependencies, CISOs can enforce patching and vulnerability management processes that enable them to respond quickly to bugs, breaches, vulnerabilities, and other potential challenges. ... The cybersecurity landscape is relentless—and the current rate of change is unlikely to shift anytime soon. While splashy headlines soak up a lot of the attention, the things keeping CISOs awake at night usually look a little different. The fundamentals we often take for granted and the fragile systems our enterprises depend on aren’t always as secure as they seem, and accounting for that risk is critical. From basic hygiene like user lifecycle management and MFA coverage to the sprawling, interdependent web of open-source software, the threats are systemic and constantly evolving.

Agents-as-a-service are poised to rewire the software industry and corporate structures

AI agents are set to change the dynamic between enterprises and software vendors in other ways, too. One major difference between software and agents is software is well-defined, operates in a particular way, and changes slowly, says Jinsook Han, chief of strategy, corporate development, and global agentic AI at Genpact. “But we expect when the agent comes in, it’s going to get smarter every day,” she says. “The world will change dramatically because agents are continuously changing. And the expectations from the enterprises are also being reshaped.” ... Another aspect of the agentic economy is instead of a human employee talking to a vendor’s AI agent, a company agent can handle the conversation on the employee’s behalf. And if a company wants to switch vendors, the experience will be seamless for employees, since they never had to deal directly with the vendor anyway. “I think that’s something that’ll happen,” says Ricardo Baeza-Yates, co-chair of the US technology policy committee at the Association for Computing Machinery. “And it makes the market more competitive, and makes integrating things much easier.” In the short term, however, it might make more sense for companies to use the vendors’ agents instead of creating their own. ... That doesn’t mean SaaS will die overnight. Companies have made significant investments in their current technology infrastructure, says Patrycja Sobera, SVP of digital workplace solutions at Unisys.

Beyond the Buzzword: The Only Question that Matters for AI in Network Operations

The problem isn’t the lack of information; it’s the volume and pace at which information arrives from a dozen different monitoring tools that can’t communicate with each other. You know the pattern: the tool sprawl problem definitely exists. A problem occurs, and it’s no longer just an alarm—it’s a full-blown storm of noise out of which you can’t differentiate the source of the problem. Our ops teams are the real heroes who keep the lights on and spend way too much of their time correlating information across various screens. They are essentially trying to jump from network to log file to application trace as the clock ticks. ... In operations, reliability is everything. You cannot build a house on shifting sand, and you certainly can’t build a reliable operational strategy on noisy, inconsistent data. If critical context is missing, even the most sophisticated model will start to drift toward educated guesswork. It’s like commissioning an architect to design a skyscraper without telling them where the foundation will be or what soil they’ll be working with. ... The biggest shortcoming of traditional tools is the isolated visibility they provide: they perceive incidents as a series of isolated points. The operator receives three notifications: one regarding the routing problem (NetOps), one regarding high CPU on the server (ITOps), and one regarding application latency (CloudOps). The platform receives three symptoms.

Architecting efficient context-aware multi-agent framework for production

To build production-grade agents that are reliable, efficient, and debuggable, the industry is exploring a new discipline: Context engineering — treating context as a first-class system with its own architecture, lifecycle, and constraints. Based on our experience scaling complex single- or multi-agentic systems, we designed and evolved the context stack in Google Agent Development Kit (ADK) to support that discipline. ADK is an open-source, multi-agent-native framework built to make active context engineering achievable in real systems. ... Early agent implementations often fall into the "context dumping" trap: placing large payloads—a 5MB CSV, a massive JSON API response, or a full PDF transcript—directly into the chat history. This creates a permanent tax on the session; every subsequent turn drags that payload along, burying critical instructions and inflating costs. ADK solves this by treating large data as Artifacts: named, versioned binary or text objects managed by an ArtifactService. Conceptually, ADK applies a handle pattern to large data. Large data lives in the artifact store, not the prompt. By default, agents see only a lightweight reference (a name and summary) via the request processor. When—and only when—an agent requires the raw data to answer a question, it uses the LoadArtifactsTool. This action temporarily loads the content into the Working Context.

Can Europe Build Digital Sovereignty While Safeguarding Its Rights Legacy?

The biggest constraint to the EuroStack vision is not technical — it is energy. AI and digital infrastructure demand massive, continuous power, and Europe’s grid is not yet built for it. ... Infrastructure sovereignty is impossible without sovereign capital. Aside from the Schwarz Digits pledge, general financing plans remain insufficient, and the venture financing landscape is fragmented. While the 2026 EU budget allocates €1.0 billion to the Digital Europe Programme, this foundational funding is not sufficient for the scale of EuroStack. The EU needs a unified capital markets union and targeted instruments to fund capital-intensive projects—specifically open-source infrastructure, which was identified as a strategic necessity by the Sovereign Tech Agency. ... Sovereignty requires control over the digital stack’s core layers, a control proprietary software inherently denies. The EU must view open-source technologies not just as a cheaper alternative for public procurement, but as the only viable path to technical autonomy, transparency, and resilience. ... Finally, Europe must abandon the fortress mentality. Sovereignty does not mean isolation; it means strategic interdependence. Inward-looking narratives risk ignoring crucial alliances with emerging economies. Europe must actively position itself as the global bridge-builder, offering an alternative to the US-China binary by advocating for its standards and co-developing interoperable infrastructure with emerging economies.

Avoiding the next technical debt: Building AI governance before it breaks

In fact, AI risks aren’t just about the future — they’re already part of daily operations. These risks arise when algorithms affect business results without clear accountability, when tools collect sensitive data and when automated systems make decisions that people no longer check. These governance gaps aren’t new. We saw the same issues with cloud, APIs, IoT and big data. The solution is also familiar: keep track, assess, control and monitor. ... Without the right guardrails, an agent can access systems it shouldn’t, expose confidential data, create unreliable information, start unauthorized transactions, skip established workflows or even act against company policy or ethics. These risks are made worse by how fast and independently agent AI works, which can cause big problems before people notice. In the rush to try new things, many companies launch these agents without basic access controls or oversight. The answer is to use proven controls like least privilege, segregation of duties, monitoring and accountability. ... Technical debt isn’t just about code anymore. It’s also about trusting your data, holding models accountable and protecting your brand’s reputation. The organizations that succeed with AI will be the ones that see governance as part of the design process, not as something that causes delays. They’ll move forward with clear plans and measure value and risk together.

How Data is Reshaping Science – Part 4: The New Trust Problem in Scientific Discovery

Scientific AI runs on architectures spread across billions (possibly trillions soon) of parameters, trained on datasets that remain partially or fully opaque. Peer review was built for methods a human could trace, not pipelines that mutate through thousands of training cycles. The trust layer that once anchored scientific work is now under strain. Traditional validation frameworks fall behind for the same reason. They were designed for fixed algorithms and stable datasets. Modern models shift with each retraining step, and disciplines lack shared benchmarks for measuring accuracy. ... The trust problems emerging across data-driven science point to a missing layer, one that operates beneath the experiments and above the compute. It is the layer that connects data, models, and decisions into a single traceable chain. Without it, every insight relies too heavily on the integrity of (potentially undocumented) the steps to reach this point. A modern governance system would require full provenance tracking. It would need permissions that define who can modify what and have audit trails that record data transformation. This is not an easy task, given how vast the datasets tend to be. Scientific AI complicates this further. These models shift as datasets change and as new configurations alter their behavior. That means science must adopt the same version control rigor seen in highly regulated industries.

Daily Tech Digest - December 05, 2025

Quote for the day:

“Failure defeats losers, failure inspires winners.” -- Robert T. Kiyosaki

The 'truth serum' for AI: OpenAI’s new method for training models to confess their mistakes

A confession is a structured report generated by the model after it provides its main answer. It serves as a self-evaluation of its own compliance with instructions. In this report, the model must list all instructions it was supposed to follow, evaluate how well it satisfied them and report any uncertainties or judgment calls it made along the way. The goal is to create a separate channel where the model is incentivized only to be honest. ... During training, the reward assigned to the confession is based solely on its honesty and is never mixed with the reward for the main task. "Like the Catholic Church’s 'seal of confession', nothing that the model reveals can change the reward it receives for completing its original task," the researchers write. This creates a "safe space" for the model to admit fault without penalty. This approach is powerful because it sidesteps a major challenge in AI training. The researchers’ intuition is that honestly confessing to misbehavior is an easier task than achieving a high reward on the original, often complex, problem. ... For AI applications, mechanisms such as confessions can provide a practical monitoring mechanism. The structured output from a confession can be used at inference time to flag or reject a model’s response before it causes a problem. For example, a system could be designed to automatically escalate any output for human review if its confession indicates a policy violation or high uncertainty.

Why is enterprise disaster recovery always such a…disaster?

One of the brutal truths about enterprise disaster recovery (DR) strategies is that there is virtually no reliable way to truly test them. ... From a corporate politics perspective, IT managers responsible for disaster recovery have a lot of reasons to avoid an especially meaningful test. Look at it from a risk/reward perspective. They’re going to take a gamble, figuring that any disaster requiring the recovery environment might not happen for a few years. And by then with any luck, they’ll be long gone. ... “Enterprises place too much trust in DR strategies that look complete on slides but fall apart when chaos hits,” he said. “The misunderstanding starts with how recovery is defined. It’s not enough for infrastructure to come back online. What matters is whether the business continues to function — and most enterprises haven’t closed that gap. ... “Most DR tools, even DRaaS, only protect fragments of the IT estate,” Gogia said. “They’re scoped narrowly to fit budget or ease of implementation, not to guarantee holistic recovery. Cloud-heavy environments make things worse when teams assume resilience is built in, but haven’t configured failover paths, replicated across regions, or validated workloads post-failover. Sovereign cloud initiatives might address geopolitical risk, but they rarely address operational realism.

The first building blocks of an agentic Windows OS

Microsoft is adding an MCP registry to Windows, which adds security wrappers and provides discovery tools for use by local agents. An associated proxy manages connectivity for both local and remote servers, with authentication, audit, and authorization. Enterprises will be able to use these tools to control access to MCP, using group policies and default settings to give connectors their own identities. ... Be careful when giving agents access to the Windows file system; use base prompts that reduce the risks associated with file system access. When building out your first agent, it’s worth limiting the connector to search (taking advantage of the semantic capabilities of Windows’ built-in Phi small language model) and reading text data. This does mean you’ll need to provide your own guardrails for agent code running on PCs, for example, forcing read-only operations and locking down access as much as possible. Microsoft’s planned move to a least-privilege model for Windows users could help here, ensuring that agents have as few rights as possible and no avenue for privilege escalation. ... Building an agentic OS is hard, as the underlying technologies work very differently from standard Windows applications. Microsoft is doing a lot to provide appropriate protections, building on its experience in delivering multitenancy in the cloud.

Syntax hacking: Researchers discover sentence structure can bypass AI safety rules

The findings reveal a weakness in how these models process instructions that may shed light on why some prompt injection or jailbreaking approaches work, though the researchers caution their analysis of some production models remains speculative since training data details of prominent commercial AI models are not publicly available. ... This suggests models absorb both meaning and syntactic patterns, but can overrely on structural shortcuts when they strongly correlate with specific domains in training data, which sometimes allows patterns to override semantic understanding in edge cases. ... In layperson terms, the research shows that AI language models can become overly fixated on the style of a question rather than its actual meaning. Imagine if someone learned that questions starting with “Where is…” are always about geography, so when you ask “Where is the best pizza in Chicago?”, they respond with “Illinois” instead of recommending restaurants based on some other criteria. They’re responding to the grammatical pattern (“Where is…”) rather than understanding you’re asking about food. This creates two risks: models giving wrong answers in unfamiliar contexts (a form of confabulation), and bad actors exploiting these patterns to bypass safety conditioning by wrapping harmful requests in “safe” grammatical styles. It’s a form of domain switching that can reframe an input, linking it into a different context to get a different result.

In 2026, Should Banks Aim Beyond AI?

Developing native AI agents and agentic workflows will allow banks to automate complex journeys while fine-tuning systems to their specific data and compliance landscapes. These platforms accelerate innovation and reinforce governance structures around AI deployment. This next generation of AI applications elevates customer service, fostering deeper trust and engagement. ... But any technological advancement must be paired with accountability and prudent risk management, given the sensitive nature of banking. AI can unlock efficiency and innovation, but its impact depends on keeping human decision-making and oversight firmly in place. It should augment rather than replace human authority, maintaining transparency and accountability in all automated processes. ... The banking environment is too risky for fully autonomous agentic AI workflows. Critical financial decisions require human judgment due to the potential for significant consequences. Nonetheless, many opportunities exist to augment decision-making with AI agents, advanced models and enriched datasets. ... As this evolution unfolds, financial institutions must focus on executing AI initiatives responsibly and effectively. By investing in home-grown platforms, emphasizing explainability, balancing human oversight with automation and fostering adaptive leadership, banks, financial services and insurance providers can navigate the complexities of AI adoption.

Building the missing layers for an internet of agents

The proposed Agent Communication Layer sits above HTTP and focuses on message structure and interaction patterns. It brings together what has been emerging across several protocols and organizes them into a common set of building blocks. These include standardized envelopes, a registry of performatives that define intent, and patterns for one to one or one to many communication. The idea is to give agents a dependable way to understand the type of communication taking place before interpreting the content. A request, an update, or a proposal each follows an expected pattern. This helps agents coordinate tasks without guessing the sender’s intention. The layer does not judge meaning. It only ensures that communication follows predictable rules that all agents can interpret. ... The paper outlines several new risks. Attackers might inject harmful content that fits the schema but tricks the agent’s reasoning. They might distribute altered or fake context definitions that mislead a population of agents. They might overwhelm a system with repetitive semantic queries that drain inference resources rather than network resources. To manage these problems, the authors propose security measures that match the new layer. Signed context definitions would prevent tampering. Semantic firewalls would examine content at the concept level and enforce rules about who can use which parts of a context.

The Rise of SASE: From Emerging Concept to Enterprise Cornerstone

The case for SASE depends heavily on the business outcomes required, and there can be multiple use cases for SASE deployment. However, not everyone is always aligned around these. Whether you’re looking to modernize systems to boost operational resilience, reduce costs, or improve security to adhere to regulatory compliance, there needs to be alignment around your SASE deployment. Additionally, because of its versatility, SASE demands expertise across networking, cloud security, zero trust, and SD-WAN, but, unfortunately, these skills are in short supply. IT teams must upskill or recruit talent capable of managing (and running) this convergence, while also adapting to new operational models and workflows. ... However, most of the reported benefits don’t focus on tangible or financial outcomes, but rather those that are typically harder to measure, namely boosting operational resilience and enhancing user experience. These are interesting numbers to explore, as SASE investments are often predicated on specific and easily measurable business cases, typically centered around cost savings or mitigation of specific cyber/operational risks. Looking at the benefits from both a networking and security perspective, the data reveals different priorities for SASE adoption: IT Network leaders value operational streamlining and efficiency, while IT Security leaders emphasize secure access and cloud protection.

Intelligent Banking: A New Standard for Experience and Trust

At its core, Intelligent Banking connects three forces that are redefining what "intelligent" really means: Rising expectations - Customers not only expect their institutions to understand them, but to intuitively put forward recommendations before they realize change is needed. All while acting with empathy while delivering secure, trusted experiences. ... Data abundance - Financial institutions have more data than ever but struggle to turn it into actionable insight that benefits both the customer and the institution. ... AI readiness - For years, AI in banking was at best a buzz word that encapsulated the standard — decision trees, models, rules. ... The next era of AI in banking will be completely different. It will be invisible. Embedded. Contextual. It will be built into the fabric of the experience, not just added on top. And while mobile apps as we know them will likely be around for a while, a fully GenAI native banking experience is both possible and imminent. ... In the age of AI, it’s tempting to see "intelligence" purely as technology alone. But the future of banking will depend just as much on human intelligence as it will artificial intelligence. The expertise, empathy, and judgement of the institutions who understand financial context and complexity blended with the speed, prediction and pattern recognition that uncover insights humans can’t see will create a new standard for banking, one where experiences feel both profoundly human and intelligently anticipatory.

Taking Control of Unstructured Data to Optimize Storage

The modern business preoccupation with collecting and retaining data has become something of a double-edged sword. On the plus side, it has fueled a transformational approach to how organizations are run. On the other hand, it’s rapidly becoming an enormous drain on resources and efficiency. The fact that 80-90% of this information is unstructured, i.e. spread across formats such as documents, images, videos, emails, and sensor outputs, only adds to the difficulty of organizing and controlling it. ... To break this down, detailed metadata insight is essential for revealing how storage is actually being used. Information such as creation dates, last accessed timestamps, and ownership highlights which data is active and requires performance storage, and which has aged out of use or no longer relates to current users. ... So, how can this be achieved? At a fundamental level, storage optimization hinges on adopting a technology approach that manages data, not storage devices; simply adding more and more capacity is no longer viable. Instead, organizations must have the ability to work across heterogeneous storage environments, including multiple vendors, locations and clouds. Tools should support vendor-neutral management so data can be monitored and moved regardless of the underlying platform. Clearly, this has to take place at petabyte scale. Optimization also relies on policy-based data mobility that enables data to be moved based on defined rules, such as age or inactivity, with inactive or long-dormant data.

W.A.R & P.E.A.C.E: The Critical Battle for Organizational Harmony

W.A.R & P.E.A.C.E, the pivotal human lens within TRIAL, designed specifically to address this cultural challenge and shepherd the enterprise toward AARAM (Agentic AI Reinforced Architecture Maturities)2 with what I term “speed 3” transformation of AI. ... The successful, continuous balancing of W.A.R. and P.E.A.C.E. is the biggest battle an Enterprise Architect must win. Just as Tolstoy explored the monumental scope of war against intimate moments of peace in his masterwork, the Enterprise Architect must balance the intense effort to build repositories against the delicate work of fostering organizational harmony. ... The W.A.R. systematically organizes information across the four critical architectural domains defined in our previous article: Business, Information, Technology, and Security (BITS). The true power of W.A.R. lies in its ability to associate technical components with measurable business and financial properties, effectively transforming technical discussions into measurable, strategic imperatives. Each architectural components across BITS are tracked across Plan, Design & Run lifecycle of change under the guardrails of BYTES. ... Achieving effective P.E.A.C.E. mandates a carefully constructed collaborative environment where diverse organizational roles work together toward a shared objective. This requires alignment across all lifecycle stages using social capital and intelligence.