Daily Tech Digest by Kannan Subbiah: 2025

Daily Tech Digest - December 25, 2025

Quote for the day:

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

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

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

How Today’s Attackers Exploit the Growing Application Security Gap

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

Data governance key to AI security

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

How the AI era is driving the resurgence in disaggregated storage

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

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

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

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

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

Leadership And The Simple Magic Of Getting Lost

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

Gen AI Paradox: Turning Legacy Code Into an Asset

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

5 stages to observability maturity

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

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

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

Daily Tech Digest - December 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.

Daily Tech Digest - December 23, 2025

Quote for the day:

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

The CIO Playbook: Reimagining Transformation in a Shifting Economy

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

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

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

Agentic AI already hinting at cybersecurity’s pending identity crisis

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

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

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

Top 5 enterprise tech priorities for 2026

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

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

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

Stop letting ‘urgent’ derail delivery. Manage interruptions proactively

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

How does Agentic AI enhance operational security

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

AI-fuelled cyber onslaught to hit critical systems by 2026

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

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

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

Daily Tech Digest - December 22, 2025

Quote for the day:

"Life isn’t about getting and having, it’s about giving and being." -- Kevin Kruse

Browser agents don’t always respect your privacy choices

A key issue is the location of the language model. Seven out of eight agents use off device models. This means detailed information about the user’s browser state and each visited webpage is sent to servers controlled by the service provider. When the model runs on remote servers, users lose control over how search queries and sensitive webpage content are processed and stored. While some providers describe limits on data use, users must rely on service provider policies. Browser version age is another factor. Browsers release frequent updates to patch security flaws. One agent was found running a browser that was 16 major versions out of date at the time of testing. ... Agents also showed weaknesses in TLS certificate handling. Two agents did not show warnings for revoked certificates. One agent also failed to warn users about expired and self signed certificates. Trusting connections with invalid certificates leaves agents open to machine-in-the-middle attacks that allow attackers to read or alter submitted information. ... Agent decision logic sometimes favored task completion over protecting user information, leading to personal data disclosure. This resulted in six vulnerabilities. Researchers supplied agents with a fictitious identity and observed whether that information was shared with websites under different conditions. Three agents disclosed personal information during passive tests, where the requested data was not required to complete the task.

What CISOs should know about the SolarWinds lawsuit dismissal

For many CISOs, the dismissal landed not as an abstract legal development, but as something deeply personal. ... Even though the SolarWinds case sparked a deeper recognition that cybersecurity responsibility should be a shared responsibility across enterprises, shifting policy priorities and future administrations could once again put CISOs in the SEC’s crosshairs, they warn. ... The judge’s reasoning reassured many security leaders, but it also exposed a more profound discomfort about how accountability is assigned inside modern organizations. “The area that a lot of us were really uncomfortable about was the idea that an operational head of security could be personally responsible for what the company says about its cybersecurity investments,” Sullivan says. He adds, “Tim didn’t have the CISO title before the incident. And so there was just a lot there that made security people very concerned. Why is this operational person on the hook for representations?” But even if he had had the CISO role before the incident, the argument still holds, according to Sullivan. “Historically, the person who had that title wasn’t a quote-unquote ‘chief’ in the sense that they’re not in the little room of people who run the company,” Sullivan says. ... If the SolarWinds case clarified anything, it’s that relief is temporary and preparation is essential. CISOs have a window of opportunity to shore up their organizational and personal defenses in the event the political pendulum swings and makes CISOs litigation targets again.

Global uncertainty is reshaping cloud strategies in Europe

Europe has been debating digital sovereignty for years, but the issue has gained new urgency amid rising geopolitical tensions. “The political environment is changing very fast,” said Ollrom. A combination of trade disputes, sanctions that affect access to technology, and the possibility of tariffs on digital services has prompted many European organizations to reconsider their reliance on US hyperscaler clouds. ... What was once largely a public-sector concern now attracts growing interest across a wide range of private organizations as well. Accenture is currently working with around 50 large European organizations on digital-sovereignty-related projects, said Capo. This includes banks, telcos, and logistics companies alongside clients in government and defense. ... Another worry is the possibility that cloud services will be swept up in future trade disputes. If the EU imposes retaliatory tariffs on digital services, the cost of using hyperscaler cloud platforms could hike overnight, and organizations heavily dependent on them may find it hard to switch to a cheaper option. There’s also the prospect that organizations could lose access to cloud services if sanctions or export restrictions are imposed, leaving them temporarily or permanently locked out of systems they rely on. It’s a remote risk, said Dario Maisto, a senior analyst at Forrester, but a material one. “We are talking of a worst-case scenario where IT gets leveraged as a weapon,” he said.

What the AWS outage taught CIOs about preparedness

For many organizations, the event felt like a cyber incident even though it wasn’t, but it raised a difficult question for CIOs about how to prepare for a disruption that lives outside your infrastructure, yet carries the same operational and reputational consequences as a security breach. ... Beyond strong cloud architecture, “Preparedness is the real differentiator,” he says. “Even the best technology teams can’t compensate for gaps in scenario planning, coordination, and governance.” ... Within Deluxe, disaster recovery tests historically focused on applications the company controlled, while cyber tabletops focused on simulated intrusions. The AWS outage exposed the gap between those exercises and real-world conditions. Shifting its applications from AWS East to AWS West was swift, and the technology team considered the recovery a success. Yet it was far from business as usual, as developers still couldn’t access critical tools like GitHub or Jira. “We thought we’d recovered, but the day-to-day work couldn’t continue because the tools we depend on were down,” he says. ... In a well-architected hybrid cloud setup, he says resilience is more often a coordination problem than a spending problem, and distributing workloads across two cloud providers doesn’t guarantee better outcomes if the clouds rely on the same power grid, or experience the same regional failure event. ... Jayaprakasam is candid about the cultural challenge that comes with resilience work.

Winning the density war: The shift from RPPs to scalable busway infrastructure in next-gen facilities

“Four or five years ago, we were seeing sub-ten-kilowatt racks, and today we're being asked for between 100 and 150 kilowatts, which makes a whole magnitude of difference,” says Osian. “And this trend is going to continue to rise, meaning we have to mobilize for tomorrow’s power challenges, today.” Rising power demands also require higher available fault currents to safely handle larger, more dynamic surges in the circuit. Supporting equipment must be more resilient and reliable to maintain safe and efficient distribution. With change happening so quickly, adopting a long-term strategy is essential. This requires building critical infrastructure with adaptability and flexibility at its core. ... A modular approach offers another tactical advantage: speed. With a traditional RPP setup, getting power physically hooked up from A to B on a per-rack basis is time and resource-consuming, especially at first installation. By reducing complexity with a plug-and-play modular design slotted in directly over the racks, the busway delivers the swift reinforcements modern facilities need to stay ahead. ... “One of the advancements we've made in the last year is creating a way for users to add a circuit from outside the arc flash boundary. While the Starline busway is already rated for live insertion – meaning it’s safe out of the box – we’ve taken safety to the next level with a device called the Remote Plugin Actuator. It allows a user to add a circuit to the busway without engaging any of the electrical contacts directly.”

Building a data-driven, secure and future-ready manufacturing enterprise: Technology as a strategic backbone

A central pillar of Prince Pipes and Fittings’ digital strategy is data democratisation. The organisation has moved decisively away from static reports towards dynamic, self-service analytics. A centralised data platform for sales and supply chain allows business users to create their own dashboards without dependence on IT teams. Desai further states, “Sales teams, for instance, can access granular data on their smartphones while interacting with customers, instantly showcasing performance metrics and trends. This empowerment has not only improved responsiveness but has also enhanced user confidence and satisfaction. Across functions, data is now guiding actions rather than merely describing outcomes.” ... Technology transformation at Prince Pipes and Fittings has been accompanied by a conscious effort to drive cultural change. Leadership recognised early that democratising data would require a mindset shift across the organisation. Initial resistance was addressed through structured training programs conducted zone-wise and state-wise, helping users build familiarity and confidence with new platforms. ... Cyber security is treated as a business-critical priority at Prince Pipes and Fittings. The organisation has implemented a phase-wise, multi-layered cyber security framework spanning both IT and OT environments. A simple yet effective risk-classification approach i.e. green, yellow, and red, was used to identify gaps and prioritise actions. ... Equally important has been the focus on human awareness.

The Next Fraud Problem Isn’t in Finance. It’s in Hiring: The New Attack Surface

The uncomfortable truth is that the interview has become a transaction. And the “asset” being transferred is not a paycheck. It’s access: to systems, data, colleagues, customers, and internal credibility. ... Payment fraud works because the system is trying to be fast. The same is true in hiring. Speed is rewarded. Friction is avoided. And that creates a predictable failure mode: an attacker’s job is to make the process feel normal long enough to get to “approved.” In payments, fraudsters use stolen cards and compromised accounts. In hiring, they can use stolen faces, voices, credentials, and employment histories. The mechanics differ, but the objective is identical: get the system to say yes. That’s why the right question for leaders is not, “Can we spot a deepfake?” It’s, “What controls do we have before we grant access?” ... Many companies verify identity late, during onboarding, after decisions are emotionally and operationally “locked.” That’s the equivalent of shipping a product and hoping the card wasn’t stolen. Instead, introduce light identity proofing before final rounds or before any access-related steps. ... In payments, the critical moment is authorization. In hiring, it’s when you provision accounts, ship hardware, grant repository permissions, or provide access to customer or financial systems. That moment deserves a deliberate gate: confirm identity through a known-good channel, verify references without relying on contact info provided by the candidate, and run a final live verification step before credentials are issued.

Agent autonomy without guardrails is an SRE nightmare

Four-in-10 tech leaders regret not establishing a stronger governance foundation from the start, which suggests they adopted AI rapidly, but with margin to improve on policies, rules and best practices designed to ensure the responsible, ethical and legal development and use of AI. ... When considering tasks for AI agents, organizations should understand that, while traditional automation is good at handling repetitive, rule-based processes with structured data inputs, AI agents can handle much more complex tasks and adapt to new information in a more autonomous way. This makes them an appealing solution for all sorts of tasks. But as AI agents are deployed, organizations should control what actions the agents can take, particularly in the early stages of a project. Thus, teams working with AI agents should have approval paths in place for high-impact actions to ensure agent scope does not extend beyond expected use cases, minimizing risk to the wider system. ... Further, AI agents should not be allowed free rein across an organization’s systems. At a minimum, the permissions and security scope of an AI agent must be aligned with the scope of the owner, and any tools added to the agent should not allow for extended permissions. Limiting AI agent access to a system based on their role will also ensure deployment runs smoothly. Keeping complete logs of every action taken by an AI agent can also help engineers understand what happened in the event of an incident and trace back the problem.

Where Architects Sit in the Era of AI

In the emerging AI-augmented ecosystem, we can think of three modes of architect involvement: Architect in the loop, Architect on the loop, and Architect out of the loop. Each reflects a different level of engagement, oversight, and trust between an Architect and intelligent systems. ... What does it mean to be in the loop? In the Architect in the Loop (AITL) model, the architect and the AI system work side by side. AI provides options, generates designs, or analyzes trade-offs, but humans remain the decision-makers. Every output is reviewed, contextualized, and approved by an architect who understands both the technical and organizational context. This is where the Architect is sat in the middle of AI interactions ... What does it mean to be on the loop? As AI matures, parts of architectural decision-making can be safely delegated. In the Architect on the Loop (AOTL) model, the AI operates autonomously within predefined boundaries, while the architect supervises, reviews, and intervenes when necessary. This is where the architect is firmly embedded into the development workflow using AI to augment and enhance their own natural abilities. ... What does it mean to be out of the loop? In the AOOTL model, we see a world where the architect is no longer required in the traditional fashion. The architectural work of domain understanding, context providing, and design thinking is simply all done by AI, with the outputs of AI being used by managers, developers, and others to build the right systems at the right time.

Cloud Migration of Microservices: Strategy, Risks, and Best Practices

The migration of microservices to the cloud is a crucial step in the digital transformation process, requiring a strategic approach to ensure success. The success of the migration depends on carefully selecting the appropriate strategy based on the current architecture's maturity, technical debt, business objectives, and cloud infrastructure capabilities. ... The simplest strategy for migrating to the cloud is Rehost. This involves moving applications as is to virtual machines in the cloud. According to research, around 40% of organizations begin their migration with Rehost, as it allows for a quick transition to the cloud with minimal costs. However, this approach often does not provide significant performance or cost benefits, as it does not fully utilize cloud capabilities. Replatform is the next level of complexity, where applications are partially adapted. For example, databases may be migrated to cloud services like Amazon RDS or Azure SQL, file storage may be replaced, and containerization may be introduced. Replatform is used in around 22% of cases where there is a need to strike a balance between speed and the depth of changes. A more time-consuming but strategically beneficial approach is Refactoring (or Rearchitecting), in which the application undergoes a significant redesign: microservices are introduced, Kubernetes, Kafka, and cloud functions (such as Lambda and Azure Functions) are utilized, as well as a service bus.

Daily Tech Digest - December 21, 2025

Quote for the day:

"Don't worry about being successful but work toward being significant and the success will naturally follow." -- Oprah Winfrey

Is it Possible to Fight AI and Win?

What’s the most important thing security teams need to figure out? Organizations must stop talking about AI like it’s a death star of sorts. AI is not a single, all-powerful, monolithic entity. It’s a stack of threats, behaviors, and operational surfaces and each one has its own kill chain, controls, and business consequences. We need to break AI down into its parts and conduct a real campaign to defend ourselves. ... If AI is going to be operationalized inside your business, it should be treated like a business function. Not a feature or experiment, but a real operating capability. When you look at it that way, the approach becomes clearer because businesses already know how to do this. There is always an equivalent of HR, finance, engineering, marketing, and operations. AI has the same needs. ... Quick fixes aren’t enough in the AI era. The bad actors are innovating at machine speed, so humans must respond at machine speed with appropriate human direction and ethical clarity. AI is a tool. And the side that uses it better will win. If that isn’t enough, AI will force another reality that organizations need to prepare for. Security and compliance will become an on-demand model. Customers will not wait for annual reports or scheduled reviews. They will click into a dashboard and see your posture in real time. Your controls, your gaps, and your response discipline will be visible when it matters, not when it is convenient.

Cybersecurity Budgets are Going Up, Pointing to a Boom

Nearly all of the security leaders (99%) in the 2025 KPMG Cybersecurity Survey plan on upping their cybersecurity budgets in the two-to-three years to come, in preparation for what may be the upcoming boom in cybersecurity. More than half (54%) say budget increases will fall between 6%-10%. “The data doesn’t just point to steady growth; it signals a potential boom. We’re seeing a major market pivot where cybersecurity is now a fundamental driver of business strategy,” Michael Isensee, Cybersecurity & Tech Risk Leader, KPMG LLP, said in a release. “Leaders are moving beyond reactive defense and are actively investing to build a security posture that can withstand future shocks, especially from AI and other emerging technologies. This isn’t just about spending more; it’s about strategic investment in resilience.” ... The security leaders recognize AI is amassing steam as a dual catalyst—38% are challenged by AI-powered attacks in the coming three years, with 70% of organizations currently committing 10% of their budgets to combating such attacks. But they also say AI is their best weapon to proactively identify and stop threats when it comes to fraud prevention (57%), predictive analytics (56%) and enhanced detection (53%). But they need the talent to pull it off. And as the boom takes off, 53% just don’t have enough qualified candidates. As a result, 49% are increasing compensation and the same number are bolstering internal training, while 25% are increasingly turning to third parties like MSSPs to fill the skills gap.

How Neuro-Symbolic AI Breaks the Limits of LLMs

While AI transforms subjective work like content creation and data summarization, executives rightfully hesitate to use it when facing objective, high-stakes determinations that have clear right and wrong answers, such as contract interpretation, regulatory compliance, or logical workflow validation. But what if AI could demonstrate its reasoning and provide mathematical proof of its conclusions? That’s where neuro-symbolic AI offers a way forward. The “neuro” refers to neural networks, the technology behind today’s LLMs, which learn patterns from massive datasets. A practical example could be a compliance system, where a neural model trained on thousands of past cases might infer that a certain policy doesn’t apply in a scenario. On the other hand, symbolic AI represents knowledge through rules, constraints, and structure, and it applies logic to make deductions. ... Neuro-symbolic AI introduces a structural advance in LLM training by embedding automated reasoning directly into the training loop. This uses formal logic and mathematical proof to mechanically verify whether a statement, program, or output used in the training data is correct. A tool such as Lean,4 is precise, deterministic, and gives provable assurance. The key advantage of automated reasoning is that it verifies each step of the reasoning process, and not just the final answer.

Three things they’re not telling you about mobile app security

With the realities of “wilderness survival” in mind, effective mobile app security must be designed for specific environmental exposures. You may need to wear some kind of jacket at your office job (web app), but you’ll need a very different kind of purpose-built jacket as well as other clothing layers, tools, and safety checks to climb Mount Everest (mobile app). Similarly, mobile app development teams need to rigorously test their code for potential security issues and also incorporate multi-layered protections designed for some harsh realities. ... A proactive and comprehensive approach is one that applies mobile application security at each stage of the software development lifecycle (SDLC). It includes the aforementioned testing in the stages of planning, design, and development as well as those multi-layered protections to ensure application integrity post-release. ... Whether stemming from overconfidence or just kicking the can down the road, inadequate mobile app security presents an existential risk. A recent survey of developers and security professionals found that organizations experienced an average of nine mobile app security incidents over the previous year. The total calculated cost of each incident isn’t just about downtime and raw dollars, but also “little things” like user experience, customer retention, and your reputation.

Cybersecurity in 2026: Fewer dashboards, sharper decisions, real accountability

The way organisations perceive risk is one of the most important changes predicted in 2026. Security teams spent years concentrating on inventory, which included tracking vulnerabilities, chasing scores and counting assets. The model is beginning to disintegrate. Attack-path modelling, on the other hand, is becoming far more useful and practical. These models are evolving from static diagrams to real-world settings where teams may simulate real attacks. Consider it a cyberwar simulation where defenders may test “what if” scenarios in real time, comprehend how a threat might propagate via systems and determine whether vulnerabilities truly cause harm to organisations. This evolution is accompanied by a growing disenchantment with abstract frameworks that failed to provide concrete outcomes. The emphasis is shifting to risk-prioritized operations, where teams start tackling the few problems that actually provide attackers access instead than responding to clutter. Success in 2026 will be determined more by impact than by activities. ... Many companies continue to handle security issues behind closed doors as PR disasters. However, an alternative strategy is gaining momentum. Communicate as soon as something goes wrong. Update frequently, share your knowledge and acknowledge your shortcomings. Post signs of compromise. Allow partners and clients to defend themselves. Particularly in the middle of disorder, this seems dangerous.

AI and Latency: Why Milliseconds Decide Winners and Losers in the Data Center Race

Many traditional workloads can tolerate latency. Batch processing doesn’t care if it takes an extra second to move data. AI training, especially at hyperscale, can also be forgiving. You can load up terabytes of data in a data center in Idaho and process it for days without caring if it’s a few milliseconds slower. Inference is a different beast. Inference is where AI turns trained models into real-time answers. It’s what happens when ChatGPT finishes your sentence, your banking AI flags a fraudulent transaction, or a predictive maintenance system decides whether to shut down a turbine. ... If you think latency is just a technical metric, you’re missing the bigger picture. In AI-powered industries, shaving milliseconds off inference times directly impacts conversion rates, customer retention, and operational safety. A stock trading platform with 10 ms faster AI-driven trade execution has a measurable financial advantage. A translation service that responds instantly feels more natural and wins user loyalty. A factory that catches a machine fault 200 ms earlier can prevent costly downtime. Latency isn’t a checkbox, it’s a competitive differentiator. And customers are willing to pay for it. That’s why AWS and others have “latency-optimized” SKUs. That’s why every major hyperscaler is pushing inference nodes closer to urban centers.

Why developers need to sharpen their focus on documentation

“One of the bigger benefits of architectural documentation is how it functions as an onboarding resource for developers,” Kalinowski told ITPro. “It’s much easier for new joiners to grasp the system’s architecture and design principles, which means the burden’s not entirely on senior team members’ shoulders to do the training," he added. “It also acts as a repository of institutional knowledge that preserves decision rationale, which might otherwise get lost when team members move to other projects or leave the company." ... “Every day, developers lose time because of inefficiencies in their organization – they get bogged down in repetitive tasks and waste time navigating between different tools,” he said. “They also end up losing time trying to locate pertinent information – like that one piece of documentation that explains an architectural decision from a previous team member,” Peters added. “If software development were an F1 race, these inefficiencies are the pit stops that eat into lap time. Every unnecessary context switch or repetitive task equals more time lost when trying to reach the finish line.” ... “Documentation and deployments appear to either be not routine enough to warrant AI assistance or otherwise removed from existing workflows so that not much time is spent on it,” the company said. ... For developers of all experience levels, Stack Overflow highlighted a concerning divide in terms of documentation activities.

AI Pilots Are Easy. Business Use Cases Are Hard

Moving from pilot to purpose is where most AI journeys lose momentum. The gap often lies not in the model itself, but in the ecosystem around it. Fragmented data, unclear ROI frameworks and organizational silos slow down scaling. To avoid this breakdown, an AI pilot must be anchored to clear business outcomes - whether that's cost optimization, data-led infrastructure or customer experience. Once the outcomes are defined, the organization can test the system with the specific data and processes that will support it. This focus sets the stage for the next 10 to 14 months of refinement needed to ready the tool for deeper integration. When implementation begins, workflows become self-optimizing, decisions accelerate and frontline teams gain real-time intelligence. As AI moves beyond pilots, systems begin spotting patterns before people do. Teams shift from retrospective analysis to live decision-making. Processes improve themselves through constant feedback loops. These capabilities unlock efficiency and insight across businesses, but highly regulated industries such as banking, insurance, and healthcare face additional hurdles. Compliance, data privacy and explainability add layers of complexity, making it essential for AI integration to include process redesign, staff retraining and organizationwide AI literacy, not just within technical teams.

Why your next cloud bill could be a trap

“AI-ready” often means “AI–deeply embedded” into your data, tools, and runtime environment. Your logs are now processed through their AI analytics. Your application telemetry routes through their AI-based observability. Your customer data is indexed for their vector search. This is convenient in the short term. In the long term, it shifts power. The more AI-native services you consume from a single hyperscaler, the more they shape your architecture and your economics. You become less likely to adopt open source models, alternative GPU clouds, or sovereign and private clouds that might be a better fit for specific workloads. You are more likely to accept rate changes, technical limits, and road maps that may not align with your interests, simply because unwinding that dependency is too painful. ... For companies not prepared to fully commit to AI-native services from a single hyperscaler or in search of a backup option, these alternatives matter. They can host models under your control, support open ecosystems, or serve as a landing zone for workloads you might eventually relocate from a hyperscaler. However, maintaining this flexibility requires avoiding the strong influence of deeply integrated, proprietary AI stacks from the start. ... The bottom line is simple: AI-native cloud is coming, and in many ways, it’s already here. The question is not whether you will use AI in the cloud, but how much control you will retain over its cost, architecture, and strategic direction.

IT and Security: Aligning to Unlock Greater Value

While many organisations have made strides in aligning IT and security, communication breakdowns can remain a challenge. Historically, friction between these two departments was driven by a lack of communication and competing priorities. For the CISO or head of the security team, reducing the company’s attack surface, limiting access privileges, or banning apps that might open their organisation up to unnecessary, additional risks are likely to be core focus areas. ... The good news is, there are more opportunities now than ever before for IT and security operations to naturally converge – in endpoint management, patch deployment, identity and access management, you name it. It can help to clearly document IT and security’s roles and responsibilities and practice scenarios with tabletop exercises to get everyone on the same page and identify coverage gaps. ... In addition to building versatile teams, organisations should focus on consolidating IT and security toolkits by prioritising solutions that expedite time to value and boost visibility. We’ve said this in security for a long time: you can’t protect (or defend against) what you can’t see. With shared visibility through integrated platforms and consolidated toolkits, both IT and security teams can gain real-time insights into infrastructure, threats, vulnerabilities, and risks before they can impact business. Solutions that help IT and security teams rapidly exchange critical information, accelerate response to incidents, and document the triaging process will make it easier to address similar instances in the future.