Daily Tech Digest by Kannan Subbiah: 2025

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.

Daily Tech Digest - December 20, 2025

Quote for the day:

"The bad news is time flies, The good news is you're the pilot." -- Elizabeth McCormick

Europe’s AI Challenge Runs Deeper Than Regulation

European firms may welcome a lessening of their regulatory burden. But Europe's problem isn’t merely regulatory drag. There's the structural gulf between what modern AI development requires and what Europe currently has the capacity to deliver. The Omnibus, helpful as it may be for legal alignment, cannot close those gaps. ... Europe has only a handful of companies, such as Aleph Alpha and Mistral, developing large-scale generative AI models domestically. Even these firms face steep structural disadvantages. A European Commission analysis has warned that such companies "require massive investment to avoid losing the race to U.S. competitors," while acknowledging that European capital markets "do not meet this need, forcing European firms to seek funding abroad." The result is a persistent leakage of ownership, control and strategic direction at precisely the moment scale matters most. ... This capital asymmetry produces powerful second-order effects. It determines who can absorb the high costs of large-scale model training, sustain loss-leading platform expansion and iterate continuously at the frontier of AI development. Over time, these dynamics create self-reinforcing structural advantages for capital-rich ecosystems. Advantages compound over time and remain largely beyond the corrective reach of regulation. These gaps are not regulatory problems.

How to Pivot When Digital Ambitions Crash into Operational Realities

Transformation usually begins with ambition. Leaders imagine a future where the bank operates more efficiently and interacts with customers the way modern platforms do. But the more I speak with people running these programs, the more I see that banks are trying to build the future without fully understanding the present. They push forward with new digital products, new interfaces, new journeys, while the actual work happening across branches, operations centers and back offices remains something of a mystery, even to the teams responsible for changing it. ... what’s less widely discussed is that banks do not fail because change is impossible, they do because too much of the real work remains invisible. Many institutions still rely on assumptions about how processes run, assumptions based on documentation that no longer reflects reality. And when a transformation is built on assumptions, the project begins to drift. What banks need is an honest picture of their operational baseline. Once leaders see how their organization works today (not how it was designed years ago and not how it is described in flowcharts) the conversation changes. Priorities become clearer. Bottlenecks reveal themselves. Entire categories of work turn out to be more manual than anyone expected. And what looked like a technology problem often turns out to be a process problem that has been accumulating for years.

Six Lessons Learned Building RAG Systems in Production

Something ships quickly, the demo looks fine, leadership is satisfied. Then real users start asking real questions. The answers are vague. Sometimes wrong. Occasionally confident and completely nonsensical. That’s usually the end of it. Trust disappears fast, and once users decide a system can’t be trusted, they don’t keep checking back to see if it has improved and will not give it a second chance. They simply stop using it. In this case, the real failure is not technical but it’s human one. People will tolerate slow tools and clunky interfaces. What they won’t tolerate is being misled. When a system gives you the wrong answer with confidence, it feels deceptive. Recovering from that, even after months of work, is extremely hard. ... Many teams rush their RAG development, and to be honest, a simple MVP can be achieved very quickly if we aren’t focused on performance. But RAG is not a quick prototype; it’s a huge infrastructure project. The moment you start stressing your system with real evolving data in production, the weaknesses in your pipeline will begin to surface. ... When we talk about data preparation, we’re not just talking about clean data; we’re talking about meaningful context. That brings us to chunking. Chunking refers to breaking down a source document, perhaps a PDF or internal document, into smaller chunks before encoding it into vector form and storing it within a database.

Enterprise reactions to cloud and internet outages

Those in the c-suite, not surprisingly, “examined” or “explored” or “assessed” their companies’ vulnerability to cloud and internet problems after the news. So what did they find? Are enterprises fleeing the cloud they now see as risky instead of protective? ... All the enterprises thought the dire comments they’d read about cloud abandonment were exaggerations, or reflected an incomplete understanding of the cloud and alternatives to cloud dependence. And the internet? “What’s our alternative there?” one executive asked me. ... The enterprise experts pointed out that the network piece of this cake had special challenges. Its critical to keep the two other layers separated, at least to ensure that nothing from the user-facing layer could see the resource layer, which of course would be supporting other applications and, in the case of the cloud, other companies. It’s also critical in exposing the features of the cloud to customers. The network layer, of course, includes the Domain Name Server (DNS) system that converts our familiar URLs to actual IP addresses for traffic routing; it’s the system that played a key role in the AWS problem, and as I’ve noted, it’s run by a different team. ... Enterprises don’t see the notion of a combined team or an overlay, every-layer team, as the solution. None of the enterprises had a view of what would be needed to fix the internet, and only a quarter of even the virtualization experts express an opinion on what the answer is for the cloud.

Offering more AI tools can't guarantee better adoption -- so what can?

After multiple years of relentless hype around AI and its promises, it's no surprise that companies have high expectations for their AI investments. But the measurable results have left a lot to be desired, with studies repeatedly showing most organizations aren't seeing the ROI they'd hoped for; in a Deloitte research report from October, only 10% of 1,854 respondents using agentic AI said they were realizing significant ROI on that investment, despite 85% increasing their spend on AI over the last 12 months. ... At face value, it seems obvious that the IT leadership team should be responsible for all things AI, since it is a technical product deployed at scale. In practice, this approach creates unnecessary hurdles to effective adoption, isolating technical decision-making from daily department workflows. And since many AI deployments are focused on equipping the workforce with new capabilities, excluding the human resources department is likely to constrain the effort. ... "If you focus on the tool, it's going to become procedural," Weed-Schertzer warned. "'Here's how to log in. This is your account.'" While technically useful, she added that she sees the biggest rewards coming from training employees on specific applications and having managers demonstrate the utility of an AI program for their teams, so that workers have a clear model from which to work. Seeing the utility is what will prompt long-term adoption, as opposed to a demo of basic tool functionality.

Why Cybersecurity Awareness Month Should Include Personal Privacy

Cybersecurity awareness campaigns tend to focus on email hygiene, secure logins, and network defense. These are key, but the boundary between internal threats and external exposure isn’t clear. An executive’s phone number leaked on a data broker’s site can become the first step in a targeted spear-phishing attack. A social media post about a trip can tip off a burglar. Forward-thinking entities know this. They tie personal privacy to enterprise risk. They integrate privacy checks into executive protection, threat monitoring, and insider-risk programs. Employees’ digital identities are treated as part of the attack surface. ... Removing data from your social profiles is only half the fight. The real struggle lives in data broker databases. These brokers compile, package, and resell personal data (addresses, phone numbers, demographics), feeding dozens of downstream systems. Together, they extend your reach into places you never directly visited. Most individuals never see their names there, never ask for removal, and never know about the pathways. Because every broker has its own rules, opt-outs require patience and effort. One broker demands forms, another wants ID, and a third ignores requests entirely. ... Awareness without action fades. However, when employees internalize privacy practices, they extend protection during their off hours and weekends. That’s when bad actors strike, during perceived downtime.

How CIOs can break free from reactive IT

Invisible IT is emerging as a practical way for CIOs to minimize disruption and improve the performance of the digital workplace. At its simplest, it’s an approach that prevents many issues from becoming problems in the first place, reducing the need for users to raise tickets or wait for help. As ecosystems scale, the gap between what organizations expect and what legacy workflows can deliver continues to widen. Lenovo’s latest research highlights invisible IT as a strategic shift toward proactive, personalized support that strengthens the performance of the digital workplace. ... In a workplace where devices, applications and services operate across different locations and conditions, this approach leaves CIOs without the early signals needed to prevent interruption. Faults often emerge gradually through performance drift or configuration inconsistencies, but traditional workflows only respond once the impact is visible to users. ... Invisible IT draws on AI to interpret device health, behavioral patterns and performance signals across the organization, giving CIOs earlier awareness of degradation and emerging risks. ... Invisible IT gives CIOs a clearer path to shaping a digital workplace that strengthens productivity and resilience by design. By shifting from user-reported issues to signal-driven insight, CIOs gain earlier visibility into risks and greater control over how disruptions are managed.

AI isn’t one system, and your threat model shouldn’t be either

The right way to partition a modern AI stack for threat modeling is not to treat “AI systems” as a monolithic risk category, we should return to security fundamentals and segment the stack by what the system does, how it is used, the sensitivity of the data it touches, and the impact its failure or breach could have. This distinguishes low risk internal productivity tools from models embedded in mission critical workflows or those representing core intellectual property and ensures AI is evaluated in context rather than by label. ... Threat modeling is a driver of higher quality that extends beyond security, and the best way to convey this to business leaders is through analogies rooted in their own domain. For example, in a car dealership, no one would allow a new salesperson to sign off on an 80 percent discount. The general manager instantly understands why that safeguard exists because it protects revenue, reputation, and operational stability. ... Tool calling patterns are one key area to incorporate into threat modeling. Most modern LLM implementations rely on external tool calls, such as web search or internal MCPs (some server side, and some client side). Unless these are tightly defined and constrained, they can drive the model to behave in unexpected or partially malicious ways. Changes in the frequency, sequence, or parameters of tool calls can indicate misuse, model confusion, or an attempted escalation path.

The Convergence Challenge: Architecture, Risk, and the Urgency for Assurance

If there was a single topic that drew the sharpest concern, it was the way organizations are adopting AI. Hayes described AI as a new threat vector that many companies have rushed into without architectural planning or governance. In his view, the industry is creating a new category of debt that may exceed what already exists in legacy systems. “AI is being adopted haphazardly in many organizations,” Hayes said. Marketing teams connect tools to mail systems. Staff paste corporate content into public models. Guardrails are light or nonexistent. In many cases no one has defined how to test models, how to check for poisoning, or how to verify that outputs remain reliable over time. Hayes argued that the field has done a poor job securing software in general, and is now repeating the same mistakes with AI, only faster. The difference is that AI systems can act and adapt at a pace human attackers cannot match. Swanson added that boards and senior leaders still struggle with their role in major technology shifts. They do not want to manage details, but they are responsible for strategy and oversight. With AI, as with earlier changes, many boards have not yet decided how to oversee investments that fundamentally reshape business operations. Ominski put a fine point on it. “We are moving into risks we have not fully imagined,” he said. “The pace alone forces us to rethink how we govern technology.”

AI Coding Agents and Domain-Specific Languages: Challenges and Practical Mitigation Strategies

DSLs are deliberately narrow, domain-targeted languages with unique syntax rules, semantics, and execution models. They often have little representation in public datasets, evolve quickly, and include concepts that resemble no mainstream programming language. For these reasons, DSLs expose the fundamental weaknesses of large language models when used as code generators. ... Many DSLs, especially new ones, lack mature Language Server Protocol (LSP) support, which provide syntax and error highlighting in the code editor. Without structured domain data for Copilot to query, the model cannot check its guesses against a canonical schema. ... Because the problem stems from missing knowledge and structure, the solution is to supply knowledge and impose structure. Copilot’s extensibility, particularly Custom Agents, project-level instruction files, and Model Context Protocol (MCP) make this possible. ... Structure matters: AI systems chunk documentation for retrieval. Keep related information proximate – constraints mentioned three paragraphs after a concept may never appear in the same retrieval context. Each section should be self-contained with necessary context included. ... AI coding agents are powerful, but they are pattern-driven tools. DSLs, by definition, lack the broad pattern exposure that enables LLMs to behave reliably.

Daily Tech Digest - December 19, 2025

Quote for the day:

"A leader's dynamic does not come from special powers. It comes from a strong belief in a purpose and a willingness to express that conviction." -- Kouzes & Posner

AI tops CEO earnings calls as bubble fears intensify

Research by examined around 10,000 earnings calls from about 5,000 global companies listed in the US. The firm's latest quarterly study found that AI rose to the top of CEO agendas for the first time in the period, while concerns about a possible AI-related asset bubble also increased sharply. Mentions of an "" climbed 64% compared with the previous quarter. IoT Analytics said executives often paired announcements of new AI investments with comments that questioned the sustainability of current market valuations and the pace of capital inflows into the sector. ... While the number of AI-related references reached a new high, comments that explicitly mentioned a "bubble" in connection with technology or financial markets grew even faster in percentage terms. The study recorded the strongest quarter-on-quarter jump in bubble-related language since it began tracking the metric. Executives used the term "bubble" in several contexts. Some discussed venture funding and valuations for private AI companies. Others raised questions about the level of spending on compute infrastructure and the potential for overcapacity. A smaller group linked bubble concerns to individual asset classes such as AI-related equities. The increase in bubble-related discussion came alongside continued announcements of long-term AI spending plans.

AI governance becomes a board mandate as operational reality lags

Executives have clearly moved fast to formalize oversight. But the foundations needed to operationalize those frameworks—processes, controls, tooling, and skills embedded in day-to-day work—have not kept pace, according to the report. ... Many organizations still lack a comprehensive view of where AI is being used across their business, explained. and unsanctioned tools proliferate, while sanctioned projects are not always cataloged in a central inventory. Without this map of AI systems and use cases, governance bodies are effectively trying to manage risk they cannot fully see. The second gap is conceptual. “There’s a myth that governance is the same as regulation,” Singh said. “Unfortunately, it’s not.” Governance, she argued, is much broader: It includes understanding and mitigating risk, but also proving out product quality, reliability, and alignment with organizational values. Treating governance as a compliance checkbox leaves major gaps in how AI actually behaves in production. The final one is AI literacy. “You can’t govern something you don’t use or understand,” Singh said. If only a small AI team truly grasps the technology while the rest of the organization is buying or deploying AI-enabled tools, governance frameworks will not translate into responsible decisions on the ground. ... What good governance looks like, Singh argued, is highly contextual. Organizations need to anchor governance in what they care about most.

Legal Issues for Data Professionals: Data Centers in Space

If data is processed, copied, or stored on satellites, courts may be forced to decide whether space-based computing falls outside the scope of a “worldwide” license. A licensor could argue that the licensee exceeded the grant by moving data “off-planet,” creating an unintended new use. Moreover, even defining the equivalent of “territory” as “throughout the universe” raises questions as well as addressing them. The legal issues and regulatory rules involving data governance and legal rights in data centers in orbit have antecedents. ... Satellite-based data centers raise new questions: Where is an unauthorized copy of copyrighted material made for legal purposes, and which jurisdiction’s laws apply? A location in space complicates these legal issues and has implications for data governance. ... On Earth, IP enforcement against infringement relies on tools like forensic imaging, seizure of hard drives, discovery of server logs, and on-site inspections. Space breaks these tools. A court cannot easily order the seizure of a satellite. Inspecting hardware in orbit is not possible without specialized spacecraft. From a user’s perspective, retrieving logs may depend entirely on a vendor’s operation. ... Most cloud contracts and cyber insurance policies assume all processing happens on Earth. They do not address such things as satellite collisions, radiation damage, solar storms, loss of access due to orbital debris, or the failure of a satellite-to-Earth data link.

DNS as a Threat Vector: Detection and Mitigation Strategies

DNS is a critical control plane for modern digital infrastructure — resolving billions of queries per second, enabling content delivery, SaaS access, and virtually every online transaction. Its ubiquity and trust assumptions make it a high‑value target for attackers and a frequent root cause of outages. Unfortunately, this essential service can be exploited as a . Attackers can harness misconfigured authoritative DNS servers, open DNS resolvers, or the networks that support such activities to initiate a flood of traffic to a target, impacting the service availability and causing disruptions in a large scale. This misuse of DNS capabilities makes it a potent tool in the hands of cybercriminals. ... DNS detection strategies focus on analyzing traffic patterns and query content for anomalies (like long/random subdomains, high volume, rare record types) to spot threats like tunneling, , or malware, using AI/ML, threat intel, and SIEMs for real-time monitoring, payload analysis, and traffic analysis, complemented by and rate limiting for prevention. Legacy security tools often miss DNS threats. ... DNS mitigation strategies involve securing servers, controlling access (, strong passwords), monitoring traffic for anomalies, rate-limiting queries, hardening configurations, and using specialized to prevent amplification, hijacking, and spoofing attacks, ensuring domain integrity and availability.

The ‘chassis strategy’: How to build an innovation system that compounds value

The chassis strategy starts with a simple principle: centralize what must be common and decentralize what should evolve. You don’t need a monolithic innovation platform. You need a spine — a shared foundation of data, models and governance — that everything else plugs into. That spine ensures no matter who builds the next great idea — your team, a startup or a strategic partner — the learning, data and IP stay inside your system. ... You don’t need five years or an enterprise overhaul. A minimal but functional chassis can be built in nine months. The first three months are about framing and simplification. Pick three or four innovation domains — formulation, packaging, pricing or supply chain. Define the shared spine: your data schema, APIs and key metrics. Draw a bright line between what you’ll own (core) and what you’ll source (modules). The next three months are about building the core. Set up a unified data layer, model registry, API gateway and an experimentation sandbox. Keep it lightweight. No monoliths, no “innovation cloud.” Just the essentials that make reuse possible. The final three months are about plugging and proving. Integrate a few external modules — a supplier-insight engine, a generative packaging designer, a formulation optimizer. Track time to activation and reuse rate. The goal isn’t more features; it’s showing that vendors can connect fast, share data safely and strengthen the system.

AI is creating more software flaws – and they're getting worse

found 10.83 issues with AI pull requests versus 6.45 for human-only ones, adding that AI pull requests were far more likely to have critical or major issues. "Even more striking: high-issue outliers were much more common in AI PRs, creating heavy review workloads," said. Logic and correctness was the worst area for AI code, followed by code quality and maintainability and security. Because of that, CodeRabbit advised reviewers to watch out for those types of errors in AI code. ... "These include business logic mistakes, incorrect dependencies, flawed control flow, and misconfigurations," Loker wrote. "Logic errors are among the most expensive to fix and most likely to cause downstream incidents." AI code was also spotted omitting null checks, guardrails, and other error checking, which Loker noted are issues that can lead to outages in the real world. When it came to security, the most common mistake by AI was improper password handling and insecure object references, Loker noted, with security issues 2.74 times more common in AI code than that written by humans. Another major difference between AI code and human written-code was readability. "AI-produced code often looks consistent but violates local patterns around naming, clarity, and structure," Loker added.

Identity risk is changing faster than most security teams expect

Two forces are expected to influence trust systems in 2026. The first is the rise of . These agents run onboarding attempts, learn from rejection, and retry with improved tactics. Their speed compresses the window for detecting weaknesses and demands faster defensive responses. The second force comes from the long tail of . Growing quantum capability is putting pressure on classical cryptographic methods, which lose strength once computation reaches certain thresholds. Data encrypted today can be harvested and unlocked in the future. In response, some organizations are adopting quantum resilient hashing and beginning the transition toward that can withstand newer forms of computational power. ... A three part structure is emerging as a practical response. Hashing establishes integrity that cannot be altered. Encryption protects data while standards evolve. Predictive analysis identifies early drift and synthetic behavior before it scales. Together these elements support a continuous trust posture that strengthens as it absorbs more identity events. This model also addresses rising threats such as , , and . All three are expected to increase in 2026 based on observed anomaly patterns. Since these vectors rely on repeated behaviors, long term monitoring is essential.

D&O liability protection rising for security leaders — unless you’re a midtier CISO

have the potential for more than one safety net, the first of which is a company’s indemnification provisions — rules typically embedded in the company’s articles of incorporation and bylaws. “The language of a company’s indemnification provisions must be properly worded — typically achieved by the general counsel and a board vote — to provide indemnification for a CISO equal to every other director or officer of a company,” explains John Peterson of World Insurance Associates, a provider of employment practice liability insurance. The second safety net for a CISO is the D&O liability insurance policy procured by the CISO’s company through an insurance broker. Even when a company has D&O insurance in place, Peterson advises CISOs to review those policies to make sure they are covered as an “insured person.” ... While enterprise CISOs often have access to legal teams and crisis PR advisors to help shield them, a midrange firm often has one or two people — possibly more — wearing multiple hats, like compliance, IT, and security all rolled into one. This can become an issue because “regulators, customers, and even the courts won’t lower the expectations just because the company is smaller,” says. “Without legal protection, CISOs face significant personal and professional risk,” Bagnall said.

The CIO Conundrum: Balancing Security and Innovation in the Age of AI SaaS

AI tools are now accessible, inexpensive, and often solve workflow friction that teams have lived with for years. The business is moving fast because the barrier to entry is low. This pace raises important questions for :Are we creating unnecessary friction where teams expect velocity? Have we made the “right path” faster than the workaround? Do our processes match how people work today? Shadow IT grows when official paths feel slow or unclear. Not because teams want to hide things, but because they feel innovation can’t wait. Governance must evolve to match that reality. ... Security should accelerate productivity, not constrain it. With strong identity controls, clear data boundaries, and automated configuration standards, we can introduce new tools without adding friction. These guardrails reduce the workload on security teams and create a predictable environment for employees. The business moves faster. IT gains visibility. The organization avoids the drift that creates risk and inefficiency. ... The question isn’t whether teams will continue exploring new tools, it’s whether we provide a responsible, scalable path forward. When intake is transparent, vetting is calibrated, and guardrails are embedded, the organization can innovate with confidence. The CIO’s job is to design frameworks that keep pace with the business, not frameworks the business waits on.

From hype to reality: The three forces defining security in 2026

Organisations should stop asking “what might agentic AI do” and start identifying the repeatable security workflows they want automated; for example: incident triage, patrol optimisation, evidence packaging; then measure agent performance against those . The winners in 2026 will be platforms that expose safe, auditable agent APIs and vendors who integrate them into end-to-end operational playbooks. ... Looking ahead, the widespread adoption of digital twins is poised to reshape the security industry’s approach to risk management and operational planning. With a unified, real-time view of complex environments, enable proactive decision-making, allowing security teams to anticipate threats, optimise resource allocation and continuously refine standard operating procedures. Over time, this capability will shift the industry from reactive incident response to predictive and preventative security strategies, where investment in training, infrastructure and technology is guided through simulated outcomes rather than historical events. ... AR and wearables have had turbulent history, but their resurgence in 2026 will be different — and AI is the reason. AI transforms wearables from simple capture devices into intelligent companions. It elevates AR from a visual overlay to a real-time, context-aware guidance layer.