Daily Tech Digest by Kannan Subbiah: 2026

Daily Tech Digest - January 13, 2026

Quote for the day:

"Don't let yesterday take up too much of today." -- Will Rogers

When AI Meets DevOps To Build Self-Healing Systems

Self-healing systems do not just react to events and incidents — they analyse historic data, identify early triggers or symptoms of failures, and act. For example, if a service is known to crash when it runs out of memory, a self-healing system can observe metrics like memory consumption, predict when the service may fail with very low memory, and take action to fix the issue—like restarting the service or allocating more memory—without human intervention. In AIOps, self-healing systems are powered by data science in terms of machine learning models, real-time analytics, and automated workflows. ... Self-healing systems don’t just rely on static rules and manual checks; they utilise real-time data streams and apply pattern and anomaly detection through machine learning to ascertain the state of the environment. A self-healing system is trying to gauge its own health all the time — CPU utilisation, latency, memory, throughput, traffic, security anomalies, etc — to preemptively address an impending failure. The key component of every self-healing system is a cycle that reflects the process followed by intelligent agents: Detect → Diagnose → Act. ... The integration of artificial intelligence and DevOps signifies an important change in the way modern IT systems are built, managed, and evolved. As we have discussed here, AIOps is not just an extension of a type of automation — it is changing the way operations are modelled from reactive to intelligent, self-healing ecosystems.

Building a product roadmap: From high-level vision to concrete plans

A roadmap provides the anchor to keep everyone aligned amid constant flux. Yet many organizations still treat roadmaps as static artifacts — a one-and-done exercise intended to appease executives or investors. That’s a mistake. The most effective roadmaps are living documents evolving with the product and market realities. ... If strategy defines direction, milestones are the engine that keeps the train moving. Too often, teams treat milestones as arbitrary checkpoints or internal deadlines. Done right, these can become powerful tools for motivation, alignment and storytelling. ... The best roadmaps aren’t written by PMs — they’re co-authored by teams. That’s why I advocate for bottom-up collaboration anchored in executive alignment. Before any roadmap offsite, sync with the CEO or leadership team. Understand what they care about and why. If they disagree with priorities, resolve those conflicts early. Then bring that context into a team workshop. During the session, identify technical leads — those trusted voices who can translate into action. Encourage them to pre-think tradeoffs and dependencies before the group session. ... The perfect roadmap doesn’t exist and that’s the point. Remember, the goal isn’t to build a flawless plan, but a resilient one. As President Dwight D. Eisenhower said, “Plans are useless, but planning is indispensable.” ... Vision without execution is hallucination. But execution without vision is chaos. The magic of product leadership lies in balancing both: crafting a roadmap that’s both inspiring and achievable.

Scattered network data impedes automation efforts

As IT organizations mature their network automation strategies, it’s becoming clear that network intent data is an essential foundation. They need reliable documentation of network inventory, IP address space, topology and connectivity, policies, and more. This requirement often kicks off a network source of truth (NSoT) project, which involves network teams discovering, validating, and consolidating disparate data in a tool that can model network intent and provide programmatic access to data for network automation tools and other systems. ... IT leaders do not understand the value of NSoT solutions. The data is already available, although it’s scattered and of dubious quality. Why should we spend money on a product or even extra engineers to consolidate it? “Part of the issue is that we’ve got leadership that are not infrastructure people,” said a network engineer with a global automobile manufacturer. “It’s kind of a heavy lift to get them to buy into it, because they see that applications are running fine over the network. ‘Why do I need to spend money on this is?’ And we tell them that the network is running fine, but there will be failures at some point and it’s worth preventing that.” ... NSoT isn’t a magic bullet for solving the problems IT organizations have with poor network documentation and scattered operational data. Network engineering teams will need to discover, validate, reconcile, and import data from multiple repositories. This process can be challenging and time-consuming. Some of this data will difficult to find.

What insurers expect from cyber risk in 2026

Cyber insurers are beginning to use LLMs to translate internet scale data into structured inputs for underwriting and portfolio analysis. These applications target specific pain points such as data gaps and processing delays. Broader change across pricing or risk selection remains gradual. ... AI supported workflows begin to reduce repetitive tasks across those stages. Automation supports data entry, document review, and routine verification. Human oversight remains central for judgment based decisions. The research links this shift to measurable operational effects. Fewer manual touches per claim reduce processing time and error rates. Claims teams gain capacity without proportional increases in staffing. ... Age verification and online safety legislation introduce unintended cyber risk. Requirements that reduce online anonymity create high value identity datasets that attract attackers. The research highlights rising exposure to identity based coercion, insider compromise, and extortion. Once personal identity data is leaked, attackers gain leverage that can translate into access to corporate systems. This dynamic supports long term campaigns by organized groups and state aligned actors. ... Data orchestration becomes a core capability. Insurers and reinsurers integrate signals including security posture, threat activity, and loss experience into shared models. Consistent views across teams and regions support portfolio governance. This shift places emphasis on actionability. Data value depends on timing and relevance within workflows rather than volume alone.

Human + AI Will Define the Future of Work by 2027: Nasscom-Indeed Report

This emerging model of Humans + AI working together is reported as the next phase of transformation, where success depends on how effectively AI will augment human capabilities, empower employees, and align with organizational purpose. The report highlights that the most effective human–AI partnerships are emerging across higher-order activities such as scope definition, system architecture, and data model design. At the same time, more routine and repeatable tasks, including boilerplate code generation and unit test creation, are expected to be increasingly automated by AI over the next two to three years. ... To stay relevant in a Human + AI workplace, the report emphasizes that individuals should build capability, adaptability, and continuous learning. This includes experience with using AI tools (prompting, critical review of output, combining AI speed with human judgment), moving up the value chain (e.g., developers from coding to architecture thinking), building multidisciplinary skills (tech + domain + professional skills), and focusing on outcomes over credentials by creating repositories of work samples showing measurable impact. ... Organizations have already started taking measures to address these challenges. Every seven in ten HR leaders are focusing on upskilling, more than half focusing on modernizing systems. With respect to AI adoption, 79% prioritize internal reskilling as a dominant strategy.

From vulnerability whack-a-mole to strategic risk operations

“Software bills of materials are just an ingredients list,” he notes. “That’s helpful because the idea is that through transparency we will have a shared understanding. The problem is that they don’t deliver a shared understanding because the expectation of anyone in security who reads the SBOM is the first job they’ll do is run those versions against vulnerability databases.” This creates a predictable problem: security teams receive SBOMs, scan them for vulnerabilities, and generate alerts for every CVE match, regardless of whether those vulnerabilities actually affect the product. ... To make SBOMs truly useful, Kreilein introduces VEX (Vulnerability Exploitability Exchange), an open standards framework that addresses the context problem. VEX provides four status messages: affected, not affected, under investigation, and fixed. “What we want to start doing is using a project called VEX that gives four possible status messages,” Kreilein explains. ... Developers aren’t refusing to patch because they don’t care about security. They’re worried that upgrading a component will break the application. “If my application is brittle and can’t take change, I cannot upgrade to the non-vulnerable version,” Kreilein explains. “If I don’t have effective test automation and integration and unit testing, I can’t guarantee that this upgrade won’t break the application.” This reframing shifts the security conversation from compliance and mandates to engineering fundamentals. Better test coverage, better reference architectures, and better secure-by-design practices become security initiatives.

AI backlash forces a reality check: humans are as important as ever

Companies are now moving beyond the hype and waking up to the consequences of AI slop, underperforming tools, fragmented systems, and wasted budgets, said Brooke Johnson, chief legal officer at Ivanti. “The early rush to adopt AI prioritized speed over strategy, leaving many organizations with little to show for their investments,” Johnson said. Organizations now need to balance AI, workforce empowerment and cybersecurity at the same they’re still formulating strategies. That’s where people come in. ... AI is becoming less a tech problem and more of an adoption hurdle, Depa said. “What we’re seeing now more and more is less of a technology challenge, more of a change management, people, and process challenge — and that’s going to continue as those technologies continue to evolve,” he said. DXC Technology is taking a similar approach, designing tools where human insight, judgment, and collaboration create value that AI can’t do alone, said Dan Gray, vice president of global technical customer operations at the company. ... Companies might have to accept underutilizing some of the AI gains in the near term. AI could help workers complete their tasks in half the time and enjoy a leisurely pace. Alternately, employees might burn out quickly by getting more work. “If you try to lay them off, you don’t have a good workforce left. If you let them be, why are you paying them? So that’s a paradox,” Seth said.

Physical AI is the next frontier - and it's already all around you

Physical AI can be generally defined as AI implemented in hardware that can perceive the world around it and then reason to perform or orchestrate actions. Popular examples including autonomous vehicles and robots -- but robots that utilize AI to perform tasks have existed for decades. So what's the difference? ... Saxena adds that while humanoid robots will be useful in instances where humans don't want to perform a task, either because it is too tedious or too risky, they will not replace humans. That's where AI wearables, such as smart glasses, play an important role, as they can augment human capabilities. But beyond that, AI wearables might actually be able to feed back into other physical AI devices, such as robots, by providing a high-quality dataset based on real-life perspectives and examples. "Why are LLMs so great? Because there is a ton of data on the internet, for a lot of the contextual information and whatnot, but physical data does not exist," said Saxena. ... Given the privacy concerns that may come from having your everyday data used to train robots, Saxena highlighted that the data from your wearables should always be kept at the highest level of privacy. As a result, the data -- which should already be anonymized by the wearable company -- could be very helpful in training robots. That robot can then create more data, resulting in a healthy ecosystem. "This sharing of context, this sharing of AI between that robot and the wearable AI devices that you have around you is, I think, the benefit that you are going to be able to accrue," added Asghar.

Unlocking the Power of Geospatial Artificial Intelligence (GeoAI)

GeoAI is more than sophisticated map analytics. It is a strategic technology that blends AI with the physical world, allowing tech experts to see, understand, and act on patterns that were previously invisible. From planning sustainable cities to protecting wildlife, it’s helping experts tackle significant challenges with precision and speed. As the world generates more location-based data every day, GeoAI is becoming a must-have tool. It’s not just tech – it’s a way to make the world work better. ... To make it simpler. Machine learning spots trends, computer vision interprets images, GIS organizes it all, and knowledge graphs tie it together. The result? GeoAI can take a chaotic pile of data and deliver clear answers, like telling a city where to build a new park or warning about a wildfire risk. It’s a powerhouse that’s making location-based decisions faster and smarter. In all, GeoAI is transforming the speed at which we extract meaning from complex datasets, thereby enabling us to address the Earth’s most pressing challenges. ... Though powerful, GeoAI is not without challenges. Effective implementation requires careful attention to data privacy, technical infrastructure, and organizational change management. ... Leaders who take GeoAI seriously stand to gain more than just incremental improvements. With the right systems in place, they can respond faster, make smarter decisions, and get better results from every field team in the network.

For application security: SCA, SAST, DAST and MAST. What next?

If you think SAST and SCA are enough, you’re already behind. The future of app security is posture, provenance and proof, not alerts. ... Posture is the ‘what.’ Provenance is the ‘how’. The SLSA framework gives us a shared vocabulary and verifiable controls to prove that artifacts were built by hardened, tamper‑resistant pipelines with signed attestations that downstream consumers can trust. When I insist on SLSA Level 2 for most services and Level 3 for critical paths, I am not chasing compliance theater; I am buying integrity that survives audit and incident. Proof is where SBOMs finally grow up. Binding SBOM generation to the build that emits the deployable bits, signing them and validating at deploy time moves SBOMs from “ingredient lists” to enforceable controls. The CNCF TAG‑Security best practices v2 paper is my practical map, personas, VEX for exploitability, cryptographic verification to ensure tests actually ran, and prescriptive guidance for cloud‑native factories. ... Among the nexts, AI is the most mercurial. NIST’s final 2025 guidance on adversarial ML split threats across PredAI and GenAI and called out prompt injection in direct and indirect form as the dominant exploit in agentic systems where trusted instructions co mingle with untrusted data. The U.S. AI Safety Institute published work on agent hijacking evaluations, which I treat as required red‑team reading for anyone delegating actions to tools.

Daily Tech Digest - January 12, 2026

Quote for the day:

"The people who 'don't have time' and the people who 'always find time' have the same amount of time." -- Unknown

7 challenges IT leaders will face in 2026

’s says that by the end of the decade organizations will see lawsuits, fines, and due to disruptions from inadequate AI controls. As a result, CIOs say, governance has become an urgent concern — not an afterthought. ... , CIO of digital identity and data protection services company , says he’s preparing for 2026 with a focus on cultural readiness, continuous learning, and preparing people and the tech stack for rapid AI-driven changes. “The CIO role has moved beyond managing applications and infrastructure,” Kaushal says. “It’s now about shaping the future. As AI reshapes enterprise ecosystems, accelerating adoption without alignment risks technical debt, skills gaps, and greater cyber vulnerabilities. Ultimately, the true measure of a modern CIO isn’t how quickly we deploy new applications or AI — it’s how effectively we prepare our people and businesses for what’s next.” ... When modernizing applications, argues that teams need to stay outcome-focused, phasing in improvements that directly support their goals. “This means application modernization and cloud cost-optimization initiatives are required to stay competitive and relevant,” he says. “The challenge is to modernize and become more agile without letting costs spiral. By empowering an organization to develop applications faster and more efficiently, we can accelerate modernization efforts, respond more quickly to the pace of tech change, and maintain control over cloud expenditures.”

Rethinking OT security for project heavy shipyards

In OT, availability always wins. If a security control interferes with operations, it will be bypassed or rejected, often for good reasons. That constraint forces a different mindset. The first mental shift is letting go of the idea that visibility requires changing the devices themselves. In many legacy environments, that simply isn’t an option. So you have to look elsewhere. In practice, meaningful visibility often starts at the network level, using passive observation rather than active interrogation. You learn what “normal” looks like by watching how systems communicate, not by poking them. ... In our environment, sustainable IT/OT integration means avoiding ad-hoc connectivity altogether. When we connect vessels, yards and on-shore systems, we do so through deliberately designed integration paths. One practical example of this approach is how we use our : secure remote access, segmentation and monitoring are treated as integral parts of the digital solution itself, not as optional add-ons introduced later. That allows us to enable innovation while retaining control as IT and OT continue to converge. ... In practice, least privilege means being disciplined about time and purpose. Access should expire by default. It should be linked to a specific task, not to a project or a person’s role in general. We have found that making access removal automatic is often more effective than adding extra approval steps at the front end. If access cannot be explained in one sentence, it probably shouldn’t exist.

Mastering the architecture of hybrid edge environments

A mature IT architecture is characterized by well-orchestrated workflows that enable compute at the edge as well as data exchanges between the edge and central IT. Throughout all processes, security must be maintained. ... Conceptually, creating an IT architecture that incorporates both central IT and the edge sounds easy -- but it isn't. What must be achieved architecturally is a synergistic blend of hardware, software, applications, security and communications that work seamlessly together, whether the technology is at the edge or in the data center. When multiple solutions and vendors are involved, the integration of these elements can be daunting -- but the way that IT can address architectural conflicts upfront is by predefining the interface protocols, devices, and the hardware and software stacks. ... The hybrid approach is a win-win for everyone. It gives users a sense of autonomy, and it saves IT from making frequent trips to remote sites. The key to it all is to clearly define the roles that IT and end users will play in edge support. In other words, what are end-user technical support people in charge of, and at what point does IT step in? ... Finally, a mature architecture must define disaster recovery. What happens if a remote edge site fails? A mature architecture must define where it fails over to, so the site can keep going even if its local systems are out. In these cases, data and systems must be replicated for redundancy in the cloud or in the corporate data center, so remote sites can fail over to these resources, with end-to-end security in place at all points.

The Push for Agentic AI Standards Is Well Underway

"Many existing trust frameworks were layered onto an internet never designed for machine-level delegation or accountability. As agents begin acting independently, those frameworks need to evolve rather than simply be imposed," said, who authored the book ": The Next Evolution of AI and the Future of Digital Interaction." The agentic AI standards debate ranges from adopting enforceable guardrails to ensuring interoperability. Hazari pointed out that innovation is already moving faster than formal standard-setting can go. Fragmentation is a natural phase that precedes consolidation and interoperability. ... The brings together early but influential agentic technologies from , and . These hyperscalers are rolling out controlled AI environments often described as "AI factories" designed to deliver AI compute at enterprise scale. Initial contributions to the foundation include Anthropic's Model Context Protocol, which focuses on standardizing how agents receive and structure context; goose, an open-source agentic framework contributed by ; and AGENTS.md from , which defines how agents describe capabilities, permissions and constraints. Rather than prescribing a single architecture, these projects aim to standardize interfaces and metadata areas where fragmentation is already creating friction. Hazari said initiatives like the Agentic AI Foundation can absorb patterns into shared frameworks as they emerge.

7 steps to move from IT support to IT strategist

The biggest obstacle holding IT professionals back is a passive mindset. Sitting back and waiting to be told what to do prevents IT teams from reaching the strategic partnership level they want, said ... , vice president of AI operations at , emphasized that strong IT leaders see their work as part of a bigger ecosystem, one that works best when people are open, share information, and collaborate. ... IT professionals need to show up as partners by truly understanding what’s going on in the business, rather than waiting for business stakeholders to come to them with problems to solve, ’s Johnson said. “When you’re engaging with your business partners, you’re bringing proactive ideas and solutions to the table,” he said. ... Rather than having an order-taking mindset, IT professionals should ask probing questions about what partners need and what’s driving that need, which shifts toward problem-solving and focuses on outcomes rather than just implementing solutions, said. ... “IT professionals should frame every initiative in terms of the business problem it solves, the risk it reduces, or the opportunity it unlocks,” he said. ... Johnson warns against constantly searching for home runs. “Those are harder to find and they’re harder to deliver on,” he said. “Within 30 to 60 days, IT pros can build understanding around metrics and target states, then look for opportunities to help, even if they start small.”

Spec Driven Development: When Architecture Becomes Executable

The name Spec Driven Development may suggest a methodology, akin to Test Driven Development. However, this framing undersells its significance. SDD is more accurately understood as an architectural pattern, one that inverts the traditional source of truth by elevating executable specifications above code itself. SDD represents a fundamental shift in how software systems are architected, governed, and evolved. At a technical level, it introduces a declarative, contract-centric control plane that repositions the specification as the system's primary executable artifact. Implementation code, in contrast, becomes a secondary, generated representation of architectural intent. ... For decades, software architecture has operated under a largely unchallenged assumption that code is the ultimate authority. Architecture diagrams, design documents, interface contracts, and requirement specifications all existed to guide implementation. However, the running system always derived its truth from what was ultimately deployed. When mismatches occurred, the standard response was to "update the documentation" SDD inverts this relationship entirely. The specification becomes the authoritative definition of system reality, and implementations are continuously derived, validated, and, when necessary, regenerated to conform to that truth. This is not a philosophical distinction; it is a structural inversion of the governance of software systems.

Decoupling architectures: building resilience against cyber attacks

The recent incidents are tied together by a common approach to digital infrastructure: tightly coupled architectures. In these environments, critical applications such as , warehouse, logistics, retail, finance are interconnected so closely that if one fails, other critical systems are unable to function. A single weak point becomes the domino that topples the rest. This design may have made sense in a simpler, more predictable IT world. But in today’s highly interconnected landscape, with constantly evolving threats accelerated thanks to the AI revolution, this once-efficient design has turned into the perfect setup for system-wide issues. ... Instead of linking systems directly, a provides a shared backbone where each system publishes what happens. That means if one system is compromised or taken offline during an incident, the others can continue to function. Business operations don’t have to come to a standstill simply because a single component is isolated — and when the affected system is restored, it can replay the missed events and rejoin the flow seamlessly. Some architectures, like event-driven data streaming, can keep that data flowing in real time despite an attack. ... For CIOs and CISOs, this shift in mindset is critical. is no longer just about or detection tools. It’s about designing systems that can limit the blast radius when hit. absorbing and isolating the damage to ensure a quick recovery.

AI, geopolitics & supply chains reshape cyber risk

Organisations are scaling AI in core operations, customer engagement and decision-making. This expansion is exposing new attack surfaces, including data inputs, model training pipelines and integration points with legacy systems. It also coincides with uncertain regulatory expectations on issues such as transparency, auditability and the handling of personal and sensitive data in machine learning models. ... Map the above challenges alongside the geopolitical fragmentation the highlights, cyber risk is really being challenged in ways many traditional compliance frameworks were not designed for, via issues such as sovereignty, supply-chain and third-party exposure. In this environment, resilience absolutely depends on an organisation's ability to integrate cyber security, information security, privacy, and AI governance into a single risk picture, and to connect that with their technology decisions, regulatory obligations, business impact, and geopolitical context. ... Hardware, software and cloud services now rely on dispersed design, manufacturing and operational ecosystems. Attackers exploit this complexity. They target upstream providers, third-party tools and managed services. ... Regulatory fragmentation around AI is emerging alongside an increase in reported misuse. This includes deepfakes, automated disinformation, fraud, model theft and prompt injection attacks, as well as concerns over opaque automated decision-making.

Five key priorities for CEOs & Governance practitioners in 2026

As are embracing cutting edge technologies, without a skilled workforce to implement these technological solutions, the financial services industry will suffer a lot. According to IDC, IT skills shortage is expected to impact 9 out of 10 organizations by 2026 with a cost of $5.5 trillion in delays, issues, and revenue loss. Thus, CEOs and governance professionals should take up skills management as their top priority ... AI’s explainability and transparency are to be addressed on priority. Finally, AI is creating lots of environmental impacts contributing to greenhouse gas emissions due to its high energy and water consumption, which leads to the Environmental, social, governance (ESG) issues to be focused on by governance professionals. ... CEOs and governance professionals must take measures towards preemptive cybersecurity. They should realise that cybersecurity gives the foundation of trust for all the stakeholders of any enterprise and they cannot afford to compromise on it. ... Traditional strategic planning involved fixed, long-term goals, detailed forecasts, and periodic reviews. This is not suitable in the face of constant disruption. by contrast is having short planning cycles, incremental objectives, and adaptive learning. ... The future of information systems management lies in the seamless integration of cloud and edge computing – a distributed intelligent architecture where data is processed wherever it is more efficient to do so.

Dark Web Intelligence: How to Leverage OSINT for Proactive Threat Mitigation

Experts say monitoring the is an early warning system. Threat actors trade stolen data or exploits before they are detected in the broader world. Security pros even call dark web monitoring an ‘early warning radar’ that flags when sensitive data is leaked in underground forums. The difference is huge: Without these signals, breaches go undetected for months. In fact, one report found that the average breach goes undiscovered for about 194 days without proactive measures. ... Gathering intel from the dark web requires specialized tools and techniques. Analysts use a combination of and commercial intelligence platforms. Basic breach-checkers (public data-leak search engines) will flag obvious exposures, but comprehensive coverage requires purpose-built scanners that constantly crawl underground forums and encrypted chat networks. ... Organizations of all sizes have seen real benefits of dark web monitoring. For example, in 2020, identified a potential supply-chain breach when threat researchers discovered guest data being sold on some underground forums. Getting that early heads up allowed Marriott to get in and investigate and inform affected customers before the incident became public. Similarly, after 700 million got scraped in 2021, the first samples of the stolen data started popping up on dark web marketplaces and got caught by monitoring tools. Those alerts prompted LinkedIn users to reset their passwords and enabled the company to sort out its credential abuse defenses.

Daily Tech Digest - January 11, 2026

Quote for the day:

"Courage doesn't mean you don't get afraid. Courage means you don't let fear stop you." -- Bethany Hamilton

From Coder to Catalyst: What They Don’t Teach About Technical Leadership

The best technical leaders don’t just solve harder problems – they multiply their impact by solving different kinds of problems. What follows is the three-tier evolution most engineers never see coming, and the skills you’ll need that no computer science program ever taught you. ... You’ll have moments of doubt. When you’re starting out, if a junior engineer falls behind, your instinct is to jump in and solve the problem yourself. You might feel like a hero, but this is bad leadership. You’re not holding the junior engineer accountable, and worse, you’re breaking trust—signaling that you don’t believe they can handle the challenge. ... When projects drift off track, you’re cutting scope, reallocating people, and making key decisions at crossroads. But there’s something more critical: risk management. You need to think one step ahead of the projects, identify key risks before they materialize, and mitigate them proactively. ... Additionally, there’s one more thing nobody mentions: managing stakeholders. Not just your team, but peers across the organization and leaders above you. Technical leadership isn’t just downward – it’s omnidirectional. ... The learning curve never ends. You never stop feeling like you’re figuring it out as you go, and that’s the point. Technical leadership is continuous adaptation. The best leaders stay humble enough to admit they’re still learning. The real measure of success isn’t in your commit history. You’re succeeding when your team can execute without you. When people you hired are better than you at things you used to do.

In an AI-perfect world, it’s time to prove you’re human

Being yourself in all communication is not only about authenticity, but individuality. By communicating in a way that only you can communicate, you increase your appeal and value in a world of generic, faceless, zero-personality AI content. For marketing communications, this goes double. The public will increasingly assume what they see is AI-generated, and therefore cheap garbage. ... Not only will the public reject what they assume to be AI, the social algorithms will increasingly reward and boost content offering the signals of authenticity. In fact, Mosseri said that within Meta there is a push to prioritize “original content” over “templated“ or “generic“ AI content that is easy to churn out at a massive scale. ... Rather than thinking of AI as a tool that replaces work and workers, we should think of it as a “scaffolding for human potential,” a way to magnify our cognitive capabilities, not replace them. In other words, instead of viewing AI as something that writes and creates pictures so we don’t have to or writes code so we don’t have to — meaning we don’t even have to learn how to code — we need to use AI to become great at writing, creating images and coding. From now on, everyone will assume everyone else has and uses AI. Content and communications will always exist on a spectrum from fully AI-generated to zero-AI human communication. The further toward the human any bit of content gets, the more valuable it will feel to both the receivers of the content and to the gatekeepers.

How to Build a Robust Data Architecture for Scalable Business Growth

As early in the process as possible, you should begin engaging with stakeholders like IT teams, business and data analysts, executives, administrators, and any other group within your organization that regularly interacts with data. Get to know their data practices and goals, which will provide insight into the requirements for your new data architecture, ensuring you have a deep well of information to draw from. ... After communicating with stakeholders and researching your organization’s current data landscape, you can determine exactly what your data architecture will need now and into the future. Some requirements you will need to precisely define the volume of data your architecture will handle, how fast data needs to move through your organization, and how secure the data needs to be. All this data about your data will guide you toward better decisions in designing and building your data architecture. ... The exact construction of your data architecture will depend largely upon the needs you outlined during the previous step, but some solutions are more advantageous for businesses looking to expand. ... While there is plenty of healthy debate regarding the merits of horizontal scaling versus vertical scaling, the truth is that the best database architectures use both. Horizontal scaling, or using multiple servers to distribute data and processes, allows an organization to have many nodes within a system so the system can dedicate resources to specific data tasks.

The Quiet Shift Changing UX

Right now, three big transformations collide. Designers are moving away from static screens, leaning into building full flows and shaping behaviours. Conversational AI redefines the user experiences from the ground up. Plus, with Gen-AI tools and mature design systems, designers shift from pixel movers to curators of experiences. All these transformations quietly reshape UX at its core. ... Back in the day, UX ‌design focused mainly on interfaces. Think pages and layouts, breakpoints, all the components, yeah, that defined the work. We’d talk about flows, sure, but really, we just built out sequences of screens. But now, that way of doing things is changing. Products are now changing and adapting depending on what’s happening around them, what the user has done before and what’s happening right now. One thing you do can lead to completely different results depending on how the user uses the system or what they know about it. Screens are becoming temporary; what really matters is what’s happening underneath and how the system changes. ... Designers now focus on curating, refining and shaping the final results, which is a strategic and decisive role. This shift does come with some risks. Sometimes, we settle for ‘good enough’ design, which can mask more serious issues. The design might look good on the surface, but it could be acting strangely beneath the surface.

What does the drought at Stack Overflow teach us?

“AI developer tools seem to be taking attention away from static question-and-answer solutions, replacing Stack Overflow with generated code without the middleman… and without waiting for a question to be answered,” said Walls. “Interestingly, AI tools lack the reputational metadata that Stack Overflow relied on: i.e. when was this solution posted and who posted it… and do they have a lot of prior answers? Developers are conferring trust to LLMs that human-sourced sites had to build over years and fight to retain. It’s much easier for developers to ask an agent for some code to accomplish a task and click accept, regardless of the provenance of that code.” ... “Today we know that LLMs like ChatGPT are already pretty good at answering common questions, which are the bulk of the questions asked at StackOverflow. Additionally, LLMs can respond in real time, so it is not a surprise that people were shifting away from StackOverflow. It might be not the only reason though – some people also reported StackOverflow moderators being rather hostile and unwelcoming towards new users, which had additional impact,” said Zaitsev. “Why would you deal with what you see as bad treatment, if an alternative exists?” ... “With AI now available directly in IDEs, engineers naturally turn to quick, contextual support as they work,” said Jackson.

Ready or Not, AI is Rewriting the Rules for Software Testing

Etan Lightstone, a product design leader at Domino Data Lab, argues that building trust in agents requires applying familiar operational principles. He suggests that for an enterprise with mature MLOps capabilities, trusting an agent is not enormously different from trusting a human user, because the same pillars of governance are in place: Robust logging of every action, complete auditability to trace what happened and the critical ability to roll back any action if something goes wrong. This product-centric mindset also extends to how we design and test the MCP tools before they ever reach production. Lightstone proposes a novel approach he calls “usability testing for AI.” Just as a product team would run usability tests with human beings to uncover design flaws before a release, he advises that MCP servers should be tested with sample AI agents. This is an effective way to discover issues in how a tool’s functions are documented and described — which is critical, since this documentation effectively becomes part of the prompt that the AI agent uses. Furthermore, he suggests we need to build “support links” for AI agents acting on our behalf. When a user gets stuck, they can often click a link to get help or submit feedback. Lightstone argues that AI agents need similar recovery mechanisms. This could be an MCP-exposed feedback tool that an agent can call if it cannot recover from an error or a dedicated function to get help from a documentation search.

Defending at Scale: The Importance of People in Data Center Security

In the tech world, the mantra of “move fast and break things” has become a badge of innovation. For cases like social platforms or mobile apps, where “breaking things” translates to inconveniences rather than catastrophes, it can work quite well. But when it comes to building critical infrastructure that supports essential functions and drives the future of society, companies must take the time to ensure they build safely and sustainably. Establishing robust physical security is already challenging, and implementing strong policies and processes to support those controls is even more difficult. Often, the core risk lies in the human layer that determines whether controls are applied consistently. ... With the promise of AI-powered efficiency gains, there’s increased pressure to move faster. When organizations take shortcuts in the name of speed, however, those shortcuts often come at the cost of consistent and thorough security. This could include gaps in training for guards, technicians, and vendors, unclear policies for after-hours access, frequent contractor changes, poorly defined emergency protocols, or procedures that only exist on paper. ... As businesses rush to meet the demand for AI, the data center boom is expected to continue rising. In all this rush, it's easy to overlook that moving fast without first establishing and reliably executing proper processes increases risk. Building too quickly without a strong security culture can lead to expensive problems down the line.

Industrial cyber governance hits inflection point, shifts toward measurable resilience and executive accountability

For industrial operators, the harder task is converting cyber exposure into defensible investment decisions. Quantified risk approaches, promoted by the World Economic Forum, are gaining traction by linking potential downtime, safety impact, and financial loss to capital planning and insurance strategy. ... “Governance should shift to a unified IT/OT risk council where safety engineers and CISOs share a common language of operational impact,” Paul Shaver, global practice leader at Mandiant’s Industrial Control Systems/Operational Technology Security Consulting practice, told Industrial Cyber. “Organizations should integrate OT-specific safety metrics into the standard IT risk framework to ensure cybersecurity decisions are made with production uptime in mind. This evolution requires aligning IT’s data confidentiality goals with OT’s requirement for high availability and human safety. ... Organizations need to move from siloed governance to a risk-first model that prioritizes the most critical threats, whether cyber or operational, and updates policies dynamically based on risk assessments, Jacob Marzloff, president and co-founder at Armexa, told Industrial Cyber. “A shared risk matrix across teams enables consistent trade-offs for safety and cybersecurity. Oversight should be centralized through a cross-functional Risk Committee rather than a single leader, ensuring expertise from IT, engineering, and operations. This committee creates a feedback loop between real-world risks and governance, building resilience.”

A Reality Check on Global AI Adoption

"AI is diffusing at extraordinary speed, but not evenly," the report said. Advanced digital economies are integrating AI into everyday work far faster than emerging markets. The findings underscore a shift in the AI race from model development to real-world deployment in which diffusion, not innovation alone, determines who benefits most. Microsoft CEO Satya Nadella in a recent blog said, "The next phase of the AI will be defined by execution at scale rather than discovery. The industry is moving from model breakthroughs to the harder work of building systems that deliver real-world value." ... Microsoft defines AI diffusion as the proportion of working-age individuals who have used generative AI tools within a defined period. This usage-based measurement shifts attention from venture funding, compute ownership or research output to real-world interaction including how AI is entering daily workflows, from coding and analysis to communication and content creation. ... Infrastructure gaps persist, language limitations reduce the effectiveness of many generative AI systems, and skills shortages constrain adoption when education and workforce training have not kept pace. Institutional capacity also plays a role, influencing trust, governance and public-sector deployment. At the same time, the diffusion metric captures breadth, not depth. A one-time interaction with a chatbot is measured the same as embedding AI into mission-critical enterprise systems.

The Hidden Resilience Gap: Why Most Organizations Are One Vendor Failure Away from Crisis

The most striking finding: when vendors lack business continuity or IT recovery plans, 43% of organizations simply ask them to create one and resubmit later. Another 32% do nothing at all. Only 13% provide structured questionnaires to actually help vendors develop meaningful plans. This means 75% of enterprises are essentially hoping their vendors figure it out on their own. ... Here’s another uncomfortable truth: 43% of organizations don’t have any system for combining operational and cyber risk indicators into a unified vendor resilience score. Another 22% track separate indicators but never connect the dots. That means nearly two-thirds of organizations can’t answer a simple question: “Which of our vendors pose the highest operational risk right now?” ... But compliance alone won’t fix this. Organizations need vendor resilience programs that actually reduce operational risk, not just check regulatory boxes. That requires moving beyond point-in-time assessments toward continuous intelligence. It means combining cyber indicators, financial health signals, operational metrics, and recovery evidence into coherent risk profiles. It demands bringing business owners, procurement teams, and risk functions into the same system with the same data. ... whatever you prioritize, make it measurable, make it continuous, and make it integrated. Fragmented data creates fragmented decisions. Point-in-time assessments create point-in-time confidence. Manual processes create manual failure modes. The organizations that crack this will have competitive advantage.

Daily Tech Digest - January 10, 2026

Quote for the day:

"To think creatively, we must be able to look a fresh at what we normally take for granted." -- George Kneller

7 cloud computing trends for leaders to watch in 2026

While many organizations will spend the year finding ways to improve the effectiveness of their cloud AI infrastructure, others might come to the realization that it just doesn’t make good sense to keep operating cloud environments dedicated to training or deploying AI workloads. These organizations will shift toward an alternative mode of AI infrastructure consumption, known as AI as a service (AIaaS). This means they’ll purchase pretrained AI models or AI-powered services from other vendors. ... No matter where cloud workloads reside, there’s probably a raft of compliance regulations that govern them, making it more critical than ever to invest in adequate governance, risk and compliance controls for the cloud. ... Of course, smart organizations won’t simply fork over more money to cloud providers just because the latter raise their prices. They’ll find ways to optimize cloud costs. Indeed, while FinOps -- a discipline focused on effective management of cloud spending -- has been around for years, cloud cost pressures, combined with more general enterprise fiscal concerns such as stubbornly high borrowing rates, mean that FinOps will likely be at the heart of more boardroom conversations over the coming year. ... The network infrastructure that connects cloud workloads and environments has long been one of the weakest links in overall cloud performance. Typically, cloud-based apps can process data much faster than they can move it over the network, which means the network often becomes the bottleneck on overall application responsiveness.

Your Teams’ Phones Are Now Your Biggest Security Hole. How to Plug It

Mobile banking adoption only continues to accelerate. Consumers are banking on their phones more than any other channel. Mobile access is another sign of the times. Yet as “bring your own device” (BYOD) expands for working, the assumptions behind “securing” personal devices are falling apart. New data from Verizon confirms what security leaders already feel: maintaining zero trust on mobile endpoints is becoming nearly impossible, even as AI-driven attacks reshape the landscape in real time. ... Agentic AI has compressed the attack lifecycle from months to minutes. This technology has transformed phishing and smishing into adaptive, multi-channel attacks. The Verizon report above found that 77% of organizations expect AI-assisted smishing to succeed. And 85% are already seeing more mobile attacks. ... Near-Field Communication and Bluetooth attacks now allow compromise by proximity. The tooling is cheap, accessible and increasingly automated. Exploits at the operating system level and firmware-level bypass mobile device management (MDM), mobile application management (MAM), antivirus and compliance controls entirely. You can have the cleanest, most “compliant” device in the world and still be wide open below the operating system. ... Institutions should assess whether their current mobile strategy depends on trusting user devices, managing them more tightly, or adding layers of software to inherently insecure endpoints.

Using unstructured data to fuel enterprise AI success

Unstructured data presents inherent difficulties due to its widely varying format, quality, and reliability, requiring specialized tools like natural language processing and AI to make sense of it. Every organization’s pool of unstructured data also contains domain-specific characteristics and terminology that generic AI models may not automatically understand. A financial services firm, for example, cannot simply use a general language model for fraud detection. Instead, it needs to adapt the model to understand regulatory language, transaction patterns, industry-specific risk indicators, and unique company context like data policies. ... “You can't assume that an out-of-the-box computer vision model is going to give you better inventory management, for example, by taking that open source model and applying it to whatever your unstructured data feeds are,” says Cealey. “You need to fine-tune it so it gives you the data exports in the format you want and helps your aims. That's where you start to see high-performative models that can then actually generate useful data insights.” ... while the AI technology mix available to companies changes by the day, they cannot eschew old-fashioned commercial metrics: clear goals. Without clarity on the business purpose, AI pilot programs can easily turn into open-ended, meandering research projects that prove expensive in terms of compute, data costs, and staffing.

Deepfake Fraud Tools Are Lagging Behind Expectations

Deepfake programs today fall into three buckets, experts say. Some are just post-production video editing tools. Some are hosted Web services. Programs that work in either of these ways might be able to create solid deepfake files, but only real-time webcam swappers threaten to trick an algorithm live and in real time. ... Thankfully, in contrast to most cybersecurity trends, the defenders are really ahead of the attackers here. Forrest attributes this, in part, to an imbalance in information. IT hackers have all the time in the world to learn about the systems they might want to attack. When it comes to KYC fraud, he says, "We learn vast amounts about every attack. We can study them. We can see what the attacker's doing. Whereas all they get back is a single yes or no answer. And so they learn nothing. They don't know if they're improving or not." Ironically, the fact that deepfakes are so realistic today is actually now working against attackers' interests. Before, they could measure their progress toward realism with their eyes. Now, they have to counteract defensive techniques they have no knowledge of. Forrest points out that "what looks really, really good to your eye is not necessarily the same as what looks very, very good to detection software. So if as a human being, you can't recognize the differences, it's very, very hard to understand how to attack them."

The Data Governance Challenge: Real-World Applications from Theory

Getting executive buy-in for and engaging the enterprise is a tricky endeavor. But, they succeeded by meeting the business where it was and applying data governance principles there. They piggybacked on business goals and requirements, acknowledged all the different needs, and tailored their messaging to each stakeholder segment. The challenge required teams to deliver a five-minute pitch and blueprint showing impact within 90 days. But what does sustained data governance look like beyond those initial wins? Cindy Hoffman, director of enterprise AI at Xcel Energy, discussed the ins and outs of sustaining a successful program in her closing keynote, “From Vision to Value – Building a Resilient Data Governance Program.” Xcel Energy started a data governance program to support an enterprise resource planning (ERP) implementation. She emphasized that implementing governance frameworks “really does take a bit of time, but it has to be something that you adopt and adapt along the way.” Her team’s recent AI-enabled metadata classification project cut a two-to-three-year data migration timeline to roughly one year – a 90% time reduction that proved governance principles drive measurable results. The key takeaway from both Hoffman’s journey and the WDMG challenge: Data governance knowledge matters most when applied to the chaos of actual business constraints. Whether you’re advocating to executives or engaging across the enterprise, that’s how data governance moves from PowerPoint to practice.

The hidden devops crisis that AI workloads are about to expose

Testing for resilience needs to happen at every layer of the stack, not just in staging or production. Can your system handle failure scenarios? Is it actually highly available? We used to wait until upper environments to add redundancy, but that doesn’t work when downtime immediately impacts AI inference quality or business decisions. The challenge is that many teams bolt on observability as an afterthought. They’ll instrument production but leave lower environments relatively blind. This creates a painful dynamic where issues don’t surface until staging or production, when they cost significantly more to fix. The solution is instrumenting at the lowest levels of the stack, even in developers’ local environments. This adds tooling overhead up front, but it allows you to catch data schema mismatches, throughput bottlenecks, and potential failures before they become production issues. ... Another common mistake is treating schema management as an afterthought. Teams hard-code data schemas in producers and consumers, which works fine initially but breaks down as soon as you add a new field. If producers emit events with a new schema and consumers aren’t ready, everything grinds to a halt. By adding a schema registry between producers and consumers, schema evolution happens automatically. ... Devops teams that cling to component-level testing and basic monitoring will struggle to keep pace with the data demands of AI.

Six for 2026: The cyber threats you can’t ignore

By generating ever more realistic content, these techniques and technologies can compromise various identity and authentication checks. Or, they can be used to manipulate insiders into establishing trust with adversaries and sharing sensitive or privileged data which could ultimately allow attackers to compromise systems or exfiltrate data. ... Thanks to AI-driven tools, finding vulnerabilities has accelerated to warp speed: vulnerabilities can be exploited in minutes not hours. Network scans that previously required human review can be analyzed, and attacks can be launched by automated agents. Now, even attacker communications can more easily hide by creating new tools and exploiting known blindspots in tunnels and through LoTL of network devices. ... Network infrastructure is dynamic: thanks to virtual machines, containers and cloud computing, servers and services come and go in a moment, often creating vulnerable entry points for attackers. As a result, nearly every static scan becomes outdated because it doesn’t capture the real-time status of your infrastructure. ... Catching multicloud threats is getting harder as adversaries get more sophisticated in bypassing existing siloed security tools such as CNAPP and EDR. Having multiple clouds is today’s norm, and that means that tools have to do a better job at having the visibility to understand how networks are constructed across clouds and how data is consumed.

Ensuring the long-term reliability and accuracy of AI systems: Moving past AI drift

AI drift is messier. When a generative model drifts, it hallucinates, fabricates, or misleads. That’s why governance needs to move from periodic check-ins to real-time vigilance. The NIST AI Risk Management Framework offers a strong foundation, but a checklist alone won’t be enough. Enterprises need coverage across two critical aspects:Ensure that the enterprise data is ready for AI. The data is typically fragmented across scores of systems and that non-coherence, along with lack of data quality and data governance, leads models to drift. The other is what I call “living governance”: Councils with the authority to stop unsafe deployments, adjust validators and bring humans back into the loop when confidence slips or rather to ensure that confidence never slips. This is where guardrails matter. ... Culture now extends beyond individuals. In many enterprises, AI agents are beginning to interact directly with one another, both agent-to-agent and human-to-agent. That’s a new collaboration loop, one that demands new norms and maturity. If the culture isn’t ready, drift doesn’t creep in through the algorithm; it enters through the people and processes surrounding it. ... Regulatory efforts are progressing, but they inevitably move more slowly than the pace of technology. In the meantime, adversaries are already exploiting the gaps with prompt injections, model poisoning and deepfake phishing.

Leadership is a choice not everyone can make

One of the rites of passage in the corporate world is when someone ceases to be an individual contributor and becomes a team leader. It seems such a natural transition that if one fails to inch up the corporate totem pole in commensuration with a receding hairline, the employee is earmarked as irksome and then some. Remaining an individual contributor for long is both a financial millstone and a social grindstone – it tires you down and doesn’t offer much social currency either. Every engineer must have a Faustian Bargain in becoming a manager – a trade in which the firm loses an able engineer and gains a lousy manager. Why? Because that’s what is expected of you—move up, amass people, and manage masses. But does an uber manager automatically become a leader? Do you keep assimilating people to a point where, someday, you metamorphose into a leader? Or, is leadership beyond management? I reckon that to manage is inherited, but to lead is earned. One doesn’t even need to have people reporting under you for you to be annotated as a leader. ... Leadership is a choice and is exercised only at the time of crisis, except that a leader can emerge from the most unexpected quarters, from down the ranks, or from outside the formation. Dhoni, Petrov, and Arkhipov were men from beyond the establishment. They absorbed immense pressure from all around, maintained a level-headed approach, and took extreme ownership of their decisions, often in the face of immediate flak from superiors and onlookers.

Program yourself: What languages should you learn in 2026?

Green coding is defined as environmentally sustainable computing practice that seeks to minimise the energy needed to process lines of code. It enables organisations to take control of their waste and consumption by prioritising responsible software usage. If this sounds appealing then why not prioritise learning a ‘green language’ for example C, Rust or Ada. These are considered among the languages that require the least amount of energy and time to execute prompts. ... Cybersecurity careers require a much higher degree of safety protocols than other professions, due to the high potential for risk, borne of both mistakes and malicious activity. With that in mind, coders looking to work in this space should ensure that the programming languages they learn have a reputation for high performance and can manage complex tasks. ... For those who want to add some flair and technical prowess to their skillset there are a range of fun and unique languages to learn, such as LaTeX, an unusual and difficult method particularly useful to those dealing with complex data and number-heavy projects. If you want something aesthetic, Piet is a really beautiful and creative language that takes data and turns it into an abstract painting in an array of colours, in the style of geometric artist Piet Mondrian. ... if you are in a STEM career and have both eyes firmly on the future, you may want to keep your skillset as up to date as possible, which means using the most modern form of programming.

Daily Tech Digest - January 09, 2026

Quote for the day:

"Always remember, your focus determines your reality." -- George Lucas

The AI plateau: What smart CIOs will do when the hype cools

During the early stages of GenAI adoption, organizations were captivated by its potential -- often driven by the hype surrounding tools like ChatGPT. However, as the technology matures, enterprises are now grappling with the complexities of scaling AI tools, integrating them into existing workflows and using them to meet measurable business outcomes. ... History has shown that transformative technologies often go through similar cycles of hype, disillusionment and eventual stabilization. ... Early on, many organizations told every department to use AI to boost productivity. That approach created energy, but it also produced long lists of ideas that competed for attention and resources. At the plateau stage, CIOs are becoming more selective. Instead of experimenting with every possible use case, they are selecting a smaller number of use cases that clearly support business goals and can be scaled. The question is no longer whether a team can use AI, but whether it should. ... CIOs should take a two-speed approach that separates fast, short-term AI projects from larger, long-term efforts, Locandro said. Smaller initiatives help teams learn and deliver quick results. Bigger projects require more planning and investment, especially when they span multiple systems. ... A key challenge CIOs face with GenAI is avoiding long, drawn-out planning cycles that try to solve everything at once. As AI technology evolves rapidly, lengthy projects risk producing outdated tools.

Middle East Tech 2026: 5 Non-AI Trends Shaping Regional Business

The Middle Eastern biotechnology market is rapidly maturing into a multi-billion-dollar industrial powerhouse, driven by national healthcare and climate agendas. In 2026, the industry is marking the shift toward manufacturing-scale deployment, as genomics, biofuels, and diagnostics projects move into operational phases. ... Quantum computing has moved past the stage of academic curiosity. In 2026, the Middle East is seeing the first wave of applied industrial pilots, particularly within the energy and material science sectors. ... While commercialization timelines remain long, the strategic value of early entry is high. Foreign suppliers who offer algorithm development or hardware-software integration for these early-stage pilots will find a highly receptive market among national energy champions. ... Geopatriation refers to the relocation of digital workloads and data onto sovereign-controlled clouds and local hardware and stands out as a major structural shift in 2026. Driven by national security concerns and the massive data requirements of AI, Middle Eastern states are reducing their reliance on cross-border digital architectures. This trend has extended beyond data residency to include the localization of critical hardware capabilities. ... the region is moving away from perimeter-based security models toward zero-trust architectures, under which no user, device, or system receives implicit trust. Security priorities now extend beyond office IT systems to cover operational technology

Scaling AI value demands industrial governance

"Capturing AI's value while minimizing risk starts with discipline," Puig said. "CIOs and their organizations need a clear strategy that ties AI initiatives to business outcomes, not just technology experiments. This means defining success criteria upfront, setting guardrails for ethics and compliance, and avoiding the trap of endless pilots with no plan for scale." ... Puig adds that trust is just as important as technology. "Transparency, governance, and training help people understand how AI decisions are made and where human judgment still matters. The goal isn't to chase every shiny use case; it's to create a framework where AI delivers value safely and sustainably." ... Data security and privacy emerge as critical issues, cited by 42% of respondents in the research. While other concerns -- such as response quality and accuracy, implementation costs, talent shortages, and regulatory compliance -- rank lower individually, they collectively represent substantial barriers. When aggregated, issues related to data security, privacy, legal and regulatory compliance, ethics, and bias form a formidable cluster of risk factors -- clearly indicating that trust and governance are top priorities for scaling AI adoption. ... At its core, governance ensures that data is safe for decision-making and autonomous agents. In "Competing in the Age of AI," authors Marco Iansiti and Karim Lakhani explain that AI allows organizations to rethink the traditional firm by powering up an "AI factory" -- a scalable decision-making engine that replaces manual processes with data-driven algorithms.

Information Management Trends in the Year Ahead

The digital workforce will make its presence felt. “Fleets of AI agents trained on proprietary data, governed by corporate policy, and audited like employees will appear in org charts, collaborate on projects, and request access through policy engines,” said Sergio Gago, CTO for Cloudera. “They will be contributing insights alongside their human colleagues.” A potential oversight framework may effectively be called an “HR department for AI.” AI agents are graduating from “copilots that suggest to accountable coworkers inside their digital environments,” agreed Arturo Buzzalino ... “Instead of pulling data into different environments, we’re bringing compute to the data,” said Scott Gnau, head of data platforms at InterSystems. “For a long time, the common approach was to move data to wherever the applications or models were running. AI depends on fast, reliable access to governed data. When teams make this change, they see faster results, better control, and fewer surprises in performance and cost.” ... The year ahead will see efforts to reign in the huge volume of AI projects now proliferating outside the scope of IT departments. “IT leaders are being called in to fix or unify fragmented, business-led AI projects, signaling a clear shift toward CIOs—like myself,” said Shelley Seewald, CIO at Tungsten Automation. The impetus is on IT leaders and managers to be “more involved much earlier in shaping AI strategy and governance.

What is outcome as agentic solution (OaAS)?

The analyst firm, Gartner predicts that a new paradigm it’s named outcome as agentic solution (OaAS) will make some of the biggest waves, by replacing software as a service (SaaS). The new model will see enterprises contract for outcomes, instead of simply buying access to software tools. Instead of SaaS, where the customer is responsible for purchasing a tool and using it to achieve results, with OaAS providers embed AI agents and orchestration so the work is performed for you. This leaves the vendor responsible for automating decisions and delivering outcomes, says Vuk Janosevic, senior director analyst at Gartner. ... The ‘outcome scenario’ has been developing in the market for several years, first through managed services then value-based delivery models. “OaAS simply formalizes it with modern IT buyers, who want results over tools,” notes Thomas Kraus, global head of AI at Onix. OaAS providers are effectively transforming systems of record (SoR) into systems of action (SoA) by introducing orchestration control planes that bind execution directly to outcomes, says Janosevic. ... Goransson, however, advises enterprises carefully evaluate several areas of risk before adopting an agentic service model, Accountability is paramount, he notes, as without clear ownership structures and performance metrics, organizations may struggle to assess whether outcomes are being delivered as intended.

Bridging the Gap Between SRE and Security: A Unified Framework for Modern Reliability

SRE teams optimize for uptime, performance, scalability, automation and operational efficiency. Security teams focus on risk reduction, threat mitigation, compliance, access control and data protection. Both mandates are valid, but without shared KPIs, each team views the other as an obstacle to progress. Security controls — patch cycles, vulnerability scans, IAM restrictions and network changes — can slow deployments and reduce SRE flexibility. In SRE terms, these controls often increase toil, create unpredictable work and disrupt service-level objectives (SLOs). The SRE culture emphasizes continuous improvement and rapid rollback, whereas security relies on strict change approval and minimizing risk surfaces. ... This disconnect impacts organizations in measurable ways. Security incidents often trigger slow, manual escalations because security and operations lack common playbooks, increasing mean time to recovery (MTTR). Risk gets mis-prioritized when SRE sees a vulnerability as non-disruptive while security considers it critical. Fragmented tooling means that SRE leverages observability and automation while security uses scanning and SIEM tools with no shared telemetry, creating incomplete incident context. The result? Regulatory penalties, breaches from failures in patch automation or access governance and a culture of blame where security faults SRE for speed and SRE faults security for friction.

The 2 faces of AI: How emerging models empower and endanger cybersecurity

More recently, the researchers at Google Threat Intelligence Group (GTIG) identified a disturbing new trend: malware that uses LLMs during execution to dynamically alter its own behavior and evade detection. This is not pre-generated code, this is code that adapts mid-execution. ... Anthropic recently disclosed a highly sophisticated cyber espionage operation, attributed to a state-sponsored threat actor, that leveraged its own Claude Codemodel to target roughly 30 organizations globally, including major financial institutions and government agencies. ... If adversaries are operating at AI speed, our defenses must too. The silver lining of this dual-use dynamic is that the most powerful LLMs are also being harnessed by defenders to create fundamentally new security capabilities. ... LLMs have shown extraordinary potential in identifying unknown, unpatched flaws (zero-days). These models significantly outperform conventional static analyzers, particularly in uncovering subtle logic flaws and buffer overflows in novel software. ... LLMs are transforming threat hunting from a manual, keyword-based search to an intelligent, contextual query process that focuses on behavioral anomalies. ... Ultimately, the challenge isn’t to halt AI progress but to guide it responsibly. That means building guardrails into models, improving transparency and developing governance frameworks that keep pace with emerging capabilities. It also requires organizations to rethink security strategies, recognizing that AI is both an opportunity and a risk multiplier.

Hacker Conversations: Katie Paxton-Fear Talks Autism, Morality and Hacking

“Life with autism is like living life without the instruction manual that everyone else has.” It’s confusing and difficult. “Computing provides that manual and makes it easier to make online friends. It provides accessibility without the overpowering emotions and ambiguities that exist in face-to-face real life relationships – so it’s almost helping you with your disability by providing that safe context you wouldn’t normally have.” Paxton-Fear became obsessed with computing at an early age. ... During the second year into her PhD study, a friend from her earlier university days invited her to a bug bounty event held by HackerOne. She went – not to take part in the event (she still didn’t think she was a hacker nor understood anything about hacking), she went to meet up with other friends from the university days. She thought to herself, ‘I’m not going to find anything. I don’t know anything about hacking.’ “But then, while there, I found my first two vulnerabilities.” ... he was driven by curiosity from an early age – but her skill was in disassembly without reassembly: she just needed to know how things work. And while many hackers are driven to computers as a shelter from social difficulties, she exhibits no serious or long lasting social difficulties. For her, the attraction of computers primarily comes from her dislike of ambiguity. She readily acknowledges that she sees life as unambiguously black or white with no shades of gray.

‘A wild future’: How economists are handling AI uncertainty in forecasts

Economists have time-tested models for projecting economic growth. But they’ve seen nothing like AI, which is a wild card complicating traditional economic playbooks. Some facts are clear: AI will make humans more productive and increase economic activity, with spillover effects on spending and employment. But there are many unknowns about AI. Economists can’t isolate AI’s impact on human labor as automation kicks in. Nailing down long-term factory job losses to AI is not possible. ... “We’re seeing an increase in terms of productivity enhancements over the next decade and a half. While it doesn’t capture AI directly… there is all kinds of upside potential to the productivity numbers because of AI. ... “There are basically two ways this can go. You can get more output for the same input. If you used to put in 100 and get 120, maybe now you get 140. That’s an expansion in total factor productivity. Or you can get the same output with fewer inputs. “It’s unclear how much of either will happen across industries or in the labor market. Will companies lean into AI, cut their workforce, and maintain revenue? Or will they keep their workforce, use AI to supplement them, and increase total output per worker? ... If AI and automation remove the human element from labor-intensive manufacturing, that cost advantage erodes. It makes it harder for developing countries to use cheap labor as a stepping stone toward industrialization.

Understanding transformers: What every leader should know about the architecture powering GenAI

Inside a transformer, attention is the mechanism that lets tokens talk to each other. The model compares every token’s query with every other token’s key to calculate a weight which is a measure of how relevant one token is to another. These weights are then used to blend information from all tokens into a new, context-aware representation called a value. In simple terms: attention allows the model to focus dynamically. If the model reads “The cat sat on the mat because it was tired,” attention helps it learn that “it” refers to “the cat,” not “the mat.” ... Transformers are powerful, but they’re also expensive. Training a model like GPT-4 requires thousands of GPUs and trillions of data tokens. Leaders don’t need to know tensor math, but they do need to understand scaling trade-offs. Techniques like quantization (reducing numerical precision), model sharding and caching can cut serving costs by 30–50% with minimal accuracy loss. The key insight: Architecture determines economics. Design choices in model serving directly impact latency, reliability and total cost of ownership. ... The transformer’s most profound breakthrough isn’t just technical — it’s architectural. It proved that intelligence could emerge from design — from systems that are distributed, parallel and context-aware. For engineering leaders, understanding transformers isn’t about learning equations; it’s about recognizing a new principle of system design.