Daily Tech Digest by Kannan Subbiah

Daily Tech Digest - February 07, 2026

Quote for the day:

"Success in almost any field depends more on energy and drive than it does on intelligence. This explains why we have so many stupid leaders." -- Sloan Wilson

Tiny AI: The new oxymoron in town? Not really!

Could SLMs and minituarised models be the drink that would make today’s AI small enough to walk through these future doors without AI bumping into carbon-footprint issues? Would model compression tools like pruning, quantisation, and knowledge distillation help to lift some weight off the shoulders of heavy AI backyards? Lightweight models, edge devices that save compute resources, smaller algorithms that do not put huge stress on AI infrastructures, and AI that is thin on computational complexity- Tiny AI- as an AI creation and adoption approach- sounds unusual and promising at the onset. ... hardware innovations and new approaches to modelling that enable Tiny AI can significantly ease the compute and environmental burdens of large-scale AI infrastructures, avers Biswajeet Mahapatra, principal analyst at Forrester. “Specialised hardware like AI accelerators, neuromorphic chips, and edge-optimised processors reduces energy consumption by performing inference locally rather than relying on massive cloud-based models. At the same time, techniques such as model pruning, quantisation, knowledge distillation, and efficient architectures like transformers-lite allow smaller models to deliver high accuracy with far fewer parameters.” ... Tiny AI models run directly on edge devices, enabling fast, local decision-making by operating on narrowly optimised datasets and sending only relevant, aggregated insights upstream, Acharya spells out.

Kali Linux vs. Parrot OS: Which security-forward distro is right for you?

The first thing you should know is that Kali Linux is based on Debian, which means it has access to the standard Debian repositories, which include a wealth of installable applications. ... There are also the 600+ preinstalled applications, most of which are geared toward information gathering, vulnerability analysis, wireless attacks, web application testing, and more. Many of those applications include industry-specific modifications, such as those for computer forensics, reverse engineering, and vulnerability detection. And then there are the two modes: Forensics Mode for investigation and "Kali Undercover," which blends the OS with Windows. ... Parrot OS (aka Parrot Security or just Parrot) is another popular pentesting Linux distribution that operates in a similar fashion. Parrot OS is also based on Debian and is designed for security experts, developers, and users who prioritize privacy. It's that last bit you should pay attention to. Yes, Parrot OS includes a similar collection of tools as does Kali Linux, but it also offers apps to protect your online privacy. To that end, Parrot is available in two editions: Security and Home. ... What I like about Parrot OS is that you have options. If you want to run tests on your network and/or systems, you can do that. If you want to learn more about cybersecurity, you can do that. If you want to use a general-purpose operating system that has added privacy features, you can do that.

Bridging the AI Readiness Gap: Practical Steps to Move from Exploration to Production

To bridge the gap between AI readiness and implementation, organizations can adopt the following practical framework, which draws from both enterprise experience and my ongoing doctoral research. The framework centers on four critical pillars: leadership alignment, data maturity, innovation culture, and change management. When addressed together, these pillars provide a strong foundation for sustainable and scalable AI adoption. ... This begins with a comprehensive, cross-functional assessment across the four pillars of readiness: leadership alignment, data maturity, innovation culture, and change management. The goal of this assessment is to identify internal gaps that may hinder scale and long-term impact. From there, companies should prioritize a small set of use cases that align with clearly defined business objectives and deliver measurable value. These early efforts should serve as structured pilots to test viability, refine processes, and build stakeholder confidence before scaling. Once priorities are established, organizations must develop an implementation road map that achieves the right balance of people, processes, and technology. This road map should define ownership, timelines, and integration strategies that embed AI into business workflows rather than treating it as a separate initiative. Technology alone will not deliver results; success depends on aligning AI with decision-making processes and ensuring that employees understand its value.

Proxmox's best feature isn't virtualization; it's the backup system

Because backups are integrated into Proxmox instead of being bolted on as some third-party add-on, setting up and using backups is entirely seamless. Agents don't need to be configured per instance. No extra management is required, and no scripts need to be created to handle the running of snapshots and recovery. The best part about this approach is that it ensures everything will continue working with each OS update. Backups can be spotted per instance, too, so it's easy to check how far you can go back and how many copies are available. The entire backup strategy within Proxmox is snapshot-based, leveraging localised storage when available. This allows Proxmox to create snapshots of not only running Linux containers, but also complex virtual machines. They're reliable, fast, and don't cause unnecessary downtime. But while they're powerful additions to a hypervised configuration, the backups aren't difficult to use. This is key since it would render the backups less functional if it proved troublesome to use them when it mattered most. These backups don't have to use local storage either. NFS, CIFS, and iSCSI can all be targeted as backup locations. ... It can also be a mixture of local storage and cloud services, something we recommend and push for with a 3-2-1 backup strategy. But there's one thing of using Proxmox's snapshots and built-in tools and a whole different ball game with Proxmox Backup Server. With PBS, we've got duplication, incremental backups, compression, encryption, and verification.

The Fintech Infrastructure Enabling AI-Powered Financial Services

AI is reshaping financial services faster than most realize. Machine learning models power credit decisions. Natural language processing handles customer service. Computer vision processes documents. But there’s a critical infrastructure layer that determines whether AI-powered financial platforms actually work for end users: payment infrastructure. The disconnect is striking. Fintech companies invest millions in AI capabilities, recommendation engines, fraud detection, personalization algorithms. ... From a technical standpoint, the integration happens via API. The platform exposes user balances and transaction authorization through standard REST endpoints. The card provider handles everything downstream: card issuance logistics, real-time currency conversion, payment network settlement, fraud detection at the transaction level, dispute resolution workflows. This architectural pattern enables fintech platforms to add payment functionality in 8-12 weeks rather than the 18-24 months required to build from scratch. ... The compliance layer operates transparently to end users while protecting platforms from liability. KYC verification happens at multiple checkpoints. AML monitoring runs continuously across transaction patterns. Reporting systems generate required documentation automatically. The platform gets payment functionality without becoming responsible for navigating payment regulations across dozens of jurisdictions.

Context Engineering for Coding Agents

Context engineering is relevant for all types of agents and LLM usage of course. My colleague Bharani Subramaniam’s simple definition is: “Context engineering is curating what the model sees so that you get a better result.” For coding agents, there is an emerging set of context engineering approaches and terms. The foundation of it are the configuration features offered by the tools, and then the nitty gritty of part is how we conceptually use those features. ... One of the goals of context engineering is to balance the amount of context given - not too little, not too much. Even though context windows have technically gotten really big, that doesn’t mean that it’s a good idea to indiscriminately dump information in there. An agent’s effectiveness goes down when it gets too much context, and too much context is a cost factor as well of course. Some of this size management is up to the developer: How much context configuration we create, and how much text we put in there. My recommendation would be to build context like rules files up gradually, and not pump too much stuff in there right from the start. ... As I said in the beginning, these features are just the foundation for humans to do the actual work and filling these with reasonable context. It takes quite a bit of time to build up a good setup, because you have to use a configuration for a while to be able to say if it’s working well or not - there are no unit tests for context engineering. Therefore, people are keen to share good setups with each other.

Reimagining The Way Organizations Hire Cyber Talent

The way we hire cybersecurity professionals is fundamentally flawed. Employers post unicorn job descriptions that combine three roles’ worth of responsibilities into one. Qualified candidates are filtered out by automated scans or rejected because their resumes don’t match unrealistic expectations. Interviews are rushed, mismatched, or even faked—literally, in some cases. On the other side, skilled professionals—many of whom are eager to work—find themselves lost in a sea of noise, unable to connect with the opportunities that align with their capabilities and career goals. Add in economic uncertainty, AI disruption and changing work preferences, and it’s clear the traditional hiring playbook simply isn’t working anymore. ... Part of fixing this broken system means rethinking what we expect from roles in the first place. Jones believes that instead of packing every security function into a single job description and hoping for a miracle, organizations should modularize their needs. Need a penetration tester for one month? A compliance SME for two weeks? A security architect to review your Zero Trust strategy? You shouldn’t have to hire full-time just to get those tasks done. ... Solving the cybersecurity workforce challenge won’t come from doubling down on job boards or resume filters. But organizations may be able to shift things in the right direction by reimagining the way they connect people to the work that matters—with clarity, flexibility and mutual trust.

News sites are locking out the Internet Archive to stop AI crawling. Is the ‘open web’ closing?

Publishers claim technology companies have accessed a lot of this content for free and without the consent of copyright owners. Some began taking tech companies to court, claiming they had stolen their intellectual property. High-profile examples include The New York Times’ case against ChatGPT’s parent company OpenAI and News Corp’s lawsuit against Perplexity AI. ... Publishers are also using technology to stop unwanted AI bots accessing their content, including the crawlers used by the Internet Archive to record internet history. News publishers have referred to the Internet Archive as a “back door” to their catalogues, allowing unscrupulous tech companies to continue scraping their content. ... The opposite approach – placing all commercial news behind paywalls – has its own problems. As news publishers move to subscription-only models, people have to juggle multiple expensive subscriptions or limit their news appetite. Otherwise, they’re left with whatever news remains online for free or is served up by social media algorithms. The result is a more closed, commercial internet. This isn’t the first time that the Internet Archive has been in the crosshairs of publishers, as the organisation was previously sued and found to be in breach of copyright through its Open Library project. ... Today’s websites become tomorrow’s historical records. Without the preservation efforts of not-for-profit organisations like The Internet Archive, we risk losing vital records.

Who will be the first CIO fired for AI agent havoc?

As CIOs deploy teams of agents that work together across the enterprise, there’s a risk that one agent’s error compounds itself as other agents act on the bad result, he says. “You have an endless loop they can get out of,” he adds. Many organizations have rushed to deploy AI agents because of the fear of missing out, or FOMO, Nadkarni says. But good governance of agents takes a thoughtful approach, he adds, and CIOs must consider all the risks as they assign agents to automate tasks previously done by human employees. ... Lawsuits and fines seem likely, and plaintiffs will not need new AI laws to file claims, says Robert Feldman, chief legal officer at database services provider EnterpriseDB. “If an AI agent causes financial loss or consumer harm, existing legal theories already apply,” he says. “Regulators are also in a similar position. They can act as soon as AI drives decisions past the line of any form of compliance and safety threshold.” ... CIOs will play a big role in figuring out the guardrails, he adds. “Once the legal action reaches the public domain, boards want answers to what happened and why,” Feldman says. ... CIOs should be proactive about agent governance, Osler recommends. They should require proof for sensitive actions and make every action traceable. They can also put humans in the loop for sensitive agent tasks, design agents to hand off action when the situation is ambiguous or risky, and they can add friction to high-stakes agent actions and make it more difficult to trigger irreversible steps, he says.

Measuring What Matters: Balancing Data, Trust and Alignment for Developer Productivity

Organizations need to take steps over and above these frameworks. It's important to integrate those insights with qualitative feedback. With the right balance of quantitative and qualitative data insights, companies can improve DevEx, increase employee engagement, and drive overall growth. Productivity metrics can only be a game-changer if used carefully and in conjunction with a consultative human-based approach to improvement. They should be used to inform management decisions, not replace them. Metrics can paint a clear picture of efficiency, but only become truly useful once you combine them with a nuanced view of the subjective developer experience. ... People who feel safe at work are more productive and creative, so taking DevEx into account when optimizing processes and designing productivity frameworks includes establishing an environment where developers can flag unrealistic deadlines and identify and solve problems together, faster. Tools, including integrated development environments (IDEs), source code repositories and collaboration platforms, all help to identify the systemic bottlenecks that are disrupting teams' workflows and enable proactive action to reduce friction. Ultimately, this will help you build a better picture of how your team is performing against your KPIs, without resorting to micromanagement. Additionally, when company priorities are misaligned, confusion and complexity follow, which is exhausting for developers, who are forced to waste their energy on bridging the gaps, rather than delivering value.

Daily Tech Digest - February 06, 2026

Quote for the day:

"When you say my team is no good, all I hear is that I failed as a leader." -- Gordon Tredgold

Everyone works with AI agents, but who controls the agents?

Over the past year, there has been a lot of talk about MCP and A2A, protocols that allow agents to communicate with each other. But more and more agents that are now becoming available support and use them. Agents will soon be able to easily exchange information and transfer tasks to each other to achieve much better results. Currently, 50 percent of AI agents in organizations still work as a silo. This means that no context or data from external systems is added. The need for context is now clear to many organizations. 96 percent of IT decision-makers understand that success depends on seamless integration. This puts renewed pressure on data silos and integrations. ... For IT decision-makers wondering what they really need to do in 2026, doing nothing is definitely not the right answer, as your competitors who do invest in AI will quickly overtake you. On the other hand, you don’t have to go all-in and blow your entire IT budget on it. ... You need to start now, so start small. Putting the three or five most frequently asked questions to your customer service or HR team into an AI agent can take a huge workload off those teams. There are now several case studies showing that this has reduced the number of tickets by as much as 50-60 percent. AI can also be used for sales reports or planning, which currently takes employees many hours each week.

Mobile privacy audits are getting harder

Many privacy reviews begin with static analysis of an Android app package (APK). This can reveal permissions requested by the app and identify embedded third-party libraries such as advertising SDKs, telemetry tools, or analytics components. Requested permissions are often treated as indicators of risk because they can imply access to contacts, photos, location, camera, or device identifiers. Library detection can also show whether an app includes known trackers. Yet, static results are only partial. Permissions may never be used in runtime code paths, and libraries can be present without being invoked. Static analysis also misses cases where data is accessed indirectly or through system behavior that does not require explicit permissions. ... Apps increasingly defend against MITM using certificate pinning, which causes the app to reject traffic interception even if a root certificate is installed. Analysts may respond by patching the APK or using dynamic instrumentation to bypass the pinning logic at runtime. Both approaches can fail depending on the app’s implementation. Mopri’s design treats these obstacles as expected operating conditions. The framework includes multiple traffic capture approaches so investigators can switch methods when an app resists a specific setup. ... Raw network logs are difficult to interpret without enrichment. Mopri adds contextual information to recorded traffic in two areas: identifying who received the data, and identifying what sensitive information may have been transmitted.

When the AI goes dark: Building enterprise resilience for the age of agentic AI

Instead of merely storing data, AI accumulates intelligence. When we talk about AI “state,” we’re describing something fundamentally different from a database that can be rolled back. ... Lose this state, and you haven’t just lost data. You’ve lost the organizational intelligence that took hundreds of human days of annotation, iteration and refinement to create. You can’t simply re-enter it from memory. Worse, a corrupted AI state doesn’t announce itself the way a crashed server does. ... This challenge is compounded by the immaturity of the AI vendor landscape. Hyperscale cloud providers may advertise “four nines” of uptime (99.99% availability, which translates to roughly 52 minutes of downtime per year), but many AI providers, particularly the startups emerging rapidly in this space, cannot yet offer these enterprise-grade service guarantees. ... When AI agents handle customer interactions, manage supply chains, execute financial processes and coordinate operations, a sustained AI outage isn’t an inconvenience. It’s an existential threat. ... Humans are not just a fallback option. They are an integral component of a resilient AI-native enterprise. Motivated, trained and prepared teams can bridge gaps when AI fails, ensuring continuity of both systems and operations. When you continually reduce your workforce to appease your shareholders, will your human employees remain motivated, trained and prepared?

The blind spot every CISO must see: Loyalty

The insider who once seemed beyond reproach becomes the very vector through which sensitive data, intellectual property, or operational integrity is compromised. These are not isolated failures of vetting or technology; they are failures to recognize that loyalty is relational and conditional, not absolute. ... Organizations have long operated under the belief that loyalty, once demonstrated, becomes a durable shield against insider risk. Extended tenure is rewarded with escalating access privileges, high performers are granted broader system rights without commensurate behavioral review, and verbal affirmations of commitment are taken at face value. Yet time and again patterns repeat. What begins as mutual confidence weakens not through dramatic betrayal but through subtle realignments in personal commitment. An employee who once identified strongly with the mission may begin to feel undervalued, overlooked for advancement, or weighed down by outside pressures. ... Positions with access to crown jewels — sensitive data, financial systems, or personnel records — or executive ranks inherently require proportionately more oversight, as regulated sectors have shown. Professionals in these roles accept this as part of the terrain, with history demonstrating minimal talent loss when frameworks are transparent and supportive.

Researchers Warn: WiFi Could Become an Invisible Mass Surveillance System

Researchers at the Karlsruhe Institute of Technology (KIT) have shown that people can be recognized solely by recording WiFi communication in their surroundings, a capability they warn poses a serious threat to personal privacy. The method does not require individuals to carry any electronic devices, nor does it rely on specialized hardware. Instead, it makes use of ordinary WiFi devices already communicating with each other nearby. ... “This technology turns every router into a potential means for surveillance,” warns Julian Todt from KASTEL. “If you regularly pass by a café that operates a WiFi network, you could be identified there without noticing it and be recognized later, for example by public authorities or companies.” Felix Morsbach notes that intelligence agencies or cybercriminals currently have simpler ways to monitor people, such as accessing CCTV systems or video doorbells. “However, the omnipresent wireless networks might become a nearly comprehensive surveillance infrastructure with one concerning property: they are invisible and raise no suspicion.” ... Unlike attacks that rely on LIDAR sensors or earlier WiFi-based techniques that use channel state information (CSI), meaning measurements of how radio signals change when they reflect off walls, furniture, or people, this approach does not require specialized equipment. Instead, it can be carried out using a standard WiFi device.

Is software optimization a lost art?

Almost all of us have noticed apps getting larger, slower, and buggier. We've all had a Chrome window that's taking up a baffling amount of system memory, for example. While performance challenges can vary by organization, application and technical stacks, it appears the worst performance bottlenecks have migrated to the ‘last mile’ of the user experience, says Jim Mercer ... “While architectural decisions and developer skills remain critical, they’re too often compromised by the need to integrate AI and new features at an exponential pace. So, a lack of due diligence when we should know better.” ... The somewhat concerning part is that AI bloat is structurally different from traditional technical debt, she points out. Rather than accumulated cruft over time, it usually manifests as systematic over-engineering from day one. ... Software optimization has become even more important due to the recent RAM price crisis, driven by surging demand for hardware to meet AI and data center buildout. Though the price increases may be levelling out, RAM is now much more expensive than it was mere months ago. This is likely to shift practices and behavior, Brock ... Security will play a role too, particularly with the growing data sovereignty debate and concerns about bad actors, she notes. Leaner, neater, shorter software is simply easier to maintain – especially when you discover a vulnerability and are faced with working through a massive codebase.

The ‘Super Bowl’ standard: Architecting distributed systems for massive concurrency

In the world of streaming, the “Super Bowl” isn’t just a game. It is a distributed systems stress test that happens in real-time before tens of millions of people. ... It is the same nightmare that keeps e-commerce CTOs awake before Black Friday or financial systems architects up during a market crash. The fundamental problem is always the same: How do you survive when demand exceeds capacity by an order of magnitude? ... We implement load shedding based on business priority. It is better to serve 100,000 users perfectly and tell 20,000 users to “please wait” than to crash the site for all 120,000. ... In an e-commerce context, your “Inventory Service” and your “User Reviews Service” should never share the same database connection pool. If the Reviews service gets hammered by bots scraping data, it should not consume the resources needed to look up product availability. ... When a cache miss occurs, the first request goes to the database to fetch the data. The system identifies that 49,999 other people are asking for the same key. Instead of sending them to the database, it holds them in a wait state. Once the first request returns, the system populates the cache and serves all 50,000 users with that single result. This pattern is critical for “flash sale” scenarios in retail. When a million users refresh the page to see if a product is in stock, you cannot do a million database lookups. ... You cannot buy “resilience” from AWS or Azure. You cannot solve these problems just by switching to Kubernetes or adding more nodes.

Cloud-native observability enters a new phase as the market pivots from volume to value

“The secret in the industry is that … all of the existing solutions are motivated to get people to produce as much data as possible,” said Martin Mao, co-founder and chief executive officer of Chronosphere, during an interview with theCUBE. “What we’re doing differently with logs is that we actually provide the ability to see what data is useful, what data is useless and help you optimize … so you only keep and pay for the valuable data.” ... Widespread digital modernization is driving open-source adoption, which in turn demands more sophisticated observability tools, according to Nashawaty. “That urgency is why vendor innovations like Chronosphere’s Logs 2.0, which shift teams from hoarding raw telemetry to keeping only high-value signals, are resonating so strongly within the open-source community,” he said. ... Rather than treating logs as an add-on, Logs 2.0 integrates them directly into the same platform that handles metrics, traces and events. The architecture rests on three pillars. First, logs are ingested natively and correlated with other telemetry types in a shared backend and user interface. Second, usage analytics quantify which logs are actually referenced in dashboards, alerts and investigations. Third, governance recommendations guide teams toward sampling rules, log-to-metric conversion or archival strategies based on real usage patterns.

How recruitment fraud turned cloud IAM into a $2 billion attack surface

The attack chain is quickly becoming known as the identity and access management (IAM) pivot, and it represents a fundamental gap in how enterprises monitor identity-based attacks. CrowdStrike Intelligence research published on January 29 documents how adversary groups operationalized this attack chain at an industrial scale. Threat actors are cloaking the delivery of trojanized Python and npm packages through recruitment fraud, then pivoting from stolen developer credentials to full cloud IAM compromise. ... Adversaries are shifting entry vectors in real-time. Trojanized packages aren’t arriving through typosquatting as in the past — they’re hand-delivered via personal messaging channels and social platforms that corporate email gateways don’t touch. CrowdStrike documented adversaries tailoring employment-themed lures to specific industries and roles, and observed deployments of specialized malware at FinTech firms as recently as June 2025. ... AI gateways excel at validating authentication. They check whether the identity requesting access to a model endpoint or training pipeline holds the right token and has privileges for the timeframe defined by administrators and governance policies. They don’t check whether that identity is behaving consistently with its historical pattern or is randomly probing across infrastructure.

The Hidden Data Access Crisis Created by AI Agents

As enterprises adopt agents at scale, a different approach becomes necessary. Instead of having agents impersonate users, agents retain their own identity. When they need data, they request access on behalf of a user. Access decisions are made dynamically, at the moment of use, based on human entitlements, agent constraints, data governance rules, and intent (purpose). This shifts access from being identity-driven to being context-driven. Authorization becomes the primary mechanism for controlling data access, rather than a side effect of authentication. ... CDOs need to work closely with IAM, security, and platform operations teams to rethink how access decisions are made. In particular, this means separating authentication from authorization and recognizing that impersonation is no longer a sustainable model at scale. Authentication teams continue to establish trust and identity. Authorization mechanisms must take on the responsibility of deciding what data should be accessible at query time, based on the human user, the agent acting on their behalf, the data’s governance rules, and the purpose of the request. ... CDOs must treat data provisioning as an enterprise capability, not a collection of tactical exceptions. This requires working across organizational boundaries. Authentication teams continue to establish trust and identity. Security teams focus on risk and enforcement. Data teams bring policy and governance context.

Daily Tech Digest - February 05, 2026

Quote for the day:

"We don't grow when things are easy. We grow when we face challenges." -- Elizabeth McCormick

AI Rapidly Rendering Cyber Defenses Obsolete

“Most organizations still don’t have a complete inventory of where AI is running or what data it touches,” he continued. “We’re talking millions of unmanaged AI interactions and untold terabytes of potentially sensitive data flowing into systems that no one is monitoring. You don’t have to be a CISO to recognize the inherent risk in that.” “You’re ending up with AI everywhere and controls nowhere,” added Ryan McCurdy ... “The risk is not theoretical,” he declared. “When you can’t inventory where AI is running and what it’s touching, you can’t enforce policy or investigate incidents with confidence.” ... While AI security discussions often focus on hypothetical future threats, the report noted, Zscaler’s red team testing revealed a more immediate reality: when enterprise AI systems are tested under real adversarial conditions, they break almost immediately. “AI systems are compromised quickly because they rely on multiple permissions working together, whether those permissions are granted via service accounts or inherited from user-level access,” explained Sunil Gottumukkala ... “We’re seeing exposed model endpoints without proper authentication, prompt injection vulnerabilities, and insecure API integrations with excessive permissions,” he said. “Default configurations are being shipped straight to production. Ultimately, it’s a fresh new field, and everyone’s rushing to stake a claim, get their revenue up, and get to market fastest.”

Offensive Security: A Strategic Imperative for the Modern CISO

Rather than remaining in a reactive stance focused solely on known threats, modern CISOs are required to adopt a proactive and strategic approach. This evolution necessitates the integration of offensive security as an essential element of a comprehensive cybersecurity strategy, rather than viewing it as a specialized technical activity. Boards now expect CISOs to anticipate emerging threats, assess and quantify risks, and clearly demonstrate how security investments contribute to safeguarding revenue, reputation, and organizational resilience. ... Offensive security takes a different approach. Rather than simply responding to threats, it actively replicates real-world attacks to uncover vulnerabilities before cybercriminals exploit them. ... Offensive security is crucial for today’s CISOs, helping them go beyond checking boxes for compliance to actively discover, confirm, and measure security risks—such as financial loss, damage to reputation, and disruptions to operations. By mimicking actual cyberattacks, CISOs can turn technical vulnerabilities into business risks, allowing for smarter resource use, clearer communication with the board, and greater overall resilience. ... Chief Information Security Officers (CISOs) are frequently required to substantiate their budget requests with clear, empirical data. Offensive security plays a critical role in demonstrating whether security investments effectively mitigate risk. CISOs must provide evidence that tools, processes, and teams contribute measurable value.

Cyber Insights 2026: Cyberwar and Rising Nation State Threats

While both cyberwar and cyberwarfare will increase through 2026, cyberwarfare is likely to increase more dramatically. The difference between the two should not be gauged by damage, but by primary intent. This difference is important because criminal activity can harm a business or industry, while nation state activity can damage whole countries. It is the primary intent or motivation that separates the two. Cyberwar is primarily motivated by financial gain. Cyberwarfare is primarily motivated by political gain, which means it could be a nation or an ideologically motivated group. ... The ultimate purpose of nation state cyberwarfare is to prepare the battlefield for kinetic war. We saw this with increased Russian activity against Ukraine immediately before the 2022 invasion. Other nations are not yet (at least we hope not) generally using cyber to prepare the battlefield. But they are increasingly pre-positioning themselves within critical industries to be able to do so. This geopolitical incentive together with the cyberattack and cyber stealth capabilities afforded by advanced AI, suggests that nation state pre-positioning attacks will increase dramatically over the next few years. Pre-positioning is not new, but it will increase. ... “Geopolitics aside, we can expect acts of cyberwar to increase over the coming years in large part thanks to AI,” says Art Gilliand, CEO at Delinea.

Cybersecurity planning keeps moving toward whole-of-society models

Private companies own and operate large portions of national digital infrastructure. Telecommunications networks, cloud services, energy grids, hospitals, and financial platforms all rely on private management. National strategies therefore emphasize sustained engagement with industry and civil society. Governments typically use consultations, working groups, and sector forums to incorporate operational input. These mechanisms support realistic policy design and encourage adoption across sectors. Incentives, guidance, and shared tooling frequently accompany regulatory requirements to support compliance. ... Interagency coordination remains a recurring focus. Ownership of objectives reduces duplication and supports faster response during incidents. National strategies frequently group objectives by responsible agency to support accountability and execution. International coordination also features prominently. Cyber threats cross borders with ease, leading governments to engage through bilateral agreements, regional partnerships, and multilateral forums. Shared standards, reporting practices, and norms of behavior support interoperability across jurisdictions. ... Security operations centers serve as focal points for detection and response. Metrics tied to detection and triage performance support accountability and operational maturity.

Should I stay or should I go?

In the big picture, CISO roles are hard, and so the majority of CISOs switch jobs every two to three years or less. Lack of support from senior leadership and lack of budget commensurate with the organization’s size and industry are top reasons for this CISO churn, according to The life and times of cybersecurity professionals report from the ISSA. More specifically, CISOs leave on account of limited board engagement, high accountability with insufficient authority, executive misalignment, and ongoing barriers to implementing risk management and resilience, according to an ISSA spokesperson. ... A common red flag and reason CISO’s leave their jobs is because leadership is paying “lip service” to auditors, customers and competitors, says FinTech CISO Marius Poskus, a popular blogger on security leadership who posted an essay about resigning from “security‑theater roles.” ... the biggest red flag is when leadership pushes against your professional and personal ethics. For example, when a CEO or board wants to conceal compliance gaps, cover up reportable breaches, and refuse to sign off on responsibility for gaps and reporting failures they’ve been made aware of. ... “A lot of red flags have to do with lack of security culture or mismatch in understanding the risk tolerance of the company and what the actual risks are. This red flag goes beyond: If they don’t want to be questioned about what they’ve done so far, that is a huge red flag that they’re covering something up,” Kabir explains.

Preparing for the Unpredictable and Reshaping Disaster Recovery

When desktops live on physical devices alone, recovery can be slow. IT teams must reimage machines, restore applications, recover files, and verify security before employees can resume work. In industries where every hour of downtime has financial, operational, or even safety implications, that delay is costly. DaaS changes the equation. With cloud-based desktops, organizations can provision clean, standardized environments in minutes. If a device is compromised, employees can simply log in from another device and get back to work immediately. This eliminates many of the bottlenecks associated with endpoint recovery and gives organizations a faster, more controlled way to respond to cyber incidents. ... However, beyond these technical benefits, the shift to DaaS encourages organizations to adopt a more proactive, strategic mindset toward resilience. It allows teams to operate more flexibly, adapt to hybrid work models, and maintain continuity through a wider range of disruptions. ... DaaS offers a practical, future-ready way to achieve that goal. By making desktops portable, recoverable, and consistently accessible, it empowers organizations to maintain operations even when the unexpected occurs. In a world defined by unpredictability, businesses that embrace cloud-based desktop recovery are better positioned not just to withstand crises, but to move through them with agility and confidence.

From Alert Fatigue to Agent-Assisted Intelligent Observability

The maintenance burden grows with the system. Teams spend significant time just keeping their observability infrastructure current. New services need instrumentation. Dashboards need updates. Alert thresholds need tuning as traffic patterns shift. Dependencies change and monitoring needs to adapt. It is routine, but necessary work, and it consumes hours that could be used building features or improving reliability. A typical microservices architecture generates enormous volumes of telemetry data. Logs from dozens of services. Metrics from hundreds of containers. Traces spanning multiple systems. When an incident happens, engineers face a correlation problem. ... The shift to intelligent observability changes how engineering work gets done. Instead of spending the first twenty minutes of every incident manually correlating logs and metrics across dashboards, engineers can review AI-generated summaries that link deployment timing, error patterns, and infrastructure changes. Incident tickets are automatically populated with context. Root cause analysis, which used to require extensive investigation, now starts with a clear hypothesis. Engineers still make the decisions, but they are working from a foundation of analyzed data rather than raw signals. ... Systems are getting more complex, data volumes are increasing, and downtime is getting more expensive. Human brains aren't getting bigger or faster.

AI is collapsing the career ladder - 5 ways to reach that leadership role now

Barry Panayi, group chief data officer at insurance firm Howden, said one of the first steps for would-be executives is to make a name for themselves. ... "Experiencing something completely different from the day-to-day job is about understanding the business. I think that exposure is what gives me confidence to have opinions on topics outside of my lane," he said. "It's those kinds of opinions and contributions that get you noticed, not being a great data person, because people will assume you're good at that area. After all, that's why the board hired you." ... "Show that you understand the organization's wider strategy and how your role and the team you lead fit within that approach," he said. "It's also about thinking commercially -- being able to demonstrate that you understand how the operational decisions you make, in whatever aspect you're leading, impact top and bottom-line business value. Think like a business shareholder, not just a manager of your team." ... "Paying it forward is really important for the next generation," she said. "And as a leader, if you're not creating the next generation and the generation after that, what are you doing?" McCarroll said Helios Towers has a strong culture of promoting and developing talent from within, including certifying people in Lean Six Sigma through a leadership program with Cranfield University, partnering closely with the internal HR department, and developing regular succession planning opportunities.

Leadership Is More Than Thinking—It's Doing

Leadership, at its core, isn't a point of view; it's a daily practice. Being an effective leader requires more than being a thinker. It's also about being a doer—someone willing to translate conviction into conduct, values into decisions and belief into behavior. ... It's often inconsistency, not substantial failure, that erodes workplace culture. Employees don't want to hear from leaders only after a decision has already been made. Being a true leader requires knowing what aspects of our environment we're willing to risk before making any decision at all. ... Every time leaders postpone necessary conversations, tolerate misaligned behavior or choose convenience over courage, they incur what I call leadership debt. Like financial debt, it compounds quietly, and it's always paid—but rarely by the leader who incurred it. ... thinking strategically has never been more important. But it's not enough to thrive. Organizations with exceptional strategic clarity can still falter because leaders underestimate the "doing" aspect of change. They may communicate the vision eloquently, then fail to stay close to employees' lived experience as they try to deliver that vision. Meanwhile, teams can rise to meet extraordinary challenges when leaders are present. Listening deeply, acknowledging uncertainty and acting with transparency foster confidence and reassurance in employees.

AI Governance in 2026: Is Your Organization Ready?

In 2026, regulators and courts will begin clarifying responsibility when these systems act with limited human oversight. For CIOs, this means governance must move closer to runtime. This includes things like real-time monitoring, automated guardrails, and defined escalation paths when systems deviate from expected behavior. ... The EU AI Act’s high-risk obligations become fully applicable in August 2026. In parallel, U.S. state attorneys general are increasingly using consumer protection and discrimination statutes to pursue AI-related claims. Importantly, regulators are signaling that documentation gaps themselves may constitute violations. ... Models that can’t clearly justify outputs or demonstrate how bias and safety risks are managed face growing resistance, regardless of accuracy claims. This trend is reinforced by guidance from the National Academy of Medicine and ongoing FDA oversight of software-based medical devices. In 2026, governance in healthcare will no longer differentiate vendors; it will determine whether systems can be deployed at all. Leaders in other regulated industries should expect similar dynamics to emerge over the next year. ... “Governance debt” will become visible at the executive level. Organizations without consistent, auditable oversight across AI systems will face higher costs, whether through fines, forced system withdrawals, reputational damage, or legal fees.

Daily Tech Digest - February 04, 2026

Quote for the day:

"The struggle you're in today is developing the strength you need for tomorrow." -- Elizabeth McCormick

A deep technical dive into going fully passwordless in hybrid enterprise environments

Before we can talk about passwordless authentication, we need to address what I call the “prerequisite triangle”: cloud Kerberos trust, device registration and Conditional Access policies. Skip any one of these, and your migration will stall before it gains momentum. ... Once your prerequisites are in place, you face critical architectural decisions that will shape your deployment for years to come. The primary decision point is whether to use Windows Hello for Business, FIDO2 security keys or phone sign-in as your primary authentication mechanism. ... The architectural decision also includes determining how you handle legacy applications that still require passwords. Your options are limited: implement a passwordless-compatible application gateway, deprecate the application entirely or use Entra ID’s smart lockout and password protection features to reduce risk while you transition. ... Start with a pilot group — I recommend between 50 and 200 users who are willing to accept some friction in exchange for security improvements. This group should include IT staff and security-conscious users who can provide meaningful feedback without becoming frustrated with early-stage issues. ... Recovery mechanisms deserve special attention. What happens when a user’s device is stolen? What if the TPM fails? What if they forget their PIN and can’t reach your self-service portal? Document these scenarios and test them with your help desk before full rollout.

When Cloud Outages Ripple Across the Internet

For consumers, these outages are often experienced as an inconvenience, such as being unable to order food, stream content, or access online services. For businesses, however, the impact is far more severe. When an airline’s booking system goes offline, lost availability translates directly into lost revenue, reputational damage, and operational disruption. These incidents highlight that cloud outages affect far more than compute or networking. One of the most critical and impactful areas is identity. When authentication and authorization are disrupted, the result is not just downtime; it is a core operational and security incident. ... Cloud providers are not identity systems. But modern identity architectures are deeply dependent on cloud-hosted infrastructure and shared services. Even when an authentication service itself remains functional, failures elsewhere in the dependency chain can render identity flows unusable. ... High availability is widely implemented and absolutely necessary, but it is often insufficient for identity systems. Most high-availability designs focus on regional failover: a primary deployment in one region with a secondary in another. If one region fails, traffic shifts to the backup. This approach breaks down when failures affect shared or global services. If identity systems in multiple regions depend on the same cloud control plane, DNS provider, or managed database service, regional failover provides little protection. In these scenarios, the backup system fails for the same reasons as the primary.

The Art of Lean Governance: Elevating Reconciliation to Primary Control for Data Risk

In today's environment comprising of continuous data ecosystems, governance based on periodic inspection is misaligned with how data risk emerges. The central question for boards, regulators, auditors, and risk committees has shifted: Can the institution demonstrate at the moment data is used that it is accurate, complete, and controlled? Lean governance answers this question by elevating data reconciliation from a back-office cleanup activity to the primary control mechanism for data risk reduction. ... Data profiling can tell you that a value looks unusual within one system. It cannot tell you whether that value aligns with upstream sources, downstream consumers, or parallel representations elsewhere in the enterprise. ... Lean governance reframes governance as a continual process-control discipline rather than a documentation exercise. It borrows from established control theory: Quality is achieved by controlling the process, not by inspecting outputs after failures. Three principles define this approach: Data risk emerges continuously, not periodically; Controls must operate at the same cadence as data movement; and Reconciliation is the control that proves process integrity. ... Data profiling is inherently inward-looking. It evaluates distributions, ranges, patterns, and anomalies within a single dataset. This is useful for hygiene, but insufficient for assessing risk. Reconciliation is inherently relational. It validates consistency between systems, across transformations, and through the lifecycle of data.

Working with Code Assistants: The Skeleton Architecture

Critical non-functional requirements- such as security, scalability, performance, and authentication- are system-wide invariants that cannot be fragmented. If every vertical slice is tasked with implementing its own authorization stack or caching strategy, the result is "Governance Drift": inconsistent security postures and massive code redundancy. This necessitates a new unifying concept: The Skeleton and The Tissue. ... The Stable Skeleton represents the rigid, immutable structures (Abstract Base Classes, Interfaces, Security Contexts) defined by the human although possibly built by the AI. The Vertical Tissue consists of the isolated, implementation-heavy features (Concrete Classes, Business Logic) generated by the AI. This architecture draws on two classical approaches: actor models and object-oriented inversion of control. It is no surprise that some of the world’s most reliable software is written in Erlang, which utilizes actor models to maintain system stability. Similarly, in inversion of control structures, the interaction between slices is managed by abstract base classes, ensuring that concrete implementation classes depend on stable abstractions rather than the other way around. ... Prompts are soft; architecture is hard. Consequently, the developer must monitor the agent with extreme vigilance. ... To make the "Director" role scalable, we must establish "Hard Guardrails"- constraints baked into the system that are physically difficult for the AI to bypass. These act as the immutable laws of the application.

8-Minute Access: AI Accelerates Breach of AWS Environment

A threat actor gained initial access to the environment via credentials discovered in public Simple Storage Service (S3) buckets and then quickly escalated privileges during the attack, which moved laterally across 19 unique AWS principals, the Sysdig Threat Research Team (TRT) revealed in a report published Tuesday. ... While the speed and apparent use of AI were among the most notable aspects of the attack, the researchers also called out the way that the attacker accessed exposed credentials as a cautionary tale for organizations with cloud environments. Indeed, stolen credentials are often an attacker's initial access point to attack a cloud environment. "Leaving access keys in public buckets is a huge mistake," the researchers wrote. "Organizations should prefer IAM roles instead, which use temporary credentials. If they really want to leverage IAM users with long-term credentials, they should secure them and implement a periodic rotation." Moreover, the affected S3 buckets were named using common AI tool naming conventions, they noted. The attackers actively searched for these conventions during reconnaissance, enabling them to find the credentials quite easily, they said. ... During this privilege-escalation part of the attack — which took a mere eight minutes — the actor wrote code in Serbian, suggesting their origin. Moreover, the use of comments, comprehensive exception handling, and the speed at which the script was written "strongly suggests LLM generation," the researchers wrote.

Ask the Experts: The cloud cost reckoning

According to the 2025 Azul CIO Cloud Trends Survey & Report, 83% of the 300 CIOs surveyed are spending an average of 30% more than what they had anticipated for cloud infrastructure and applications; 43% said their CEOs or boards of directors had concerns about cloud spend. Moreover, 13% of surveyed CIOs said their infrastructure and application costs increased with their cloud deployments, and 7% said they saw no savings at all. Other surveys show CIOs are rethinking their cloud strategies, with "repatriation" -- moving workloads from the cloud back to on-premises -- emerging as a viable option due to mounting costs. ... "At Laserfiche we still have a hybrid environment. So we still have a colocation facility, where we house a lot of our compute equipment. And of course, because of that, we need a DR site because you never want to put all your eggs in that one colo. We also have a lot of SaaS services. We're in a hyperscaler environment for Laserfiche cloud. "But the reason why we do both is because it actually costs us less money to run our own compute in a data center colo environment than it does to be all in on cloud." ,,, "The primary reason why the [cloud] costs have been increasing is because our use of cloud services has become much more sophisticated and much more integrated. "But another reason cloud consumption has increased is we're not as diligent in managing our cloud resources in provisioning and maintaining."

NIST develops playbook for online use cases of digital credentials in financial services

The objective is to develop what a panel description calls a “playbook of standards and best practices that all parties can use to set a high bar for privacy and security.” “We really wanted to be able to understand, what does it actually take for an organization to implement this stuff? How does it fit into workflows? And then start to think as well about what are the benefits to these organizations and to individuals.” “The question became, what was the best online use case?” Galuzzo says. “At which point our colleagues in Treasury kind of said, hey, our online banking customer identification program, how do we make that both more usable and more secure at the same time? And it seemed like a really nice fit. So that brought us to both the kind of scope of what we’re focused on, those online components, and the specific use case of financial services as well.” ... The model, he says, “should allow you to engage remotely, to not have to worry about showing up in person to your closest branch, should allow for a reduction in human error from our side and should allow for reduction in fraud and concern over forged documents.” It should also serve to fulfil the bank’s KYC and related compliance requirements. Beyond the bank, the major objective with mDLs remains getting people to use them. The AAMVA’s Maru points to his agency’s digital trust service, and to its efforts in outreach and education – which are as important in driving adoption as anything on the technical side.

Designing for the unknown: How flexibility is reshaping data center design

Rapid advances in compute architectures – particularly GPUs and AI-oriented systems – are compressing technology cycles faster than many design and delivery processes can respond. In response, flexibility has shifted from a desirable feature to the core principle of successful data center design. This evolution is reshaping how we think about structure, power distribution, equipment procurement, spatial layout, and long-term operability. ... From a design perspective, this means planning for change across several layers: Structural systems that can accommodate higher equipment loads without reinforcement; Spatial layouts that allow reconfiguration of white space and service zones; and Distribution pathways that support future modifications without disrupting live operations. The objective is not to overbuild for every possible scenario, but to provide a framework that can absorb change efficiently and economically. ... Another emerging challenge is equipment lead time. While delivery periods vary by system, generators can now carry lead times approaching 12 months, particularly for higher capacities, while other major infrastructure components – including transformers, UPS modules, and switchgear – typically fall within the 30- to 40-week range. Delays in securing these items can introduce significant risk when procurement decisions are deferred until late in the design cycle.

Onboarding new AI hires calls for context engineering - here's your 3-step action plan

In the AI world, the institutional knowledge is called context. AI agents are the new rockstar employees. You can onboard them in minutes, not months. And the more context that you can provide them with, the better they can perform. Now, when you hear reports that AI agents perform better when they have accurate data, think more broadly than customer data. The data that AI needs to do the job effectively also includes the data that describes the institutional knowledge: context. ... Your employees are good at interpreting it and filling in the gaps using their judgment and applying institutional knowledge. AI agents can now parse unstructured data, but are not as good at applying judgment when there are conflicts, nuances, ambiguity, or omissions. This is why we get hallucinations. ... The process maps provide visibility into manual activities between applications or within applications. The accuracy and completeness of the documented process diagrams vary wildly. Front-office processes are generally very poor. Back-office processes in regulated industries are typically very good. And to exploit the power of AI agents, organizations need to streamline them and optimize their business processes. This has sparked a process reengineering revolution that mirrors the one in the 1990s. This time around, the level of detail required by AI agents is higher than for humans.

Q&A: How Can Trust be Built in Open Source Security?

The security industry has already seen examples in 2025 of bad actors deploying AI in cyberattacks – I’m concerned that 2026 could bring a Heartbleed- or Log4Shell-style incident involving AI. The pace at which these tools operate may outstrip the ability of defenders to keep up in real time. Another focus for the year ahead: how the Cyber Resilience Act (CRA) will begin to reshape global compliance expectations. Starting in September 2026, manufacturers and open source maintainers must report exploited vulnerabilities and breaches to the EU. This is another step closer to CRA enforcement and other countries like Japan, India and Korea are exploring similar legislation. ... The human side of security should really be addressed just as urgently as the technical side. The way forward involves education, tooling and cultural change. Resilient human defences start with education. Courses from the Linux Foundation like Developing Secure Software and Secure AI/ML‑Driven Software Development equip users with the mindset and skills to make better decisions in an AI‑enhanced world. Beyond formal training, reinforcing awareness creating a vigilant community is critical. The goal is to embed security into culture and processes so that it’s not easily overlooked when new technology or tools roll around. ... Maintainers and the community projects they lead are struggling without support from those that use their software.

Daily Tech Digest - February 03, 2026

Quote for the day:

"In my whole life, I have known no wise people who didn't read all the time, none, zero." -- Charlie Munger

How risk culture turns cyber teams predictive

Reactive teams don’t choose chaos. Chaos chooses them, one small compromise at a time. A rushed change goes in late Friday. A privileged account sticks around “temporarily” for months. A patch slips because the product has a deadline, and security feels like the polite guest at the table. A supplier gets fast-tracked, and nobody circles back. Each event seems manageable. Together, they create a pattern. The pattern is what burns you. Most teams drown in noise because they treat every alert as equal and security’s job. You never develop direction. You develop reflexes. ... We’ve seen teams with expensive tooling and miserable outcomes because engineers learned one lesson. “If I raise a risk, I’ll get punished, slowed down or ignored.” So they keep quiet, and you get surprised. We’ve also seen teams with average tooling but strong habits. They didn’t pretend risk was comfortable. They made it speakable. Speakable risk is the start of foresight. Foresight enables the right action or inaction to achieve the best result! ... Top teams collect near misses like pilots collect flight data. Not for blame. For pattern. A near miss is the attacker who almost got in. The bad change that almost made it into production. The vendor who nearly exposed a secret. The credential that nearly shipped in code. Most organizations throw these away. “No harm done.” Ticket closed. Then harm arrives later, wearing the same outfit.

Why CIOs are turning to digital twins to future-proof the supply chain

The ways in which digital twin models differ from traditional models are that they can be run as what-if scenarios and simulated by creating models based on cause-and-effect. Examples of this would include a demand increase in volume of supply chain product in a short time frame, or changes involving a facility shutting down because of severe weather conditions. The model will look at how this will affect a supply chain’s inventory levels, shipping schedule and delivery date, and even worker availability if any. All of this allows companies to move their decision-making process away from reactive firefighting to the more proactive planning process. For a CIO, using a digital twin model eliminates the historical siloing of enterprise architecture of supply chain-related data. ... Although the value of the digital twin technology is evident, scaling digital twins remains a significant challenge. Integration of data from multiple sources including ERP, WMS, IoT, and partner systems is a primary challenge for all. High fidelity simulation requires high computational capacity, which in turn requires trade-offs between realism, performance, and cost. There are also governance issues associated with digital twins. As digital twin models drift or are modified due to the physical state of the model changing, potential security vulnerabilities also increase as continuing data is streamed from cloud and edge environments.

Quantum computing is getting closer, but quantum-proof encryption remains elusive

“Everybody’s well into the belief that we’re within five years of this cryptocalypse,” says Blair Canavan, director of alliances for the PKI and PQC portfolio at Thales, a French multinational company that develops technologies for aerospace, defense, and digital security. “I see it and hear it in almost every circle.” Fortunately, we already have new, quantum-safe encryption technology. NIST released its fifth quantum-safe encryption algorithm in early 2025. The recommended strategy is to build encryption systems that make it easy to swap out algorithms if they become obsolete and new algorithms are invented. And there’s also regulatory pressure to act. ... CISA is due to release its PQC category list, which will establish PQC standards for data management, networking, and endpoint security. And early this year, the Trump administration is expected to release a six-pillar cybersecurity strategy document that includes post-quantum cryptography. But, according to the Post Quantum Cryptography Coalition’s state of quantum migration report, when it comes to public standards, there’s only one area in which we have broad adoption of post-quantum encryption, and that’s with TLS 1.3, and only with hybrid encryption — not pre or post quantum encryption or signatures. ... The single biggest driver for PQC adoption is contractual agreements with customers and partners, cited by 22% of respondents.

From compliance to competitive edge: How tech leaders can turn data sovereignty into a business advantage

Data sovereignty - where data is subject to the laws and governing structures of the nation in which it is collected, processed, or held - means that now more than ever, it’s incredibly important that you understand where your organization’s data comes from, and how and where it’s being stored. Understandably, that effort is often seen through the lens of regulation and penalties. If you don’t comply with GDPR, for example, you risk fines, reputational damage, and operational disruption. But the real conversation should be about the opportunities it could bring, and that involves looking beyond ticking boxes, towards infrastructure and strategy. ... Complementing the hybrid hub-and-spoke model, distributed file systems synchronize data across multiple locations, either globally or only within the boundaries of jurisdictions. Instead of maintaining separate, siloed copies, these systems provide a consistent view of data wherever it is needed and help teams collaborate while keeping sensitive information within compliant zones. This reduces delays and duplication, so organizations can meet data sovereignty obligations without sacrificing agility or teamwork. Architecture and technology like this, built for agility and collaboration, are perfectly placed to transform data sovereignty from a barrier into a strategic enabler. They support organizations in staying compliant while preserving the speed and flexibility needed to adapt, compete, and grow.

Why digital transformation fails without an upskilled workforce

“Capability” isn’t simply knowing which buttons to click. It’s being able to troubleshoot when data doesn’t reconcile. It’s understanding how actions in the system cascade through downstream processes. It’s recognizing when something that’s technically possible in the system violates a business control. It’s making judgment calls when the system presents options that the training scenarios never covered. These capabilities can’t be developed through a three-day training session two weeks before go-live. They’re built through repeated practice, pattern recognition, feedback loops and reinforcement over time. ... When upskilling is delayed or treated superficially, specific operational risks emerge quickly. In fact, in the implementations I’ve supported, I’ve found that organizations routinely experience productivity declines of as much as 30-40% within the first 90 days of go-live if workforce capability hasn’t been adequately addressed. ... Start by asking your transformation team this question: “Show me the behavioral performance standards that define readiness for the roles, and show me the evidence that we’re meeting them.” If the answer is training completion dashboards, course evaluation scores or “we have a really good training vendor,” you have a problem. Next, spend time with actual end users not power users, not super users, but the people who will do this work day in and day out.

How Infrastructure Is Reshaping the U.S.–China AI Race

Most of the early chapters of the global AI race were written in model releases. As LLMs became more widely adopted, labs in the U.S. moved fast. They had support from big cloud companies and investors. They trained larger models and chased better results. For a while, progress meant one thing. Build bigger models, and get stronger output. That approach helped the U.S. move ahead at the frontier. However, China had other plans. Their progress may not have been as visible or flashy, but they quietly expanded AI research across universities and domestic companies. They steadily introduced machine learning into various industries and public sector systems. ... At the same time, something happened in China that sent shockwaves through the world, including tech companies in the West. DeepSeek burst out of nowhere to show how AI model performance may not be as contrained by hardware as many of us thought. This completely reshaped assumptions about what it takes to compete in the AI race. So, instead of being dependent on scale, Chinese teams increasingly focused on efficiency and practical deployment. Did powerful AI really need powerful hardware? Well, some experts thought DeepSeek developers were not being completely transparent on the methods used to develop it. However, there is no doubt that the emergence of DeepSeek created immense hype. ... There was no single turning point for the emergence of the infrastructure problem. Many things happened over time.

Why AI adoption keeps outrunning governance — and what to do about it

The first problem is structural. Governance was designed for centralized, slow-moving decisions. AI adoption is neither. Ericka Watson, CEO of consultancy Data Strategy Advisors and former chief privacy officer at Regeneron Pharmaceuticals, sees the same pattern across industries. “Companies still design governance as if decisions moved slowly and centrally,” she said. “But that’s not how AI is being adopted. Businesses are making decisions daily — using vendors, copilots, embedded AI features — while governance assumes someone will stop, fill out a form, and wait for approval.” That mismatch guarantees bypass. Even teams with good intentions route around governance because it doesn’t appear where work actually happens. ... “Classic governance was built for systems of record and known analytics pipelines,” he said. “That world is gone. Now you have systems creating systems — new data, new outputs, and much is done on the fly.” In that environment, point-in-time audits create false confidence. Output-focused controls miss where the real risk lives. ... Technology controls alone do not close the responsible-AI gap. Behavior matters more. Asha Palmer, SVP of Compliance Solutions at Skillsoft and a former US federal prosecutor, is often called in after AI incidents. She says the first uncomfortable truth leaders confront is that the outcome was predictable. “We knew this could happen,” she said. “The real question is: why didn’t we equip people to deal with it before it did?”

How AI Will ‘Surpass The Boldest Expectations’ Over The Next Decade And Why Partners Need To ‘Start Early’

The key to success in the AI era is delivering fast ROI and measurable productivity gains for clients. But integrating AI into enterprise workflows isn’t simple; it requires deep understanding of how work gets done and seamless connection to existing systems of record. That’s where IBM and our partners excel: embedding intelligence into processes like procurement, HR, and operations, with the right guardrails for trust and compliance. We’re already seeing signs of progress. A telecom client using AI in customer service achieved a 25-point Net Promoter Score (NPS) increase. In software development, AI tools are boosting developer productivity by 45 percent. And across finance and HR, AI is making processes more efficient, error-free, and fraud-resistant. ... Patience is key. We’re still in the early innings of enterprise AI adoption — the players are on the field, but the game is just beginning. If you’re not playing now, you’ll miss it entirely. The real risk isn’t underestimating AI; it’s failing to deploy it effectively. That means starting with low-risk, scalable use cases that deliver measurable results. We’re already seeing AI investments translate into real enterprise value, and that will accelerate in 2026. Over the next decade, AI will surpass today’s boldest expectations, driving a tenfold productivity revolution and long-term transformation. But the advantage will go to those who start early.

Five AI agent predictions for 2026: The year enterprises stop waiting and start winning

By mid-2026, the question won't be whether enterprises should embed AI agents in business processes—it will be what they're waiting for if they haven't already. DIY pilot projects will increasingly be viewed as a risker alternative to embedded pre-built capabilities that support day-to-day work. We're seeing the first wave of natively embedded agents in leading business applications across finance, HR, supply chain, and customer experience functions. ... Today's enterprise AI landscape is dominated by horizontal AI approaches: broad use cases that can be applied to common business processes and best practices. The next layer of intelligence - vertical AI - will help to solve complex industry-specific problems, delivering additional P&L impact. This shift fundamentally changes how enterprises deploy AI. Vertical AI requires deep integration with workflows, business data, and domain knowledge—but the transformative power is undeniable. ... Advanced enterprises in 2026 will orchestrate agent teams that automatically apply business rules, maintain a tight control on compliance, integrate seamlessly across their technology stack, and scale human expertise rather than replace it. This orchestration preserves institutional knowledge while dramatically multiplying its impact. Organizations that master multi-agent workflows will operate with fundamentally different economics than those managing point automation solutions.

How should AI agents consume external data?

Agents benefit from real-time information ranging from publicly accessible web data to integrated partner data. Useful external data might include product and inventory data, shipping status, customer behavior and history, job postings, scientific publications, news and opinions, competitive analysis, industry signals, or compliance updates, say the experts. With high-quality external data in hand, agents become far more actionable, more capable of complex decision-making and of engaging in complex, multi-party flows. ... According to Lenchner, the advantages of scraping are breadth, freshness, and independence. “You can reach the long tail of the public web, update continuously, and avoid single‑vendor dependencies,” he says. Today’s scraping tools grant agents impressive control, too. “Agents connected to the live web can navigate dynamic sites, render JavaScript, scroll, click, paginate, and complete multi-step tasks with human‑like behavior,” adds Lenchner. Scraping enables fast access to public data without negotiating partnership agreements or waiting for API approvals. It avoids the high per-call pricing models that often come with API integration, and sometimes it’s the only option, when formal integration points don’t exist. ... “Relying on official integrations can be positive because it offers high-quality, reliable data that is clean, structured, and predictable data through a stable API contract,” says Informatica’s Pathak. “There is also legal protection, as they operate under clear terms of service, providing legal clarity and mitigating risk.”