Daily Tech Digest by Kannan Subbiah: trend

Showing posts with label trend. Show all posts

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.”

Daily Tech Digest - January 27, 2026

Quote for the day:

"Supreme leaders determine where generations are going and develop outstanding leaders they pass the baton to." -- Anyaele Sam Chiyson

Why code quality should be a C-suite concern

At first, speed feels like progress. Then the hidden costs begin to surface: escalating maintenance effort, rising incident frequency, delayed roadmaps and growing organizational tension. The expense of poor code slowly eats into return on investment — not always in ways that show up neatly on a spreadsheet, but always in ways that become painfully visible in daily operations. ... During the planning phase, rushed architectural decisions often lead to tightly coupled, monolithic systems that are expensive and risky to change. During development, shortcuts accumulate into what we call technical debt: duplicated logic, brittle integrations and outdated dependencies that appear harmless at first but quietly erode system stability over time. Like financial debt, technical debt compounds. ... Architecture always comes first. I advocate for modular growth — whether through a well- structured modular monolith that can later evolve into microservices, or through service-oriented architectures with clear domain boundaries. Platforms such as Kubernetes enable independent scaling of components, but only when the underlying architecture is cleanly segmented. Language and framework choices matter more than most leaders realize. ... The technologies we select, the boundaries we define and the failure modes we anticipate all place invisible limits on how far an organization can grow. From what I’ve seen, you simply cannot scale a product on a foundation that was never designed to evolve.

How to regulate social media for teens (and make it stick)

Noting that age assurance proposals have broad support from parents and educators, Allen says “the question is not whether children deserve safeguarding (they do) but whether prohibition is an effective tool for achieving it.” “History suggests that bans succeed or fail not on the basis of intention, but on whether they align with demand, supply, moral legitimacy and enforcement capacity. Prohibition does not remove human desire; it reallocates who fulfils it. Whether that reallocation reduces harm or increases it depends on how well policy engages with the underlying economics and psychology of behaviour.” ... “There is little evidence that young people themselves view social media as morally repugnant. On the contrary, it is where friendships are maintained, identities are explored and social status is negotiated. That does not mean it is harmless. It means it is meaningful.” “This creates a problem for prohibition. Where demand remains strong, supply will be found.” Here, Allen’s argument falters somewhat, in that it follows the logic that says bans push kids onto less regulated and more dangerous platforms. I.e., “the risk is not simply that prohibition fails. It is that it succeeds in changing who supplies children’s social connectivity.” The difference is that, while a basket of plums and some ingenuity are all you need to produce alcohol, social media platforms have their value in the collective. Like Star Trek’s Borg, they are more powerful the more people they assimilate.

The era of agentic AI demands a data constitution, not better prompts

If a data pipeline drifts today, an agent doesn't just report the wrong number. It takes the wrong action. It provisions the wrong server type. It recommends a horror movie to a user watching cartoons. It hallucinates a customer service answer based on corrupted vector embeddings. ... In traditional SQL databases, a null value is just a null value. In a vector database, a null value or a schema mismatch can warp the semantic meaning of the entire embedding. Consider a scenario where metadata drifts. Suppose your pipeline ingests video metadata, but a race condition causes the "genre" tag to slip. Your metadata might tag a video as "live sports," but the embedding was generated from a "news clip." When an agent queries the database for "touchdown highlights," it retrieves the news clip because the vector similarity search is operating on a corrupted signal. The agent then serves that clip to millions of users. At scale, you cannot rely on downstream monitoring to catch this. By the time an anomaly alarm goes off, the agent has already made thousands of bad decisions. Quality controls must shift to the absolute "left" of the pipeline. ... Engineers generally hate guardrails. They view strict schemas and data contracts as bureaucratic hurdles that slow down deployment velocity. When introducing a data constitution, leaders often face pushback. Teams feel they are returning to the "waterfall" era of rigid database administration.

QA engineers must think like adversaries

Test engineers are now expected to understand pipelines, cloud-native architectures, and even prompt engineering for AI tools. The mindset has become more preventive than detective. AI has become part of QA’s toolkit, helping predict weak spots and optimise testing. At the same time, QA must validate the integrity and fairness of AI systems — making it both a user and a guardian of AI. ... With DevOps, QA became embedded into the pipeline — automated test execution, environment provisioning, and feedback loops are all part of CI/CD now. With SecOps, we’re adding security scans and penetration checks earlier, creating a DevTestSecOps model. QA is no longer a separate stage. It’s a mindset that exists throughout the lifecycle — from requirements to observability in production. ... Regression testing has become AI-augmented and data-driven. Instead of re-running all test cases, systems now prioritise based on change impact analysis. The SDET role is also evolving — they now bridge coding, observability, and automation frameworks, often owning quality gates within CI/CD. ... Security checks are now embedded as automated gates within pipelines. Performance testing, too, is moving earlier — with synthetic monitoring and API-level load simulations. In effect, security and speed can coexist, provided teams integrate validation rather than treat it as an afterthought.

The biggest AI bottleneck isn’t GPUs. It’s data resilience

The risks of poor data resilience will be magnified as agentic AI enters the mainstream. Whereas generative AI applications respond to a prompt with an answer in the same manner as a search engine, agentic systems are woven into production workflows, with models calling each other, exchanging data, triggering actions and propagating decisions across networks. Erroneous data can be amplified or corrupted as it moves between agents, like the party game “telephone.” ... Experts cite numerous reasons data protection gets short shrift in many organizations. A key one is an overly intense focus on compliance at the expense of operational excellence. That’s the difference between meeting a set of formal cybersecurity metrics and being able to survive real-world disruption. Compliance guidelines specify policies, controls and audits, while resilience is about operational survivability, such as maintaining data integrity, recovering full business operations, replaying or rolling back actions and containing the blast radius when systems fail or are attacked. ... “Resilience and compliance-oriented security are handled by different teams within enterprises, leading to a lack of coordination,” said Forrester’s Ellis. “There is a disconnect between how prepared people think they are and how prepared they actually are.” ... Missing or corrupted data can lead models to make decisions or recommendations that appear plausible but are far off the mark.

When open science meets real-world cybersecurity

If there is no collaboration, usually the product that emerges is a great scientific specimen with very risky implementations. The risk is usually caught by normal cyber processes and reduced accordingly; however, scientists who see the value in IT/cyber collaboration usually also end up with a great scientific specimen. There is also managed risk in the implementation with almost no measurable negative impacts or costs. We’ve seen that if collaboration is planned into the project very early on, cybersecurity can provide value. ... Cybersecurity researchers often are confused and look for issues on the internet where they stumble onto the laboratory IT footprint and make claims that we are leaking non-public information. We clearly label and denote information that is releasable to the public, but it always seems there are folks who are quicker to report than to read the dissemination labels. ... Encryption at rest (EIR) is really a control to prevent data loss when the storage medium is no longer in your control. So, when the data has been reviewed for public release, we don’t spend the extra time, effort, and money to apply a control to data stores that provide no value to either the implementation or to a cyber control. ... You can imagine there are many custom IT and OT parts that run that machine. The replacement of components is not on a typical IT replacement schedule. This can present longer than ideal technology refresh cycles. The risk here is that integrating modern cyber technology into an older IT/OT technology stack has its challenges.

4 issues holding back CISOs’ security agendas

CISOs should aim to have team members know when and how to make prioritization calls for their own areas of work, “so that every single team is focusing on the most important stuff,” Khawaja says. “To do that, you need to create clear mechanisms and instructions for how you do decision-support,” he explains. “There should be criteria or factors that says it’s high, medium, low priority for anything delivered by the security team, because then any team member can look at any request that comes to them and they can confidently and effectively prioritize it.” ... According to Lee, the CISOs who keep pace with their organization’s AI strategy take a holistic approach, rather than work deployment to deployment. They establish a risk profile for specific data, so security doesn’t spend much time evaluating AI deployments that use low-risk data and can prioritize work on AI use cases that need medium- or high-risk data. They also assign security staffers to individual departments to stay on top of AI needs, and they train security teams on the skills needed to evaluate and secure AI initiatives. ... the challenge for CISOs not being about hiring for technical skills or even soft skills, but what he called “middle skills,” such as risk management and change management. These skills he sees becoming more crucial for aligning security to the business, getting users to adopt security protocols, and ultimately improving the organization’s security posture. “If you don’t have [those middle skills], there’s only so far the security team can go,” he says.

Rethinking data center strategy for AI at scale

Traditional data centers were engineered for predictable, transactional workloads. Your typical enterprise rack ran at 8kW, cooled with forced air, powered through 12-volt systems. This worked fine for databases, web applications, and cloud storage. Yet, AI workloads are pushing rack densities past 120kW. That's not an incremental change—it's a complete reimagining of what a data center needs to be. At these densities, air cooling becomes physically impossible. ... Walk into a typical data center today. The HVAC system has its own monitoring dashboard. Power distribution runs through a separate SCADA system. Compute performance lives in yet another tool. Network telemetry? Different stack entirely. Each subsystem operates in isolation, reporting intermittently through proprietary interfaces that don't talk to each other. Operators see dashboards, not decisions. ... Cooling systems can respond instantly to thermal changes, and power orchestration becomes adaptive rather than provisioned for theoretical peaks. AI clusters can scale based not just on demand, but in coordination with available power, cooling capacity, and network bandwidth. ... Real-time visibility, unified data architectures, and adaptive control will define performance, efficiency, and competitiveness in AI-ready data centers. The organizations that thrive in the AI era won't necessarily be those with the most data centers or the biggest chips; they'll be the ones that treat infrastructure as an intelligent, responsive system capable of sensing, adapting, and optimizing in real time.

Microsoft handed over BitLocker keys to law enforcement, raising enterprise data control concerns

The US Federal Bureau of Investigation approached Microsoft with a search warrant in early 2025, seeking keys to unlock encrypted data stored on three laptops in a case of alleged fraud involving the COVID unemployment assistance program in Guam. As the keys were stored on a Microsoft server, Microsoft adhered to the legal order and handed over the encryption keys ... While the encryption of BitLocker is robust, enterprises need to be mindful of who has custody of the keys, as this case illustrates. ... Enterprises using BitLocker should treat the recovery keys as highly sensitive, and avoid default cloud backup unless there is a clear business requirement and the associated risks are well understood and mitigated. ... CISOs should also ensure that when devices are repurposed, decommissioned, or moved across jurisdictions, keys should be regenerated as part of the workflow to ensure old keys cannot be used. ... If recovery keys are stored with a cloud provider, that provider may be compelled, at least in its home jurisdiction, to hand them over under lawful order, even if the data subject or company is elsewhere without notifying the company. This becomes even more critical from the point of view of a pharma company, semiconductor firm, defence contractor, or critical-infrastructure operator, as it exposes them to risks such as exposure of trade secrets in cross‑border investigations.

Moore’s law: the famous rule of computing has reached the end of the road, so what comes next?

For half a century, computing advanced in a reassuring, predictable way. Transistors – devices used to switch electrical signals on a computer chip – became smaller. Consequently, computer chips became faster, and society quietly assimilated the gains almost without noticing. ... Instead of one general-purpose processor trying to do everything, modern systems combine different kinds of processors. Traditional processing units or CPUs handle control and decision-making. Graphics processors, are powerful processing units that were originally designed to handle the demands of graphics for computer games and other tasks. AI accelerators (specialised hardware that speeds up AI tasks) focus on large numbers of simple calculations carried out in parallel. Performance now depends on how well these components work together, rather than on how fast any one of them is. Alongside these developments, researchers are exploring more experimental technologies, including quantum processors (which harness the power of quantum science) and photonic processors, which use light instead of electricity. ... For users, life after Moore’s Law does not mean that computers stop improving. It means that improvements arrive in more uneven and task-specific ways. Some applications, such as AI-powered tools, diagnostics, navigation, complex modelling, may see noticeable gains, while general-purpose performance increases more slowly.

Daily Tech Digest - January 24, 2026

Quote for the day:

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

When a new chief digital officer arrives, what does that mean for the CIO?

One reason the CDO can unsettle CIOs is that the title has never had a consistent meaning. , president and founder of , said organizations typically create the role for one of two reasons. "Some organizations split off a CDO role because the CIO is overly focused on infrastructure and operations, and the business's customer and employee experiences, AI and data initiatives, and other innovations aren't meeting expectations," Sacolick said. "In other organizations, the CDO is a C-level title for the head of product management and , and reports to the CIO." Those two models lead to very different outcomes. In the first, the CDO is positioned as a corrective measure; in the second, the role is an extension of the CIO's broader operating model. Without clarity on which model is being pursued, confusion tends to follow. ... Across the experts, there was strong agreement on one point: The CIO remains central to the enterprise digital operating model, even as new roles emerge. "CIOs need to own the digital operating model and evolve it for the AI era," Sacolick said, noting that this increasingly involves "product-centric, agile, multi-disciplinary team organizational models." echoed that sentiment, emphasizing accountability and trust. "The CIO should be the single point of ownership with the deep expertise feeding into it so there is consistency, business acumen and trust built within the technology function," he said.

Responsible AI moves from principle to practice, but data and regulatory gaps persist: Nasscom

The data shows a strong correlation between AI maturity and responsible practices. Nearly 60% of companies that say they are confident about scaling AI responsibly already have mature RAI frameworks in place. Large enterprises are leading this transition, with 46% reporting mature practices. Startups and SMEs trail behind at 16% and 20% respectively, but Nasscom sees this as ecosystem-wide momentum rather than a gap, given the growing willingness among smaller firms to learn, comply, and invest. ... Workforce enablement has become a central pillar of this transition. Nearly nine out of ten organisations surveyed are investing in sensitisation and training around Responsible AI. Companies report the highest confidence in meeting data protection obligations—reflecting relatively mature privacy frameworks—but monitoring-related compliance continues to be a concern. Accountability for AI governance still sits largely at the top. ... As AI systems become more autonomous, Responsible AI is increasingly seen as the deciding factor for whether organisations can scale with confidence. Nearly half of mature organisations believe their current frameworks are prepared to handle emerging technologies such as agentic AI. At the same time, industry experts caution that most existing frameworks will need substantial updates to address new categories of risk introduced by more autonomous systems. The report concludes that sustained investment in skills, governance mechanisms, high-quality data, and continuous monitoring will be essential.

AI-induced cultural stagnation is no longer speculation − it’s already happening

Regardless of how diverse the starting prompts were – and regardless of how much randomness the systems were allowed – the outputs quickly converged onto a narrow set of generic, familiar visual themes: atmospheric cityscapes, grandiose buildings and pastoral landscapes. Even more striking, the system quickly “forgot” its starting prompt. ... For the past few years, skeptics have warned that generative AI could lead to cultural stagnation by flooding the web with synthetic content that future AI systems then train on. Over time, the argument goes, this recursive loop would narrow diversity and innovation. Champions of the technology have pushed back, pointing out that fears of cultural decline accompany every new technology. Humans, they argue, will always be the final arbiter of creative decisions. ... The study shows that when meaning is forced through such pipelines repeatedly, diversity collapses not because of bad intentions, malicious design or corporate negligence, but because only certain kinds of meaning survive the text-to-image-to-text repeated conversions. This does not mean cultural stagnation is inevitable. Human creativity is resilient. Institutions, subcultures and artists have always found ways to resist homogenization. But in my view, the findings of the study show that stagnation is a real risk – not a speculative fear – if generative systems are left to operate in their current iteration.

Europe votes to tackle deep dependence on US tech in sovereignty drive

The depth of European reliance on foreign technology providers varies across sectors but remains substantial throughout the stack. In cloud infrastructure alone, Amazon, Microsoft, and Google command 70% of the European market, while local providers including , , and collectively hold just 15%. ... “Recent geopolitical tensions show that the issue of Europe’s digital sovereignty is of the utmost importance,” , the who negotiated the report text, said in a statement. “If we do not act now to reduce Europe’s technological dependence on foreign actors, we run the risk of becoming a digital colony.” ... “Due to geopolitical tensions, the driver has shifted to reducing foreign digital dependency across the entire technology stack. are now tasked with redesigning their approach to semiconductors, cloud, software, and AI, upending two decades of established strategy. It’s not going to be easy, it’s not going to be cheap, and it’s going to span multiple generations of CIOs.” When asked whether will see viable sovereign alternatives across core technology areas, said: The answer is yes, but the time horizon is potentially more than a decade. Europe has been supporting US technology providers through licensing agreements for the better part of the last two decades. ... A key question is whether the report’s proposed preferential procurement policies can actually change market realities, given the

One-time SMS links that never expire can expose personal data for years

One of the most significant findings involved how long these links remained active. All 701 confirmed URLs still worked when the researchers accessed them, often long after the original message was sent. More than half of the exposed links were between one and two years old. About 46% were older than two years. Some dated back to 2019. Public SMS gateways rarely retain messages for that long, which suggests that the actual lifetime of many links may extend even further. The risk starts as soon as a private link is exposed, but it grows with time. The longer a link stays active, the more chances there are for abuse through logs, forwarding, compromised devices, message interception, phone number recycling, or third-party access. ... In many services, the link carried a token passed to backend APIs. Some pages rendered data server side, while others fetched information after load. Only five services placed personal data directly inside the URL itself, though access results were similar once the link was opened. This design assumes the link remains private. According to Danish, product pressure plays a central role in keeping this pattern widespread. ... In one case, an order tracking page displayed an address, while API responses included phone numbers, geolocation data, and driver details. In another, a loan service returned bank routing numbers and Social Security numbers that were only visible in network logs. This data became reachable as soon as the link was opened, even before the page finished loading.

How enterprise architecture and start-up thinking drive strategic success

Strategy is now judged less by the quality of vision decks and more by how quickly enterprises can test, learn and scale what works and is valuable. To beat the heat, enterprises increasingly combine the discipline of enterprise architecture with the speed and adaptability associated with a start-up mindset. ... is less about cataloging systems and more about shaping how an enterprise senses opportunities, mobilizes resources and transforms at pace. In a high-performing enterprise, it acts as a bridge between strategy and execution in three concrete ways, i.e., alignment and clarity, transparency and risk management and decision support and adaptive governance. ... Start-ups and scale-ups operate under uncertainty, but they thrive by learning in short cycles, minimizing waste and scaling only what demonstrates traction. When large enterprises infuse enterprise architecture with similar principles, the function becomes a multiplier for speed rather than a constraint. ... Cross-functional innovation and flexible governance complete the picture. In many enterprises, architects now embed directly in domain or platform teams, joining strategic backlog refinement, incident reviews and design sessions as peers. In a large healthcare network, for instance, enterprise architecture practitioners joined clinical, operations and analytics teams to co-design a data platform that could support both operational reporting and AI-driven decision support.

From Conflict To Collaboration: How Tension Can Strengthen Your Team

Letting tensions simmer is one of the most common leadership mistakes. The longer a disagreement sits in the corner, the more toxic it becomes. ... Teams function better when they normalize honest conversation before things go sideways. A simple practice—opening meetings with "wins and worries"—creates a habit of surfacing concerns early. echoes this principle: "Only say about someone what you will say to their face." It’s a powerful expectation. Candor reduces gossip, eliminates guesswork and gives leaders clarity long before emotions get out of hand. ... When conflict arises, people don’t immediately need solutions. What they need is to feel heard. It’s vital to fully understand their concerns so there is no ambiguity. Repeat your understanding of their position before giving your input. It’s remarkable how much progress can be made when people feel genuinely heard. ... Compromise has an unfair reputation in business culture, as if giving an inch signals defeat. In practice, it’s a recognition that multiple perspectives may hold merit. Good leaders invite both sides to walk through their rival viewpoints together. When people better understand the context behind each position, they’re far more willing to find common ground that moves the team forward. ... Many conflicts resurface not because the solution was wrong, but because leaders assumed the first conversation fixed everything.

Six tips to gain control over your cloud spending

The first step any organization should take before shifting a workload to the cloud is performing proper due diligence on ROI. It isn’t always the case that moving workloads to the cloud will translate into financial savings. Many variables should be considered when calculating ROI, including current infrastructure, licensing and hiring. ... A formal cloud governance framework establishes rules, policies, and processes that formalize how cloud resources will be accessed, used, and retired. Accurately matching cloud resources to workload demands improves resource utilization and minimizes waste. ... , short for financial operations, is a management discipline that involves collaboration between finance, operations and development teams to manage cloud spending. By implementing tools and processes for cost tracking, budgeting, and forecasting, businesses can gain insights into their cloud expenses and identify areas for optimization. ... Providers offer a variety of discounts that can significantly reduce cloud costs. For example, reserved instance pricing models offer discounts to customers who reserve cloud resources over a fixed period. Some providers offer tiered pricing models in which the cost per unit decreases as you consume more resources. ... You may find that moving some workloads to the cloud offers no significant performance advantages. Repatriating some applications, data and workloads back to on-premises infrastructure can often improve performance while reducing cloud spending.

These 4 big technology bets will reshape the global economy in 2026

The impact of disruptive technologies will have a material impact on real GDP growth. suggested that capital investment alone, catalyzed by disruptive innovation platforms, could add 1.9% to annualized real GDP growth this decade. Each innovation platform, AI, public blockchains, robotics, energy storage, and , should provide a structural boost to global growth. ... According to ARK research, hyperscalers are expected to spend more than $500 billion on capital expenditures (Capex) in 2026, nearly four times the $135 billion spent in 2021, the year before the launch of in 2022. ... ARK forecasted that could facilitate more than $8 trillion in online consumption by 2030. ARK noted that as consumers delegate more decisions to intelligent systems, AI agents should capture an increasing share of digital transactions, from 2% of online spend in 2025 to around 25% by 2030 ... AI agents are becoming more productive. ARK found that advances in reasoning capability, tool use, and extended context are driving an exponential increase in the capability of AI agents. The duration of tasks these agents can complete reliably increased 5 times, from six minutes to 31 minutes, in 2025. ... ARK suggested robots are a growing part of the labor force and took a historical look at productivity and labor hours. As productivity increased, each hour of labor became more valuable, enabling increased output with fewer hours, as living standards continued to rise

Half of agentic AI projects are still stuck at the pilot stage

The main barriers to full implementation, respondents said, are concerns with security, privacy, or compliance, cited by 52%, followed by technical challenges to managing agents at scale, at 51%. “Organizations are not slowing adoption because they question the value of AI, but because scaling autonomous systems safely requires confidence that those systems will behave reliably and as intended in real-world conditions,” said , chief technology strategist at . Seven-in-ten agentic AI–powered decisions are still verified by humans, and 87% of organizations are actively building or deploying agents that require human supervision. ... A recurring pain point for enterprises tinkering with agentic AI tools lies in observability, according to Dynatrace. Observability of these autonomous systems is needed across every stage of the life cycle, from development and implementation through to operationalization. Observability is most used in implementation, at 69%, followed by operationalization at 57% and development at 54%. “Observability is a vital component of a successful agentic AI strategy. As organizations push toward greater autonomy, they need real-time visibility into how AI agents behave, interact, and make decisions,” Reitbauer said. “Observability not only helps teams understand performance and outcomes, but it provides the transparency and confidence required to scale agentic AI responsibly and with appropriate oversight.”

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.

Daily Tech Digest - January 01, 2026

Quote for the day:

"It always seems impossible until it’s done." -- Nelson Mandela

Why data trust is the missing link in digital transformation

Data trust is often framed as a technical issue, delegated to IT or data teams. In reality, it is a business capability with direct implications for growth, risk, and reputation. Trusted data enables organisations to: Confidently automate customer and operational workflows; Personalise experiences without introducing errors; Improve forecasting and performance reporting; and Reduce operational rework and exception handling When data cannot be trusted, leaders are forced to rely on manual checks, conservative assumptions, and duplicated processes. This increases cost and slows decision-making - the opposite of what digital transformation aims to achieve. .... Establishing data trust is not a one-time project. It requires a shift in mindset across the organisation. Data quality should be viewed as a shared responsibility, supported by the right processes and tools. Leading organisations embed data validation into their digital workflows, measure data quality as part of system health, and treat trusted data as a strategic asset. Over time, this creates a culture where decisions are made with confidence and transformation initiatives are more likely to succeed. ... Digital transformation is ultimately about enabling better decisions, faster execution, and stronger customer relationships. None of these goals can be achieved without trusted data. As organisations continue to modernise their platforms and processes, data quality should be treated as core infrastructure, not an afterthought.

Health Data Privacy, Cyber Regs: What to Watch in 2026

When federal regulators hesitate, states often jump into filing privacy and security gaps involving health data. That includes mandates in New York to shore up cybersecurity at certain hospitals (see: New York Hospitals Are Facing Tougher Cyber Rules Than HIPAA). Also worth watching is the New York Health Information Privacy Act, Greene said. "It was passed by both New York legislative chambers in January but has not yet been formally submitted to the governor for signature, with lobbying efforts underway to amend it." "In its most recent version, it would be the toughest health privacy law in the country in many respects, including a controversial prohibition on obtaining consents for secondary uses of data until at least 24 hours after an individual creates an account or first uses the requested product or service," Greene said. ... Greene predicted HIPAA resolution agreements and civil monetary penalties will continue much as they have in years past, with one to two dozen such cases next year. HHS has recently indicated that it intends to begin enforcing the Information Blocking Rule. "The primary target will be health IT developers," Greene said. "I expect that there are less information blocking issues with health information networks and believe that the statute and regulation's knowledge standard makes it more challenging to enforce against healthcare providers because the government must prove that a healthcare provider knew its practice to be unreasonable."

From integration pain to partnership gain: How collaboration strengthens cybersecurity

When collaborators leverage data in specific cybersecurity work, they unlock several valuable benefits, especially since no organization has complete insight into every possible threat. A shared, data-driven cybersecurity framework can offer both sides a better understanding of existing and emerging threats that could undermine one or both collaborators. Data-driven collaboration also enables partners to become more proactive in their cybersecurity posture. Coordinated data can give business partners insights into where there’s greater exposure for a cyberattack, allowing partners to work together with data-backed guidance on how to better prepare. ... The Vested model — an innovative approach based on research from the University of Tennessee — focuses on shared goals and outcomes rather than traditional transactional buyer and seller agreements. Both companies agreed on a specific set of KPIs they could use to measure the health of the partnership and keep their security goals on track, allowing them to continue to adapt cybersecurity initiatives as needs and threats evolve. “You have to build, maintain and exercise the right partnerships with business units and shared services across the enterprise so continuity plans identify the issue quickly, deploy appropriate mitigations, and ultimately restore client and business services as quickly as possible,” says Royce Curtin, IBM’s former VP of corporate security.

AI governance: A risk and audit perspective on responsible AI adoption

AI governance refers to the policies, procedures, and oversight mechanisms that guide how AI systems are developed, deployed, and monitored. It ensures that AI aligns with business objectives, complies with applicable laws, and operates in a way that is ethical and transparent. Regulatory scrutiny is increasing. The EU AI Act is setting a precedent for global standards, and U.S. agencies are signaling more aggressive enforcement, particularly in sectors like healthcare, finance, and employment. Organizations are expected to demonstrate accountability in how AI systems make decisions, manage data, and interact with users. Beyond regulation, there is growing pressure from customers, employees, and investors. ... Audit teams also help boards and audit committees understand the risks associated with AI. Their work supports transparency and builds trust with regulators and stakeholders. As AI becomes more embedded in business operations, internal audit must expand its scope to include model governance, data lineage, and ethical risk. ... Organizations that treat AI as a strategic risk are better positioned to scale it responsibly. Risk and internal audit teams have a central role in ensuring that AI systems are secure, compliant, and aligned with business goals. Citrin Cooperman helps organizations navigate AI adoption with confidence by combining deep risk expertise, practical governance frameworks, and advanced technology solutions that support secure, scalable, and compliant growth.

Six data shifts that will shape enterprise AI in 2026

While RAG won't entirely disappear in 2026, one approach that will likely surpass it in terms of usage for agentic AI is contextual memory, also known as agentic or long-context memory. This technology enables LLMs to store and access pertinent information over extended periods. Multiple such systems emerged over the course of 2025 including Hindsight, A-MEM framework, General Agentic Memory (GAM), LangMem, and Memobase. RAG will remain useful for static data, but agentic memory is critical for adaptive assistants and agentic AI workflows that must learn from feedback, maintain state, and adapt over time. In 2026, contextual memory will no longer be a novel technique; it will become table stakes for many operational agentic AI deployments. ... In 2025, we saw numerous innovations, like the notion that an AI is able to parse data from an unstructured data source like a PDF. That's a capability that has existed for several years, but proved harder to operationalize at scale than many assumed. Databricks now has an advanced parser, and other vendors, including Mistral, have emerged with their own improvements. The same is true with natural language to SQL translation. While some might have assumed that was a solved problem, it's one that continued to see innovation in 2025 and will see more in 2026. It's critical for enterprises to stay vigilant in 2026.

Communicating AI Risk to the Board With Confidence

Most board members can comprehend that AI will drive growth. What they fail to grasp concretely is how the technology introduces a massive amount of exposure. This predicament is typically a result of how information is presented. Security and risk managers (SRMs) often describe AI incidents in the vocabulary of adversarial inputs, model drift, and architecture choices, which matter deeply but rarely answer the questions that directors tackle during their meetings. High-level stakeholders, in reality, are concerned with issues such as revenue protection, operational continuity, and competitive differentiation, creating a gap that requires more than translating acronyms. ... Traditional discussions about technology risk revolve around the triad of confidentiality, integrity, and availability. Boards know these categories well, and over the past few decades, they have learned that cybersecurity failures directly affect the business along these lines. GenAI has formidably challenged this familiar structure, with its associated risks not limited to one of these three domains. ... When the conversation begins with the business consequence, though, the relevance is immediate. The most effective approach involves replacing those mechanics that mean so much to the internal teams with the strategic information boards need to operate. These details open a path for meaningful conversations that encourage directors to think through the implications and make more informed decisions.

The six biggest security challenges coming in 2026

For many organizations, cybersecurity and resilience is a compliance exercise. But it must evolve into “a core intentional cybersecurity capability”, says Dimitriadis. “In 2026, organizations will need to build the capacity to anticipate regulatory changes, understand their strategic implications, and embed them into long-term planning.” ... Attackers are leveraging AI to create convincing email templates and fake websites “almost indistinguishable” from real ones – and without the common warning signs employees are trained to identify, says Mitchell. AI is also being used in vishing attacks, with deepfakes making it easier to clone the voice of high-ranking company executives to trick victims. In 2026, there will be more attacks utilizing realistic voice cloning and high-quality video deepfakes, says Joshua Walsh ... There is a current shift towards agentic AI that can take real-world actions, such as adjusting configurations, interacting with APIs, booking services and initiating financial tasks. This can increase efficiency, but it can also lead to unsafe decisions made at speed, says rradar’s Walsh. An agent told to "optimize performance" might disable logging or bypass authentication because it views security controls as delays, he suggests. Prompt injection is a hidden issue to look out for, he adds. “If a threat actor slips hidden instructions into data that the agent consumes, they can make it run actions on internal systems without anyone realising.”

5 Changes That Will Define AI-Native Enterprises in 2026

As enterprises scale to multi-agent systems, the engineering focus will shift from creating prompts to architecting context. Multi-agent workflows rapidly expand requirements with tool definitions, conversation history, and data from multiple sources. This creates two challenges: context windows fill up, and models suffer from “context rot,” forgetting information buried in lengthy prompts. By mid-2026, context engineering will emerge as a distinct discipline with dedicated teams and specialized infrastructure, serving the minimal but complete information agents need. The best context engineers will understand both LLM constraints and their business domain’s semantic structure. ... Enterprises are realizing that AI agents need both data and meaning. Companies that spent years perfecting data lakes are already finding those assets are insufficient. AI can retrieve data, but without semantic context, it can’t interpret action or intent. That’s why teams will move beyond vector search toward building knowledge graphs, ontologies, and metadata-driven maps that teach AI how their business works. The battleground will shift from owning raw data to owning its interpretation. Off-the-shelf agents will struggle in complex domains because semantics are domain-specific. ... The AI-native enterprise looks very different from what came before. It serves machine customers, treats context as critical infrastructure, and has the tools to escape decades of technical debt.

Microsegmentation: the unsung hero of cybersecurity (and why it should be your top priority)

Think of your network like an apartment building. You’ve got a locked front door — that’s your perimeter. But once someone gets inside, there’s no front desk checking IDs, no elevator security and the same outdated lock on every unit. An intruder can roam freely, entering any apartment they choose. Microsegmentation is the internal security system. It’s the keycard for the elevator, the camera in the hallway, the unique lock on your door. It’s what stops one compromised device from becoming a full-blown breach. ... OT environments are different. They’re often built on legacy systems, lack patching and operate in real-time. You can’t just drop an agent or reroute traffic without risking downtime. That’s why agencies need solutions that are agentless, software-defined and tailored to the unique constraints of OT. Otherwise, you’re only protecting half the house. ... Microsegmentation also plays a critical role in enabling zero trust. It enforces least privilege at the network level. It’s not just about who gets in; it’s about what they can touch once they’re inside. For agencies building toward zero trust, microsegmentation isn’t an afterthought. It’s a foundation. Despite all this, microsegmentation remains underutilized. According to TechTarget’s Enterprise Strategy Group, only 36% of organizations use it today, even though it’s foundational to zero trust. Why? Because 28% believe it’s too complex. But that perception is often rooted in outdated tooling.

Beyond Chatbots: What Makes an AI Agent Truly Autonomous

Autonomous agents must retain and use context over time. Memory enables an agent to recall previous interactions, data, and decisions—allowing it to continue a process seamlessly without restarting each time. That persistence turns single exchanges into long-running workflows. In enterprise settings, it means an agent can track a contract review across multiple sessions or follow a complex support case without losing context. ... Traditional automation runs on fixed, rule-based workflows. Autonomous agents build and revise their own plans on the fly, adapting to results and feedback. This ability to plan dynamically—think, act, observe, and adjust—is what differentiates agentic AI from robotic process automation (RPA) or prompt chaining. In practice, an agent might be tasked with analyzing a set of contracts, then automatically decide how to proceed: extract key terms, assess risk, and summarize results. ... Resilient agents are designed to operate across models, retry failed actions, or launch sub-agents to handle specialized work—all within defined guardrails. That adaptability is what separates a proof of concept (POC) from a production-ready system. ... All the reasoning in the world means little if an agent can’t execute. Tools are what translate intelligence into impact. They’re the functions, APIs, and integrations that allow agents to interact with business systems—searching systems, generating documents, updating records, or triggering workflows across CRMs, ERPs, and analytics platforms.