Daily Tech Digest - March 05, 2026

---

Quote for the day:

"To get a feel for the true essence of leadership, assume everyone who works for you is a volunteer." -- Kouzes and Posner

---

CISOs Are Now AI Guardians of the Enterprise

CISOs are managing risk, talent and digital resilience that underpins critical business outcomes - a reality that demands new approaches to leadership and execution. Security leaders are quantifying and communicating ROI to executive leadership, developing the next generation of cybersecurity talent, and responsibly deploying emerging technologies - including generative and agentic AI ... While CISOs approach AI with cautious optimism, 86% fear agentic AI will increase the sophistication of social engineering attacks and 82% worry it will increase deployment speed and complexity of persistence mechanisms. "This is happening primarily because AI accelerates existing weaknesses in how organizations understand and control their data. The solution to both is not more tools, but [to implement] a strong and well-understood data governance model across the organization," said Kim Larsen, group CISO at Keepit. ... Despite the rise of AI, CISOs know that human intelligence and judgement supersede even the most intelligent tools, because of their ability to understand context. Their primary strategies include upskilling current workforces, hiring new full-time employees and engaging contractors, especially for nuanced tasks like threat hunting. "AI risk management, cloud security architecture, automation skills and the ability to secure AI-driven systems will be far more valuable in senior cybersecurity hires in 2026 than they were three years ago," said Latesh Nair

The right way to architect modern web applications

A single modern SaaS platform often contains wildly different workloads. Public-facing landing pages and documentation demand fast first contentful paint, predictable SEO behavior, and aggressive caching. Authenticated dashboards, on the other hand, may involve real-time data, complex client-side interactions, and long-lived state where a server round trip for every UI change would be unacceptable. Trying to force a single rendering strategy across all of that introduces what many teams eventually recognize as architectural friction. ... Modern server-rendered applications behave very differently. The initial HTML is often just a starting point. It is “hydrated,” enhanced, and kept alive by client-side logic that takes over after the first render. The server no longer owns the full interaction loop, but it hasn’t disappeared either. ... Data volatility matters. Content that changes once a week behaves very differently from real-time, personalized data streams. Performance budgets matter too. In an e-commerce flow, a 100-millisecond delay can translate directly into lost revenue. In an internal admin tool, the same delay may be irrelevant. Operational reality plays a role as well. Some teams can comfortably run and observe a fleet of SSR servers. Others are better served by static-first or serverless approaches simply because that’s what their headcount and expertise can support. ... When something breaks, the hardest part is often figuring out where it broke. This is where staged architectures show a real advantage. 

Safeguarding biometric data through anonymization

Biometric anonymization refers to a range of approaches that remove Personally Identifiable Information (PII) from biometric data so that an individual can no longer be identified from the data alone. If, after anonymization, the retained data or template can still perform its required function, then we have successfully removed the risk of the identifiers being compromised. An anonymized biometric template in the wrong hands then has no meaningful value, as it can’t be used to identify the individual from whom it originated. As a result, there is great interest in anonymization approaches that can meet the needs of different business applications. ... While biometrics deliver significant value across a wide range of use cases, safeguarding data privacy and meeting regulatory obligations remain top priorities for most organizations. Biometric anonymization can help reduce risk by limiting the exposure of sensitive personal data. Taken together, anonymization approaches address different dimensions of risk – from inference and reporting exposure to vulnerabilities at the template level. They are not one-size-fits-all solutions. Organizations must evaluate which method aligns with their functional requirements, risk tolerance, and compliance obligations, while ensuring that only the minimum necessary personal data is retained for the intended purpose. Anonymization is no longer a peripheral consideration. 

Security leaders must regain control of vendor risk, says Vanta’s risk and compliance director

The rise of AI technologies has made vendor networks increasingly harder to manage. Shadow supply chains (untracked vendor networks), fast-moving subcontracting, model updates, data-sharing and embedded tooling all compound the complexities. Particularly for large enterprises with a network of tens of thousands of suppliers or more, traditional vendor management relying on legacy infrastructure and manual operations is no longer adequate. This is where the Cyber Security and Resilience Bill comes in, forcing a shift toward continuous monitoring which should match the speed of AI threats. ... By implementing evidence-led reporting templates, automated control validation, and continuous monitoring of supplier security posture, businesses can provide the board with real-time assurance, not point-in-time attestations. This approach demonstrates that systemic supplier risk is actively managed without diverting disproportionate time away from frontline threat detection and response. At an operational level, leaders shouldn’t wait for the bill to be finalised to find out who their ‘critical suppliers’ are. ... Upcoming changes to the bill will likely encourage tighter contractual obligations. Businesses should get ahead of this mandate and implement measures such as incident notification service-level agreements, rights-to-audit and evidence provisions, continuous monitoring, and Software bill of Materials.

Inspiration And Aspiration: Why Feel-Good Leadership Rarely Changes Outcomes

Inspiration is fancy. It makes ideas feel noble, futures feel possible and leadership feel virtuous—all without demanding immediate action or sacrifice. We feel moved, aligned and temporarily elevated. It’s a dream we see others have achieved through their actions. Aspiration is different. It is inconvenient. It’s our own dream, our desire to see ourselves in a certain spot or a way in the future. It requires disproportionate effort, new skills and a willingness to confront the uncomfortable gap between who we are today and who we say we want to become. ... That gap between intent and impact was uncomfortable. I told myself "I can't" and then took a step back, which was the easiest thing to do. What I realized is this: Aspiration without action becomes self-deception. Inspiration without action becomes mere admiration. And leadership that relies on either one eventually stagnates. Real change happens only when inspiration and aspiration move together, dance together—not sequentially, not occasionally, but in constant unison. ... Belief does not close gaps; capability and capacity do. Until the distance between intention and reality is acknowledged, effort will always be miscalculated. This gap should evoke and cement commitment, rather than creating drag. One needs to be very careful at this stage, as most people stop here. We may get inspired by mountaineers climbing Everest, but when we do a mental assessment about ourselves, we assume we are incapable of the task of bridging the gap, and we take a step back.

Most Organizations Plan Strategically. Few Manage It That Way

The report segments respondents into two categories: “Dynamic Planners,” characterized by frequent review cycles, cross-functional integration, high portfolio visibility, and active use of scenario planning; and “Plodders,” defined by siloed operations, infrequent reassessment, and limited real-time visibility into execution data. The performance difference between them is sharp enough to be operationally relevant. Eighty-one percent of Planners’ projects deliver measurable ROI or strategic value. Among Plodders, that figure is 45%. That’s a 36-point spread. That’s not measuring financial metrics; it’s about whether projects are doing what they were supposed to do. The survey also found that 30% of projects are not delivering meaningful ROI or strategic value. That leaves nearly one in three funded initiatives operating at levels ranging from marginal to counterproductive. ... Over a third of projects across the survey population are stopped early due to misalignment or insufficient ROI. The report treats this not as a problem to fix but as a sign of mature portfolio management. Chynoweth frames it in capital terms: “Cancellation is not failure. It’s disciplined capital allocation.” Most enterprises reward launch momentum, delivery against plan, and continuation of funded initiatives. Budget cycles create sunk-cost inertia. Career incentives favor project sponsors who ship, not those who cancel. 

Malicious insider threats outpace negligence in Australia

John Taylor, Mimecast's Field Chief Technical Officer for APAC, said organisations are seeing more cases where insiders are used to bypass established security controls. "We're seeing a concerning acceleration in malicious insider threats across Australia. While negligence has traditionally been the primary insider concern, intentional betrayal is now growing at a faster rate. ..." The report described AI as a factor that can increase the speed and scale of attacks, citing more convincing social engineering messages and automated reconnaissance. It also raised the prospect of AI being used to help recruit insiders. Taylor said older assumptions about a clear boundary between internal and external users no longer match how organisations operate, particularly with distributed workforces and widespread cloud adoption. ... Governance and compliance over communications data emerged as another concern. Mimecast found 91% of Australian organisations face challenges maintaining governance and compliance across communications data, and 53% lack confidence in quickly locating data to meet regulatory or legal requirements. These issues can slow incident response by delaying investigations and limiting the ability to reconstruct timelines across messaging platforms, email, and file stores. They can also increase risk during regulatory inquiries when organisations must produce relevant records quickly. Taylor said visibility is central to improving governance, culture, and response.

AI fatigue is real and it’s time for leaders to close the organizational gap

AI has been pitched as the next great accelerant of productivity. But inside many enterprises, teams are still recovering from years’ worth of transformation programs—cloud migrations, ERP upgrades, data modernization. Adding AI to an already overloaded change agenda can feel less like innovation and more like yet another disruption to absorb. The result is a predictable backlash. Tools in the industry are dismissed as “just another license”. Expectations are sky high; lived experience is often underwhelming. And when the novelty wears off, employees revert to old behavior fast. ... A pervasive misconception is that adopting AI is mostly about selecting and deploying the right technology. But tooling alone doesn’t redesign workflows. It doesn’t train employees. It doesn’t embed new decision making patterns. Some of the highest spending organizations are seeing the least value from AI precisely because investment has been concentrated at the technology layer rather than the organizational one. Without true operational change, AI tools risk becoming surface level enhancements rather than business accelerators. ... AI is not a spectator sport. Employees must understand how to use it, when to trust it, and how it adds value to their role. Organizations that invest early in skills from prompting to automation design will see dramatically higher adoption rates. The companies scaling fastest are those that build internal capability, not dependency on a small number of specialists.

Measuring What Matters in Large Language Model Performance

The study is timely, as LLM innovation increasingly targets skills and traits that are difficult to benchmark. “There’s been a shift towards testing AI systems for more complex capabilities like reasoning, helpfulness, and safety, which are very hard to measure,” said Rocher. “We wanted to look at whether evaluations are doing a good job capturing these sorts of skills.” Historically, AI innovators focused on equipping programs with easy-to-measure skills, like the ability to play chess and other strategy games. Today’s general-purpose LLMs, including popular models like ChatGPT, feature more flexible, open-ended strengths and traits. These attributes are notoriously difficult to operationalize, or to define in a way that’s precise enough to work in AI program measurement but broad enough to encompass the many different ways that the attribute might show up in the real world. Reasoning is one such skill. While most people are able to tell what counts as good or bad reasoning on a case-by-case basis, it’s not easy to describe reasoning in general terms. ... Towards this end, “Measuring what Matters” includes a set of guidelines to promote precision, thoroughness, rigor, and transparency in benchmark development. The first two recommendations, “define the phenomenon” and “measure the phenomenon and only the phenomenon,” encourage benchmark authors to be direct and specific as they define their target phenomena. 

Hallucination is not an option when AI meets the real world

For Boeckem, the most consequential AI applications are not advisory. They are autonomous. “In industrial environments, AI doesn’t just recommend,” he says. “It acts.” That shift, from insight to action, raises the stakes dramatically. Autonomous systems operate in safety-critical environments where failure can result in physical damage, financial loss, or human harm. “When generative AI went mainstream in 2022, it was exciting,” Boeckem says. “But professional environments need AI that is grounded in reality. These systems must always know where they are, what obstacles exist, and what the consequences of an action might be.” ... Despite the growing popularity of digital twins, many enterprises struggle to make them operational. According to Boeckem, the problem is not ambition, but misunderstanding. “A digital twin must be fit for purpose,” he says. “And above all, it must be dimensionally accurate.” Accuracy is non-negotiable. A flood simulation requires a watertight model. Urban planning demands precise representations of sunlight, shadows, and surroundings. Aesthetic simulations require photorealistic textures and material properties. At the most complex end of the spectrum, Hexagon models human faces. “A human face is not static,” Boeckem explains. “It’s soft-body material. When you smile, when you’re angry, when you’re sad, it changes. If you want to do diagnosis or therapy, you have to account for that.” 

Daily Tech Digest - January 03, 2026

---

Quote for the day:

“Some people dream of great accomplishments, while others stay awake and do them.” -- Anonymous

---

Cloud costs now No. 2 expense at midsize IT companies behind labor

The Cloud Capital survey shows midsize IT vendor CFOs and their CIO partners struggling to contain cloud spending, with significant cost volatility from month to month. Three-quarters of IT org CFOs report cloud spending forecasts varying between 5% and 10% of company revenues each month, Pingry notes. Costs of AI workloads are harder to predict than traditional SaaS infrastructure, Pingry adds, and organizations running major AI workloads are more likely to report margin declines tied to cloud spending than those with moderate AI exposure. “Training spikes, usage-driven inference, and experimentation noise introduce non-linear patterns that break the forecasting assumptions finance relies on,” says a report from Cloud Capital. “The challenge will intensify as AI’s share of cloud spend continues scaling.” ... Cloud services in themselves aren’t inherently too expensive, but many organizations shoot themselves in the foot through unintentional consumption, Clark adds. “Costs rise when the system is built without a clear understanding of the value it is meant to deliver,” he adds. ... “No CxO wants to explain to the board why another company used AI to leap ahead,” Clark adds. “This has created a no-holds-barred spending spree on training, inference, and data movement, often layered on top of architectures that were already economically incoherent.”

Securing Integration of AI into OT Technology

For critical infrastructure owners and operators, the goal is to use AI to increase efficiency and productivity, enhance decision-making, save costs, and improve customer experience – much like digitalization. However, despite the many benefits, integrating AI into operational technology (OT) environments that manage essential public services also introduces significant risks – such as OT process models drifting over time or safety-process bypasses – that owners and operators must carefully manage to ensure the availability and reliability of critical infrastructure. ... Understand the unique risks and potential impacts of AI integration into OT environments, the importance of educating personnel on these risks, and the secure AI development lifecycle. ... Assess the specific business case for AI use in OT environments and manage OT data security risks, the role of vendors, and the immediate and long-term challenges of AI integration. ... Implement robust governance mechanisms, integrate AI into existing security frameworks, continuously test and evaluate AI models, and consider regulatory compliance. ... Implement oversight mechanisms to ensure the safe operation and cybersecurity of AI-enabled OT systems, maintain transparency, and integrate AI into incident response plans.The agencies said critical infrastructure owners and operators should review this guidance so they can safely and securely integrate AI into OT systems. 

Rethinking Risk in a Connected World

As consumer behavior data proliferates and becomes increasingly available, it presents both an opportunity and a challenge for actuaries, Samuell says. Actuaries have the opportunity to better align expected and actual outcomes, while also facing the challenge of accounting for new sources of variability that traditional data does not capture. ... Keep in mind that incorporating behavioral factors into risk models does not guarantee certainty. A customer whom the model predicts to be at high risk of dishonesty may actually act honestly. “Ethical insurers must avoid treating predictive categories as definitive labels,” Samuell says. “Operational guidelines should ensure that all customers are treated with fairness and dignity, even as insurers make better use of available data.” ... Behavioral analytics is also changing how insurers engage with their customers. For example, by understanding how policyholders interact with digital platforms—including how often they log in, which features they use, and where they disengage—insurers can identify friction points and design more intuitive, personalized services. ... Consumer behavior data can also inform communication strategies for insurers. For example, “actuaries often want to be very precise, but data shows that can diminish comprehension of communications,” Stevenson says. ... In addition to data generated by insured individuals through technology, some insurance companies also use data from government and other sources in risk modeling. 

Inside the Cyber Extortion Boom: Phishing Gangs and Crime-as-a-Service Trends

Phishing attempts are growing in volume partly because organized crime groups no longer need technical knowledge to launch ransomware or other forms of cyber extortion: they can simply buy in the services they need. This ongoing trend is combined with emerging social engineering techniques, including multi-channel attacks, deep fakes and ClickFix exploits. Cybercriminals are also using AI to fine tune their operations, with more persuasive personalization, better translation into other languages and easier reconnaissance against high-value targets. It is becoming harder to detect and block attacks, and harder to train workforces to spot suspicious activity. ... “AI has increased the accuracy of a lot of phishing emails. Everybody was familiar with phishing emails you could spot it by the bad grammar and the poor formatting and stuff like that. Previously, a good attacker could create a good phishing email. All AI has done is allowed the attacker to generate good quality phishing emails at speed and at scale,” explained Richard Meeus, EMEA director of security strategy and technology at Akamai. ... For CISOs, wider cybersecurity and fraud prevention teams, recent developments in phishing and cyber extortion schemes will pose real challenges in the coming year. “User awareness still matters, but it isn’t enough,” cautioned Forescout’s Ferguson. “In a world of deepfake video, cloned voices and perfect written English, your control point can’t be ‘would our users spot this?’”

AI Fatigue: Is the backlash against AI already here?

The problem of AI fatigue is inevitable, but also to be expected, according to Dr Clare Walsh, director of education at the Institute of Analytics (IoA). “For those working in digital long enough. They know there is always a period after the initial excitement at the launch of a new technology when ordinary users start to see the costs and limitations of the latest technologies,” she says. “After 10 years of non-stop exciting advancements – from the first neural nets in 2016 to RAG solutions today – we may have forgotten this phase of disappointment was coming. It doesn’t negate the potential of AI technology – it is just an inevitable part of the adoption curve.” ... Holding back the tide of AI fatigue is also about not presenting it as the only solution to every problem, warns Claus Jepsen, Unit4’s CTO. “It is absolutely critical the IT team is asking the right questions and thoroughly interrogating the brief from the business,” he explains. “Quite often, AI is not the right answer. If you foist AI onto the business when they don’t want or need it, you’ll get a backlash. You can avoid the threat of AI fatigue if you listen carefully to your team and really appreciate how they want to interact with technology, where its use can be improved, and where it adds absolutely no value.” ... “AI fatigue is not just a productivity issue; it is a board-level risk,” she says. “When workflows are interrupted, or systems overlap, trust in technology erodes, driving disengagement, errors, and higher attrition. ...”

Why Cybersecurity Risk Management Will Continue to Increase in Complexity in 2026

The year 2026 ushers in tougher rules across regions and industries. Compliance pressure continues to build from multiple directions. By 2026, sector-specific and regional rules will grow tighter, from NIS2 enforcement across Europe to updated PCI DSS controls, alongside firmer privacy and AI oversight. Privacy laws continue tightening while new AI regulations add requirements around algorithmic transparency and data handling. Organizations are now juggling NIST frameworks, ISO 27001 certifications, and sector-specific mandates simultaneously. Each framework arrives with a valid intent, yet together they create layers of obligation that rarely align cleanly. This tension surfaced clearly in 2025, when more than forty CISOs from global enterprises urged the G7 and OECD to push for closer regulatory coordination. Their message was simple. Fragmented rules drain limited security resources and weaken collective response. ... The majority of organizations no longer run security in isolation. Daily operations depend on cloud providers, managed service partners, niche SaaS tools, and open-source libraries pulled into production without much ceremony. The problem keeps compounding: your vendors have their own vendors, creating chains of dependency that stretch impossibly far. You can secure your own network perfectly and still get breached because a third-party contractor left credentials exposed.

Seven steps to AI supply chain visibility — before a breach forces the issue

NIST’s AI Risk Management Framework, released in 2023, explicitly calls for AI-BOMs as part of its “Map” function, acknowledging that traditional software SBOMs don’t capture model-specific risks. But software dependencies resolve at build time and stay fixed. Conversely, model dependencies resolve at runtime, often fetching weights from HTTP endpoints during initialization, and mutate continuously through retraining, drift correction, and feedback loops. LoRA adapters modify weights without version control, making it impossible to track which model version is actually running in production. ... AI-BOMs are forensics, not firewalls. When ReversingLabs discovered nullifAI-compromised models, documented provenance would have immediately identified which organizations downloaded them. That’s invaluable to know for incident response, while being practically useless for prevention. Budgeting for protecting AI-BOMs needs to take that factor into account. The ML-BOM tooling ecosystem is maturing fast, but it's not where software SBOMs are yet. Tools like Syft and Trivy generate complete software inventories in minutes. ML-BOM tooling is earlier in that curve. Vendors are shipping solutions, but integration and automation still require additional steps and more effort. Organizations starting now may need manual processes to fill gaps. AI-BOMs won't stop model poisoning as that happens during training, often before an organization ever downloads the model.

Power, compute, and sovereignty: Why India must build its own AI infrastructure in 2026

Digital infrastructure decisions made in 2026 will shape India’s technological posture well into the 2040s. Data centers, power systems, and AI platforms are not short-cycle investments; they are multi-decade commitments. In this context, policy clarity becomes a prerequisite for execution rather than an afterthought. Clear, stable frameworks around data governance, AI regulation, cross-border compute flows, and energy integration reduce long-term risk and enable infrastructure to be designed correctly the first time. Ambiguity forces fragmentation capital hesitates, architectures become reactive, and systems are retrofitted instead of engineered. As India accelerates its AI ambitions, predictability in policy will be as important as speed in deployment. ... In India’s context, sovereignty does not imply isolation. It implies resilience. Compliance, data residency, and AI governance cannot be retrofitted into infrastructure after it is built. They must be embedded from inception governing where data resides, how it moves, how workloads are isolated, audited, and secured, and how infrastructure responds to evolving regulatory expectations. Systems designed this way reduce friction for enterprises operating in regulated environments and provide governments with confidence in domestic digital capability. This reality also reframes the role of domestic technology firms. 

Why AI Risk Visibility Is the Future of Enterprise Cybersecurity Strategy

Vulnerabilities arise from two sources: internal infrastructure and third-party tools that companies rely on. Organizations typically have stronger control over internally developed systems. The complexity stems from third-party software that introduces new risks whenever a new version or patch is released. A comprehensive asset inventory is essential for documenting the software and hardware resources in use. Once the enterprise knows what it has, it can evaluate which systems pose the highest risk. Asset management, infrastructure, and information security teams, along with audit functions, all contribute to that assessment. Together, they can determine where remediation must occur first. Cloud service providers are responsible for cloud-based Software as a Service (SaaS) applications. It’s vital, however, for the company to take on data governance and service offboarding responsibilities. Contracts must clearly specify how data is handled, transferred, or destroyed at the end of the relationship. ... Alignment between business and IT leadership is essential. The chief information officer (CIO) approves the IT project kickoff and allocates the required budget and other resources. The business analysis team translates those needs into technical requirements. Quarterly scorecards and governance checkpoints create visibility, enabling leaders to make decisions that balance business outcomes and technical realities.

Why are IT leaders optimistic about future AI governance

IT leaders are optimistic about AI’s transformative potential. This optimism extends to AI governance, where the strategic integration of NHI management enhances security and enables organizations to confidently pursue AI initiatives. It’s essential to ensure that security measures evolve alongside technological advancements, safeguarding AI systems without stifling innovation. ... Can robust security and innovation coexist harmoniously? The answer lies in striking a balance between rigorous security measures and fostering an environment conducive to innovation. Properly managing NHIs equips organizations with the flexibility to innovate while maintaining a fortified security posture. With advancements in artificial intelligence and automation progress, machine identities play an increasingly pivotal role in enabling these technologies. By ensuring that machine interactions are secure and transparent, businesses can confidently explore the transformative potential of AI without compromising on security. Herein lies the essence of responsible AI governance: leveraging data-driven insights to enable ethical and sustainable technological growth while safeguarding against inherent risks. ... What can organizations do to harness the collective expertise of stakeholders? Where cyber threats are increasingly sophisticated, collaboration becomes the cornerstone of a resilient cybersecurity framework.