Daily Tech Digest by Kannan Subbiah: tech talk

Showing posts with label tech talk. Show all posts

Daily Tech Digest - May 31, 2026

Quote for the day:

“Make sure you don’t start seeing yourself through the eyes of those who don’t value you.” -- Anonymous

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Duration: 21 mins • Perfect for listening on the go.

AI observability: How CIOs can see past their org blind spots

The article discusses AI observability, highlighting how traditional IT monitoring tools are insufficient for evaluating artificial intelligence performance. As AI applications expand across modern businesses, CIOs frequently struggle with deep blind spots regarding system usage, model drift, performance degradation, and unauthorized "shadow AI" tools. Unlike standard software that relies on predictable metrics like uptime, AI systems operate probabilistically, meaning the exact same inputs can yield wildly varying outcomes. This inherent unpredictability creates compounding risks, especially as enterprises connect multiple autonomous agents into complex workflows where minor data issues can quietly corrupt downstream results for weeks before finally breaking. To address these organizational vulnerabilities, experts suggest shifting from front-loaded risk assessments to continuous, full-stack visibility. This comprehensive approach involves setting up automated guardrails for model outputs, maintaining a clear catalog of active systems, and establishing an integrated control plane. By compiling system telemetry, semantic mapping, and risk thresholds into a single shared interface, different corporate stakeholders, such as finance, human resources, and security teams, can easily monitor the metrics relevant to their own departments. Ultimately, treating observability as a core design principle rather than an afterthought enables leadership to safely scale their AI initiatives, manage ballooning costs, and build lasting organizational trust.

The Validation Gap Is Costing You More Than You Think

According to a report on software delivery, development teams are writing more code than ever, but less of it is actually reaching production. Analysis of millions of workflows reveals that while development throughput has spiked, main branch success rates have fallen to a five-year low of roughly seventy percent. This drop stems from a gap in how software is validated. Traditional continuous integration systems were designed for humans who commit code gradually. Today, automated artificial intelligence tools generate code at a rapid pace that completely overwhelms traditional review processes. When errors are caught late in the shared integration system, it results in expensive compute costs, wasted time, and broken focus as the automated tools have already moved on to other tasks. To solve this dilemma, engineering teams must shift testing much earlier into the initial writing phase. By running smaller, targeted tests while the automated code generator is still actively focused on a task, teams can fix errors immediately without draining infrastructure resources. When this early testing stage and the final integration pipeline share historical information, the entire delivery system becomes smarter and more efficient. Ultimately, addressing this validation imbalance helps organizations safely increase their software output without absorbing downstream failures.

Why Attack Surface Management Breaks in OT (and What Actually Works)

Traditional Attack Surface Management (ASM) fails in Operational Technology (OT) environments because industrial infrastructure operates on fundamentally different principles than standard enterprise IT systems. Many legacy industrial protocols, such as Modbus, DNP3, and BACnet, were created decades ago without built-in encryption, session management, or authentication mechanisms. Consequently, their lack of security is an inherent property of the system design rather than a simple configuration mistake that can easily be patched. Furthermore, the active interrogation techniques standard in IT security can severely disrupt operational networks; sending aggressive probes often overwhelms the limited network stacks of Programmable Logic Controllers (PLCs), causing critical physical machinery to misbehave or shut down entirely. Because these industrial environments do not support software agents or standard diagnostic queries, establishing a reliable asset inventory is remarkably difficult. To mitigate risks effectively, security teams must reverse their usual enterprise instincts by defaulting to passive network monitoring and treating active probing as a tightly managed privilege. Utilizing passive internet search data allows analysts to map exposed external components safely without introducing disruptive traffic to live plants. Ultimately, embedding clear safety workflows and strict rate limits into automated security tools ensures that scanning efforts do not cause unintended physical operational downtime.

Backup and recovery architecture best practices for UK SMEs

The Security Boulevard article explains that smaller businesses in the UK should treat backup and recovery as a practical safety measure rather than a simple file storage task. A sensible backup plan focuses entirely on restoration outcomes, ensuring a company can keep trading after an incident like an accidental deletion, system failure, or cyberattack. Instead of buying expensive software tools first, these organizations should prioritize their systems based on how a disruption directly impacts their daily operations, clearly defining how much downtime and data loss they can realistically handle. To build stronger protection, companies must keep multiple copies of their files across separate locations and accounts so that a single compromise or mistake cannot destroy both the live data and the backups. Furthermore, restricting access to named administrative accounts, applying settings that prevent recent copies from being altered or deleted, and choosing backup styles that match different types of systems will lower overall risk. Because copying data does not automatically mean a system can be successfully rebuilt, regular testing is necessary to catch unexpected delays and overlooked technical connections. Ultimately, the article recommends documenting these steps in short, straightforward guides with clear ownership so that staff can respond calmly when an unexpected outage occurs.

Challenging AI Assumptions

In his Forbes article, John Werner encourages readers to reconsider common assumptions about artificial intelligence that might limit our ability to effectively navigate the future. He notes that early technology milestones, such as the IBM Watson era, conditioned the public to view machine intelligence as a centralized database focused entirely on factual recall, rapid calculation, and deterministic logic. However, as the field quickly moves toward a future centered on autonomous software agents, Werner argues that continuing to rely on these old centralized frameworks is a foundational mistake. Drawing from insights shared at a recent MIT-linked conference, he suggests that the true development of artificial intelligence will ultimately mirror biological organisms and complex economic networks rather than centralized computer hardware. Because the long-term impact of this technology on global society is frequently compared to foundational discoveries like fire or electricity, our structural approach must evolve accordingly. Instead of designing isolated, top-down systems, we should foster collaborative, decentralized, and biologically inspired ecosystems of digital agents. By shifting our perspective away from rigid central control, human society can establish cooperative frameworks that allow these increasingly autonomous systems to be integrated smoothly, sustainably, and safely into everyday life.

The Architecture Questions I Ask Before an Initiative Starts

In his article, Eetu Niemi outlines three practical architectural questions to ask before any major business project begins, aiming to clarify scope and prevent costly downstream surprises. The first question focuses on what is actually changing within the organization. Project names can often be deceptive, so teams must carefully distinguish between a project's stated scope and its actual, wider impact. If a change only alters a single isolated system, heavy architectural planning is rarely needed. The second question addresses visible dependencies, identifying which software applications, data streams, teams, or external vendors the project relies upon. Uncovering this scattered knowledge early helps avoid scheduling or financial surprises down the line without over-documenting every minor connection. The final question evaluates which decisions would be expensive to reverse later on. While choices regarding technology platforms, data models, or core software might seem like minor delivery choices initially, they quickly harden into fixed constraints once other systems are built around them. By addressing what is changing, identifying dependencies, and flagging irreversible choices early on, architects can guide decision-making through plain conversations and basic diagrams. This upfront evaluation allows organizations to balance development speed with long-term operational stability without drowning teams in unnecessary paperwork or rigid governance structures.

Building a Quantum-Safe Foundation: WWT and Cisco Accelerate Post-Quantum Readiness

The article outlines how World Wide Technology and Cisco are working together to help organizations secure their networks against future quantum computing threats. Central to this effort is the use of Cisco 8000 Series Secure Routers, which address post-quantum security in two main areas: protecting data in transit with encryption that resists quantum attacks, and maintaining internal device integrity through hardware-anchored trust and secure boot processes. Importantly, these routers already contain the necessary hardware components to run these new cryptographic standards, meaning companies do not need to replace their existing infrastructure and can implement the updates through straightforward configuration changes. This compatibility allows quantum-safe equipment to run on the same network as older systems, removing the need for a risky, immediate complete network overhaul. To guide organizations through this transition, World Wide Technology provides planning and deployment support through its specialized security division and its Advanced Technology Center lab facility. In this testing lab, engineering teams can evaluate encryption tunnel behaviors and test fallback systems under realistic network conditions before rolling them out. Ultimately, the collaboration highlights that achieving security against quantum threats is an ongoing program requiring careful testing, technical depth, and phased adjustments rather than a simple product purchase.

The Next Wow Factor: A Conversation with Sidney Lu, Chairman and CEO, Foxconn Interconnect Technology (FIT)

In this interview, Sidney Lu, the chairman and chief executive officer of Foxconn Interconnect Technology, reflects on his forty year career and personal leadership philosophy. He oversees a large global workforce that manufactures vital electrical parts, such as connectors and cables, for common electronics like smartphones, electric vehicles, and computer servers. Lu credits his way of leading to a balance of Eastern discipline and Western workplace confidence, which he gained while studying and working in the United States. A foundational lesson from his mother taught him to take full responsibility, avoid self pity, and quickly move past mistakes, a clear mindset he later applied to difficult engineering problems. As a leader, Lu strongly emphasizes supporting his employees by taking personal blame for business setbacks rather than shifting it downward to others. To stay relevant and avoid falling behind, he consistently challenges his team to deliver an unexpected, fresh product or advancement every three years. Under his quiet guidance, the company has expanded significantly while building long lasting relationships with clients based on deep trust. Ultimately, Lu attributes his steady motivation to a simple, genuine enjoyment of his daily work and a constant curiosity about what comes next.

Post-quantum cryptography is not the future. It is your current reality

The article explains that post-quantum cryptography is an immediate operational necessity rather than a distant concern. Major tech companies and governments are already deploying these new algorithms because waiting for a functional quantum computer introduces severe, immediate risks to digital infrastructure. Chief among these is the "Harvest Now, Decrypt Later" strategy, where adversaries actively intercept and store encrypted network traffic today with the intention of decrypting it once advanced quantum hardware becomes available. Additionally, existing digital signatures and root certificates face future retroactive forgery, threatening the core authenticity of secure software supply chains. Successfully upgrading an enterprise is rarely an issue of funding or algorithm selection; the real challenge is an absolute lack of visibility. Modern corporate networks contain countless forgotten encryption points hidden within legacy software, cloud environments, and device firmware. To address this, organizations must establish a continuous inventory, known as a Cryptography Bill of Materials, to locate and evaluate their vulnerable assets. Once an organization maps these internal elements, it can cultivate true cryptographic agility, enabling systems to swap underlying protocols smoothly without disrupting daily operations or breaking system compatibility. Rather than delaying, companies must prioritize data based on its overall longevity and methodically adapt to finalized standards, securing their systems before the available implementation runway runs out entirely.

Non-Human Identities Are Outgrowing Your Governance Model

Many companies have developed dependable systems to manage human user identities, but they are falling behind when it comes to non-human accounts. Machine identities, such as service accounts, API keys, security certificates, and automated workloads, now vastly outnumber human credentials, particularly in cloud computing environments. Because these digital entities lack individual managers, specific start dates, or standard offboarding processes, they often slip through traditional corporate tracking systems completely unnoticed. This ongoing management gap leads to significant security problems, including orphaned accounts that maintain high-level administrative access years after a project ends, static passwords that are never rotated, and old third-party integrations that leave access doors wide open to former external vendors. Additionally, neglecting these machine identities creates serious compliance exposure during regulatory audits under strict frameworks like SOC 2 or ISO 27001, which mandate clear internal accountability and regular access reviews. To fix these issues, organizations need to update their tracking strategies and treat non-human credentials with the exact same discipline applied to human staff. This approach means assigning clear owners to every automated account, mapping their actual usage patterns, setting up predictable update cycles, and deleting them automatically when software is retired. By establishing this structured oversight, security teams can successfully close dangerous operational loopholes and maintain control.

Daily Tech Digest - May 07, 2026

Quote for the day:

"You learn more from failure than from success. Don't let it stop you. Failure builds character." -- Unknown

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Duration: 21 mins • Perfect for listening on the go.

Designing front-end systems for cloud failure

In the InfoWorld article "Designing front-end systems for cloud failure," Niharika Pujari argues that frontend resilience is a critical yet often overlooked aspect of engineering. Since cloud infrastructure depends on numerous moving parts, failures are frequently partial rather than absolute, manifesting as temporary network instability or slow downstream services. To maintain a usable and calm user experience during these hiccups, developers should adopt a strategy of . This begins with distinguishing between critical features, which are essential for core tasks, and non-critical components that provide extra richness. When non-essential features fail, the interface should isolate these issues—perhaps by hiding sections or displaying cached data—to prevent a total system outage. Technical implementation involves employing controlled retries with exponential backoff and jitter to manage transient errors without overwhelming the backend. Additionally, protecting user work in form-heavy workflows is vital for maintaining trust. Effective failure handling also requires a shift in communication; specific, reassuring error messages that explain what still works and provide a clear recovery path are far superior to generic "something went wrong" alerts. Ultimately, resilient frontend design focuses on isolating failures, rendering partial content, and ensuring that the interface remains functional and informative even when underlying cloud dependencies falter.

Scaling AI into production is forcing a rethink of enterprise infrastructure

The article "Scaling AI into production is forcing a rethink of enterprise infrastructure" explores the critical shift from AI experimentation to large-scale deployment across real business environments. As organizations move beyond proofs of concept, executives Tarkan Maner and Thomas Cornely argue that the emergence of is a primary driver of this transformation. Agentic systems introduce complex, autonomous, multi-step workflows that traditional infrastructures are often unequipped to handle efficiently. These sophisticated agents require real-time orchestration and secure, on-premises data access to protect sensitive enterprise information. While many organizations initially utilized the public cloud for rapid experimentation, the transition to production highlights serious concerns regarding ongoing cost, strict governance, and data control, prompting a significant shift toward private or hybrid environments. The article emphasizes that AI is designed to augment human capability rather than replace it, seeking a harmonious integration between human decision-making and automated agentic workflows. Practical applications are already emerging across various sectors, from retail’s cashier-less checkouts and targeted marketing to healthcare’s remote diagnostic tools. Ultimately, scaling AI successfully necessitates a foundational rethink of how modern enterprises coordinate their underlying infrastructure, data, and security protocols to support unpredictable workloads while maintaining overall operational stability and long-term cost efficiency.

Why ransomware attacks succeed even when backups exist

The BleepingComputer article "Why ransomware attacks succeed even when backups exist" explains that modern ransomware operations have evolved into sophisticated campaigns that systematically target and destroy an organization's backup infrastructure before deploying encryption. Rather than just locking files, attackers follow a predictable sequence: gaining initial access, stealing administrative credentials, moving laterally across the network, and then identifying and deleting backups. This includes wiping , hypervisor snapshots, and cloud repositories to ensure no easy recovery path remains. Several common organizational failures contribute to this vulnerability, such as the lack of network isolation between production and backup environments, weak access controls like shared admin credentials or missing multi-factor authentication, and the absence of . Furthermore, many organizations suffer from untested recovery processes or siloed security tools that fail to detect attacks on backup systems. To combat these threats, the article emphasizes the necessity of integrated cyber protection, featuring immutable backups with enforced retention locks, dedicated credentials, and continuous monitoring. By neutralizing the traditional "safety net" of backups, ransomware gangs effectively force victims into paying ransoms. This strategic shift highlights that basic, unprotected backups are no longer sufficient in the face of modern, targeted ransomware tactics.

Document as Evidence vs. Data Source: Industrial AI Governance

In the article "Document as Evidence vs. Data Source: Industrial AI Governance," Anthony Vigliotti highlights a critical distinction in how organizations manage information for . Most current programs utilize a "data source" model, where documents are treated as raw material; data is extracted, and the original document is archived or orphaned. This terminal approach severs the link between data and its context, creating significant governance risks, particularly in brownfield manufacturing where legacy records carry decades of operational history. Conversely, the "evidence" model treats documents as permanent artifacts with ongoing legal and operational standing. This framework ensures documents are preserved with high fidelity, validated before downstream use, and permanently linked to any derived data through a navigable citation trail. By adopting an evidence-based posture, organizations can build a robust "Accuracy and Trust Layer" that makes AI-driven decisions defensible and auditable. This is essential for safety-critical operations and regulatory compliance, where being able to prove the provenance of data is as vital as the accuracy of the AI output itself. Transitioning from a throughput-focused extraction mindset to one centered on trust allows industrial enterprises to scale AI safely while mitigating the long-term governance debt associated with disconnected data silos.

Method for stress-testing cloud computing algorithms helps avoid network failures

Researchers at MIT have developed a groundbreaking method called to stress-test cloud computing algorithms, helping prevent large-scale network failures and service outages that impact millions of users. In massive cloud environments, engineers often rely on ""—simplified shortcut algorithms that route data quickly but can unexpectedly break down under unusual traffic patterns or sudden demand spikes. Traditionally, stress-testing these heuristics involved manual, time-consuming simulations using human-designed test cases, which frequently missed critical "blind spots" where the algorithm might fail. MetaEase revolutionizes this evaluation process by utilizing to analyze an algorithm’s source code directly. By mapping out every decision point within the code, the tool automatically searches for and identifies worst-case scenarios where performance gaps and underperformance are most significant. This automated approach allows engineers to proactively catch potential failure modes before deployment without requiring complex mathematical reformulations or extensive manual labor. Beyond standard networking tasks, the researchers highlight MetaEase’s potential for auditing risks associated with AI-generated code, ensuring these systems remain resilient under unpredictable real-world conditions. In comparative experiments, this technique identified more severe performance failures more efficiently than existing state-of-the-art methods. Moving forward, the team aims to enhance MetaEase’s scalability and versatility to process more complex data types and applications.

Hacker Conversations: Joey Melo on Hacking AI

In the SecurityWeek article "Hacker Conversations: Joey Melo on Hacking AI," Principal Security Researcher Joey Melo shares his journey and methodology within the evolving field of artificial intelligence red teaming. Melo, who developed a passion for manipulating software environments through childhood gaming, now applies that curiosity to "" and "data poisoning" AI models. Unlike traditional penetration testing, focuses on bypassing sophisticated guardrails without altering source code. Melo describes jailbreaking as a process of "liberating" bots via complex context manipulation—such as tricking an LLM into believing it is operating in a future where current restrictions no longer apply. Furthermore, he explores data poisoning, where researchers test if models can be influenced by malicious prompt ingestion or untrustworthy web scraping. Despite possessing the skills to exploit these vulnerabilities for personal gain, Melo emphasizes a commitment to ethical, responsible disclosure. He views his work as a vital contribution to an ongoing "cat-and-mouse game" aimed at hardening machine learning defenses against increasingly creative threats. Ultimately, Melo believes that while AI security will continue to improve, the constant evolution of technology ensures that red teaming will remain a necessary, creative endeavor to identify and mitigate emerging risks.

Global Push for Digital KYC Faces a Trust Problem

The global movement toward digital Know Your Customer (KYC) frameworks is gaining significant momentum, as evidenced by the United Arab Emirates’ recent launch of a standardized national platform designed to streamline onboarding and bolster anti-money laundering efforts. While domestic systems are becoming increasingly sophisticated, the concept of portable, cross-border KYC remains largely elusive due to a fundamental lack of trust between international regulators. Governments and financial institutions are eager to reduce duplication and speed up compliance processes to match the rapid growth of instant payments and digital banking. However, significant hurdles persist because KYC extends beyond simple identity verification to include complex assessments of ownership structures and risk profiles, which are heavily influenced by local market contexts and legal frameworks. National regulators often prioritize sovereign control and data protection, making them hesitant to rely on third-party verification performed in different jurisdictions. Consequently, even when countries share broad anti-money laundering goals, their divergent definitions of adequate due diligence and monitoring requirements create a fragmented landscape. Ultimately, the transition to a unified digital identity ecosystem depends less on technological innovation and more on establishing mutual recognition and trust among global supervisory bodies, ensuring that sensitive identity data can be securely and reliably shared across borders.

How To Ensure Business Continuity in the Midst of IT Disaster Recovery

The content provided by the Disaster Recovery Journal (DRJ) at the specified URL serves as a foundational guide for professionals navigating the complexities of organizational stability through the lens of business continuity (BC) and disaster recovery (DR) planning. The material emphasizes that while these two disciplines are closely interconnected, they serve distinct roles in safeguarding an organization. Business continuity is presented as a holistic, high-level strategy focused on maintaining essential operations across all departments during a crisis, ensuring that personnel, facilities, and processes remain functional. In contrast, disaster recovery is defined as a specialized technical subset of BC, primarily concerned with the restoration of information technology systems, critical data, and infrastructure following a disruptive event. A primary theme of the planning process is the requirement for a structured lifecycle, which begins with a rigorous Business Impact Analysis (BIA) and Risk Assessment to identify vulnerabilities and prioritize critical functions. By defining clear Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO), organizations can create targeted response strategies that minimize operational downtime. Furthermore, the resource highlights that modern planning must evolve to address contemporary challenges, such as cyber threats, hybrid work environments, and artificial intelligence integration. Regular testing, cross-functional collaboration, and plan maintenance are essential to transform static documentation into a dynamic, resilient framework capable of withstanding diverse disasters.

The Agentic AI Challenge: Solve for Both Efficiency and Trust

According to the article from The Financial Brand, agentic artificial intelligence represents the next inevitable evolution in banking, marking a fundamental shift from reactive generative AI chatbots to autonomous, proactive systems. While nearly all financial institutions are currently exploring agentic technology, a significant "execution gap" persists; most organizations remain stuck in the pilot phase due to legacy infrastructure, fragmented data silos, and outdated governance frameworks. Unlike traditional AI that merely offers recommendations, agentic systems are designed to act—executing complex workflows, coordinating multi-step transactions, and managing customer financial health in real time with minimal human intervention. The report emphasizes that while banks have historically prioritized low-value applications like back-office automation and fraud prevention, the true potential of agentic AI lies in fulfilling broader ambitions for hyper-personalization and revenue growth. As fintech competitors increasingly rebuild their transaction stacks for real-time execution and autonomous validation, traditional banks face a critical strategic choice. They must modernize their leadership mindset and core technical architecture to support the "self-driving bank" model or risk being permanently outpaced. Ultimately, embracing agentic AI is not merely a technological upgrade but a necessary structural evolution required for banks to remain competitive in an increasingly automated financial ecosystem.

Multi-model AI is creating a routing headache for enterprises

According to F5’s 2026 State of Application Strategy Report, enterprises are rapidly transitioning AI inference into core production environments, with 78% of organizations now operating their own inference services. As 77% of firms identify inference as their primary AI activity, the focus has shifted from experimentation to operational integration within hybrid multicloud infrastructures. Organizations currently manage or evaluate an average of seven distinct AI models, reflecting a diverse landscape where no single model fits every use case. This multi-model approach creates significant architectural complexities, turning AI delivery into a sophisticated traffic management challenge and AI security into a rigorous governance priority. Companies are increasingly adopting identity-aware infrastructure and centralized control planes to manage the routing, observability, and protection of inference workloads. To mitigate operational strain and rising costs, enterprises are integrating shared protection systems and cross-model observability tools. Furthermore, the convergence of AI delivery and security around inference highlights the necessity of managing multiple services to ensure availability and compliance. Ultimately, the report emphasizes that successful AI adoption depends on treating inference as a managed workload subject to the same delivery and resilience requirements as traditional enterprise applications, ensuring faster and safer operational execution.

Daily Tech Digest - November 07, 2025

Quote for the day:

"The best teachers are those who don't tell you how to get there but show the way." -- @Pilotspeaker

AI spending may slow down as ROI remains elusive

Some AI experts agree with Forrester that an AI market correction is on the way. Microsoft founder Bill Gates recently talked about the existence of an AI bubble, and industry observers have noted that some AI excitement is dimming. Many don’t see an AI bubble that will burst in the near future, but it’s deflating a bit. Still others don’t see much of a slowdown in the near term. ... Some organizations are not achieving the accuracy they need from AI tools, and others are not finding their data to be easily accessible or properly structured, says Sam Ferrise, CTO of IT consulting firm Trinetix. “Many organizations are realizing that their expectations for AI accuracy and performance don’t always align with the level of investment they’re willing — or able — to make,” he says. “The key is calibrating expectations relative to both the investment and the use case.” In other cases, enterprises deploying AI are running into privacy or security problems, he adds. “Many teams successfully prove a use case with clear ROI, only to realize later that they must harden the solution before it can safely move into production,” Ferrise says. “When that alignment isn’t there, it’s natural for organizations to pause or delay spending until they can justify the value.” The prospect of a bubble bursting may be an overly dramatic scenario, although not impossible, he adds. It’s been easy for organizations to overlook intangible costs such as training, compliance, and governance.

Why can’t enterprises get a handle on the cloud misconfiguration problem?

“Microsoft, Google, and Amazon have handed us a problem,” says Andrew Wilder, CSO at Vetcor, a national network of more than 900 veterinary hospitals. “By default, everything is insecure, and you have to put security on top of it. It would be much better if they just gave us out-of-the-box secure stuff. Would you buy a car that doesn’t have locks? They wouldn’t even sell that car.” This security gap is what allows third-party vendors to exist, he says. “You should be building products — and I’m talking to you, Google, Microsoft, and Amazon — that are secure by design, so you don’t have to get a third-party tool. They should be out of the box secure.” ... When administrators or users make changes to cloud configurations in the cloud management consoles, it’s difficult to track those changes and to revert them if something goes wrong. Plus, humans can easily make mistakes. The solution experts advise is to adopt the principle of “infrastructure as code” and use configuration management tools so that all changes are checked against policies, tracked and audited, and can easily be rolled back. ... Companies will often have monitoring for major cloud services, but shadow IT deployments are left in the dark. This is less a technology problem than a management one and can be addressed by better communications with business units and a more disciplined approach to deploying technology on an enterprise-wide level.

The Supply Chain Blind Spot: Protecting Data in Expanding IT Ecosystems

Data growth is no longer linear, it is exponential. The rise of AI, automation, and digital platforms has transformed how information is created, stored, and shared. In India, this acceleration is particularly visible. The country’s data centre industry has grown from 590 MW in 2019 to 1.4 GW in 2024, a 139% jump, and is projected to reach 3 GW by 2030, driven by cloud adoption, AI demand, and data localisation initiatives. This infrastructure boom, while positive, brings new operational realities. Most enterprises now operate across hybrid environments, combining on-premises, public cloud and SaaS-based data stores. Without unified oversight, these fragmented environments risk becoming silos. True resilience depends not just on protecting data but understanding where it lives, how it moves, and who controls it. ... Globally, enterprises are reframing resilience as a core business capability. This approach requires integrating resilience principles into decision-making: from procurement and architecture design to crisis response. Simulated attacks, failover testing and dependency audits are becoming part of daily operational culture, not annual exercises. For Indian organizations, this mindset shift is vital. RBI’s ICT risk management directives and the DPDP Act establish the baseline; the differentiator lies in how proactively organizations operationalize these expectations.

The power of low-tech in a high-tech world

Our high-tech society is impressive in the collective. But it robs individuals of skills. Most kids now can’t write cursive. And they can’t read it, either. They can’t read an analog clock or a paper map. The acceleration of technological innovation also accelerates the rate at which we lose skills. Videogames, smartphones, and dating apps — aided and abetted by the trauma of the COVID-19 lockdowns a few years ago — have left many young people alone without the skills to meet and connect with anyone, leading to a loneliness epidemic among the young. But losing old-fashioned skills and old-school tech knowledge is a choice we don’t have to make. ... Thousands of scientific reports all lead us to the same conclusion: Over-reliance on advanced technologies dulls critical thinking, weakens memory, reduces problem-solving skills, limits creativity, erodes attention spans, and fosters passive dependence on automated systems. ... What all these old-school approaches have in common is that they’re harder and take longer — and they leave you smarter and better connected. In other words, if you strategically cultivate the skills, habits, discipline and practice of older tech, you’ll be much more successful in your career and your life. And here’s one final point: The more high-tech our culture becomes, the more impactful old-school tech will be. So yes, by all means become brilliantly skilled at AI chatbot prompt engineering.

Why Leaders Cannot Outsource Communication

When communication is delegated to a proxy, that signal weakens. Employees notice the gap between what the leader says or doesn’t say, and what the organization does. This is why communication has an outsized impact on engagement. Gallup finds that 70% of the variance in employee engagement is explained by managers and leaders, not perks or policies. When leaders own the message, they create psychological safety: the sense that it’s safe to commit, speak up and take risks. When they don’t, that safety erodes. ... Delegating communication is tempting. Leaders are busy. They hire communications officers and agencies to manage the message. These roles are valuable, but they can’t substitute for the leader’s voice. A speechwriter can shape phrasing and a PR team can guide timing, but only the leader can deliver authenticity. As Murphy has written, “Leaders are accountable to employees: Candor about bad news as well as the good, and feedback that aligns with expectations.” Authenticity requires candor, even when the message is difficult. When communication comes from anyone else, it’s interpreted as institutional rather than personal. And people follow people, not institutions. ... The Operator Economy demands a new kind of scale, one built not on capital or code, but on human alignment. Communication is infrastructure. The CEO becomes the signal source around which all systems calibrate. When leaders “scale themselves” through clarity and consistency, they convert trust into throughput.

Breaking the Burnout Cycle: How Smart Automation and ASPM Can Restore Developer Joy

Smart automation can rescue developers from repetitive drudgery by using AI to handle routine tasks like test writing, bug fixing, and documentation. Modern application security posture management (ASPM) platforms exemplify this approach by providing contextualized risk assessments rather than overwhelming vulnerability dumps, helping security teams first understand which issues actually matter and then giving developers actionable info on the risk and how it should be fixed. These platforms excel at managing the volume and unpredictability of AI-generated code, turning what was once a blind spot into manageable, prioritized work. ... Technology alone isn't enough. Organizations must also prioritize developer growth by creating opportunities for experimentation, architectural decisions, and end-to-end project ownership while automation handles routine tasks. This means shifting from measuring output volume to focusing on meaningful metrics like code quality and developer satisfaction. AI represents an opportunity for developers to gain expertise in an emerging technology. ... The developer talent crisis is solvable. While AI has introduced new complexities to the software development and security landscape, it also presents unprecedented opportunities for organizations willing to rethink how they support their development teams.

The CIO’s Role In Data Democracy: Empowering Teams Without Losing Control

The modern CIO is at a point where they can choose between innovation and control. In the past, IT departments were thought of as people who took care of infrastructure and enforced strict regulations about who could access data. The CIO needs to reassess this way of doing things today. They shouldn’t prohibit access; instead, they should make it safe by building frameworks. The job has changed from saying “no” to making sure that when the company says “yes,” it does it smartly. The CIO is now both an architect and a guardian. They create systems that make data easy to get to, understand, and act on, all while keeping security and compliance in mind. ... The CIO is no longer a gatekeeper; they are instead a designer of trust. The goal is to make governance a part of systems such that it is seamless, automatic, and easy to use. This change lets companies keep an eye on things and stay in control without making decisions take longer. Unified data taxonomies are the first step in building this framework. This means that all departments use the same naming standards and definitions. When everyone uses the same “data language,” there is less confusion and more cooperation. ... Effective governance demands collaboration between IT, compliance, and business leaders. The CIO must champion cross-functional alignment where all parties share responsibility for data integrity and use.

What keeps phishing training from fading over time

Employees who want to be helpful or appear responsive can become easier targets than those reacting to fear or haste. For CISOs, this reinforces the need to teach users about manipulation through trust and cooperation, not just the warning signs of urgent or threatening messages. ... Dubniczky said maintaining employee engagement over time is a major challenge for most organizations. “In contrast with other research in the area, a key contribution of ours was a mandatory training after each failed phishing attack,” he explained. “This strikes a good balance between not needlessly bothering careful employees with monthly or quarterly trainings while making sure that the highest risk individuals are constantly trained.” He recommended that organizations vary their phishing simulations to keep users alert. “We’d recommend performing monthly penetration tests on smaller groups of people in diverse departments of the organization with a seemingly random pattern, and making re-training mandatory in case of successful attacks,” he said. “It’s also difficult to generalize on this, but this approach seems much more effective than periodic presentation-style trainings.” ... One of the most striking findings involves the timing of feedback. When employees clicked a phishing link and then received an immediate explanation and training prompt, they were far less likely to repeat the behavior. Around seven in ten employees who failed once did not do so again.

The new QA playbook: Leveraging AI to amplify expertise, not replace it

Many quality teams have been part of the AI journey from the very beginning, contributing from concept to implementation and helping evaluate large language models to ensure quality and reliability. However, many AI features are not developed by QA practitioners, so it is essential to evaluate them through a QA lens. First, ensure the system can produce what your teams actually use, whether that is step lists, BDD-style scenarios, or free text that fits your templates and automation. Next, map the full data journey. Know whether prompts or results are kept, how encryption and minimization are applied, and where any content is stored. Finally, require fine-grained controls so you can limit usage by environment, project, and role. Regulated teams require an audit trail and clear accountability, which means governance must keep pace with adoption, or speed will outpace safety. Once review-first habits are in place, build on them. True oversight requires more than simply checking AI outputs; it demands deeper knowledge and understanding than the AI itself to spot gaps, inaccuracies, or misleading information. That’s what separates a passive reviewer from an effective human in the loop. ... Real gains from AI will not come from automation alone but from people who know how to guide it with clarity, context, and care. The future of testing depends on professionals who can combine technical fluency with critical thinking, ethical judgment, and a sense of ownership over quality.

Your outage costs more than you think – so design with resilience in mind

Service providers are under strain to deliver the rapid speeds and constant network uptime that modern life demands, with areas like remote working, financial transactions, cloud access and streaming services expected to work seamlessly as part of the daily lives of many end users. For many enterprises, their business depends on this connectivity. Even a single hour of network disruption can cost an organisation more than $300,000, and the long-term damage to customer trust often exceeds any immediate financial loss. Despite this, many organisations still rely on outdated infrastructure that cannot support the requirements of today’s end users. Legacy environments struggle with explosive data growth, the soaring demands of AI, and the complexity of distributed, cloud-first applications. At the same time, power limitations, infrastructure strain and inconsistent service levels put businesses at risk of falling behind. The gap between what service providers and enterprises need, and what their infrastructure can deliver, is widening. ... For service providers, investing in robust colocation and high-performance networking is not just about upgrading infrastructure, but enabling customers and partners worldwide to thrive in today’s fast-paced digital landscape. By offering resilient and scalable connectivity, providers can differentiate their service offering, attract high-value enterprise clients, and create new revenue streams based on reliability and performance.

Daily Tech Digest - September 18, 2025

Quote for the day:

"When your life flashes before your eyes, make sure you’ve got plenty to watch.” -- Anonymous

The new IT operating model: cloud-managed networking as a strategic lever

Enterprises are navigating an environment where the complexity of IT is increasing exponentially. Hybrid work requires consistent connectivity across homes, offices, and campuses. Edge computing and IoT generate massive volumes of data at distributed sites. Security risks escalate as the attack surface grows. Traditional, hardware-centric approaches leave IT teams struggling to keep up. Managing dozens or hundreds of controllers, patching firmware manually, and troubleshooting issues site by site is not sustainable. Cloud-managed networking changes that equation. By centralizing management, applying AI-driven intelligence, and extending visibility across distributed environments, it enables IT to shift from reactive firefighting to proactive strategy. ... Enterprises adopting cloud-managed networking are making a decisive shift from complexity to clarity. Success requires more than technology alone. It demands a partner that understands how to translate advanced capabilities into measurable business outcomes. ... Cloud-managed networking is not just another IT trend. It is the operating model that will define enterprise technology for the next decade. By elevating the network from infrastructure to strategy, it enables organizations to move faster, stay secure, and innovate with confidence.

Why Shadow AI Is the Next Big Governance Challenge for CISOs

In many respects, shadow AI is a subset of a broader shadow IT problem. Shadow IT is an issue that emerged more than a decade ago, largely emanating from employee use of unauthorized cloud apps, including SaaS. Lohrmann noted that cloud access security broker (CASB) solutions were developed to deal with the shadow IT issue. These tools are designed to provide organizations with full visibility of what employees are doing on the network and on protected devices, while only allowing access to authorized instances. However, shadow AI presents distinct challenges that CASB tools are unable to adequately address. “Organizations still need to address other questions related to licensing, application sprawl, security and privacy policies, procedures and more ..,” Lohrmann noted. A key difference between IT and AI is the nature of data, the speed of adoption and the complexity of the underlying technology. In addition, AI is often integrated into existing IT systems, including cloud applications, making these tools more difficult to identify. Chuvakin added, “With shadow IT, unauthorized tools often leave recognizable traces – unapproved applications on devices, unusual network traffic or access attempts to restricted services. Shadow AI interactions, however, often occur entirely within a web browser or personal device, blending seamlessly with regular online activity or not leaving any trace on any corporate system at all.”

Cisco strengthens integrated IT/OT network and security controls

Melding IT and OT networking and security is not a new idea, but it’s one that has seen growing attention from Cisco. ... Cisco also added a new technology called AI-powered asset clustering to its Cyber Vision OT management suite. Cyber Vison keeps track of devices connected to an industrial network, builds a real-time map of how these devices talk to each other and to IT systems, and can detect abnormal behavior, vulnerabilities, or policy violations that could signal malware, misconfigurations, or insider threats, Cisco says. ... Another significant move that will help IT/OT integration is the planned integration of the management console for Cisco’s Catalyst and Meraki networks. That combination will allow IT and OT teams to see the same dashboard for industrial OT and IT enterprise/campus networks. Cyber Vision will feeds into the dashboard along with other Cisco management offerings such as ThousandEyes, which gives customers a shared inventory of assets, traffic flows and security. “What we are focusing on is helping our customers have the secure networking foundation and architecture that lets IT teams and operational teams kind of have one fabric, one architecture, that goes from the carpeted spaces all the way to the far reaches of their OT network,” Butaney said.

Global hiring risks: What you need to know about identity fraud and screening trends

Most organizations globally include criminal record checks in their pre-employment screening. Employment and education verifications are also common, especially in EMEA and APAC. ... “Employers that fail to strengthen their identity verification processes or overlook recurring discrepancy patterns could face costly consequences, from compliance failures to reputational harm,” said Euan Menzies, President and CEO of HireRight. ... More than three-quarters of businesses globally found at least one discrepancy in a candidate’s background over the past year. Thirteen percent reported finding one discrepancy for every five candidates screened. Employment verification remains the area where most inconsistencies are discovered, especially in APAC and EMEA. These discrepancies range from minor errors like incorrect dates to more serious issues such as fabricated job histories. ... Companies are increasingly adopting post-hire screening to address risks that emerge after someone is hired. In North America, only 38 percent of companies now say they do no post-hire screening, a sharp drop from 57 percent last year. Common post-hire checks include driver monitoring and periodic rescreening for regulated roles. These efforts help companies catch new issues such as undisclosed criminal activity, changes in legal eligibility to work, or evolving insider threats.

Doomprompting: Endless tinkering with AI outputs can cripple IT results

Some LLMs appear to be designed to encourage long-lasting conversation loops, with answers often spurring another prompt. ... “When an individual engineer is prompting an AI, they get a pretty good response pretty quick,” he says. “It gets in your head, ‘That’s pretty good; surely, I could get to perfect.’ And you get to the point where it’s the classic sunk-cost fallacy, where the engineer is like, ‘I’ve spent all this time prompting, surely I can prompt myself out of this hole.’” The problem often happens when the project lacks definitions of what a good result looks like, he adds. “Employees who don’t really understand the goal they’re after will spin in circles not knowing when they should just call it done or step away,” Farmer says. “The enemy of good is perfect, and LLMs make us feel like if we just tweak that last prompt a little bit, we’ll get there.” ... Govindarajan has seen some IT teams get stuck in “doom loops” as they add more and more instructions to agents to refine the outputs. As organizations deploy multiple agents, constant tinkering with outputs can slow down deployments and burn through staff time, he says. “The whole idea of doomprompting is basically putting that instruction down and hoping that it works as you set more and more instructions, some of them contradicting with each other,” he adds. “It comes at the sacrifice of system intelligence.”

Vanishing Public Record Makes Enterprise Data a Strategic Asset

“We are rapidly running out of public data that is credible and usable. More and more enterprises will start to assign value to their data and go beyond partnerships to monetize it. For example, wind measurements captured by a wind turbine company could be helpful to many businesses that are not competitors,” said Olga Kupriyanova, principal consultant of AI and data engineering at ISG. ... "We’re entering a defining moment in AI where access to reliable, scalable, and ethical data is quickly becoming the central bottleneck, and also the most valuable asset. As legal and regulatory pressure tightens access to public data, due to copyright lawsuits, privacy concerns, or manipulation of open data repositories, enterprises are being forced to rethink where their AI advantage will come from,” said Farshid Sabet, CEO and co-founder at Corvic AI, developer of a GenAI management platform. ... The economic consequences of such data loss are already visible. Analysts estimate that U.S. public data underpinned nearly $750 billion of business activity as recently as 2022, according to the Department of Commerce. The loss of such data blinds companies that build models for everything from supply chain forecasting to investment strategy and predictions.

The Architecture of Responsible AI: Balancing Innovation and Accountability

The field of AI governance suffers from what Mackenzie et al reaffirm as the “principal-agent problem,” where one party (the principal) delegates tasks to another party (the agent). But their interests are not perfectly aligned, leading to potential conflicts and inefficiencies. ... Architects occupy a unique position in this landscape. Unlike regulators who may impose constraints post-design, architects work at the intersection of possibility and constraint. They must balance competing requirements, such as performance and privacy, efficiency and equity, speed and safety, within coherent system designs. Every architectural decision must embed values, priorities, and assumptions about how systems should behave. ... current AI guidance suffers from systematic weaknesses: evidence quality is sacrificed for speed, commercial interests masquerade as objective advice, and some perspectives dominate while broader stakeholder voices remain unheard ... Architects, being well-placed to bridge the gap between strategy and technology, hold a key role in establishing the principles that govern how systems behave, interact, and evolve. In the context of AI, this principle set extends beyond technical design. It encompasses the ethical, social, and legal aspects as well. .

AI will make workers ‘busier in the future’ – so what’s the point exactly?

“I have to admit that I’m afraid to say that we are going to be busier in the future than now,” he told host Liz Claman. “And the reason for that is because a lot of different things that take a long time to do are now faster to do. I’m always waiting for work to get done because I’ve got more ideas.” ... “The more productive we are, the more opportunity we get to pursue new ideas,” Huang continued. Reading between the lines here, it seems the so-called efficiency gains afforded by AI will mean workers have more work dumped in their laps – onto the next task, no rest for the wicked, etc. Huang’s comments run counter to the prevailing sentiment among big tech executives on exactly what AI will deliver for both enterprises and individual workers. ... We’ve all read the marketing copy and heard it regurgitated by tech leaders on podcasts and keynote stages – AI will allow us to focus on the “more rewarding” aspects of our jobs. They’ve never fully explained what this entails, or how it will pan out in the workplace. To be quite honest, I don’t think they know what it means. Marketing probably made it up and they’ve stuck with it. ... Will we be busier spending time on those rewarding aspects of our jobs? I have to say, I’m doubtful. The reality is that workers will be pulled into other tasks and merely end up drowning in the same cumbersome workloads they’ve been dealing with since the pandemic.

Building Safer Digital Experiences Through Robust Testing Practices

Secure software testing forms the bedrock of resilient applications, proactively uncovering flaws before they become critical. Early testing practices can significantly reduce risks, costs, and exposure to threats. According to Global Market Insights, the growing number and size of data breaches have increased the need for security testing services. Organizations that heavily use security AI and automation save an average of USD 1.76 million compared to those that don’t. About 51% plan to increase their security spending. Early integration of techniques like Static Application Security Testing (SAST) can detect vulnerabilities in existing code. It can also help to fix bugs during development. ... Organizations must verify that their systems handle personal data securely and comply with global regulations like GDPR and CCPA. Testing ensures sensitive information is protected from leaks or unauthorized use. Americans are highly concerned about how companies use their private data. ... Stress testing evaluates how applications perform under extreme loads. It helps identify potential failures in scalability, response times, and resource management. Vulnerability assessments concentrate on uncovering security gaps. Verified Market Reports notes that, after recent financial crises, governments are putting stronger emphasis on stress testing.

Prompt Engineering Is Dead – Long Live PromptOps

PromptOps is gaining traction rapidly because it has the potential to address major challenges in the use of LLMs, such as prompt drift and suboptimal output. Yet incorporating PromptOps effectively into an organization is far from simple, requiring a structured and clear process, the right tools, and a mindset that enables collaboration and effective centralization. Digging deeper into what PromptOps is, why it is needed, and how it can be implemented effectively can help companies to find the right approach when incorporating this methodology for improving their LLM applications usage. ... Before PromptOps is implemented, an organization typically has prompts scattered across multiple teams and tools, with no structured management in place. The first stage of implementing PromptOps involves gathering every detail on LLM applications usage within an organization. It is essential to understand precisely which prompts are being used, by which teams, and with which models. The next stage is to build consistency into this practice by incorporating versioning and testing. Adding secure access control at this stage is also important, in order to ensure only those who need it have access to prompts. With these practices in place, organizations will be well-positioned to introduce cross-model design and embed core compliance and security practices into all prompt crafting.

Daily Tech Digest - September 09, 2025

Quote for the day:

“The greatest leader is not necessarily the one who does the greatest things. He is the one that gets the people to do the greatest things.” -- Ronald Reagan

Neuromorphic computing and the future of edge AI

While QC captures the mainstream headlines, neuromorphic computing has positioned itself as a force in the next era of AI. While conventional AI relies heavily on GPU/TPU-based architectures, neuromorphic systems mimic the parallel and event-driven nature of the human brain. ... Neuromorphic hardware has shown promise in edge environments where power efficiency, latency and adaptability matter most. From wearable medical devices to battlefield robotics, systems that can “think locally” without requiring constant cloud connectivity offer clear advantages. ... As neuromorphic computing matures, ethical and sustainability considerations will shape adoption as much as raw performance. Spiking neural networks’ efficiency reduces carbon footprints by cutting energy demands compared to GPUs, aligning with global decarbonization targets. At the same time, ensuring that neuromorphic models are transparent, bias‑aware and auditable is critical for applications in healthcare, defense and finance. Calls for AI governance frameworks now explicitly include neuromorphic AI, reflecting its potential role in high‑stakes decision‑making. Embedding sustainability and ethics into the neuromorphic roadmap will ensure that efficiency gains do not come at the cost of fairness or accountability.

10 security leadership career-killers — and how to avoid them

“Security has evolved from being the end goal to being a business-enabling function,” says James Carder, CISO at software maker Benevity. “That means security strategies, communications, planning, and execution need to be aligned with business outcomes. If security efforts aren’t returning meaningful ROI, CISOs are likely doing something wrong. Security should not operate as a cost center, and if we act or report like one, we’re failing in our roles.” ... CISOs generally know that the security function can’t be the “department of no.” But some don’t quite get to a “yes,” either, which means they’re still failing their organizations in a way that could stymie their careers, says Aimee Cardwell, CISO in residence at tech company Transcend and former CISO of UnitedHealth Group. ... CISOs who are too rigid with the rules do a disservice to their organizations and their professional prospects, says Cardwell. Such a situation recently came up in her organization, where one of her team members initially declined to permit a third-party application from being used by workers, pointing to a security policy barring such apps. ... CISOs who don’t have a firm grasp on all that they must secure won’t succeed in their roles. “If they don’t have visibility, if they can’t talk about the effectiveness of the controls, then they won’t have credibility and the confidence in them among leadership will erode,” Knisley says.

A CIO's Evolving Role in the Generative AI Era

The dual mandate facing CIOs today is demanding but unavoidable. They must deliver quick AI pilots that boards can take to the shareholders while also enforcing guardrails on security, ethics and cost aspects. Too much caution can make CIOs irrelevant. This balancing act requires not only technical fluency but also narrative skill. The ability to translate AI experiments into business outcomes that CEOs and boards can trust can make CIOs a force. The MIT report highlights another critical decision point: whether to build or buy. Many enterprises attempt internal builds, but externally built AI partnerships succeed twice as often. CIOs, pressured for fast results, must be pragmatic about when to build and when to partner. Gen AI does not - and never will - replace the CIO role. But it demands corrections. The CIO who once focused on alignment must now lead business transformation. Those who succeed will act less as CIOs and more as AI diplomats, bridging hype with pragmatism, connecting technological opportunities to shareholder value and balancing the boardroom's urgency with the operational reality. As AI advances, so does the CIO's role - but only if they evolve. Their reporting line to the CEO symbolizes greater trust and higher stakes. Unlike previous technology cycles, AI has brought the CIO to the forefront of transformation.

Building an AI Team May Mean Hiring Where the Talent Is, Not Where Your Bank Is

Much of the adaptation of banking to AI approaches requires close collaboration between AI talent with people who understand how the banking processes involved need to work. This will put people closer together, literally, to facilitate both quick and in-depth but always frequent interactions to make collaboration work — paradoxically, increased automation needs more face-to-face dealings at the formative stages. However, the "where" of the space will also hinge on where AI and innovation talent can be recruited, where that talent is being bred and wants to work, and the types of offices that talent will be attracted to. ... "Banks are also recruiting for emerging specialties in responsible AI and AI governance, ensuring that their AI initiatives are ethical, compliant and risk-managed," the report says. "As ‘agentic AI’ — autonomous AI agents — and generative AI gain traction, firms will need experts in these cutting-edge fields too." ... Decisions don’t stop at the border anymore. Jesrani says that savvy banks look for pockets of talent as well. ... "Banks are contemplating their global strategies because emerging markets can provide them with talent and capabilities that they may not be able to obtain in the U.S.," says Haglund. "Or there may be things happening in those markets that they need to be a part of in order to advance their core business capabilities."

How Data Immaturity is Preventing Advanced AI

Data immaturity, in the context of AI, refers to an organisation’s underdeveloped or inadequate data practices, which limit its ability to leverage AI effectively. It encompasses issues with data quality, accessibility, governance, and infrastructure. Critical signs of data immaturity include inconsistent, incomplete, or outdated data leading to unreliable AI outcomes; data silos across departments hindering access and comprehensive analysis, as well as weak data governance caused by a lack of policies on data ownership, compliance and security, which introduces risks and restricts AI usage. ... Data immaturity also leads to a lack of trust in analysis and predictability of execution. That puts a damper on any plans to leverage AI in a more autonomous manner—whether for business or operational process automation. A recent study by Kearney found that organisations globally are expecting to increase data and analytics budgets by 22% in the next three years as AI adoption scales. Fragmented data limits the predictive accuracy and reliability of AI, which are crucial for autonomous functions where decisions are made without human intervention. As a result, organisations must get their data houses in order before they will be able to truly take advantage of AI’s potential to optimise workflows and free up valuable time for humans to focus on strategy and design, tasks for which most AI is not yet well suited.

From Reactive Tools to Intelligent Agents: Fulcrum Digital’s AI-First Transformation

To mature, LLM is just one layer. Then you require the integration layer, how you integrate it. Every customer has multiple assets in their business which have to connect with LLM layers. Every business has so many existing applications and new applications; businesses are also buying some new AI agents from the market. How do you bring new AI agents, existing old systems, and new modern systems of the business together — integrating with LLM? That is one aspect. The second aspect is every business has its own data. So LLM has to train on those datasets. Copilot and OpenAI are trained on zillions of data, but that is LLM. Industry wants SLM—small language models, private language models, and industry-orientated language models. So LLMs have to be fine-tuned according to the industry and also fine-tuned according to their data. Nowadays people come to realise that LLMs will never give you 100 per cent accurate solutions, no matter which LLM you choose. That is the phenomenon customers and everybody are now learning. The difference between us and others: many players who are new to the game deliver results with LLMs at 70–75 per cent. Because we have matured this game with multiple LLMs coexisting, and with those LLMs together maturing our Ryze platform, we are able to deliver more than 93–95 per cent accuracy.

You Didn't Get Phished — You Onboarded the Attacker

Many organizations respond by overcorrecting: "I want my entire company to be as locked down as my most sensitive resource." It seems sensible—until the work slows to a crawl. Without nuanced controls that allow your security policies to distinguish between legitimate workflows and unnecessary exposure, simply applying rigid controls that lock everything down across the organization will grind productivity to a halt. Employees need access to do their jobs. If security policies are too restrictive, employees are either going to find workarounds or continually ask for exceptions. Over time, risk creeps in as exceptions become the norm. This collection of internal exceptions slowly pushes you back towards "the castle and moat" approach. The walls are fortified from the outside, but open on the inside. And giving employees the key to unlock everything inside so they can do their jobs means you are giving one to Jordan, too. ... A practical way to begin is by piloting ZSP on your most sensitive system for two weeks. Measure how access requests, approvals, and audits flow in practice. Quick wins here can build momentum for wider adoption, and prove that security and productivity don't have to be at odds. ... When work demands more, employees can receive it on request through time-bound, auditable workflows. Just enough access is granted just in time, then removed. By taking steps to operationalize zero standing privileges, you empower legitimate users to move quickly—without leaving persistent privileges lying around for Jordan to find.

OT Security: When Shutting Down Is Not an Option

some of the most urgent and disruptive threats today are unfolding far from the keyboard, in operational technology environments that keep factories running, energy flowing and transportation systems moving. In these sectors, digital attacks can lead to physical consequences, and defending OT environments demands specialized skills. Real-world incidents across manufacturing and critical infrastructure show how quickly operations can be disrupted when OT systems are not adequately protected. Just this week, Jaguar Land Rover disclosed that a cyberattack "severely disrupted" its automotive manufacturing operations. ... OT environments present challenges that differ sharply from traditional IT. While security is improving, OT security teams must protect legacy control systems running outdated firmware, making them difficult to patch. Operators need to prioritize uptime and safety over system changes; and IT and OT teams frequently work in silos. These conditions mean that breaches can have physical as well as digital consequences, from halting production to endangering lives. Training tailored to OT is essential to secure critical systems while maintaining operational continuity. ... An OT cybersecurity learning ecosystem is not a one-time checklist but a continuous program. The following elements help organizations choose training that meets current needs while building capacity for ongoing improvement.

Connected cars are racing ahead, but security is stuck in neutral

Connected cars are essentially digital platforms with multiple entry points for attackers. The research highlights several areas of concern. Remote access attacks can target telematics systems, wireless interfaces, or mobile apps linked to the car. Data leaks are another major issue because connected cars collect sensitive information, including location history and driving behavior, which is often stored in the cloud. Sensors present their own set of risks. Cameras, radar, lidar, and GPS can be manipulated, creating confusion for driver assistance systems. Once inside a vehicle, attackers can move deeper by exploiting the CAN bus, which connects key systems such as brakes, steering, and acceleration. ... Most drivers want information about what data is collected and where it goes, yet very few said they have received that information. Brand perception also plays a role. Many participants prefer European or Japanese brands, while some expressed distrust toward vehicles from certain countries, citing political concerns, safety issues, or perceived quality gaps. ... Manufacturers are pushing out new software-defined features, integrating apps, and rolling out over the air updates. This speed increases the number of attack paths and makes it harder for security practices and rules to keep up.

Circular strategies for data centers

Digital infrastructure is scaling rapidly, with rising AI workloads and increased compute density shaping investment decisions. Growth on that scale can generate unnecessary waste unless sustainability is integrated into planning. Circular thinking makes it possible to expand capacity without locking facilities into perpetual hardware turnover. Operators can incorporate flexibility into refresh cycles by working with vendors that design modular platforms or by adopting service-based models that build in maintenance, refurbishment, and recovery. ... Sustainable planning also involves continuous evaluation. Instead of defaulting to wholesale replacement, facilities can test whether assets still meet operational requirements through reconfiguration, upgrades, or role reassignment. This kind of iterative approach gives operators a way to match innovation with responsibility, ensuring that capacity keeps pace with demand without discarding equipment prematurely. ... The transition to circular practices is more than an environmental gesture. For data centers, it is a strategic shift in how infrastructure is procured, maintained, and retired. Extending lifecycles, redeploying equipment internally, refurbishing where possible, and ensuring secure, responsible recycling at the end of use all contribute to a more resilient operation in a resource-constrained and tightly regulated industry.