Daily Tech Digest by Kannan Subbiah: SMB

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Daily Tech Digest - June 20, 2026

Quote for the day:

"Outstanding leaders go out of their way to boost the self-esteem of their personnel." -- Sam Walton

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

Why AI coding debt is different

The rapid adoption of artificial intelligence in software development is generating an entirely new challenge: cognitive debt. Unlike traditional technical debt, which usually involves poorly written or messy code, cognitive debt arises when software works perfectly but no human understands exactly how or why it was built. Because AI tools generate code at unprecedented speeds, developers often bypass the crucial, slower process of thinking through specific scenarios and internalizing the underlying logic. Furthermore, many AI tools operate without essential background knowledge, such as past design choices or specific security rules, resulting in code that may function in isolation but lacks overall coherence. To prevent this accumulation of invisible debt, organizations must shift their focus from merely generating code to rigorously checking it. This involves building strong internal practices that provide AI with necessary historical knowledge before it writes a single line. Most importantly, engineering teams must establish strict human ownership, ensuring a developer takes the time to thoroughly review and comprehend the final product. By balancing the speed of AI generation with careful oversight and deep understanding, companies can maintain healthy, reliable systems without sacrificing their future stability or falling into irreversible complications.

Why Every CISO Needs a Head of AppSec in the Age of Vibecoding

The rise of AI-assisted software development has drastically increased the speed at which code is generated and deployed. While this shift enhances developer productivity, it also introduces subtle flaws and misconfigurations at a scale that outpaces traditional security measures. For a Chief Information Security Officer (CISO), directly overseeing application security is no longer practical. To maintain control without slowing down engineering, organizations must introduce a dedicated Head of Application Security. This role acts as a vital bridge between the security and development teams, turning abstract vulnerabilities into clear, actionable fixes that fit naturally into everyday workflows. Instead of treating security as a roadblock, a capable Head of Application Security enables developers to build safely and efficiently. Furthermore, while automated tools handle known issues, this leader ensures human testers remain focused on uncovering complex attack paths that machines miss. By delegating the daily operational details of application security to a specialized leader, the CISO can step back and focus on broader risk management and strategy. Ultimately, restructuring security leadership is essential for companies wanting to build software quickly without taking on unmanaged risks.

A perfect storm: data centers and tornadoes

The article examines the growing collision between data center expansion and the rising threat of tornadoes. As the demand for digital infrastructure pushes these vital facilities into regions known for volatile weather patterns, operators face a complex challenge. The piece highlights that relying on standard commercial building practices is no longer sufficient to protect critical hardware and ensure uninterrupted operations. Instead, modern data centers must incorporate specialized physical hardening from the ground up. This involves constructing reinforced concrete walls and specialized roofing designed to withstand extreme wind speeds and dangerous flying debris. Beyond structural defenses, the analysis strongly emphasizes the necessity of implementing comprehensive disaster recovery strategies. A key component is building geographic redundancy into the network architecture, ensuring that if one specific facility goes offline, other locations can seamlessly manage the computing load. Maintaining reliable backup power generation and secondary cooling systems is also essential to survive the immediate aftermath of a storm when local utility grids fail. Ultimately, securing digital assets against nature's unpredictability requires a steady, proactive approach, blending structural engineering with thorough contingency planning to keep essential services running smoothly.

OT vs IT Security: Key Differences Explained for Controls Engineers

Operational Technology (OT) security and Information Technology (IT) security serve different purposes and operate under distinct priorities. While IT security safeguards corporate data networks with a primary focus on keeping information confidential, intact, and available, OT security protects industrial control systems like programmable logic controllers and manufacturing lines. Because a failure in these industrial environments can lead to damaged equipment or physical harm, OT flips the traditional model to prioritize availability and safety above all else, often minimizing confidentiality. A major challenge for controls engineers is that standard IT practices do not easily transfer to the plant floor. For example, you cannot simply update an industrial controller the way you patch a laptop. These devices require uninterrupted operation, rigorous testing, and strict vendor approvals, making routine updates costly and disruptive. Furthermore, as enterprise networks increasingly connect with industrial systems to share data—a trend known as IT/OT convergence—traditional boundaries disappear. This connectivity introduces new vulnerabilities to legacy equipment that was never designed for modern internet threats. Bridging this gap requires careful network segmentation and a shared understanding between IT departments and plant engineers to keep production running safely.

AI Governance vs Data Governance: Why They Need Opposite Approaches

The article highlights the distinct but complementary needs of data and artificial intelligence governance within modern organizations. It points out that traditional data management programs often fail within their first year because they rely on rigid, centralized control that internal teams actively resist. To succeed, these data initiatives must instead link directly to specific business goals and decentralize their efforts across departments. Conversely, managing artificial intelligence requires the exact opposite organizational approach. Because AI development usually begins in isolated, scattered teams, it actually requires a centralized strategy to mature effectively and deliver consistent value. To resolve this structural tension, the text advocates for an adaptable framework that thoughtfully balances central standards with flexible, everyday execution. This method adjusts the level of control based on the organization's maturity and the specific risks involved in each project. Furthermore, the rapid adoption of modern AI tools demands a renewed focus on unstructured information, such as plain text documents, which is inherently harder to organize than traditional databases. Companies are strongly advised to systematically discover, tag, and connect this unstructured information to ensure their automated systems remain reliable and safe for long-term enterprise use.

Security considerations for adopting Claude Code and Cowork for SMBs

When small and medium-sized businesses decide to adopt AI tools like Claude, security leaders must carefully balance rapid deployment with essential safety measures. The primary step is understanding the specific plan your organization requires, as advanced security features like single sign-on and compliance tools are restricted to higher-tier subscriptions. Rather than granting broad access, it is safer to control your exposure by selectively assigning licenses for different products—such as Chat, Code, or Cowork—based on actual employee needs. As you introduce these tools, avoid turning on every feature at once. Instead, evaluate the risks of each capability and roll them out gradually. Features like web search or automated skills introduce vulnerabilities, making strict management of API keys and data access critical. Limit the number of people who can generate administrative keys to maintain tight control. Additionally, remember that you cannot outsource your data governance. It is your responsibility to monitor what information flows into the system and verify the accuracy of what comes out. By relying on a phased approach and leveraging existing security vendors, you can confidently integrate new technologies while keeping your business secure.

Every AI Agent Is an Identity. Most Organizations Don't Treat Them That Way

As AI agents evolve from simple productivity tools into powerful actors that can trigger workflows, write code, and update records, they are effectively becoming new digital identities within enterprise networks. However, most organizations are failing to secure them as such. According to the article, security teams traditionally focus on managing the identities of human employees and service accounts, leaving AI agents largely ungoverned. These agents are frequently connected to critical business platforms like Salesforce, GitHub, and production databases, often receiving overly broad permissions just to ensure they work smoothly. This creates a sprawling network of hidden actors with high levels of system access. While much of the AI security conversation has centered on software risks like bad prompts or incorrect outputs, the greater threat lies in what these tools can actually access. An overprivileged AI agent compromised by a malicious plugin can become a dangerous pathway for major data theft or system damage. To safely adopt AI technology, organizations must start treating AI agents exactly like standard network identities. This requires continuous tracking, strictly restricting their permissions to match their exact purpose, and systematically applying the same exact security rules used for human employees.

CIOs: tear down the wall between resilience and data security

For years, organizations have treated keeping systems online and keeping data safe as two separate jobs handled by different teams. However, the rapid adoption of artificial intelligence is proving that this separation is no longer practical. Rather than creating entirely new problems, AI is exposing existing flaws in how companies manage their files and information. When employees use AI assistants, these tools can easily find and share old or sensitive documents that were left unsecured, revealing a severe lack of basic organization and control. To solve this, technology leaders must unite their safety and system recovery efforts. First, companies need to understand exactly what information they have, where it lives, and who should see it before they roll out new tools. Second, they must use automated systems to manage rules and access, because human review simply cannot keep up with the speed of automated requests. Finally, businesses must clearly track what automated programs are doing and why, to ensure they meet future legal standards. Ultimately, attempting to block these new tools will fail. Instead, leaders must safely guide their use by building a unified, trustworthy foundation.

France and Germany Boost Digital Sovereignty Push

France and Germany are strengthening their commitment to European digital sovereignty through a coordinated approach and substantial new funding. To reduce reliance on foreign technology, the French government announced an initial 13 billion euro investment fund, expected to grow to 15 billion euros by the end of the year, aimed at supporting domestic and regional technology firms. Institutional investors, including aerospace and defense partners, are backing this initiative. Half of the capital is dedicated to deep technology sectors such as artificial intelligence, quantum computing, biotechnology, and space exploration. This focus on artificial intelligence is particularly timely given recent United States export controls that restricted European access to advanced models from companies like Anthropic. These restrictions have intensified demands for regional self-sufficiency and highlighted the strategic importance of European developers like France's Mistral AI. The new funding represents the third phase of a broader effort to close the financing gap for scaling tech businesses in the region. Although Germany previously approached such initiatives with caution, shifting geopolitical dynamics and concerns over the reliability of American technology services have united the two nations in their drive to secure technological independence.

Data Observability: Guidance for Data Leaders

Many organizations struggle to ensure their artificial intelligence systems receive reliable information. Although experts recognize the necessity of tracking data as it moves through systems, many leaders still treat this practice as a future goal rather than an immediate requirement. Without a clear view into their data systems, companies are left guessing whether their information is accurate and safe to use. As artificial intelligence shifts from simply providing answers to taking independent actions, relying on guesswork is no longer acceptable. Information pathways are becoming increasingly complicated, making it easier for mistakes to happen or for incorrect details to reach the wrong destination. Proper oversight helps address these complications, including the growing challenge of fragmented systems. Fundamentally, observing your data means proving that the right information arrives exactly when and where it is needed. This practice requires finding and fixing errors before they impact the business. Instead of merely checking if a system is turned on, organizations must validate that the information flowing through it is completely trustworthy. By maintaining a continuous, clear view of their data, organizations can confidently support their advanced technologies and ensure reliable outcomes.

Daily Tech Digest - March 31, 2026

Quote for the day:

“A bad system will beat a good person every time.” -- W. Edwards Deming

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Duration: 22 mins • Perfect for listening on the go.

World Backup Day warnings over ransomware resilience gaps

serves as a critical reminder of the widening gap between traditional backup strategies and the sophisticated demands of modern ransomware resilience. Industry experts emphasize that many organizations are failing to evolve their recovery plans alongside increasingly complex, fragmented cloud environments spanning AWS, Azure, and SaaS platforms. A major concern highlighted is the tendency for businesses to treat backups as a narrow IT task rather than a foundational pillar of security governance. Statistics from incident response specialists reveal a troubling reality: over half of organizations experience backup failures during significant breaches, and nearly 84% lack a single survivable data copy when first facing an attack. Experts warn that standard native tools often lack the unified visibility and immutability required to withstand malicious encryption or intentional destruction by threat actors. To address these vulnerabilities, the article advocates for a shift toward "breach-informed" recovery orchestration, which includes rigorous, real-world scenario testing and the reduction of internal "blast radiuses." Ultimately, as ransomware attacks surge by over 50% annually, the message is clear: simple data replication is no longer sufficient. True resilience requires a continuous, holistic approach that integrates people, processes, and hardened technology to ensure data is not just stored, but truly recoverable under extreme pressure.

APIs are the new perimeter: Here’s how CISOs are securing them

The rapid proliferation of application programming interfaces (APIs) has fundamentally shifted the cybersecurity landscape, making them the new organizational perimeter. As traditional endpoint protections and web application firewalls struggle to detect sophisticated business-logic abuse, Chief Information Security Officers (CISOs) are adapting their strategies to address this expanding attack surface. The rise of and autonomous agentic systems has further exacerbated risks by enabling low-skill adversaries to exploit vulnerabilities and automating high-speed interactions that can bypass legacy defenses. To counter these threats, security leaders are implementing robust governance frameworks that include comprehensive API inventories to eliminate "shadow APIs" and integrating automated security validation directly into CI/CD pipelines. A critical component of this modern defense is a shift toward identity-aware security, prioritizing the management of non-human identities and service accounts through least-privilege access. Furthermore, CISOs are centralizing third-party credential management and utilizing specialized API gateways to enforce consistent security policies across diverse cloud environments. By treating APIs as critical business infrastructure rather than mere plumbing, organizations can maintain visibility and control, ensuring that every integration is threat-modeled and continuously monitored for behavioral anomalies in an increasingly interconnected and AI-driven digital ecosystem.

Q&A: What SMBs Need To Know About Securing SaaS Applications

In this BizTech Magazine interview, Shivam Srivastava of Palo Alto Networks highlights the critical need for small to medium-sized businesses (SMBs) to secure their Software as a Service (SaaS) environments as the web browser becomes the modern workspace’s primary operating system. With SMBs typically managing dozens of business-critical applications, they face significant risks from visibility gaps, misconfigurations, and the rising threat of AI-powered attacks, which hit smaller firms significantly harder than large enterprises. Srivastava emphasizes that traditional antivirus solutions are insufficient in this browser-centric era, particularly when employees use unmanaged devices or accidentally leak sensitive data into generative AI tools. To mitigate these risks, he advocates for a "crawl, walk, run" strategy that prioritizes the adoption of a secure browser as the central command center for security. This approach allows businesses to fulfill their side of the shared responsibility model by protecting the "last mile" where users interact with data. By implementing secure browser workspaces, multi-factor authentication, and AI data guardrails, SMBs can establish a manageable yet highly effective defense. As the landscape evolves toward automated AI agents and app-to-app integrations, centering security on the browser ensures that small businesses remain protected against the next generation of automated, browser-based threats.

Developers Aren't Ignoring Security - Security Is Ignoring Developers

The article "Developers Aren’t Ignoring Security, Security is Ignoring Developers" on DEVOPSdigest argues that the traditional disconnect between security teams and developers is not due to developer negligence, but rather a failure of security processes to integrate with modern engineering workflows. The central premise is that developers are fundamentally committed to quality, yet they are often hindered by security tools that prioritize "gatekeeping" over enablement. These tools frequently generate excessive false positives, leading to alert fatigue and friction that slows down delivery cycles. To bridge this gap, the author suggests that security must "shift left" not just in timing, but in mindset—moving away from being a final hurdle to becoming an automated, invisible part of the development lifecycle. This involves implementing security-as-code, providing actionable feedback within the Integrated Development Environment (IDE), and ensuring that security requirements are defined as clear, achievable tasks rather than abstract policies. Ultimately, the piece contends that for DevSecOps to succeed, security professionals must stop blaming developers for gaps and instead focus on building developer-centric experiences that make the secure path the path of least resistance.

Beyond the Sandbox: Navigating Container Runtime Threats and Cyber Resilience

In the article "Beyond the Sandbox: Navigating Container Runtime Threats and Cyber Resilience," Kannan Subbiah explores the evolving landscape of , emphasizing that traditional "Shift Left" strategies are no longer sufficient against 2026’s sophisticated runtime threats. Unlike virtual machines, containers share the host kernel, creating an inherent "isolation gap" that attackers exploit through container escapes, poisoned runtimes, and resource exhaustion. To bridge this gap, Subbiah advocates for advanced isolation technologies such as Kata Containers, gVisor, and Confidential Containers, which provide hardware-level protection and secure data in use. Central to building a "digital immune system" is the implementation of cyber resilience strategies, including eBPF for deep kernel observability, that prioritize service identity, and immutable infrastructure to prevent configuration drift. Furthermore, the article highlights the increasing importance of regulatory compliance, referencing global standards like NIST SP 800-190, the EU’s DORA and NIS2, and Indian frameworks like KSPM. Ultimately, the author argues that true resilience requires shifting from a "fortress" mindset to an automated, proactive approach where containers are continuously monitored and secured against the volatility of the runtime environment, ensuring robust defense in a high-density, multi-tenant cloud ecosystem.

AI-first enterprises must treat data privacy as architecture, not an afterthought

In an exclusive interview, Roshmik Saha, Co-founder and CTO of Skyflow, argues that AI-first enterprises must transition from viewing data privacy as a compliance checklist to treating it as a foundational architectural requirement. As organizations accelerate their AI journeys, Saha emphasizes the necessity of isolating personally identifiable information (PII) into a dedicated data privacy vault. Because PII constitutes less than one percent of enterprise data but represents the majority of regulatory risk, treating it as a distinct data layer allows for better protection through tokenization and encryption. This approach is particularly critical for AI integration, where sensitive data often leaks into logs, prompts, and models that lack inherent access controls or deletion capabilities. Saha warns that once PII enters a large language model, remediation is nearly impossible, making prevention the only viable strategy. By embedding “privacy by design” directly into the technical stack, companies can ensure that AI systems utilize behavioral patterns rather than raw identifiers. Ultimately, this architectural shift not only simplifies compliance with regulations like India’s DPDP Act but also serves as a strategic enabler, removing legal bottlenecks and allowing businesses to innovate with confidence while safeguarding their long-term data integrity and customer trust.

The Balance Between AI Speed and Human Control

The article "The Balance Between AI Speed and Human Control" explores the critical tension between rapid technological advancement and the necessity of human oversight. It argues that issues like are often inherent design consequences of prioritizing fluency and speed over safety safeguards. Currently, global governance is fragmented: the European Union emphasizes rigid regulation, the United States favors innovation with limited accountability, and India seeks a middle path focusing on deployment scale. However, each model faces significant challenges, such as algorithmic bias or systemic failures. The author suggests moving toward a "copilot" framework where AI serves as decision support rather than an autocrat. This requires implementing three interconnected architectural pillars: impact-aware modeling, context-grounded reasoning, and governed escalation with explicit thresholds for human intervention. As develops incrementally, nations must shift from treating human judgment as a bottleneck to viewing it as a vital safeguard. Ultimately, the goal is to harmonize efficiency with empathy, ensuring that technological progress does not come at the cost of moral accountability or human potential. By adopting binding technical standards for human overrides in consequential decisions, society can ensure that AI remains a tool for empowerment rather than an uncontrolled force.

Securing agentic AI is still about getting the basics right

As transform the enterprise landscape, Sam Curry, CISO of Zscaler, emphasizes that robust security remains grounded in fundamental principles. Speaking at the RSAC 2026 Conference, Curry highlights a major shift toward silicon-based intelligence, where AI agents will eventually conduct the majority of internet transactions. This evolution necessitates a renewed focus on two primary pillars: identity management and runtime workload security. Unlike traditional methods, securing these agents requires sophisticated frameworks like SPIFFE and SPIRE to ensure rigorous identification, verification, and authentication. Organizations must implement granular authorization controls and zero-trust architectures to contain risks, such as autonomous agent sprawl or unauthorized data access. Furthermore, while automation can streamline governance and compliance, Curry warns that security in adversarial environments still requires human judgment to counter unpredictable threats. Ultimately, the successful deployment of agentic AI depends on mastering the basics—cleaning infrastructure, establishing clear accountability, and ensuring auditability. By treating AI agents as distinct identities within a segmented network, businesses can foster innovation without sacrificing security. This balanced approach ensures that as technology advances, the underlying security architecture remains resilient against emerging threats in a world increasingly dominated by autonomous digital entities.

Can Your Bank’s IT Meet the Challenge of Digital Assets?

The article from The Financial Brand examines the "side-core" (or sidecar) architecture as a transformative solution for traditional banks seeking to integrate and into their operations. Traditional banking core systems are often decades old and technically incapable of supporting the high-precision ledgers—often requiring eighteen decimal places—and the 24/7/365 real-time settlement demands of blockchain-based assets. Rather than attempting a costly and risky "rip-and-replace" of these legacy cores, financial institutions are increasingly adopting side-cores: modern, cloud-native platforms that run in parallel with the main system. This specialized architecture allows banks to issue tokenized deposits, manage stablecoins, and facilitate instant cross-border payments while maintaining their established systems for traditional functions. By leveraging a side-core, banks can rapidly deploy crypto-native services, attract younger demographics, and secure new deposit streams without significant operational disruption. The article highlights that as regulatory clarity improves through frameworks like the GENIUS Act, the ability to operate these dual systems will become a key competitive advantage for regional and community banks. Ultimately, the side-core approach provides a modular path toward modernization, allowing traditional institutions to remain relevant in an era defined by and digital-native commerce.

Everything You Think Makes Sprint Planning Work, Is Slowing Your Team Down!

In his article, Asbjørn Bjaanes argues that traditional Sprint Planning "best practices"—such as assigning work and striving for accurate estimation—actually undermine team agility by stifling ownership and clarity. He identifies several key pitfalls: first, leaders who assign stories strip developers of their internal sense of control, turning owners into compliant executors. Instead, teams should self-select work to foster initiative. Second, estimation should be viewed as an alignment tool rather than a forecasting exercise; "estimation gaps" are vital opportunities to surface hidden complexities and synchronize mental models. Third, the author warns against mid-sprint interruptions and automatic story rollovers. Rolling over unfinished work without scrutiny ignores shifting priorities and cognitive biases, while unplanned additions break the sanctity of the team’s commitment. Furthermore, Bjaanes emphasizes that a Sprint Backlog without a clear, singular goal is merely a "to-do list" that leaves teams directionless under pressure. Ultimately, real improvement requires shifting underlying beliefs about control and trust rather than simply refining process steps. By embracing healthy disagreement during planning and protecting the team’s autonomy, organizations can move beyond mere compliance toward true high performance, ensuring that planning serves as a strategic compass rather than an administrative burden.

Daily Tech Digest - February 24, 2026

Quote for the day:

"Transparent reviews create fairness. Subjective reviews create frustration." -- Gordon Tredgold

AI agents and bad productivity metrics

The great promise of was that it would finally clear our backlogs. Coding agents would churn out boilerplate at superhuman speeds, and teams would finally ship exactly what the business wants. The reality, as we settle into 2026, is far more uncomfortable. Artificial intelligence is not going to save developer productivity because writing code was never the bottleneck in software engineering. ... For decades, one of the most common debugging techniques was entirely social. A production alert goes off. You look at the version control history, find the person who wrote the code, ask them what they were trying to accomplish, and reconstruct the architectural intent. But what happens to that workflow when no one actually wrote the code? What happens when a human merely skimmed a 3,000-line agent-generated pull request, hit merge, and moved on to the next ticket? When an incident happens, where is the deep knowledge that used to live inside the author? ... The metrics that matter are still the boring ones because they measure actual business outcomes. The remain the best sanity check we have because they tie delivery speed directly to system stability. They measure deployment frequency, lead time for changes, change failure rate, and time to restore service. None of those metrics cares about the number of commits your agents produced today. They only care about whether your system can absorb change without breaking.

How vertical SaaS is redefining enterprise efficiency

For the past decade, horizontal SaaS has been the defining force in enterprise technology. Platforms like CRMs, ERP suites and collaboration tools promised universality, offering a single platform to manage every business function across all industries. The strategy made sense: a large total addressable market, reusable architecture and marketing scale. flips that model. It is narrow by design but deep in impact. A report by Strategy& found that B2B vertical software companies are now growing faster than their horizontal peers, thanks to higher retention rates, lower churn rates and better unit economics. When software mirrors how a business already works, people stop treating it like a tool they tolerate and start relying on it like infrastructure. ... In regulated industries, isn’t a feature; it’s the baseline for trust. I learned early that trying to retrofit audit trails or data retention policies after go-live only creates technical debt. Instead, design for compliance as a first-class product layer: immutable logs, permission hierarchies and exportable compliance reports built into the system. ... Vertical products don’t thrive in isolation. Integration with industry hardware, marketplaces and regulatory systems drives adoption. In one case, we partnered with a hardware vendor to automatically sync manifest data from their devices, cutting onboarding time in half and unlocking co-marketing opportunities.

API Security Standards: 10 Essentials to Get You Started

Most API security flaws are created during the design phase. You're too late if you're waiting until deployment to think about threats. Shift-left principles mean integrating security early, especially at the design phase, where flawed assumptions become future exploits. Start by mapping out each endpoint's purpose, what data it touches, and who should access it. Identify where trust is assumed (not earned), roles blur, and inputs aren't validated. ... Every API has a breaking point. If you don't define it, attackers will. prevent denial-of-service (DoS) attacks, and they're also your first defense against scraping, brute-forcing, enumeration, and even accidental misuse by poorly built integrations. APIs, by nature, invite automation. Without guardrails, that openness turns into a floodgate. And in some cases, unchecked abuse opens the door to far worse issues, like remote code execution, where improperly scoped input or lack of throttling leads directly to exploitation. ... APIs are built to accept input. Attackers find ways to exploit it. The core rule is this - if you didn't expect it, don't process it. If you didn't define it, don't send it. Define request and response schemas explicitly using tools like or JSON Schema, as recommended by leading API security standards. Then enforce them — at the gateway, app layer, or both. Don't just use validation as linting; treat it as a runtime contract. If the payload doesn't match the spec, reject it.

Why AI Urgency Is Forcing a Data Governance Reset

The cost of weak governance shows up in familiar ways: teams can’t find data, requirements arrive late in the process, and launches stall when compliance realities collide with product timelines. Without governance, argues, organizations “ultimately suffer from higher cost basis,” with downstream consequences that “impact the bottom line.” ... McQuillan sees a clear step-change in executive urgency since generative AI (GenAI) became mainstream. “There’s been a rapid adoption, particularly since the advent of GenAI and the type of generative and agentic technologies that a lot of C-suites are taking on,” he says. But he also describes a common leadership gap: many executives feel pressure to become “AI-enabled” without a clear definition of what that means or how to build it sustainably. “There’s very much a well-understood need across all companies to become AI-enabled in some way,” he says. “But the problem is a lot of folks don’t necessarily know how to define that.” In the absence of clarity, organizations often fall into scattershot experimentation. What concerns McQuillan the most is how the pace of the “race” shapes priorities. ... When asked whether the long-running mantra “data is the new oil” still holds in the era of large language models and agentic workflows, McQuillan is direct. “It holds true now more than ever,” he says. He acknowledges why attention drifts: “It’s natural for people to gravitate toward things that are shiny,” and “AI in and of itself is an absolutely magnificent space.”

Building a Least-Privilege AI Agent Gateway for Infrastructure Automation with MCP, OPA, and Ephemeral Runners

An agent misinterpreting an instruction can initiate destructive infrastructure changes, such as tearing down environments or modifying production resources. A compromised agent identity can be abused to exfiltrate secrets, create unauthorized workloads, or consume resources at scale. In practice, teams often discover these issues late, because traditional logs record what happened, but not why an agent decided to act in the first place. For organizations, this liability creates operational and governance challenges. Incidents become harder to investigate, change approvals are bypassed unintentionally, and security teams are left with incomplete audit trails. Over time, this problem erodes trust in automation itself, forcing teams to either roll back agent usage or accept increasing levels of unmanaged risk. ... A more sustainable approach is to introduce an explicit control layer between agents and the systems they operate on. In this article, we focus on an AI Agent Gateway, a dedicated boundary that validates intent, enforces policy as code, and isolates execution before any infrastructure or service API is invoked. Rather than treating agents as privileged actors, this model treats them as untrusted requesters whose actions must be authorized, constrained, observed, and contained. ... In the context of AI-driven automation, defense in depth means that no single component, neither the agent, nor the gateway, nor the execution environment, has enough authority on its own to cause damage.

Demystifying CERT‑In’s Elemental Cyber Defense Controls: A Guide for MSMEs

For India’s Micro, Small, and Medium Enterprises (MSMEs), cybersecurity is no longer a “big company problem.” With digital payments, SaaS adoption, cloud-first operations, and supply‑chain integrations becoming the norm, MSMEs are now prime targets for cyberattacks. To help these organizations build a strong foundational security posture, the Indian Computer Emergency Response Team (CERT-In) has released CIGU-2025-0003, outlining a baseline of Cyber Defense Controls, which prescribes 15 Elemental Cyber Security Controls—a pragmatic, baseline set of safeguards designed to uplift the nation’s cyber hygiene. ... These controls, mapped to 45 recommendations, enable essential digital hygiene, protect against ransomware, ensure regulatory compliance, and are required for annual audits. CERT‑In’s Elemental Controls are designed as minimum essential practices that every Indian organization—regardless of size—should implement. ... The CERT-In guidelines offer a simplified, actionable starting point for MSMEs to benchmark their security. These controls are intentionally prescriptive, unlike ISO or NIST, which are more framework‑oriented. ... Because threats constantly evolve and MSMEs face unique risks depending on their industry and data sensitivity, organizations should view this framework not as an endpoint, but as the first critical step toward building a comprehensive security program akin to ISO 27001 or NIST CSF 2.0.

AI-fuelled cyber attacks hit in minutes, warns CrowdStrike

CrowdStrike reports a sharp acceleration in cyber intrusions, with attackers moving from initial access to lateral movement in less than half an hour on average as widely available artificial intelligence tools become embedded in criminal workflows. Its latest Global Threat Report puts average eCrime "breakout time" at 29 minutes in 2025, a 65% improvement on the prior year. ... Alongside generative AI use in preparation and execution, the report describes attempts to exploit AI systems directly. Adversaries injected malicious prompts into GenAI tools at more than 90 organisations, using them to generate commands associated with credential theft and cryptocurrency theft. ... Incidents linked to North Korea rose more than 130%, while activity by the group CrowdStrike tracks as FAMOUS CHOLLIMA more than doubled. The report says DPRK-nexus actors used AI-generated personas to scale insider operations. It also cites a large cryptocurrency theft attributed to the actor it calls PRESSURE CHOLLIMA, valued at USD $1.46 billion and described as the largest single financial heist ever reported. The report also references AI-linked tooling used by other state and criminal groups. Russia-nexus FANCY BEAR deployed LLM-enabled malware, which it named LAMEHUG, for automated reconnaissance and document collection. The eCrime actor tracked as PUNK SPIDER used AI-generated scripts to speed up credential dumping and erase forensic evidence.

Shadow mode, drift alerts and audit logs: Inside the modern audit loop

When systems moved at the speed of people, it made sense to do compliance checks every so often. But AI doesn't wait for the next review meeting. The change to an inline audit loop means audits will no longer occur just once in a while; they happen all the time. Compliance and risk management should be "baked in" to the AI lifecycle from development to production, rather than just post-deployment. This means establishing live metrics and guardrails that monitor AI behavior as it occurs and raise red flags as soon as something seems off. ... Cultural shift is equally important: Compliance teams must act less like after-the-fact auditors and more like AI co-pilots. In practice, this might mean compliance and AI engineers working together to define policy guardrails and continuously monitor key indicators. With the right tools and mindset, real-time AI governance can “nudge” and intervene early, helping teams course-correct without slowing down innovation. In fact, when done well, continuous governance builds trust rather than friction, providing shared visibility into AI operations for both builders and regulators, instead of unpleasant surprises after deployment. ... Shadow mode is a way to check compliance in real time: It ensures that the model handles inputs correctly and meets policy standards before it is fully released. One AI security framework showed how this method worked: Teams first ran AI in shadow mode, then compared AI and human inputs to determine trust.

Making AI Compliance Practical: A Guide for Data Teams Navigating Risk, Regulation, and Reality

As AI tools become more embedded in enterprise workflows, data teams are encountering a growing reality: compliance isn’t only a legal concern but also a design constraint, a quality signal, and, often, a competitive differentiator. But navigating compliance can feel complex, especially for teams focused on building and shipping. What is the good news? It doesn’t have to be. When approached intentionally, compliance becomes a pathway to better decisions, not a barrier. ... Automation can help with regulations, but only if it's used correctly. I've looked at a tool before that used algorithms to find private information. It worked well with English, but when tested with material in more than one language, it missed a few personal identifiers. The group thought it was "smart enough." It wasn't. We kept the automation, but we added human review for rare cases, confidence levels to make checks happen, and alerts for input formats that aren't common. The automation stayed the same, but there were built-in checks and balances. ... The biggest compliance failures don’t come from bad people. They come from good teams moving fast, skipping hard questions, and assuming nothing will go wrong. But compliance isn’t a blocker. It’s a product quality signal. People will trust you more if they are aware that your team has carefully considered the details.

Tata Communications’ Andrew Winney on why SASE is now non-negotiable

is often discussed as a product decision, but in reality it is a journey. Many enterprises start with a few use cases, such as securing internet access or enabling remote access to private applications. But they do not always extend those principles across contractors, third-party users, software-as-a-service applications and hybrid environments. Practical Zero Trust requires enterprises to rethink access fundamentally. Every request must be evaluated based on who the user is, the context from which they are accessing, the device they are using and the resource they are requesting. Access must then be granted only to that specific resource. ... represents a structural convergence of networking and security rather than a simple technology swap. What are the most critical architectural and change-management considerations enterprises must address during this transition? SASE is not a one-time technology change. It represents the convergence of networking and security under unified orchestration and policy management. That transition takes time and must be managed carefully. We typically work with enterprises through phased transition plans. If an organisation’s immediate priority is securing internet access or private application access for remote users, we begin there and expand to additional use cases over time. Integration is critical. Enterprises have existing investments in cloud platforms, local area networks and security tools.

Daily Tech Digest - February 08, 2026

Quote for the day:

"The litmus test for our success as Leaders is not how many people we are leading, but how many we are transforming into leaders" -- Kayode Fayemi

Why agentic AI and unified commerce will define ecommerce in 2026

Agentic AI and unified commerce are set to shape ecommerce in 2026 because the foundations are now in place: consumers are increasingly comfortable using AI tools, and retailers are under pressure to operate seamlessly across channels. ... When inventory, orders, pricing, and customer context live in disconnected systems, both humans and AI struggle to deliver consistent experiences. When those systems are unified, retailers can enable more reliable automation, better availability promises, and more resilient fulfillment, especially at peak. ... Unified commerce platforms matter because they provide a single operational framework for inventory, orders, pricing, and customer context. That coordination is increasingly critical as more interactions become automated or AI-assisted. ... The shift toward “agentic” happens when AI can safely take actions, like resolving a customer service step, updating a product feed, or proposing a replenishment recommendation, based on reliable data and explicit rules. That’s why unified commerce matters: it reduces the risk of automation acting on partial truth. Because ROI varies dramatically by category, maturity, and data quality, it’s safer to avoid generic percentage claims. The defensible message is: companies that pair AI with clean operational data and clear governance will unlock automation faster and with fewer reputational risks. ... Ultimately, success in 2026 will not be defined by how many AI features a retailer deploys, but by how well their systems can interpret context, act reliably, and scale under pressure.

EU's Digital Sovereignty Depends On Investment In Open-Source And Talent

We argue that Europe must think differently and invest where it matters, leveraging its strengths, and open technologies are the place to look. While Europe does not have the tech giants of the US and China, it possesses a huge pool of innovation and human capital, as well as a small army of capable and efficient technology service providers, start-ups, and SMEs. ... Recent data shows that while Europe accounts for a substantial share of global open source developers, its contribution to open source-derived infrastructure remains fragmented across countries, with development being concentrated in a small number of countries. ... Europe may not have a Silicon Valley, but it has something better: a robust open source workforce. We are beginning to recognize this through fora such as the recent European Open Source Awards, which celebrated European citizens and residents working on things ranging from the Linux kernel and open office suites to open hardware and software preservation. ... Europe has a chance of succeeding. Historically, Europe has done a good job in making open source and open standards a matter of public policy. For example, the European Commission's DG DIGIT has an open source software strategy which is being renewed this year, and Europe possesses three European Standards Organizations, including CEN, CENELEC, and ETSI. While China has an open source software strategy, Europe is arguably leading the US in harnessing the potential of open technologies as a matter of public and industrial policy, and it has a strong foundation for catching up to China.

Is artificial general intelligence already here? A new case that today's LLMs meet key tests

Approaching the AGI question from different disciplinary perspectives—philosophy, machine learning, linguistics, and cognitive science—the four scholars converged on a controversial conclusion: by reasonable standards, current large language models (LLMs) already constitute AGI. Their argument addresses three key questions: What is general intelligence? Why does this conclusion provoke such strong reactions? And what does it mean for ... "There is a common misconception that AGI must be perfect—knowing everything, solving every problem—but no individual human can do that," explains Chen, who is lead author. "The debate often conflates general intelligence with superintelligence. The real question is whether LLMs display the flexible, general competence characteristic of human thought. Our conclusion: insofar as individual humans possess general intelligence, current LLMs do too." ... "This is an emotionally charged topic because it challenges human exceptionalism and our standing as being uniquely intelligent," says Belkin. "Copernicus displaced humans from the center of the universe, Darwin displaced humans from a privileged place in nature; now we are contending with the prospect that there are more kinds of minds than we had previously entertained." ... "We're developing AI systems that can dramatically impact the world without being mediated through a human and this raises a host of challenging ethical, societal, and psychological questions," explains Danks.

Biometrics deployments at scale need transparency to help businesses, gain trust

As adoption invites scrutiny, more biometrics evaluations, completed assessments and testing options come available. Communication is part of the same issue, with major projects like EES, U.S. immigration and protest enforcement, and more pedestrian applications like access control and mDLs all taking off. ... Biometric physical access control is growing everywhere, but with some key sectorial and regional differences, Goode Intelligence Chief Analyst Alan Goode explains in a preview of his firm’s latest market research report on the latest episode of the Biometric Update Podcast. Imprivata could soon be on the market, with PE owner Thoma Bravo working with JPMorgan and Evercore to begin exploring its options. ... A panel at the “Identity, Authentication, and the Road Ahead 2026” event looked at NIST’s work on a playbook to help businesses implement mDLs. Representatives from the NCCoE, Better Identity Coalition, PNC Bank and AAMVA discussed the emerging situation, in which digital verifiable credentials are available, but people don’t know how to use them. ... DHS S&T found 5 of 16 selfie biometrics providers met the performance goals of its Remote Identity Validation Rally, Shufti and Paravision among them. RIVR’s first phase showed that demographically similar imposters still pose a significant problem for many face biometrics developers.

The Invisible Labor Force Powering AI

A low-cost labor force is essential to how today’s AI models function. Human workers are needed at every stage of AI production for tasks like creating and annotating data, reinforcing models, and moderating content. “Today’s frontier models are not self-made. They’re socio-technical systems whose quality and safety hinge on human labor,” said Mark Graham, a professor at the University of Oxford Internet Institute and a director of the Fairwork project, which evaluates digital labor platforms. In his book Feeding the Machine: the Hidden Human Labor Powering AI (Bloomsbury, 2024), Graham and his co-authors illustrate that this global workforce is essential to making these systems usable. “Without an ongoing, large human-in-the-loop layer, current capabilities would be far more brittle and misaligned, especially on safety-critical or culturally sensitive tasks,” Graham said. ... The industry’s reliance on a distributed, gig-work model goes back years. Hung points to the creation of the ImageNet database around 2007 as the moment that set the referential data practices and work organization for modern AI training. ... However, cost is not the only factor. Graham noted that cost arbitrage plays a role, but it is not the whole explanation. AI labs, he said, need extreme scale and elasticity, meaning millions of small, episodic tasks that can be staffed up or down at short notice, as well as broad linguistic and cultural coverage that no single in-house team can reproduce.

Code smells for AI agents: Q&A with Eno Reyes of Factory

In order to build a good agent, you have to have one that's model agnostic. It needs to be deployable in any environment, any OS, any IDE. A lot of the tools out there force you to make a hard trade off that we felt wasn't necessary. You either have to vendor lock yourself to one LLM or ask everyone at your company to switch IDEs. To build like a true model agnostic, vendor agnostic coding agent, you put in a bunch of time and effort to figure out all the harness engineering that's necessary to make that succeed, which we think is a fairly different skillset from building models. And so that's why we think companies like us actually are able to build agents that outperform on most evaluations from our lab. ... All LLMs have context limits so you have to manage that as the agent progresses through tasks that may take as long as eight to ten hours of continuous work. There are things like how you choose to instruct or inject environment information. It's how you handle tool calls. The sum of all of these things requires attention to detail. There really is no individual secret. Which is also why we think companies like us can actually do this. It's the sum of hundreds of little optimizations. The industrial process of building these harnesses is what we think is interesting or differentiated. ... Of course end-to-end and unit tests. There are auto formatters that you can bring in, SaaS static application security testers and scanners: your sneaks of the world.

Software-Defined Vehicles Transform Auto Industry With Four-Stage Maturity Framework For Engineers

More refined software architectures in both edge and cloud enable the interpretation of real-time data for predictive maintenance, adaptive user interfaces, and autonomous driving functions, while cloud-based AI virtualized development systems enable continuous learning and updates. Electrification has only further accelerated this evolution as it opened the door for tech players from other industries to enter the automotive market. This represents an unstoppable trend as customers now expect the same seamless digital experiences they enjoy on other devices. ... Legacy vehicle systems rely on dozens of electronic control units (ECUs), each managing isolated functions, such as powertrain or infotainment systems. SDVs consolidate these functions into centralized compute domains connected by high-speed networks. This architecture provides hardware and software abstraction, enabling OTA updates, seamless cross-domain feature integration, and real-time data sharing, are essential for continuous innovation. ... Processing sensor data at the edge – directly within the vehicle – enables highly personalized experiences for drivers and passengers. It also supports predictive maintenance, allowing vehicles to anticipate mechanical issues before they occur and proactively schedule service to minimize downtime and improve reliability. Equally important are abstraction layers that decouple software applications from underlying hardware.

Cybersecurity and Privacy Risks in Brain-Computer Interfaces and Neurotechnology

Neuromorphic computing is developing faster than predicted by replicating the human brain's neural architecture for efficient, low-power AI computation. As highlighted in talks around brain-inspired chips and meshing, these systems are blurring distinctions between biological and silicon-based computation. In the meanwhile, bidirectional communication is made possible by BCIs, such as those being developed by businesses and research facilities, which can read brain activity for feedback or control and possibly write signals back to affect cognition. ... Neural data is essentially personal. Breaches could expose memories, emotions, or subconscious biases. Adversaries may reverse-engineer intentions for coercion, fraud, or espionage as AI decodes brain scans for "mind captioning" or talent uploading. ... Compromised BCIs blur cyber-physical boundaries farther than OT-IT convergence already has. A malevolent actor might damage medical implants, alter augmented reality overlays, or weaponize neurotech in national security scenarios. ... Implantable devices rely on worldwide supply chains prone to tampering. Neuromorphic hardware, while efficient, provides additional attack surfaces if not designed with zero-trust principles. Using AI to process neural signals can introduce biases, which may result in unfair treatment in brain-augmented systems

Designing for Failure: Chaos Engineering Principles in System Design

To design for failure, we must understand how the system behaves when failure inevitably happens. What is the cost? What is the impact? How do we mitigate it? How do we still maintain over 99% uptime? This requires treating failure as a default state, not an exception. ... The first step is defining steady-state behavior. Without this, there is no baseline to measure against. ... Chaos experiments are most valuable in production. This is where real traffic patterns, real user behavior, and real data shapes exist. That said, experiments must be controlled. ... Chaos Engineering is not a one-off exercise. Systems evolve. Dependencies change. Teams rotate. Experiments should be automated, repeatable, and run continuously, either as scheduled jobs or integrated into CI/CD pipelines. Over time, experiments can be expanded to test higher-impact scenarios. ... Additional considerations include health checks, failover timing, and data consistency. Strong consistency simplifies reasoning but reduces availability. Eventual consistency improves availability but introduces complexity and potential inconsistency windows. ... Network failures are unavoidable in distributed systems. Latency spikes, packets get dropped, DNS fails, and sometimes the network splits entirely. Many system outages are not caused by servers crashing, but by slow or unreliable communication between otherwise healthy components. This is where several of the classic fallacies of distributed computing show up, especially the assumption that the network is reliable and has zero latency.

Why SMBs Need Strong Data Governance Practices

Good data governance for small businesses is about building trust, control and scalability into your data from day one. Governance should be built into the data foundation, not bolted on later. Small businesses move fast, and governance works best when it’s native to how data is managed. That means choosing platforms that apply security, access controls and compliance consistently across all data, without requiring manual oversight or specialized teams. Additionally, clear visibility and control over what data exists and who can access it is essential. Even at a smaller scale, businesses handle sensitive information ranging from customer and financial data to operational insights. ... Governance also future proofs the business. Regulations are becoming more complex, customer expectations for data protection are rising, and AI systems must have high-quality, well-governed data to perform reliably. Small businesses that treat governance as a foundation are better positioned to adopt AI and safely expand into new use cases, markets and regulatory environments without needing to rearchitect later. At the same time, strong data governance improves day-to-day efficiency. When data is well governed, teams can spend more time acting on insights and less time questioning data quality, managing access manually or duplicating work. ... From a cybersecurity perspective, governance provides the controls and visibility needed to reduce attack surfaces and detect misuse.

Daily Tech Digest - December 10, 2025

Quote for the day:

"Develop success from failures. Discouragement and failure are two of the surest stepping stones to success." -- Dale Carnegie

Design in the age of AI: How small businesses are building big brands faster

Instead of hiring separate agencies for naming, logo design, and web development, small businesses are turning to unified AI platforms that handle the full early-stage design stack. Tools like Design.com merge naming, logo creation, and website generation into a single workflow — turning an entrepreneur’s first sketch into a polished brand system within minutes. ... Behind the surge in AI design tools lies a broader ecosystem shift. Companies like Canva and Wix made design accessible; the current wave — led by AI-native platforms like Design.com — is more personal and adaptive. Unlike templated platforms, these tools understand context. A restaurant founder and a SaaS startup will get not just different visuals, but different copy tones, typography systems, and user flows — automatically. “What we’re seeing,” Lynch explains, “isn’t just growth in one product category. It’s a movement toward connected creativity — where every part of the brand experience learns from every other.” ... Imagine naming a company and watching an AI instantly generate a logo, color palette, and homepage layout that all reflect the same personality. As your audience grows, the same system helps you update your visual identity or tone to match new goals — while preserving your original DNA.

Henkel CISO on the messy truth of monitoring factories built across decades

On the factory floor, it is common to find a solitary engineering workstation that holds the only up-to-date copies of critical logic files, proprietary configuration tools, and project backups. If that specific computer suffers a hardware failure or is compromised by ransomware, the maintenance team loses the ability to diagnose errors or recover the production line. ... If the internet connection is severed, or if the third-party cloud provider suffers an outage, the equipment on the floor stops working. This architecture fails because it prioritizes connectivity over local autonomy, creating a fragile ecosystem where a disruption in a remote cloud environment creates a “digital brick” out of physical machinery. ... An attacker does not need sophisticated “zero-day” exploits to compromise a fifteen-year-old human-machine interface, they often just need publicly known vulnerabilities that will never be fixed by the vendor. By compromising a peripheral camera or an outdated visualization node, they gain a persistence mechanism that security teams rarely monitor, allowing them to map the operational technology network and prepare for a disruptive attack on the critical control systems at their leisure. ... A critical question for CISOs to ask is: “Can you provide a continuously updated Software Bill of Materials for your firmware, and what is your specific process for mitigating vulnerabilities in embedded third-party libraries?”

AI churn has IT rebuilding tech stacks every 90 days

Even without full production status, the fact that so many organizations are rebuilding components of their agent tech stacks every few months demonstrates not only the speed of change in the AI landscape but also a lack of faith in agentic results, Northcutt claims. Changes in the agent tech stack range from something as simple as updating the underlying AI model’s version, to moving from a closed-source to an open-source model or changing the database where agent data is stored, he notes. In many cases, replacing one component in the stack sets off a cascade of changes downstream, he adds. ... While the speed of AI evolution can drive frequent rebuilds, part of the problem lies in the way AI models are tweaked, she says. “The deeper issue is that many agent systems rely on behaviors that sit inside the model rather than on clear rules,” Hashem explains. “When the model updates, the behavior drifts. When teams set clear steps and checks for the agent, the stack can evolve without constant breakage.” ... “What works now may become suboptimal later on,” he says. “If organizations don’t actively keep up to date and refresh their stack, they risk falling behind in performance, security, and reliability.” Constant rebuilds don’t have to create chaos, however, Balabanskyy adds. CIOs should take a layered approach to their agent stacks, he recommends, with robust version control, continuous monitoring, and a modular deployment approach.

Why Losing One Security Engineer Can Break Your Defences

When tools are hard to manage – or if you need to bundle numerous tools from different vendors together – tribal knowledge builds up in one engineer’s head. It’s unrealistic to expect them to document it. Gartner recently said that organizations use an average of 45 cybersecurity tools and called for security leaders to optimize their toolsets. And in that context, losing the one person who understands how these systems actually work is not just inconvenient: it's a structural risk. And the impact this has is seen in the data from the State of AI in Security & Development report; using numerous vendors for security tools correlates with more incidents, more time spent prioritising alerts and slower remediation. In short, a security engineer has too much on their plate, and most security tools aren’t making their job any easier. ... “Organisations tend to be all looking for the same blend of technical cloud, integration, SecOps, IAM experience but with extensive knowledge in each pillar,” says James Walsh, National Lead for Cyber, Data & Cloud UK&I at Hays. “Everyone wants the unicorn security engineer whose experience spans all of this, but it comes at too high a price for lots of organisations,” he adds. Walsh notes that hiring is often driven by teams below the CISO — such as Heads of SecOps — which can create inconsistent expectations of what a ‘fully competent’ engineer should look like.

Overload Protection: The Missing Pillar of Platform Engineering

Some limits exist to protect systems. Others enforce fairness between customers or align with contractual tiers. Regardless of the reason, these limits must be enforced predictably and transparently. ... In data-intensive environments, bottlenecks often appear in storage, compute, or queueing layers. One unbounded query or runaway job can starve others, impacting entire regions or tenants. Without a unified overload protection layer, every team becomes a potential failure domain. ... Enterprise customers often face challenges when quota systems evolve organically. Quotas are published inconsistently, counted incorrectly, or are not visible to the right teams. Both external customers and internal services need predictable limits. A centralized Quota Service solves this. It defines clear APIs for tracking and enforcing usage across tenants, resources, and time intervals. ... When overload protection is not owned by the platform, teams reinvent it repeatedly. Each implementation behaves differently, often under pressure. The result is a fragile ecosystem where: Limits are enforced inconsistently, for example, some endpoints apply resource limits, while others run requests without enforcing any limits, leading to unpredictable behavior and downstream problems; Failures cascade unpredictably, for example, a runaway data pipeline job can saturate a shared database, delaying or failing unrelated jobs and triggering retries and alerts across teams

Is your DR plan just wishful thinking? Prove your resilience with chaos engineering

At its core, it’s about building confidence in your system’s resilience. The process starts with understanding your system's steady state, which is its normal, measurable, and healthy output. You can't know the true impact of a failure without first defining what "good" looks like. This understanding allows you to form a clear, testable hypothesis: a statement of belief that your system's steady state will persist even when a specific, turbulent condition is introduced. To test this hypothesis, you then execute a controlled action, which is a precise and targeted failure injected into the system. This isn't random mischief; it's a specific simulation of real-world failures, such as consuming all CPU on a host (resource exhaustion), adding network latency (network failure), or terminating a virtual machine (state failure). While this action is running, automated probes act as your scientific instruments, continuously monitoring the system's state to measure the effect. ... Beyond simply proving system availability, chaos engineering builds trust in your reliability metrics, ensuring that you meet your SLOs even when services become unavailable. An SLO is a specific, acceptable target level of your service's performance measured over a specified period that reflects the user's experience. SLOs aren't just internal goals; they are the bedrock of customer trust and the foundation of your contractual service level agreements (SLAs).

The data center of the future: high voltage, liquid cooled, up to 4 MW per rack

Developments such as microfluidic cooling could have a profound impact on how racks and accompanying infrastructure will be built towards the future. Also, it is not all about the type of cooling, but also about the way chips communicate with each other and communicate internally. What will the impact of an all-photonics network be on cooling, for example? The first couple of stages building that type of end-to-end connection have been completed. The interesting parts for the discussion we have here are next on the roadmap for all-photonics networks: using photonics connections between and inside silicon on boards. ... However, there are many moving parts to take into account. It will need a more dynamic approach to selling space in data centers, which is usually based on the amount of watts a customer wants. Irrespective of the actual load, the data center reserves that for the customer. If data centers need to be more dynamic, so do the contracts. ... The data center of the future will be characterized by high-density computing, liquid cooling, sustainable power sources, and a more integrated role in the grid ecosystem. As technology continues to advance, data centers will become more efficient, flexible, and environmentally responsible. That may sound like an oxymoron to many people nowadays, but it’s the only way to get to the densities we need moving forward.

Vietnam integrating biometrics into daily life in digital transformation drive

Vietnam is rapidly integrating biometrics and digital identity into everyday life, rolling out identity‑based systems across public transport, air travel and banking as part of an ambitious national digital transformation drive. New deployments in Hanoi’s metro, airports nationwide and the financial sector show how VNeID and biometric verification increasingly constitute Vietnam’s infrastructure. ... Officials argue the initiative strengthens Hanoi’s ambitions as a smart city and improves interoperability across transport modes. It also introduces a unified digital identity layer for public transit, which no other Vietnamese city can yet boast. Passenger data, operations and transactions are now centralized on a single platform, enabling targeted subsidies based on usage patterns rather than flat‑rate models. The Hanoi Metro app, available on major app stores, supports tap‑and‑go access and discounted fares for verified digital identities. ... The new rules require banks to conduct face‑to‑face identity checks and verify biometric data, such as facial information, before issuing cards to individual customers. The same requirement applies to the legal representatives of corporate clients, with limited exceptions, reports Vietnam Plus. ... Foreigners without electronic identity credentials, as well as Vietnamese nationals with undetermined citizenship status, will undergo in‑person biometric collection using data from the National Population Database.

Why 2025 broke the manager role — and what it means for leadership ahead

Managers did far more than supervise. “They became mentors, skill-builders, culture carriers and the first line of emotional support,” Tyagi said. They coached diverse teams, supported women and marginalised groups entering new roles, and navigated talent crunches by building internal pipelines. They adopted learning apps, facilitated experience-sharing sessions and absorbed the emotional load of stretched teams. ... Sustaining morale amid continual uncertainty was the most difficult task, Tyagi said. Workloads were redistributed constantly. Managers had to reassure employees while balancing performance expectations with wellbeing. Chopra saw the same tensions. Recognition and feedback remained inconsistent. Gallup research showed a gap between managers’ belief that they offered regular feedback and employees’ experience that they rarely received it. Remote work deepened disconnection. “Creating team cohesion, trust and belonging when people are dispersed remains difficult,” she said. ... Empathy dominated the management skill-set in 2025. Transparency, communication and emotional intelligence were indispensable as uncertainty persisted. Coaching and talent development grew central, especially in organisations investing in women, new hires and marginalised communities. Chopra pointed to several non-negotiables: emotional intelligence, tech literacy, outcome-focused leadership, psychological safety, coaching and ethical awareness in technology use.

The Missing Link in AI Scaling: Knowledge-First, Not Data-First

Organizations today need to ensure data readiness to avoid failures in model performance, system trust, and strategic alignment. To succeed, CIOs must shift from a “data-first” to a “knowledge-first” approach in order to capitalize on the true benefits of AI. ... Domain-specific reasoning capabilities provide context and meaning to data, which is crucial for professional and reliable advice. A semantic layer across silos creates unified views of all data, enabling comprehensive insights that are otherwise impossible to achieve. Another benefit is its ability to support AI governance and explainability by ensuring that AI systems are not “black boxes,” but are transparent and trustworthy. Lastly, it acts as an agentic AI backbone by orchestrating a workforce of AI agents that can execute complex tasks with reliability and context. ... Shifting to a knowledge-first architecture is not just an option, but a necessity, and is a direct challenge to the conventional data-first mindset. For decades, enterprises have focused on accumulating vast lakes of data, believing that more data inherently leads to better insights. However, this approach created fragmented, context-poor data silos. This “digital quicksand” is the root of the “Semantic Challenge” because data is siloed and heterogeneous. ... A knowledge-first approach fundamentally changes the goal from simply storing data to building an interconnected, enterprise-wide, knowledge graph. This architecture is built on the principle of “things, not strings”.