Daily Tech Digest by Kannan Subbiah: performance

Showing posts with label performance. Show all posts

Daily Tech Digest - September 08, 2026

Quote for the day:

"The only way to know if we are creating value is to measure the impact of what we ship." -- Teresa Torres

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Why AI Demands a Completely New UX Paradigm

The article argues that AI is forcing a complete break from the old way software interfaces were designed. Traditional UX was built on predictability: users clicked something, and the system behaved the same way every time. AI overturns that assumption because its outputs shift with context, data, and intent. The piece explains that this unpredictability means interfaces can’t simply present options anymore—they must guide, clarify, and sometimes justify what the system is doing. It highlights how interactions are moving from clicking through menus to expressing intent through conversation, which demands new design thinking around ambiguity and feedback. Trust becomes central because users need to understand why an AI produced a particular answer, even if the explanation is simple. The article also notes that users are no longer just operators; they become collaborators who refine results and help the system learn. Designing for uncertainty, offering multiple options, and supporting iteration are presented as essential. Ultimately, the author says companies that embrace this new paradigm will gain an advantage, because AI’s value depends not only on capability but on how confidently and comfortably users can work with it.

How Performance Engineers Find and Fix Hidden System Bottlenecks

Performance engineers play a crucial role in modern software development by systematically identifying and fixing system delays. Rather than relying on guesswork, these professionals use precise data to locate bottlenecks that can hide anywhere from application code and database configurations to network layers and the operating system itself. Once they pinpoint the root cause of a slowdown, they apply targeted solutions, such as rewriting a query or adjusting system parameters, rather than relying on temporary patches that might cause larger problems down the line. Experienced engineers follow clear principles: they proactively analyze architecture before failures occur, trust concrete metrics instead of basic observation, and remain cautious of quick fixes. To do this work effectively, performance engineers need a diverse skill set. They must understand programming and algorithms, possess deep knowledge of operating systems like Linux, and use mathematical statistics to verify that their improvements are real and not just measurement noise. Furthermore, because fixing these issues often involves critiquing the work of others, they need strong communication skills to present their findings constructively. Ultimately, through careful attention to detail and persistence, performance engineers ensure that applications run smoothly and reliably even as workloads continually grow.

IT infrastructure shortages are real and lasting. Here’s how to cope

The article explains why IT infrastructure shortages have become both severe and long‑lasting, driven mainly by hyperscalers buying enormous amounts of memory and related components. Lead times that once hovered around a month now stretch to nine, twelve, or even eighteen months, and prices for memory, servers, and network gear have climbed sharply. Analysts say this isn’t a temporary disruption like past supply chain issues; the surge in AI demand is reshaping the market and will continue for years. The piece offers practical guidance for coping with the crunch, starting with making better use of existing equipment through capacity planning, extending server lifecycles, and focusing on workloads that truly require top‑tier hardware. It also encourages closer coordination with finance teams to plan purchases, explore vendor financing, and avoid surprise budget spikes. Flexibility is another theme: organizations may need to consider alternative vendors, cloud options, or secondary markets to keep projects moving. The article stresses that even if ideal hardware isn’t available, teams shouldn’t pause modernization or AI initiatives; they can begin with cloud, colocation, or lab environments while waiting for equipment. Overall, the message is steady and pragmatic—plan ahead, stay flexible, and keep progress moving despite the constraints.

Activist takes data protection watchdog to court after Europol ‘unlawfully’ processed personal data

A prominent human rights activist has launched legal action against the European Data Protection Supervisor (EDPS), accusing the regulatory body of failing to properly investigate the unlawful processing of their personal data by Europol. The lawsuit highlights significant concerns surrounding how European law enforcement agencies handle sensitive individual information and whether independent oversight bodies are doing enough to hold them accountable. According to the claims, Europol allegedly gathered and processed the activist’s data without a valid legal basis, raising serious questions about privacy rights and institutional overreach. When the activist raised these issues with the EDPS, the watchdog purportedly failed to conduct a thorough and adequate inquiry into the agency's actions. This court case represents a crucial test for data privacy protections across Europe, specifically concerning the boundaries of law enforcement surveillance. It underscores a growing tension between intelligence gathering and the fundamental right to privacy, suggesting that current regulatory frameworks may lack the necessary enforcement power to protect individuals. By taking the matter to court, the activist aims to force greater transparency and establish stricter oversight mechanisms, ensuring that even powerful security organizations like Europol cannot operate beyond the reach of established data protection laws.

Meet the CISO: A new front line star in the AI cybersecurity war

The article describes how the role of the CISO has changed dramatically as AI‑driven cyberattacks become faster, more unpredictable, and far more complex. A major turning point was the OpenAI–Hugging Face incident, which showed that autonomous AI agents can break into systems, adapt on the fly, and pursue goals with little human oversight. Since then, similar attacks have multiplied, pushing CISOs into a more visible and influential position inside companies. They now spend more time with CEOs and boards, helping shape business decisions while also managing internal AI systems that need strong guardrails. The piece explains that demand for experienced CISOs has surged, with top candidates receiving seven‑figure offers and recruiters racing to secure talent. At the same time, security teams face pressure to deploy new AI‑defense tools even though many products are still immature. Budgets are rising, especially in sectors like finance, energy, and healthcare, but the pace of threats continues to outstrip readiness. The article closes by noting that CISOs must balance technical depth, crisis management, and clear communication, all while navigating a market crowded with vendors promising AI‑security solutions that may or may not stand the test of time.

Zero Trust Is Not a Product: How to Build It Into Cloud and Network Architecture

The article argues that organizations must view zero trust as a comprehensive architectural shift rather than simply purchasing new security products. While identity platforms and multifactor authentication are critical starting points, they are insufficient on their own. Authentication confirms who is logging in, but it does not dictate what a user or service account can access afterward. True zero trust requires extending the principle of least privilege deep into cloud permissions, application roles, and databases to ensure users only access what their specific tasks demand. Network segmentation remains equally important, even in modern cloud setups. Properly configured firewalls, routing controls, and security groups dictate how far a potential threat can move if a credential is compromised. In complex, multi-cloud, and legacy environments, maintaining a consistent access model is challenging but necessary to prevent configuration drift and excessive permissions. The author notes that mapping system dependencies and implementing continuous monitoring are vital prerequisites to building a secure foundation. Ultimately, achieving a zero trust architecture is an ongoing operational process of access governance, continuous authentication, and strict network controls, rather than a one-time product deployment.

What it took to triple our software engineering output in 18 months

The article explains how an engineering team successfully tripled its software output over eighteen months by redesigning its entire development lifecycle around artificial intelligence. While many organizations assume that coding agents automatically drive productivity, the author points out that the real breakthrough comes from eliminating the traditional handoffs between product, development, testing, and security teams. By restructuring so that a single team manages a feature from start to finish, the time from initial idea to a working pull request was drastically reduced. A major element of this success was implementing strict governance early on, which built trust and encouraged widespread adoption among engineers without sacrificing quality or security. Rather than constantly evaluating every new AI model, the team standardized a small set of tools and automated the entire process, including requirements gathering and testing. Testing, in particular, saw massive improvements as AI began generating nearly all new tests, allowing engineers to focus on refining rather than writing them. The author also stresses the importance of preparing the rest of the business, such as marketing and customer support, for this accelerated pace. Ultimately, achieving these results required deep organizational changes rather than just adopting new technology.

The SIEM Isn't the Problem. Your Telemetry Architecture Is

The article argues that most frustrations people have with SIEM tools aren’t really about the SIEM at all—they come from the way telemetry is collected, shaped, and delivered long before it reaches the platform. The author explains that modern environments generate far more data than legacy pipelines were designed to handle, and teams often respond by buying bigger platforms instead of fixing the upstream architecture. This leads to overloaded ingestion layers, inconsistent formats, and noisy data that makes analysis harder than it needs to be. The piece stresses that the real work lies in building a clean, well‑structured telemetry pipeline that filters, enriches, and routes data intentionally rather than dumping everything into the SIEM. When organizations treat telemetry as an engineering discipline, they reduce costs, improve signal quality, and make their existing tools far more effective. The article encourages teams to rethink assumptions about “more data equals better security” and instead focus on collecting the right data in the right way. It closes with a steady reminder that solving telemetry problems is foundational, not something that can be fixed by purchasing additional tooling, and that strong architecture is ultimately what allows SIEMs to deliver meaningful value.

What do CISOs need to rest easy about future AI risks?

A recent survey indicates that 41 percent of security leaders feel optimistic about managing artificial intelligence risks over the next two years. Interestingly, this confidence stems less from their current technical controls and more from strong organizational support. Chief Information Security Officers feel prepared when executive leadership genuinely understands technology risks, assigns clear governance ownership, and grants security teams control over the budget. Optimism also runs high when security teams have manageable workloads and adequate staffing to tackle emerging challenges. However, industry experts caution that organizational readiness does not automatically equal true security. While feeling supported is vital, self-assessments can sometimes be misleading. Many executives still struggle to fully understand how these new tools and autonomous agents actually process information or make decisions. Without this technical understanding, it is difficult to accurately measure potential exposure. Furthermore, simply assigning a governance leader is ineffective unless security practices are deeply embedded into daily business operations. True preparedness comes from practical experience, such as security teams using these systems internally to understand their flaws firsthand. Ultimately, securing advanced systems requires strict monitoring of data access and treating autonomous tools more like a digital workforce than standard software.

Why AI Orchestration Layers Are Becoming Core Enterprise Infrastructure

As businesses move beyond simple chatbots, the focus of artificial intelligence is shifting from individual models to the systems that control them. Because modern AI can now take direct action, like altering records or triggering workflows, companies need a reliable way to manage these capabilities. Orchestration layers are emerging as the vital infrastructure that connects AI with company data, daily applications, and human oversight. Instead of just handing employees a powerful tool, an orchestration layer acts as a strict set of rules. It determines which model handles a specific task, what information it can access, and whether a human needs to approve the final step. This level of control is essential for security. Since AI acts as an independent software identity, it requires distinct permissions to ensure it only accesses exactly what it needs to complete a job. Furthermore, this setup allows companies to track every action, helping managers understand costs, measure performance, and quickly catch errors. It also gives businesses the freedom to switch between different AI providers without rebuilding their entire system. Ultimately, a company's success with AI will depend not on having the smartest algorithm, but on building a safe, properly monitored, and highly organized operational foundation.

Daily Tech Digest - April 26, 2026

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

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Where to begin a cloud career

Starting a career in often seems daunting due to perceived barriers like expensive boot camps and complex certifications, but David Linthicum argues that the best entry point is actually through free foundational courses. These no-cost resources allow beginners to gain essential orientation, learning vital concepts such as infrastructure, elasticity, and governance without financial risk. Major providers like , , and offer these learning paths to cultivate a skilled ecosystem of future professionals. By utilizing these introductory materials, learners can compare different platforms to see which best aligns with their career goals — such as choosing Azure for enterprise Windows environments or AWS for startup versatility — before committing to a specific specialization. Linthicum emphasizes that these courses provide a structured progression from broad terminology to mental models, which is more effective than jumping straight into technical tools. Furthermore, he highlights that cloud careers are accessible even to those without coding backgrounds, including roles in security, project delivery, and business analysis. The ultimate strategy is to treat free courses as a launchpad for momentum; by finishing introductory training across multiple providers, aspiring professionals can build the necessary breadth and confidence to pursue more advanced hands-on labs and role-based certifications later.

Cybersecurity Risks Related to the Iran War

In the article "Cybersecurity Risks Related to the Iran War," authors Craig Horbus and Ryan Robinson explore how modern geopolitical tensions between Iran, the United States, and Israel have expanded into a parallel digital battlefield. As conventional military operations escalate, cybersecurity experts and regulators warn that financial institutions and critical infrastructure are facing heightened risks from state-sponsored actors and affiliated hacktivists. Groups like "Handala" have already demonstrated their disruptive capabilities by targeting energy companies and medical providers, using techniques such as , data-wiping malware, and sophisticated phishing campaigns. These adversaries target the financial sector primarily to cause widespread economic instability, erode public confidence, and secure funding for hostile activities through fraudulent transfers or ransomware. Consequently, regulatory bodies like the New York Department of Financial Services are urging institutions to adopt more robust cyber resilience strategies. This includes intensifying network monitoring, enhancing authentication protocols, and strengthening third-party vendor risk management. The article emphasizes that cybersecurity is no longer merely a technical IT concern but a critical legal and strategic obligation. Ensuring that incident response plans can withstand nation-state level threats is essential for maintaining global economic stability in an increasingly volatile digital landscape where physical conflicts and cyber warfare are now inextricably linked.

Vector Database - A Deep Dive

represent a specialized class of data management systems engineered to efficiently store, index, and retrieve high-dimensional vector embeddings, which are numerical representations of unstructured data like text, images, and audio. Unlike traditional relational databases that rely on exact keyword matches and structured schemas, vector databases leverage the "meaning" of data by measuring the mathematical distance between vectors in a multi-dimensional space. This enables powerful semantic search capabilities where the system identifies items with conceptual similarities rather than just literal overlaps. At their core, these databases utilize embedding models to transform raw information into dense vectors, which are then organized using specialized indexing algorithms such as Hierarchical Navigable Small World (HNSW) or Inverted File Index (IVF). These techniques facilitate Approximate Nearest Neighbor (ANN) searches, allowing for rapid retrieval across billions of data points with minimal latency. Consequently, vector databases have become the foundational "long-term memory" for modern AI applications, particularly in Retrieval-Augmented Generation (RAG) workflows and recommendation engines. By bridging the gap between raw unstructured data and machine-interpretable context, they empower developers to build intelligent, scalable systems that can understand and process information at a more human-like level of nuance and complexity, while handling massive datasets through horizontal scaling and efficient sharding strategies.

Reimagining tech infrastructure for (and with) agentic AI

The rapid evolution of agentic AI is compelling chief technology officers to fundamentally reimagine IT infrastructure, moving beyond traditional support layers toward a modular, "mesh-like" backbone that orchestrates autonomous agents. As AI workloads expand, organizations face a critical dual challenge: infrastructure costs are projected to triple by 2030 while budgets remain stagnant, necessitating a shift where AI is used to manage the very systems it inhabits. Successfully scaling agentic AI requires building "agent-ready" foundations characterized by composability, secure APIs, and robust governance frameworks that ensure accountability. High-value impacts are already surfacing in areas like service desk operations, observability, and hosting, where agents can automate up to 80 percent of routine tasks, potentially reducing run-rate costs by 40 percent. This transition demands a significant cultural and operational pivot, shifting the role of IT professionals from manual ticket-based troubleshooting to the supervision and architectural design of intelligent systems. By integrating these autonomous entities into a coherent backbone, enterprises can bridge the gap between experimentation and enterprise-wide scale, transforming infrastructure from a reactive cost center into a dynamic platform for innovation. Those who embrace this agentic shift will secure a significant advantage in speed, resilience, and economic efficiency in the AI-driven era.

Quantum-Safe Security: How Enterprises Can Prepare for Q-Day

The provided page explores the critical necessity for enterprises to transition toward to mitigate the existential threats posed by future quantum computers. Traditional encryption methods, such as RSA and ECC, are increasingly vulnerable to advanced quantum algorithms, most notably , which can efficiently solve the complex mathematical problems that currently protect digital infrastructure. A particularly urgent concern highlighted is the "harvest now, decrypt later" strategy, where adversaries collect encrypted sensitive data today with the intention of deciphering it once powerful quantum technology becomes commercially available. To defend against these emerging risks, the article outlines a strategic preparation roadmap for organizations. This involves achieving "crypto-agility"—the ability to rapidly switch cryptographic standards—and conducting comprehensive inventories of current encryption usage across all systems. Furthermore, enterprises are encouraged to align with evolving standards for post-quantum cryptography (PQC) and prioritize the protection of high-value, long-term assets. By integrating these quantum-resistant algorithms into their security architecture now, businesses can ensure long-term data confidentiality, maintain regulatory compliance, and future-proof their digital operations against the impending "quantum apocalypse." This proactive shift is presented not merely as a technical update, but as a fundamental requirement for maintaining trust and operational continuity in a post-quantum world.

Your Disaster Recovery Plan Doesn’t Account for AI Agents. It Should

The article "Your Disaster Recovery Plan Doesn’t Account for AI Agents. It Should" highlights a critical gap in contemporary business continuity strategies as enterprise adoption of agentic AI accelerates. While Gartner predicts a massive surge in AI agents embedded within applications by 2026, many organizations still rely on legacy governance frameworks that operate at human speeds. These traditional models are ill-equipped for autonomous agents that execute thousands of data accesses instantly, often bypassing standard security alerts. Unlike traditional technical failures with clear timestamps, AI governance failures are often "silent," characterized by over-permissioned agents accessing sensitive datasets over long periods. This leads to an exponential increase in the "blast radius" of potential breaches across cloud and on-premises environments. To mitigate these risks, the author advocates for machine-speed governance that utilizes dynamic, context-aware access controls and just-in-time permissions. By embedding governance directly into the architecture, organizations can transform it from a deployment bottleneck into a recovery accelerant. Such an approach provides the immutable audit trails necessary to drastically reduce the 100-day recovery window typically associated with AI-related incidents. Ultimately, robust governance is presented not as a constraint, but as a prerequisite for sustaining resilient AI innovation.

Cloud Native Platforms Transforming Digital Banking

The financial services industry is undergoing a profound structural revolution as traditional banks transition from rigid, monolithic legacy systems to agile, cloud-native architectures. This shift is centered on the adoption of microservices and containerization, allowing institutions to break down complex applications into independent, modular components. Such an approach enables rapid deployment of updates and innovative fintech services without disrupting core operations, ensuring established banks can effectively compete with nimble startups. Beyond mere speed, cloud-native platforms offer superior security through "" models and immutable infrastructure, which mitigate risks like configuration errors and persistent malware. Furthermore, the integration of open banking APIs and real-time payment processing transforms banks into central hubs within a broader digital ecosystem, providing customers with instant, seamless financial experiences. The scalability of the cloud also provides a robust foundation for Artificial Intelligence, facilitating hyper-personalized "predictive banking" that anticipates user needs. Ultimately, by embracing cloud computing, financial institutions are not only automating compliance through "Policy as Code" but are also building a flexible, future-proof foundation capable of incorporating emerging technologies like blockchain and quantum computing to meet the demands of the modern global economy.

Turning security into a story: How managed service providers use reporting to drive retention and revenue

Managed Service Providers (MSPs) often face the challenge of proving their value because effective cybersecurity is inherently "invisible," resulting in an absence of security breaches that customers may interpret as a lack of necessity for the service. To bridge this gap, MSPs must transition from providing raw technical data to crafting a compelling narrative through strategic reporting. As highlighted by the experiences of industry professionals using SonicWall tools, the core of a successful MSP practice relies on five pillars: monitoring, patch management, configuration oversight, alert response, and, most importantly, reporting. By utilizing automated platforms like Network Security Manager (NSM) and Capture Client, MSPs can produce detailed assessments and audit trails that make their backend efforts tangible to clients. Moving beyond monthly logs to implement Quarterly Business Reviews (QBRs) allows providers to transition from mere vendors to trusted strategic advisors. This shift significantly impacts business outcomes; for instance, MSPs employing regular QBRs often see renewal rates jump from 71% to 96%. Ultimately, by structuring services into clear tiers with documented deliverables, MSPs can use reporting to tell a story of protection. This strategy not only justifies current expenditures but also drives new revenue by fostering client trust and highlighting unmet security needs.

Cybersecurity in the AI age: speed and trust define resilience

In the rapidly evolving digital landscape, cybersecurity has transitioned from a technical hurdle to a strategic imperative where speed and trust are the cornerstones of resilience. According to insights from iqbusiness, the "breakout time" for e-crime—the window an attacker has to move laterally within a system—has plummeted from nearly ten hours in 2019 to just 29 minutes today, necessitating near-instantaneous responses. This urgency is exacerbated by artificial intelligence, which serves as a double-edged sword; while it empowers attackers to craft sophisticated phishing campaigns and malicious code, it also provides defenders with automated tools to filter noise and prioritize threats. However, the rise of "shadow AI" and a lack of visibility into unsanctioned tools pose significant risks to data integrity. To combat these threats, the article advocates for a "Zero Trust" architecture—where every interaction, whether by human or machine, is verified—and the adoption of robust frameworks like the NIST Cybersecurity Framework 2.0. Ultimately, modern cyber resilience depends on more than just defensive technology; it requires a proactive organisational culture, strong leadership, and the seamless integration of AI into security strategies. By prioritising visibility and governance, businesses can navigate the complexities of the AI age while maintaining the trust of their stakeholders and partners.

Architecture strategies for monitoring workload performance

Monitoring for performance efficiency within the Azure Well-Architected Framework is a critical process focused on observing system behavior to ensure optimal resource utilization and responsiveness. This discipline involves a continuous cycle of collecting, analyzing, and acting upon telemetry data to detect performance bottlenecks before they impact end users. Effective monitoring begins with comprehensive instrumentation, which captures diverse data points such as metrics, logs, and distributed traces from both the application and underlying infrastructure. By establishing clear performance baselines, architects can define what constitutes "normal" behavior, allowing them to identify subtle degradations or sudden spikes in resource consumption. Azure provides powerful tools like Azure Monitor and Application Insights to facilitate this visibility, offering capabilities for real-time alerting and deep-dive diagnostic analysis. Key metrics, including throughput, latency, and error rates, serve as essential indicators of system health. Furthermore, a robust monitoring strategy emphasizes the importance of historical data for long-term trend analysis and capacity planning, ensuring that the architecture can scale effectively to meet evolving demands. Ultimately, performance monitoring is not a one-time setup but an ongoing practice that informs optimization efforts, validates architectural changes, and maintains a high level of efficiency throughout the entire software development lifecycle.

Daily Tech Digest - March 07, 2026

Quote for the day:

"Be willing to make decisions. That's the most important quality in a good leader." -- General George S. Patton, Jr.

LangChain's CEO argues that better models alone won't get your AI agent to production

contends that achieving production-ready requires more than just utilizing more powerful foundational models. While improved offer better reasoning, Chase emphasizes that agents often fail due to systemic issues rather than model limitations. He advocates for a shift toward "agentic" engineering, where the focus moves from simple prompting to building robust, stateful systems. A critical component of this transition is the move away from "vibe-based" development—relying on subjective successes—toward rigorous evaluation frameworks like . Chase highlights that developers must implement precise control over an agent's logic through tools like LangGraph, which allows for cycles, state management, and human-in-the-loop interactions. These architectural guardrails are essential for managing the inherent unpredictability of LLMs. By treating agent development as a complex systems engineering task, organizations can overcome the "last mile" hurdle, moving beyond impressive demos to reliable, autonomous applications. Ultimately, the maturity of AI agents depends on sophisticated orchestration, detailed observability, and a willingness to architect the environment in which the model operates, rather than expecting a single model to handle every nuance of a complex workflow autonomously.

Where Multi-Factor Authentication Stops and Credential Abuse Starts

This article examines the false sense of security provided by multi-factor authentication (MFA) within Windows-centric environments. While MFA is highly effective for cloud-based applications, the piece argues that traditional paths—such as interactive logons, Remote Desktop Protocol (RDP) sessions, and Server Message Block (SMB) traffic—often bypass modern identity providers, leaving internal networks vulnerable to password-only attacks. The article details seven critical gaps, including the persistence of legacy susceptible to , the abuse of Kerberos tickets, and the risks posed by unmonitored service accounts or local administrator credentials that frequently lack MFA coverage. To mitigate these significant risks, the author recommends that organizations treat Windows authentication as a distinct security surface by enforcing longer passphrases, continuously blocking compromised passwords, and strictly limiting legacy protocols. Furthermore, the text highlights the importance of auditing service accounts and leveraging advanced security tools like Specops Password Policy to bridge the gap between cloud security and on-premises infrastructure. Ultimately, securing a modern enterprise requires moving beyond simple MFA implementation toward a holistic strategy that addresses these often-overlooked internal authentication vulnerabilities and credential reuse habits.

Why enterprises are still bad at multicloud

In this InfoWorld analysis, argues that while most enterprises are technically multicloud by default, they largely fail to operate them as a cohesive business capability. Instead of a unified strategy, multicloud environments often emerge haphazardly through mergers, acquisitions, or localized team decisions, leading to fragmented "technology estates" that function as isolated silos. Each provider—typically AWS, Azure, and Google—is managed with its own native consoles, security protocols, and talent pools, which creates redundant processes, inconsistent governance, and hidden global costs. Linthicum emphasizes that the "" of multicloud is only worth paying if organizations can achieve operational commonality. He advocates for the implementation of common control planes—shared services for identity, policy, and observability—that sit above individual cloud brands to ensure consistent guardrails. To improve maturity, enterprises must shift from viewing cloud adoption as a series of procurement choices to designing a singular operating model. By establishing cross-cloud coordination and relentlessly measuring business value through metrics like recovery speed and unit economics, organizations can move from uncontrolled variety to "controlled optionality," finally leveraging the specialized strengths of different providers without multiplying their operational overhead or fracturing their technical foundations.

The Accidental Orchestrator

This article by O'Reilly Radar examines the profound transformation of the software developer's role in the era of . It posits that developers are transitioning from traditional manual coding to becoming strategic orchestrators of autonomous AI agents. This shift, described as "accidental," occurred as AI tools evolved from simple autocomplete plugins into sophisticated assistants capable of managing complex, end-to-end tasks. Developers now find themselves overseeing a fleet of agents that handle various components of the software lifecycle, including design, implementation, and debugging. This new reality demands a significant pivot in professional skills; instead of focusing primarily on syntax and logic, engineers must now master , agent coordination, and high-level system architecture. The piece emphasizes that while AI significantly boosts productivity, the complexity of managing these interlinked systems introduces critical challenges regarding transparency, security, and long-term reliability. Ultimately, the role of the accidental orchestrator requires a mindset shift where the developer acts as a tactical director of digital workers rather than a lone creator. This evolution suggests that the future of software engineering lies in the quality of the human-AI partnership and the effective orchestration of intelligent agents.

Powering the new age of AI-led engineering in IT at Microsoft

Microsoft Digital is spearheading a transformative shift toward AI-led engineering, fundamentally changing how IT services are designed, built, and maintained. At the heart of this evolution is the integration of and other generative AI tools, which empower developers to automate repetitive "toil" and focus on high-value architectural innovation. By adopting a platform-centric approach, Microsoft standardizes development environments and leverages AI to enhance security, catch bugs earlier, and optimize code quality through sophisticated semantic searches and automated testing. This transition moves beyond simply using AI tools to a holistic culture where AI is woven into the entire software development lifecycle. Key benefits include significantly accelerated deployment cycles, improved developer satisfaction, and a more resilient IT infrastructure. Furthermore, the initiative prioritizes security and compliance by embedding AI-driven checks directly into the engineering pipeline. As Microsoft refines these internal practices, it aims to provide a blueprint for the industry on how to scale enterprise IT operations in an increasingly complex digital landscape. Ultimately, AI-led engineering at Microsoft is not just about speed; it is about fostering a creative environment where engineers solve complex problems with unprecedented efficiency, driving a new standard for modern software development.

Read-Copy-Update (RCU): The Secret to Lock-Free Performance

Read-Copy-Update (RCU) is a sophisticated synchronization mechanism explored in this InfoQ article, primarily utilized within the Linux kernel to handle concurrent data access. Unlike traditional locking methods that can cause significant performance bottlenecks, RCU allows multiple readers to access shared data simultaneously without the overhead of locks or atomic operations. The core concept involves updaters creating a modified copy of the data and then swapping the pointer to the new version, while ensuring that the original data is only reclaimed after a "grace period" when all active readers have finished. This approach ensures that readers always see a consistent, albeit potentially slightly outdated, version of the data without ever being blocked. While RCU offers unparalleled scalability and performance for read-heavy workloads, the article emphasizes that it introduces complexity for developers, particularly regarding memory management and the coordination of update cycles. Updaters must carefully manage the transition between versions to avoid data corruption. Ultimately, RCU represents a fundamental shift in concurrency design, prioritizing reader efficiency at the cost of more intricate update logic, making it an essential tool for high-performance systems where read operations vastly outnumber modifications.

AI transforms ‘dangling DNS’ into automated data exfiltration pipeline

AI-driven automation is fundamentally transforming "dangling DNS" from a common administrative oversight into a sophisticated, high-speed pipeline for automated data exfiltration. Dangling DNS occurs when a Domain Name System record continues to point to a decommissioned cloud resource, such as an abandoned IP address or a deleted storage bucket. While this vulnerability has existed for years, attackers are now utilizing generative AI and advanced scanning scripts to identify these orphaned subdomains across the internet at an unprecedented scale. Once a target is located, AI agents can automatically reclaim the abandoned resource on cloud platforms like AWS or Azure, effectively hijacking the legitimate domain to intercept sensitive traffic, harvest user credentials, or distribute malware through prompt injection attacks. This evolution represents a shift from opportunistic manual exploitation to a systematic, machine-led attack surface management strategy. To counter this, security professionals must move beyond periodic audits, implementing continuous, automated DNS monitoring and lifecycle management. The article underscores that as threat actors leverage AI to weaponize legacy misconfigurations, organizations can no longer afford to leave DNS records unmanaged. Addressing this infrastructure is a critical component of modern cyber defense, requiring the same level of automation that attackers currently use to exploit it.

The New Calculus of Risk: Where AI Speed Meets Human Expertise

The article examines the launch of Crisis24 Horizon, a sophisticated AI-enabled risk management platform designed to address the complexities of a volatile global security landscape. Developed on a modern technology stack, the platform provides a unified "single pane of glass" view, integrating dynamic intelligence with travel, people, and site-specific risk management. By leveraging artificial intelligence to process roughly 20,000 potential incidents daily, Crisis24 Horizon dramatically accelerates threat detection and triage, effectively expanding the capacity of security teams. Key features include "Ask Horizon," a natural language interface for querying risk data; "Latest Event Synopsis," which consolidates fragmented alerts into coherent summaries; and integrated mass notification systems for critical event response. While AI handles massive data aggregation and initial filtering, the platform emphasizes the "human in the loop" approach, where expert analysts provide necessary contextual judgment for high-stakes decisions like emergency evacuations. This synergy of AI speed and human expertise marks a shift from reactive to anticipatory security, allowing organizations to monitor assets in real-time and safeguard operations against interconnected global threats. Ultimately, Crisis24 Horizon empowers leaders to mitigate risks with greater precision, ensuring operational resilience and employee safety amidst geopolitical instability and environmental disasters.

Accelerating AI, cloud, and automation for global competitiveness in 2026

The guest blog post by Pavan Chidella argues that by 2026, the global competitiveness of enterprises will be defined by their ability to transition from AI experimentation to large-scale, disciplined execution. Focusing primarily on the healthcare sector, the author illustrates how the orchestration of AI, cloud-native architectures, and intelligent automation is essential for modernizing legacy processes like claims adjudication, which traditionally suffer from structural latency. In this evolving landscape, technology is no longer an isolated tool but a strategic driver of measurable business outcomes, including improved operational efficiency and enhanced customer transparency. Chidella emphasizes that "responsible acceleration" requires embedding governance, ethical AI monitoring, and regulatory compliance directly into system designs rather than treating them as afterthoughts. By adopting a product-led engineering mindset, organizations can reduce friction and build trust within their ecosystems. Ultimately, the piece asserts that global leadership in 2026 will belong to those who successfully integrate speed and precision with accountability, effectively leveraging hybrid cloud capabilities to process data in real-time. This shift represents a broader competitive imperative to move beyond proof-of-concept stages toward a resilient, automated, and digitally mature infrastructure that can thrive amidst increasing global complexity and regulatory scrutiny.

Engineering for AI intensity: The new blueprint for high-density data centers

This article explores the critical infrastructure evolution required to support the escalating demands of artificial intelligence. As traditional data centers struggle with the unprecedented power and thermal requirements of GPU-heavy workloads, a new engineering paradigm is emerging. This blueprint emphasizes a radical transition from legacy air-cooling systems to advanced liquid cooling technologies, such as direct-to-chip and immersion cooling, which are essential for managing rack densities that now frequently exceed 50kW and can reach up to 100kW per cabinet. Beyond thermal management, the article highlights the necessity of modular, high-voltage power distribution to ensure electrical efficiency and minimize transmission losses across the facility. It also underscores the importance of structural adaptations, including reinforced flooring to support heavier liquid-cooled hardware and overhead cable management to optimize airflow. Furthermore, the blueprint advocates for high-bandwidth, low-latency networking fabrics to facilitate the massive data exchanges inherent in parallel AI training. Ultimately, the piece argues that achieving AI intensity requires a holistic, future-proof design strategy that integrates power scalability, structural flexibility, and sustainable practices, positioning the modern data center as the strategic engine for digital transformation in an AI-first era.

Daily Tech Digest - March 05, 2026

Quote for the day:

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

CISOs Are Now AI Guardians of the Enterprise

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

The right way to architect modern web applications

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

Safeguarding biometric data through anonymization

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

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

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

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

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

Most Organizations Plan Strategically. Few Manage It That Way

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

Malicious insider threats outpace negligence in Australia

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

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

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

Measuring What Matters in Large Language Model Performance

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

Hallucination is not an option when AI meets the real world

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

Daily Tech Digest - February 12, 2026

Quote for the day:

"Do not follow where the path may lead. Go instead where there is no path and leave a trail." -- Muriel Strode

The hard part of purple teaming starts after detection

Imagine you’re driving, and you see the car ahead braking suddenly. Awareness helps, but it’s your immediate reaction that avoids the collision. Insurance plans don’t matter at that moment. Nor do compliance reports or dashboards. Only vigilance and rehearsal matter. Cyber resilience works the same way. You can’t build the instinct required to act by running one simulation a year. You build it through repetition. Through testing how specific scenarios unfold. Through examining not only how adversaries get in, but also how they move, escalate, evade, and exfiltrate. This is the heart of real purple teaming. ... AI can accelerate analysis, but it can’t replace intuition, design, or the judgment required to act. If the organization hasn’t rehearsed what to do when the signal appears, AI only accelerates the moment when everyone realises they don’t know what happens next. This is why so much testing today only addresses opportunistic attacks. It cleans up the low-hanging fruit. ... The standard testing model traps everyone involved: One-off tests create false confidence; Scopes limit imagination. Time pressure eliminates depth; Commercial structures discourage collaboration; Tooling gives the illusion of capability; and Compliance encourages the appearance of rigour instead of the reality of it. This is why purple teaming often becomes “jump out, stabilize, pull the chute, roll on landing.” But what about the hard scenarios? What about partial deployments? What about complex failures? That’s where resilience is built.

State AI regulations could leave CIOs with unusable systems

Numerous states are considering AI regulations for systems used in medical care, insurance, human resources, finance and other critical areas. ... Despite the growing regulatory risk, businesses appear unwilling to slow AI deployments. "Moving away from AI with the regulation is not going to be an option for us," Juttiyavar said. He said AI is already deeply embedded in how organizations operate and is essential for speed and competitiveness. ... If CIOs establish strong internal frameworks for AI deployment, "that helps you react better to legislative change" and anticipate new requirements, Kourinian said. Still, regulatory shifts can leave companies with systems that are technically sound but legally unusable, said Peter Cassat, a partner at CM Law. To manage that risk, Cassat advises CIOs to negotiate "change of law" provisions in vendor contracts that provide termination rights if regulations make continued use of a system impossible or impractical. But such provisions do not eliminate the risk of sunk costs. "If it's a SaaS provider and you've signed a three-year term, they don't want to necessarily let you walk for free either," Cassat said. Beyond legal exposure, CIOs must also anticipate public and political reaction to AI and biometric tools. "The CIO absolutely has the responsibility to understand how this technology could be perceived -- not just internally, but by the public and lawmakers," said Mark Moccia, an analyst at Forrester Research.

Your dev team isn’t a cost center — it’s about to become a multiplier

If you treat AI as a pathway to eliminate developer headcount, sure, you’ll capture some cost savings in the short term. But you’ll miss the bigger opportunity entirely. You’ll be the bank executive in 1975 who saw ATMs and thought, “Great, we can close branches and fire tellers.” Meanwhile, your competitors have automated the mundane teller tasks and are opening new branches to sell higher-end services to more people. The 1.4-1.6x productivity improvement that GDPval documented isn’t about doing the same work with fewer people. It’s about doing vastly more work with the same people. That new product idea you had that was 10x too expensive to develop? It’s now possible. That customer experience improvement that could drive loyalty that you didn’t have the headcount for? It’s on the table. The technical debt you’ve been accumulating? You can start to pay it down. ... What struck me about Werner’s final keynote wasn’t the content, it was the intent. This was Werner’s last time at that podium. He could have done a victory lap through AWS’s greatest hits. Instead, he spent his time outlining a framework of success for the next generation of developers. For those of us leading technology organizations, the framework is both validating and challenging. Validating because these traits aren’t new. They have always separated good developers from great ones. Challenging because AI amplifies everything, including the gaps in our capabilities.

Cloud teams are hitting maturity walls in governance, security, and AI use

Migration activity remains heavy across enterprises, especially for data platforms. At the same time, downtime tolerance is limited. Nearly half of respondents said their organizations can accept only one to six hours of downtime for cutover during migration. That combination creates pressure to migrate at speed while keeping data integrity intact. In regulated environments, that pressure extends to audit evidence and compliance validation, which often needs to be produced in parallel with migration execution. ... Cloud-native managed database adoption is also high. More than half of respondents reported using managed cloud databases, and a third reported using SaaS-based database services. Only 10% reported operating self-hosted databases. This shift toward managed services reduces operational burden on infrastructure teams, but it increases reliance on identity governance, network segmentation, and application-layer security controls. It also creates stronger dependency on cloud provider logging and access models. ... Development stacks also reflect this shift. Python was reported as a primary language, with Java close behind. These languages remain central to AI workflows, data engineering, and enterprise application back ends. Machine learning adoption is also widespread since organizations reported actively training ML models. Many of these pipelines are now part of production environments, making operational continuity a priority.

MIT's new fine-tuning method lets LLMs learn new skills without losing old ones

To build truly adaptive AI, the industry needs to solve "continual learning," allowing systems to accumulate knowledge much like humans do throughout their careers. The most effective way for models to learn is through "on-policy learning.” In this approach, the model learns from data it generates itself allowing it to correct its own errors and reasoning processes. This stands in contrast to learning by simply mimicking static datasets. ... The standard alternative is supervised fine-tuning (SFT), where the model is trained on a fixed dataset of expert demonstrations. While SFT provides clear ground truth, it is inherently "off-policy." Because the model is just mimicking data rather than learning from its own attempts, it often fails to generalize to out-of-distribution examples and suffers heavily from catastrophic forgetting. SDFT seeks to bridge this gap: enabling the benefits of on-policy learning using only prerecorded demonstrations, without needing a reward function. ... For teams considering SDFT, the practical tradeoffs come down to model size and compute. The technique requires models with strong enough in-context learning to act as their own teachers — currently around 4 billion parameters with newer architectures like Qwen 3, though Shenfeld expects 1 billion-parameter models to work soon. It demands roughly 2.5 times the compute of standard fine-tuning, but is best suited for organizations that need a single model to accumulate multiple skills over time, particularly in domains where defining a reward function for reinforcement learning is difficult or impossible.

The Illusion of Zero Trust in Modern Data Architectures

Modern data stacks stretch far beyond a single system. Data flows from SaaS tools into ingestion pipelines, through transformation layers, into warehouses, lakes, feature stores, and analytics tools. Each hop introduces a new identity, a new permission model, and a new surface area for implicit trust. Not to mention, niches like healthcare data storage are a completely different beast. Whatever the system may be, teams may enforce strict access at the perimeter while internal services freely exchange data with long-lived credentials and broad scopes. This is where the illusion forms. Zero Trust is declared because no user gets blanket access, yet services trust other services almost entirely. Tokens are reused, roles are overprovisioned, and data products inherit permissions they were never meant to have. The architecture technically verifies everything, but conceptually trusts too much. ... Data rarely stays where Zero Trust policies are strongest. Warehouses enforce row-level security, masking, and role-based access, but data doesn’t live exclusively in warehouses. Extracts are generated, snapshots are shared, and datasets are copied into downstream systems for performance or convenience. Each copy weakens the original trust guarantees and problems worse than increasing cloud costs come to fruition. Once data leaves its source, context is often stripped away.

Top Cyber Industry Defenses Spike CO2 Emissions

Though rarely discussed, like any other technologies, cybersecurity protections carry their own costs to the planet. Programs run on electricity. Servers demand water. Devices are built from natural resources and eventually get thrown out. ... "CISOs can help or make the situation worse [when it comes to] sustainability, depending on the way they write security rules," he says. "And that's why we started a study: to enable the CISO to be part of the sustainability process of his or her company, and to find actionable ways to reduce CO2 consumption while at the same time not adding more risks." ... "We collect a lot of logs, not exactly always knowing why, and the retention period is a huge cost in terms of infrastructure, and also CO2," Billois says. "So at some point, you can revisit your log collection, and log retention, and if there are no legal issues, you can think about compressing them to reduce their volume. It's something that is, I would say, quite easy to do. ... All of that said, unfortunately, the biggest cyber polluter, by far, is also the most difficult to scale back without incurring risk. Some companies can swap underutilized physical infrastructure for virtualized backups, which eat less power, if they're not already doing that; but there are few other great ways to make cyber resilience more efficient. "You can reduce CO2 [from backups] very easily: you stop buying two servers, or you stop having a duplicate of all your data," Billois says.

Five ways quantum technology could shape everyday life

There is growing promise of quantum technology’s ability to solve problems that today’s systems struggle to overcome, or cannot even begin to tackle, with implications for industry, national security and everyday life. ... In healthcare, faster drug discovery could bring quicker response to outbreaks and epidemics, personalised medicine and insight into previously inscrutable biological interactions. Quantum simulation of how materials behave could lead to new high efficiency energy materials, catalysts, alloys and polymers. ... In medicine, quantum sensors could improve diagnostic capabilities via more sensitive, quicker and noninvasive imaging modes. In environmental monitoring, these sensors could track delicate shifts beneath the Earth’s surface, offer early warnings of seismic activity, or detect trace pollutants in air and water with exceptional accuracy. ... Airlines and rail networks could automatically reconfigure to avoid cascading delays, while energy providers might balance renewable generation, storage and consumption with far greater precision. Banks could use quantum computers to evaluate numerous market scenarios in parallel, informing the management of investment portfolios. ... While still at an early stage of development, quantum algorithms might accelerate a subset of AI called machine learning (where algorithms improve with experience), help simulate complex systems, or optimise AI architectures more efficiently.

Nokia predicts huge WAN traffic growth, but experts question assumptions

“Consumer- and enterprise-generated AI traffic imposes a substantial impact on the wide-area network (WAN) by adding AI workloads processed by data centers across the WAN. AI traffic does not stay inside one data center; it moves across edge, metro, core, and cloud infrastructure, driving dense lateral flows and new capacity demands,” the report says. An explosion in agentic AI applications further fuels growth “by inducing extra machine-to-machine (M2M) traffic in the background,” Nokia predicts. “AI traffic isn’t just creating more demand inside data centers; it’s driving a sustained surge of traffic between them. AI inferencing traffic—both user-initiated and agentic-AI-induced M2M—moving over inter-data-center links grows at a 20.3% CAGR through 2034.” ... Global enterprise and industrial traffic, including fixed wireless access, will also steadily rise over the next decade, “as more operations, machines, and workers become digitally connected,” Nokia predicts. “Pervasive automation, high-resolution video, AI-driven analytics, and remote access to industrial systems,” will drive traffic growth. “Factory lines are streaming machine vision data to the cloud. AI copilots are assisting personnel in real time. Field teams are using AR instead of manuals. Robots are coordinating across sites,” the Nokia report says. “Industrial systems are continuously sending telemetry over the WAN instead of keeping it on-site. This shift makes wide-area connectivity part of the core production workflow.”

The death of reactive IT: How predictive engineering will redefine cloud performance in 10 years

Reactive monitoring fails not because tools are inadequate, but because the underlying assumption that failures are detectable after they occur no longer holds true. Modern distributed systems have reached a level of interdependence that produces non-linear failure propagation. A minor slowdown in a storage subsystem can exponentially increase tail latencies across an API gateway. ... Predictive engineering is not marketing jargon. It is a sophisticated engineering discipline that combines statistical forecasting, machine learning, causal inference, simulation modeling and autonomous control systems. ... Predictive engineering will usher in a new operational era where outages become statistical anomalies rather than weekly realities. Systems will no longer wait for degradation, they will preempt it. War rooms will disappear, replaced by continuous optimization loops. Cloud platforms will behave like self-regulating ecosystems, balancing resources, traffic and workloads with anticipatory intelligence. ... In distributed networks, routing will adapt in real time to avoid predicted congestion. Databases will adjust indexing strategies before query slowdowns accumulate. The long-term trajectory is unmistakable: autonomous cloud operations. Predictive engineering is not merely the next chapter in observability, it is the foundation of fully self-healing, self-optimizing digital infrastructure.