Daily Tech Digest by Kannan Subbiah: Manufacturing

Showing posts with label Manufacturing. Show all posts

Daily Tech Digest - June 14, 2026

Quote for the day:

“If you think compliance is expensive, try non‑compliance.” -- Paul McNulty

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Duration: 24 mins • Perfect for listening on the go.

Segmentation Works for OT If Operators Are Paying Attention

Network segmentation remains a foundational strategy for securing operational technology, but its ultimate effectiveness relies heavily on active and continuous human oversight. Many organizations mistakenly view network segmentation as a static, one-time project designed during a workshop, rather than as an ongoing operational practice that evolves over time. This fixed mindset creates dangerous security gaps, as real-world industrial environments change quickly while network diagrams remain completely outdated. Furthermore, the practical execution of traditional segmentation and newer microsegmentation models faces severe real-world hurdles. Traditional firewalls are frequently undermined by user convenience workarounds, such as technicians introducing unmanaged, internet-connected personal laptops onto the factory floor, or by unpatched vulnerabilities within the firewalls themselves. Meanwhile, microsegmentation is regularly impossible to implement because older legacy infrastructure cannot accommodate security software agents or survive the disruptive downtime required for vital updates. Compounding the issue, companies often overuse segmentation by dumping too many diverse industrial systems into a single isolated zone, meaning one compromised machine can expose the entire segment. To fix these systemic flaws, security experts recommend adopting enforceable policies that continuously verify user access. Operators must look past static blueprints, regularly auditing endpoint logs and identifying unrecognizable addresses to catch unauthorized connections before clever attackers can exploit them.

In Conversation with Simon Stone and Simon Barrows: Adventures in Architecture as Code

As organizations grow in scale and speed, traditional architecture diagrams often become outdated, subjective, and disconnected from actual operations. A recent interview with Simon Stone and Simon Barrows explores the transition from relying on these static diagrams to adopting Architecture as Code, a method that treats architectural knowledge as living, version-controlled data. This shift is increasingly practical today because modern artificial intelligence can efficiently gather and organize data from various scattered sources. By keeping architecture as structured data, teams can automatically generate up-to-date diagrams on demand, test for consistency, and cleanly link business strategies directly to technology investments. This approach changes the architect's role from drawing static pictures to managing data quality, working more like a software engineer. Instead of constantly updating documents, architects can rely on automated tests for routine checks and focus their time on complex decisions. However, converting old, fragmented documents into a single, reliable dataset remains a significant challenge. To succeed, the speakers advise starting small. Rather than attempting a massive overhaul all at once, organizations should identify a specific, high-value problem to solve first. By focusing on a clear initial use case, companies can build a solid foundation and gradually expand their structured architecture, ultimately creating a more transparent, efficient, and well-aligned technical environment.

10 Indispensable Prompts Our Team Refuses to Build Without

The recent Google Cloud blog post highlights a collection of practical prompts that their engineering teams rely on to build better software. Rather than using AI just to write code faster, these developers use specific prompts to challenge their own assumptions and catch mistakes early. The shared prompts cover a wide range of everyday programming tasks. For example, some developers ask the AI to act as a strict architect to help refine product requirements without making the design too complex. Others use it to run thorough code reviews, instructing the tool to grade their work on a harsh scale to ensure systems are truly reliable. There are also prompts designed to build testing plans, clean up unused code and forgotten comments, check software permissions for compliance, and weigh the pros and cons of different technical choices. Additionally, the team uses prompts to automatically review code changes and identify potential flaws in code that was generated by AI itself. Ultimately, the article suggests that treating AI as a critical partner rather than a simple code generator helps developers release software with greater confidence. By routinely asking hard questions and checking for hidden weaknesses, engineering teams can improve the overall quality of their work and avoid unexpected failures.

AI Governance in Enterprise Adoption: Why Trust Will Define the Next Wave of Innovation

Artificial intelligence is steadily moving from isolated experiments into the daily operations of the financial services sector. As companies integrate these systems into everything from fraud detection to customer service, the primary challenge is no longer about the technology itself, but rather about building institutional trust. With the arrival of more autonomous systems, financial organizations must handle complex new risks that go beyond simple technical errors. These risks involve broad operational dependencies, data security, and the complications of unapproved tool usage by employees. Because of this, companies are shifting away from unrestricted public tools and moving toward carefully governed internal environments. Setting clear rules and maintaining structured oversight should not be viewed as an obstacle to progress. Instead, sensible governance provides the necessary foundation for organizations to innovate safely and reliably. By establishing clear boundaries and maintaining accountability, businesses give their teams the confidence to adopt new capabilities while assuring regulators and customers that their data remains secure. Ultimately, the companies that succeed in this new landscape will not necessarily be the fastest to implement the latest tools. They will be the ones that recognize safe, transparent, and continuous oversight as a strategic advantage, proving that responsible management is a fundamental requirement for sustainable growth in modern finance.

Rethinking MDR as Attackers and Defenders Embrace AI

Traditional managed detection and response models are struggling to keep pace with modern cybersecurity threats. Historically, these services relied on human analysts to monitor networks and investigate potential issues. However, as attackers increasingly use advanced automation to launch faster and more complex campaigns, human-led teams simply cannot process the massive volume of alerts generated daily. Because of this, analysts are forced to prioritize severe warnings, leaving roughly sixty percent of alerts unreviewed. Unfortunately, attackers know this and deliberately hide their activity within these overlooked, low-severity notifications. Furthermore, the quality of human investigation can vary depending on shift times and workload, leading to inconsistent security outcomes. To address these vulnerabilities, organizations are moving toward automated systems. In this new approach, computers automatically investigate every single alert, regardless of its initial severity rating or the time of day. Instead of acting as a simple filter, the system conducts a deep, technical analysis of all warnings in seconds, providing a consistent and thorough review. This allows human security teams to shift their focus from manual discovery to making informed decisions based on the system's verified findings. Ultimately, adopting this automated approach ensures complete alert coverage, eliminates blind spots, and provides organizations with full ownership of their own network data.

The Intelligent Factory: Navin Nathani on How Manufacturing’s Next Competitive Edge Is Being Built on Data, Resilience, and Industrial AI

In modern manufacturing, competitive advantage no longer relies solely on scale and cost, but on the speed and quality of broad company decisions. Navin Nathani emphasizes that navigating current disruptions requires connected operations rather than delayed reporting. To achieve this, technology is shifting from a supportive background function to the core operating system of the business. Organizations are focusing on practical technology updates, such as modernizing resource planning software and moving information storage to the internet. These practical upgrades establish stability and build trust among employees, making them more open to further changes. As office networks and factory machinery converge, manufacturing plants become more connected, which necessitates a stronger focus on security to protect production from emerging online threats. Furthermore, the industry is gradually adopting artificial intelligence for specific applications like anticipating equipment repairs and better supply planning. Rather than serving as a replacement for human workers, this technology acts as a useful assistant that helps identify patterns and prevent equipment failures before they occur. However, successful implementation relies heavily on maintaining disciplined processes and accurate data. Ultimately, the future of manufacturing lies in using connected information to shift from reacting to problems to preventing them, ensuring that daily operations remain stable in an unpredictable environment.

Knowing When To Let Go Is A Leadership Skill

In her article, Kendra MacDonald explains that true leadership requires knowing when to persevere and when to simply let go. Drawing from her personal experiences with family planning, she notes that while society often celebrates grit and determination, effective leaders must also exercise clear judgment. They need to recognize whether their ongoing efforts are actually helpful or just delaying an inevitable outcome. MacDonald highlights that some situations and relationships cannot be repaired, and forcing people to agree is not always the answer. Instead, she advises leaders to accept differences as realities rather than problems to solve. When setbacks occur, it is essential to learn from them without taking the failure personally or letting emotions cloud objective facts. Furthermore, she stresses the importance of facing difficult conversations directly, as avoiding them only prolongs frustration for everyone involved. Honest communication, even when disappointing, is far more useful than giving false hope. Most importantly, MacDonald points out that holding onto the wrong opportunity or strategy drains team energy. By walking away from poorly fitting client relationships or unworkable strategies, leaders create space for fresh ideas and better matches. Ultimately, stepping back from a failing path is not a lack of resilience; rather, it is often the clearest demonstration of confident leadership.

The Real Cost of Unclear Technology Ownership

Unclear technology ownership is a direct threat to a company's operational stability and financial health. When no single person is accountable for a specific technology, organizations suffer from chronic delays, wasted spending, and repeated audit failures. Teams might look busy with meetings and project updates, but without a clear decision maker, this activity often hides a lack of actual progress. The costs show up as hidden labor, duplicated efforts, and lingering security vulnerabilities. This lack of ownership usually breaks down in critical areas like access management, data reporting, and vendor relationships. When systems fail or security incidents occur, fragmented responsibility means no one knows who should act first. As a result, small problems quickly escalate into costly crises. Furthermore, when executives and board members receive vague answers or see the same issues repeatedly, they quickly lose trust in the team's ability to manage risk. To fix this, companies do not need massive new programs. Instead, they must assign one accountable executive to each major risk area and give them the real authority to make decisions and control budgets. Organizations should establish a clear path for reporting bad news and ensure that board updates focus on actionable decisions rather than just listing activities. Clear ownership replaces confusion with stable, reliable progress.

AI Is Here to Stay. The Real Challenge Is Operating It Securely

Artificial intelligence is now a standard tool for writing software, with AI-generated code already running in major projects like OpenStack. However, its rapid adoption introduces significant operational and security challenges. Because AI produces code so quickly, human reviewers struggle to keep up, making it harder to ensure software remains secure and maintainable. Even more concerning is the rise of autonomous AI agents. Organizations often grant these agents broad permissions to access production environments, ignoring decades of security practices like the principle of least privilege. While AI capabilities advance rapidly, security features like containment and auditing lag behind. To operate AI securely, teams must apply proven engineering practices. First, organizations should use automated gating systems like Zuul. By testing how new code interacts with dependencies before it merges, gating prevents errors from reaching production. This acts as a vital check against the high volume of AI-written code. Second, teams should use strong hardware isolation, such as Kata Containers, to protect sensitive information. Standard containers share a core operating system, posing security risks in shared environments. Kata provides lightweight virtual machine isolation, ensuring data processed by an agent remains secure. Ultimately, enforcing strict access limits, adopting automated quality checks, and maintaining reliable backups are essential steps for operating AI safely.

Security in the Post-Mythos Era

The emergence of advanced artificial intelligence capable of instantly discovering and exploiting software vulnerabilities has fundamentally shifted the timeline of cybersecurity. While the core principles of network defense remain unchanged, the sheer speed at which new threats materialize means organizations can no longer rely on software patching as their primary shield. Because AI systems can weaponize flaws in minutes, human-driven patching cycles simply cannot keep pace. To survive, organizations must adopt a layered strategy that holds strong when patching inevitably falls behind. The first critical step is returning to basic system hardening. This means strictly enforcing multi-factor authentication, removing unnecessary network services, and dividing networks into isolated segments to prevent attackers from moving freely. When preventive measures fail, robust detection and response systems serve as the vital safety net. Security teams must assume some attacks will break through and focus on identifying the behavioral signs of an intruder, rather than relying solely on known threat lists. Finally, organizations must actively test these defenses. Regularly checking network boundaries and practicing response plans ensures that controls work in reality, not just on paper. AI has accelerated the speed of risk, making foundational preparation and rigorous testing the most reliable path to security.

Daily Tech Digest - June 10, 2026

Quote for the day:

“Bad companies are destroyed by crisis. Good companies survive them. Great companies are improved by them.” -- Andy Grove

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Duration: 17 mins • Perfect for listening on the go.

Beware of the Generative AI token trap

Organizations are rapidly adopting generative artificial intelligence without realizing the long-term financial risks hidden in how these services are priced. Right now, major tech providers are offering their intelligence capabilities at artificially low rates to capture market share and encourage companies to build deep dependencies on their platforms. However, this subsidy phase will not last forever. Providers charge by the token, a small unit of processing that acts as a tollbooth for every prompt, response, and automated action. As businesses transition from simple chat tools to more advanced, autonomous systems that loop through multiple steps behind the scenes, token usage multiplies exponentially. If an organization relies entirely on external providers for these capabilities, a pilot project that seems affordable today could become a crippling expense in just a few years when the market inevitably matures and prices increase. To avoid repeating the costly mistakes of the early cloud computing era, companies must treat artificial intelligence as a strategic architectural decision rather than a simple software subscription. The safest approach is prioritizing artificial intelligence sovereignty by building, hosting, and managing smaller, purpose-built models internally. By owning the technology for critical everyday tasks instead of renting massive public models, organizations can maintain control over their data, secure their operating flexibility, and keep their future costs predictable.

Six layers between your LLM and a production agent

The 2026 edition of the AI agents stack outlines six essential layers connecting language models to reliable production systems. This updated framework reflects practical shifts in how developers build these applications. Three major developments redefined the stack: the widespread adoption of the Model Context Protocol (MCP) for standardizing tool connections, the rise of reasoning models that handle complex tasks in a single step, and the evolution of memory into an architectural core rather than a simple database add-on. When evaluating these layers, development teams must consider how much state they need to manage, their tolerance for vendor lock-in, and the effort required to move from prototype to production. The foundation layer, models and inference, is increasingly commoditized, with open-weight options closing the performance gap and making cost and latency the primary considerations. The second layer, protocols and tools, is now dominated by MCP, though securing these connections remains a clear challenge. The third layer, memory and knowledge, shifts the focus toward managing exactly what an agent sees and retains across interactions, utilizing structured fields rather than basic prompts. Ultimately, the guide advises a measured approach to building systems: developers should start with a minimal stack and only introduce additional complexity when a specific component fails.

UK promises age assurance for social media, device-level child safety controls

The UK government is preparing new legislation to restrict children’s access to social media and protect them from online harm. Led by Prime Minister Keir Starmer, the proposed laws are expected to set a minimum age of 16 for social media accounts, similar to recent measures introduced in Australia. Beyond simple age limits, the government is specifically targeting the growing threat of explicit AI-generated content, such as deepfakes. Officials are pressuring tech companies to implement device-level safety controls that would block nudity by default across smartphones and tablets. If tech leaders fail to introduce these protections within three months, the government has threatened to mandate them by law and may even hold executives criminally liable. While these safety measures address urgent concerns, the government’s overall technology policy reveals a notable contradiction. Leaders are heavily promoting the rapid expansion of artificial intelligence infrastructure, yet they are simultaneously trying to manage the severe risks generated by those very technologies. Additionally, officials acknowledge that smartphones themselves, with their inherently addictive designs, are fundamentally part of the problem. As the UK navigates these complex challenges, other nations are taking similar steps; for example, Canada is currently preparing its own age-restriction laws, focusing on temporary safety compliance before allowing younger users back onto major platforms.

Segment With Purpose: A Zero Trust Blueprint For OT Network Segmentation In Manufacturing

Historically, factory floor equipment operated in complete isolation from the rest of the world. Today, manufacturers routinely connect these industrial machines to standard office networks to improve efficiency and gather data. While this connectivity offers benefits, it also creates severe security vulnerabilities. If a network remains completely open, a threat originating in a standard office computer can easily spread to critical production machinery, causing dangerous physical disruptions. To prevent this, manufacturers must deliberately divide their networks into smaller, isolated sections based on specific functional needs. This strategy relies on the principle that no device, user, or system should ever be trusted by default, regardless of its location within the facility. Before making any changes, companies must carefully map every piece of equipment and understand exactly how these machines need to communicate to keep production running smoothly. Once this normal behavior is understood, administrators can implement strict rules that allow only necessary communications while blocking everything else. By grouping similar assets and restricting access to the absolute minimum required, organizations effectively create barriers that contain potential security incidents to a single small area. This methodical, practical approach allows manufacturers to steadily protect their most critical physical operations from modern digital threats without accidentally causing downtime or interrupting daily production schedules.

7 sources of AI debt and how to avoid them

As companies rush to implement artificial intelligence, they risk accumulating a new form of technical burden known as AI debt. Driven by the pressure to move early concepts into active production, teams often bypass critical testing and governance, leaving major improvements for later. This debt typically arises from seven common mistakes. First, running experiments without clear, measurable business goals leads to systems that lack practical value. Second, feeding poor quality data into models simply amplifies errors at a massive scale. Third, failing to monitor systems causes model drift, where performance degrades over time as real-world data changes. Fourth, granting AI agents overly broad access permissions creates severe security and compliance vulnerabilities. Fifth, applying automation over broken or inefficient business processes only worsens existing operational flaws. Sixth, deploying too many unmanaged agents results in sprawl, where abandoned tools compound security risks and duplicate logic. Finally, relying on code generated by AI without proper security reviews can introduce hidden vulnerabilities. To avoid these issues, organizations must slow down and apply strong management practices. By setting clear objectives, enforcing strict data quality standards, monitoring system performance, and implementing robust security checks, companies can confidently deploy AI tools that deliver genuine value instead of future headaches.

From Prediction to Intervention: Integrating Counterfactual Reasoning into AI Decision-Making

As artificial intelligence matures, organizations are realizing that simply predicting the future based on past data is no longer enough. Traditional predictive models can forecast what might happen, but they do not understand the underlying reasons behind those events. This limitation becomes obvious when teams try to make strategic decisions, as predictive models cannot accurately simulate what would occur if a company actively intervened to change its current course of action. To solve this problem, the focus is shifting toward causal reasoning. Instead of just identifying patterns, causal models allow teams to test alternative scenarios and understand cause and effect. By using these systems, organizations can ask what-if questions, helping them separate true drivers of success from mere coincidences. For example, a causal model can clearly reveal whether increased sales were actually caused by a recent marketing push or just a predictable seasonal trend. Implementing this approach helps close the trust gap often found in complex software systems, providing clear explanations that are grounded in logic rather than hidden assumptions. While the transition requires employees to build stronger statistical skills and entirely new ways of thinking, the shift is highly valuable. Moving from basic prediction to true causal understanding gives teams the solid confidence to make clearer, more effective decisions.

How Leaders Can Break Their Team’s Habit Of Safe Thinking

While artificial intelligence can rapidly analyze data and generate standard solutions, true breakthroughs still rely entirely on human imagination. However, extensive industry experience often traps teams in a pattern where past successes and ingrained habits prevent them from exploring new directions. To break this cycle of safe thinking, leaders must intentionally create an environment that fosters creativity rather than simply rewarding efficiency and certainty. First, leaders should adopt a 'yes, and' mindset instead of instinctively dismissing ideas with 'no, because.' This approach keeps unconventional ideas alive long enough to evolve into viable solutions. Second, they must regularly reframe challenges. By changing the core question, such as focusing on solving a customer's problem instead of just increasing sales, teams can escape familiar patterns and discover completely different paths. Third, leaders need to deliberately carve out time for quiet reflection, as continuous pressure from emails, meetings, and tight deadlines stifles fresh ideas. The best thoughts often occur when the brain is allowed to rest and wander. Finally, organizations must reward curiosity just as highly as technical expertise. When leaders encourage their teams to ask deep questions and challenge accepted processes, innovation naturally surfaces. Ultimately, businesses do not necessarily need more creative employees; they just need leaders who understand how to cultivate conditions for new ideas to thrive.

Autonomous Malware Is No Longer Theoretical: AI Worm Proof Of Concept Created In A Lab

Security researchers have recently demonstrated that autonomous AI malware is no longer just a theoretical concept. In a controlled lab environment, a team successfully built a proof-of-concept worm that uses open-weight AI models to independently find vulnerabilities, exploit them, and spread across network systems without any human guidance. Although this specific lab experiment moved slowly and deliberately lacked advanced evasion techniques, it clearly highlights a significant shift in the cyber threat landscape. The economics of cyberattacks are changing; adversaries can now use low-cost AI models to automate and scale their operations. This reality means defensive teams can no longer rely solely on predictable attack patterns or traditional behavioral detection methods, as attackers may soon use AI to generate new tools faster than analysts can classify them. To prepare for these emerging challenges, organizations must focus on complete visibility and strict enforcement across their networks. Understanding exactly which AI agents are operating, what data they access, and what permissions they hold is crucial. Any agent that cannot be monitored must be removed. Additionally, basic patching is no longer enough. IT leaders need to implement strong compensating controls, utilize microsegmentation to limit lateral movement, and strengthen their overall zero-trust security strategies to protect against increasingly sophisticated, autonomous threats.

How cyber-risk can fall flat in the boardroom

When IT leaders present cybersecurity updates to a corporate board of directors, their message often gets lost in highly technical details. While security teams naturally focus on vulnerabilities, threat activities, and audit scores, board members need to understand how these issues affect the actual business. To get real support from the boardroom, technology leaders must stop treating cyber risk as a separate technical problem and start framing it as a core business challenge. This means translating security gaps into measurable business consequences, such as potential financial losses, operational downtime, legal liabilities, or delays to strategic projects. Instead of simply reporting that a system is weak or a patch is delayed, leaders should explain what the organization stands to lose if a failure occurs and what choices are involved in fixing it. Using practical scenario analysis, like estimating the recovery cost if a major vendor goes offline, helps directors weigh priorities and allocate limited resources effectively. Honesty is also essential; leaders should clearly prioritize the most significant exposures without treating every new threat as an overwhelming emergency. By presenting clear, disciplined business cases rather than overwhelming metrics, security leaders can help the board govern cyber risk as a standard part of overall corporate resilience and stability.

From critical to controlled: Cutting vulnerabilities in a live manufacturing environment

Managing software security alerts in a live manufacturing plant is much more complicated than in a standard office setting. When a critical warning pops up, you cannot simply shut down production to install a quick update. Instead, you need a practical process to figure out if that specific alert actually threatens your equipment. The first step is maintaining an automated list of all your machines so you can confirm exactly where the flagged device lives on your network. Next, verify if the reported flaw is truly present, as scanners often guess based on outdated version numbers rather than deep checks. Even if the flaw exists, its real-world risk depends heavily on how easily someone can reach the machine. A vulnerable device hidden securely behind strict network boundaries, jump servers, and custom firewalls is far less dangerous than one exposed to the internet. By tracing the exact steps an attacker would need to take, you can apply focused fixes, like blocking specific network pathways or enforcing strong passwords, without risking a system crash. If you cannot fix the issue right away because the equipment is too old or cannot be turned off, you must formally document the risk alongside extra safety measures. Ultimately, this approach helps you confidently separate genuine threats from harmless alerts, keeping your factory running safely.

Daily Tech Digest - May 12, 2026

Quote for the day:

"Leadership seems mystical. It's actually methodical. The method is learnable and repeatable — and when followed, produces results that feel magical." -- Gordon Tredgold

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

The ghost in the machine: Why AI ROI dies at the human finish line

In "The Ghost in the Machine," Andrew Hallinson argues that the primary barrier to achieving a return on investment for artificial intelligence is not technical inadequacy but human psychological resistance. Despite multi-million dollar investments in advanced data stacks, many organizations suffer from what Hallinson terms an "aversion tax"—the significant loss of potential value caused by low adoption rates and human friction. This resistance stems from three psychological barriers: the "black box paradox," where lack of transparency breeds distrust; "identity threat," where employees feel the technology undermines their professional intuition and autonomy; and the "perfection trap," which involves holding algorithms to much higher standards than human peers. Hallinson illustrates a solution through his experience at ADP, where success was achieved by shifting the focus from restrictive data governance to empowering data democratization. By treating employees as strategic partners and behavioral architects rather than just data processors, leaders can overcome these hurdles. Ultimately, the article posits that technical excellence is wasted if cultural integration is ignored. For executives, the mandate is clear: building an AI-ready culture is just as critical as the engineering itself, as ignoring the human element transforms expensive AI tools into mere "shelfware" that fails to deliver on its mathematical promise.

AI Finds Code Vulnerabilities – Fixing Them Is the Real Challenge

The article "AI Finds Code Vulnerabilities – Fixing Them is the Real Challenge," published on DevOps Digest, explores the double-edged sword of utilizing artificial intelligence in software security. While AI-driven tools have revolutionized the ability to scan vast codebases and identify potential security flaws with unprecedented speed, the author argues that the industry's bottleneck has shifted from detection to remediation. Automated scanners often generate an overwhelming volume of alerts, many of which are false positives or lack the necessary context for immediate action. This "security debt" places a significant burden on development teams who must manually verify and patch each issue. Furthermore, the piece highlights that while AI can identify a problem, it often struggles to understand the complex business logic required to fix it without breaking existing functionality. The real challenge lies in integrating AI into the developer's workflow in a way that provides actionable, verified suggestions rather than just a list of problems. The article concludes that for AI to truly enhance cybersecurity, organizations must focus on automating the "fix" phase through sophisticated generative AI and better developer-security collaboration, ensuring that the speed of remediation finally matches the efficiency of automated detection.

Data Replication Strategies: Enterprise Resilience Guide

The article "Data Replication Strategies: Enterprise Resilience Guide" from Scality explores the critical methodologies for ensuring data durability and availability across physical systems. At its core, the guide highlights the fundamental tradeoff between consistency and availability, a tension that dictates how organizations architect their storage infrastructure. Synchronous replication is presented as the gold standard for zero-data-loss scenarios (RPO of zero) because it requires all replicas to acknowledge a write before completion; however, this introduces significant write latency. Conversely, asynchronous replication optimizes for performance and long-distance fault tolerance by propagating changes in the background, which decouples write speed from network latency but risks losing data not yet synchronized. Beyond timing, the content details architectural models like active-passive, where one primary site handles writes, and active-active, where multiple sites simultaneously serve traffic. The article also addresses consistency models such as strong, causal, and session consistency, emphasizing that the choice depends on specific application requirements. By aligning replication strategies with Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO), the guide argues that organizations can build a resilient infrastructure capable of surviving data center failures while balancing cost, bandwidth, and performance.

When Should a DevOps Agent Act Without Human Approval?

The article titled "When Should a DevOps Agent Act Without Human Approval?" by Bala Priya C. outlines a comprehensive framework for navigating the transition from manual oversight to autonomous operations in DevOps. Central to this transition is a six-point autonomy spectrum, ranging from basic observation at Level 0 to full autonomy at Level 5. The author highlights that determining the appropriate level of independence for an agent depends on four critical factors: the reversibility of the action, the potential blast radius, the quality of incoming signals, and time sensitivity. For most organizations, the author suggests maintaining agents within Levels 1 through 3, where humans remain primary decision-makers or provide explicit approval for suggested actions. Level 4, which involves agents executing tasks and then notifying humans with a defined override window, should be reserved for narrowly defined, low-risk activities. Full Level 5 autonomy is only recommended after an agent has established a consistent, documented track record of success at lower levels. To manage these shifts safely, the article emphasizes the necessity of robust guardrails, including progressive rollouts, granular approval gates, and high signal-quality thresholds. This structured approach ensures that automation enhances operational efficiency without compromising the security or stability of the production environment, ultimately allowing engineers to focus on higher-value strategic innovation and developmental work.

8 guiding principles for reskilling the SOC for agentic AI

The article "8 guiding principles for reskilling the SOC for agentic AI" outlines a strategic roadmap for Security Operations Centers (SOCs) transitioning toward an AI-driven future. The first principle, embracing the agentic imperative, highlights that moving at "machine speed" is essential to counter advanced adversaries effectively. Leadership plays a critical role by setting a tone of rapid experimentation and "failing fast" to foster internal innovation. While cultural resistance—particularly fears regarding job displacement—is common, the article suggests addressing this by redefining roles around high-value tasks such as AI safety and governance. Hands-on training in secure sandboxes is vital for building practitioner confidence and "model intuition," allowing analysts to recognize when AI outputs are structurally flawed. Crucially, the "human-in-the-loop" principle ensures that non-deterministic AI remains under human oversight through clear escalation paths and audit trails. Beyond technology, the shift requires rethinking organizational structures to move from siloed disciplines to holistic, outcome-based orchestration. Ultimately, fostering collaboration between humans and machines allows analysts to relocate from "inside the process" to a supervisory position above it. By reimagining the operating model, CISOs can transform chaotic environments into calm, efficient hubs where agentic AI handles automated triage while humans provide strategic judgment and effective long-term accountability.

New DORA Report Claims Strong Engineering Foundations Drive AI RoI

The May 2026 InfoQ article summarizes Google Cloud's DORA report, "ROI of AI-Assisted Software Development," which offers a structured framework for calculating financial returns from AI adoption. The research argues that AI acts primarily as an amplifier; rather than repairing flawed processes, it magnifies existing organizational strengths and weaknesses. Consequently, achieving sustainable ROI necessitates robust engineering foundations, including quality internal platforms, disciplined version control, and clear workflows. A central concept introduced is the "J-Curve of value realization," where organizations typically face a temporary productivity dip due to the "tuition cost of transformation"—incorporating learning curves, verification taxes for AI-generated code, and essential process adaptations. Despite this initial drop, the report models a substantial first-year ROI of 39% for a typical 500-person organization, with a payback period of approximately eight months. However, leaders are cautioned against an "instability tax," as increased delivery speed may overwhelm manual review gates and elevate failure rates if not balanced with automated testing and continuous integration. Looking ahead, the research predicts compounding gains in years two and three, potentially reaching a 727% return as teams transition toward autonomous agentic workflows. Ultimately, the report emphasizes that AI’s true value lies in clearing systemic bottlenecks and unlocking latent human creativity, rather than pursuing simple headcount reduction.

Compliance Without Chaos In Modern Delivery

The article "Compliance Without Chaos In Modern Delivery" emphasizes transforming compliance from a disruptive, quarterly hurdle into a seamless, integrated component of the software delivery lifecycle. Rather than treating audits as high-stakes oral exams, the author advocates for building automated controls directly into existing engineering workflows. This "Policy as Code" approach effectively eliminates the ambiguity of "folklore" policies by enforcing rules through CI/CD gates, such as mandatory pull request reviews, automated testing, and artifact traceability. To maintain a state of continuous readiness, teams should implement automated evidence collection, ensuring that audit trails for changes, access, and security checks are generated as a natural byproduct of daily development work. The piece also highlights the importance of robust access management, favoring short-lived privileges and group-based permissions over static, high-risk credentials. Furthermore, continuous monitoring is described as essential for identifying silent failures in critical areas like encryption, log retention, and vulnerability status before they escalate into major incidents. By maintaining an updated evidence map and an "audit-ready pack" year-round, organizations can achieve a "boring" compliance posture. Ultimately, the goal is to shift from reactive manual efforts to a disciplined, automated machine that consistently proves security and regulatory adherence without sacrificing delivery speed or engineering focus.

Ask a Data Ethicist: What Are the Legal and Ethical Issues in Summarizing Text with an AI Tool?

The use of AI tools for text summarization introduces significant legal and ethical challenges that organizations must navigate carefully. Legally, the primary concern revolves around copyright infringement, as these tools are often trained on large datasets containing proprietary data without explicit consent, potentially leading to complex intellectual property disputes. Furthermore, privacy risks emerge when users input sensitive or personally identifiable information into external AI systems, potentially violating strict regulations like the GDPR or CCPA. From an ethical standpoint, the article highlights the danger of algorithmic bias, where AI might inadvertently emphasize or distort certain viewpoints based on inherent flaws in its training data. Hallucinations represent another critical ethical risk, as AI can generate plausible-looking but factually incorrect summaries, leading to the spread of misinformation. To mitigate these systemic issues, the author emphasizes the importance of implementing robust data governance frameworks and maintaining a consistent "human-in-the-loop" approach. This ensures that summaries are rigorously reviewed for accuracy and fairness before being utilized in professional decision-making processes. Transparency regarding the use of automated tools is also paramount to maintaining public and stakeholder trust. Ultimately, while AI summarization offers immense efficiency, its deployment requires a balanced strategy that prioritizes legal compliance and ethical integrity.

UK chief executives make AI priority but delay plans

A recent report from Dataiku, based on a Harris Poll survey of nine hundred global chief executives, indicates that UK leaders are positioning artificial intelligence as a paramount corporate priority while simultaneously exercising significant caution in its implementation. The study, which focused on organizations with annual revenues exceeding five hundred million dollars, revealed that eighty-one percent of UK CEOs rank AI strategy as a top or high priority, a figure that notably surpasses the global average of seventy-three percent. However, this high level of ambition is tempered by a growing fear of financial waste; seventy-seven percent of British respondents expressed greater concern about over-investing in the technology than under-investing, compared to sixty-five percent of their international peers. This fiscal wariness has led to tangible delays in project rollouts across the country. Specifically, fifty-one percent of UK executives admitted to postponing AI initiatives due to regulatory uncertainty, a sharp increase from twenty-six percent just one year prior. As questions regarding return on investment and governance persist, a widening gap has emerged between boardroom aspirations and practical execution. UK leaders are increasingly weighing their expenditures more carefully, shifting from rapid adoption toward a more calculated approach that prioritizes oversight and navigates the evolving legislative landscape to avoid costly mistakes.

Open Innovation and AI will define the next generation of manufacturing: Annika Olme, CTO, SKF

Annika Olme, the CTO of SKF, emphasizes that the future of manufacturing lies at the intersection of open innovation and advanced technology like Artificial Intelligence. She highlights how SKF is transitioning from being a traditional bearing manufacturer to a digital-first, data-driven leader. By fostering a culture of deep collaboration with startups, academia, and technology partners, the company accelerates the development of smart solutions that optimize industrial processes globally. AI and machine learning are central to this evolution, particularly in predictive maintenance, which allows customers to anticipate failures and reduce downtime significantly. Olme also underscores the critical role of sustainability, noting that digital transformation is intrinsically linked to circularity and energy efficiency. By leveraging sensors and real-time data analysis, SKF helps various industries minimize waste and lower their carbon footprint. The “Smart Factory” vision involves integrating these technologies into every stage of the product lifecycle, from design to end-of-use recycling. Ultimately, the goal is to create a seamless synergy between human ingenuity and machine intelligence, ensuring that manufacturing remains both competitive and environmentally responsible. This holistic approach to innovation not only boosts productivity but also redefines how global industrial leaders address modern challenges like climate change, resource scarcity, and supply chain volatility.

Daily Tech Digest - December 22, 2025

Quote for the day:

"Life isn’t about getting and having, it’s about giving and being." -- Kevin Kruse

Browser agents don’t always respect your privacy choices

A key issue is the location of the language model. Seven out of eight agents use off device models. This means detailed information about the user’s browser state and each visited webpage is sent to servers controlled by the service provider. When the model runs on remote servers, users lose control over how search queries and sensitive webpage content are processed and stored. While some providers describe limits on data use, users must rely on service provider policies. Browser version age is another factor. Browsers release frequent updates to patch security flaws. One agent was found running a browser that was 16 major versions out of date at the time of testing. ... Agents also showed weaknesses in TLS certificate handling. Two agents did not show warnings for revoked certificates. One agent also failed to warn users about expired and self signed certificates. Trusting connections with invalid certificates leaves agents open to machine-in-the-middle attacks that allow attackers to read or alter submitted information. ... Agent decision logic sometimes favored task completion over protecting user information, leading to personal data disclosure. This resulted in six vulnerabilities. Researchers supplied agents with a fictitious identity and observed whether that information was shared with websites under different conditions. Three agents disclosed personal information during passive tests, where the requested data was not required to complete the task.

What CISOs should know about the SolarWinds lawsuit dismissal

For many CISOs, the dismissal landed not as an abstract legal development, but as something deeply personal. ... Even though the SolarWinds case sparked a deeper recognition that cybersecurity responsibility should be a shared responsibility across enterprises, shifting policy priorities and future administrations could once again put CISOs in the SEC’s crosshairs, they warn. ... The judge’s reasoning reassured many security leaders, but it also exposed a more profound discomfort about how accountability is assigned inside modern organizations. “The area that a lot of us were really uncomfortable about was the idea that an operational head of security could be personally responsible for what the company says about its cybersecurity investments,” Sullivan says. He adds, “Tim didn’t have the CISO title before the incident. And so there was just a lot there that made security people very concerned. Why is this operational person on the hook for representations?” But even if he had had the CISO role before the incident, the argument still holds, according to Sullivan. “Historically, the person who had that title wasn’t a quote-unquote ‘chief’ in the sense that they’re not in the little room of people who run the company,” Sullivan says. ... If the SolarWinds case clarified anything, it’s that relief is temporary and preparation is essential. CISOs have a window of opportunity to shore up their organizational and personal defenses in the event the political pendulum swings and makes CISOs litigation targets again.

Global uncertainty is reshaping cloud strategies in Europe

Europe has been debating digital sovereignty for years, but the issue has gained new urgency amid rising geopolitical tensions. “The political environment is changing very fast,” said Ollrom. A combination of trade disputes, sanctions that affect access to technology, and the possibility of tariffs on digital services has prompted many European organizations to reconsider their reliance on US hyperscaler clouds. ... What was once largely a public-sector concern now attracts growing interest across a wide range of private organizations as well. Accenture is currently working with around 50 large European organizations on digital-sovereignty-related projects, said Capo. This includes banks, telcos, and logistics companies alongside clients in government and defense. ... Another worry is the possibility that cloud services will be swept up in future trade disputes. If the EU imposes retaliatory tariffs on digital services, the cost of using hyperscaler cloud platforms could hike overnight, and organizations heavily dependent on them may find it hard to switch to a cheaper option. There’s also the prospect that organizations could lose access to cloud services if sanctions or export restrictions are imposed, leaving them temporarily or permanently locked out of systems they rely on. It’s a remote risk, said Dario Maisto, a senior analyst at Forrester, but a material one. “We are talking of a worst-case scenario where IT gets leveraged as a weapon,” he said.

What the AWS outage taught CIOs about preparedness

For many organizations, the event felt like a cyber incident even though it wasn’t, but it raised a difficult question for CIOs about how to prepare for a disruption that lives outside your infrastructure, yet carries the same operational and reputational consequences as a security breach. ... Beyond strong cloud architecture, “Preparedness is the real differentiator,” he says. “Even the best technology teams can’t compensate for gaps in scenario planning, coordination, and governance.” ... Within Deluxe, disaster recovery tests historically focused on applications the company controlled, while cyber tabletops focused on simulated intrusions. The AWS outage exposed the gap between those exercises and real-world conditions. Shifting its applications from AWS East to AWS West was swift, and the technology team considered the recovery a success. Yet it was far from business as usual, as developers still couldn’t access critical tools like GitHub or Jira. “We thought we’d recovered, but the day-to-day work couldn’t continue because the tools we depend on were down,” he says. ... In a well-architected hybrid cloud setup, he says resilience is more often a coordination problem than a spending problem, and distributing workloads across two cloud providers doesn’t guarantee better outcomes if the clouds rely on the same power grid, or experience the same regional failure event. ... Jayaprakasam is candid about the cultural challenge that comes with resilience work.

Winning the density war: The shift from RPPs to scalable busway infrastructure in next-gen facilities

“Four or five years ago, we were seeing sub-ten-kilowatt racks, and today we're being asked for between 100 and 150 kilowatts, which makes a whole magnitude of difference,” says Osian. “And this trend is going to continue to rise, meaning we have to mobilize for tomorrow’s power challenges, today.” Rising power demands also require higher available fault currents to safely handle larger, more dynamic surges in the circuit. Supporting equipment must be more resilient and reliable to maintain safe and efficient distribution. With change happening so quickly, adopting a long-term strategy is essential. This requires building critical infrastructure with adaptability and flexibility at its core. ... A modular approach offers another tactical advantage: speed. With a traditional RPP setup, getting power physically hooked up from A to B on a per-rack basis is time and resource-consuming, especially at first installation. By reducing complexity with a plug-and-play modular design slotted in directly over the racks, the busway delivers the swift reinforcements modern facilities need to stay ahead. ... “One of the advancements we've made in the last year is creating a way for users to add a circuit from outside the arc flash boundary. While the Starline busway is already rated for live insertion – meaning it’s safe out of the box – we’ve taken safety to the next level with a device called the Remote Plugin Actuator. It allows a user to add a circuit to the busway without engaging any of the electrical contacts directly.”

Building a data-driven, secure and future-ready manufacturing enterprise: Technology as a strategic backbone

A central pillar of Prince Pipes and Fittings’ digital strategy is data democratisation. The organisation has moved decisively away from static reports towards dynamic, self-service analytics. A centralised data platform for sales and supply chain allows business users to create their own dashboards without dependence on IT teams. Desai further states, “Sales teams, for instance, can access granular data on their smartphones while interacting with customers, instantly showcasing performance metrics and trends. This empowerment has not only improved responsiveness but has also enhanced user confidence and satisfaction. Across functions, data is now guiding actions rather than merely describing outcomes.” ... Technology transformation at Prince Pipes and Fittings has been accompanied by a conscious effort to drive cultural change. Leadership recognised early that democratising data would require a mindset shift across the organisation. Initial resistance was addressed through structured training programs conducted zone-wise and state-wise, helping users build familiarity and confidence with new platforms. ... Cyber security is treated as a business-critical priority at Prince Pipes and Fittings. The organisation has implemented a phase-wise, multi-layered cyber security framework spanning both IT and OT environments. A simple yet effective risk-classification approach i.e. green, yellow, and red, was used to identify gaps and prioritise actions. ... Equally important has been the focus on human awareness.

The Next Fraud Problem Isn’t in Finance. It’s in Hiring: The New Attack Surface

The uncomfortable truth is that the interview has become a transaction. And the “asset” being transferred is not a paycheck. It’s access: to systems, data, colleagues, customers, and internal credibility. ... Payment fraud works because the system is trying to be fast. The same is true in hiring. Speed is rewarded. Friction is avoided. And that creates a predictable failure mode: an attacker’s job is to make the process feel normal long enough to get to “approved.” In payments, fraudsters use stolen cards and compromised accounts. In hiring, they can use stolen faces, voices, credentials, and employment histories. The mechanics differ, but the objective is identical: get the system to say yes. That’s why the right question for leaders is not, “Can we spot a deepfake?” It’s, “What controls do we have before we grant access?” ... Many companies verify identity late, during onboarding, after decisions are emotionally and operationally “locked.” That’s the equivalent of shipping a product and hoping the card wasn’t stolen. Instead, introduce light identity proofing before final rounds or before any access-related steps. ... In payments, the critical moment is authorization. In hiring, it’s when you provision accounts, ship hardware, grant repository permissions, or provide access to customer or financial systems. That moment deserves a deliberate gate: confirm identity through a known-good channel, verify references without relying on contact info provided by the candidate, and run a final live verification step before credentials are issued.

Agent autonomy without guardrails is an SRE nightmare

Four-in-10 tech leaders regret not establishing a stronger governance foundation from the start, which suggests they adopted AI rapidly, but with margin to improve on policies, rules and best practices designed to ensure the responsible, ethical and legal development and use of AI. ... When considering tasks for AI agents, organizations should understand that, while traditional automation is good at handling repetitive, rule-based processes with structured data inputs, AI agents can handle much more complex tasks and adapt to new information in a more autonomous way. This makes them an appealing solution for all sorts of tasks. But as AI agents are deployed, organizations should control what actions the agents can take, particularly in the early stages of a project. Thus, teams working with AI agents should have approval paths in place for high-impact actions to ensure agent scope does not extend beyond expected use cases, minimizing risk to the wider system. ... Further, AI agents should not be allowed free rein across an organization’s systems. At a minimum, the permissions and security scope of an AI agent must be aligned with the scope of the owner, and any tools added to the agent should not allow for extended permissions. Limiting AI agent access to a system based on their role will also ensure deployment runs smoothly. Keeping complete logs of every action taken by an AI agent can also help engineers understand what happened in the event of an incident and trace back the problem.

Where Architects Sit in the Era of AI

In the emerging AI-augmented ecosystem, we can think of three modes of architect involvement: Architect in the loop, Architect on the loop, and Architect out of the loop. Each reflects a different level of engagement, oversight, and trust between an Architect and intelligent systems. ... What does it mean to be in the loop? In the Architect in the Loop (AITL) model, the architect and the AI system work side by side. AI provides options, generates designs, or analyzes trade-offs, but humans remain the decision-makers. Every output is reviewed, contextualized, and approved by an architect who understands both the technical and organizational context. This is where the Architect is sat in the middle of AI interactions ... What does it mean to be on the loop? As AI matures, parts of architectural decision-making can be safely delegated. In the Architect on the Loop (AOTL) model, the AI operates autonomously within predefined boundaries, while the architect supervises, reviews, and intervenes when necessary. This is where the architect is firmly embedded into the development workflow using AI to augment and enhance their own natural abilities. ... What does it mean to be out of the loop? In the AOOTL model, we see a world where the architect is no longer required in the traditional fashion. The architectural work of domain understanding, context providing, and design thinking is simply all done by AI, with the outputs of AI being used by managers, developers, and others to build the right systems at the right time.

Cloud Migration of Microservices: Strategy, Risks, and Best Practices

The migration of microservices to the cloud is a crucial step in the digital transformation process, requiring a strategic approach to ensure success. The success of the migration depends on carefully selecting the appropriate strategy based on the current architecture's maturity, technical debt, business objectives, and cloud infrastructure capabilities. ... The simplest strategy for migrating to the cloud is Rehost. This involves moving applications as is to virtual machines in the cloud. According to research, around 40% of organizations begin their migration with Rehost, as it allows for a quick transition to the cloud with minimal costs. However, this approach often does not provide significant performance or cost benefits, as it does not fully utilize cloud capabilities. Replatform is the next level of complexity, where applications are partially adapted. For example, databases may be migrated to cloud services like Amazon RDS or Azure SQL, file storage may be replaced, and containerization may be introduced. Replatform is used in around 22% of cases where there is a need to strike a balance between speed and the depth of changes. A more time-consuming but strategically beneficial approach is Refactoring (or Rearchitecting), in which the application undergoes a significant redesign: microservices are introduced, Kubernetes, Kafka, and cloud functions (such as Lambda and Azure Functions) are utilized, as well as a service bus.

Daily Tech Digest - December 16, 2025

Quote for the day:

"Worry less, smile more. Don't regret, just learn and grow." -- @Pilotspeaker

The battle for agent connectivity: Can MCP survive the enterprise?

"MCP is the UI for agents. The future of asking to book an and have a pizza available when you arrive at the hotel only works if we have the connectivity," said Dag Calafell III, director of Technology Innovation at MCA Connect, an IT consultancy for manufacturers. But while seamless connectivity might be the Holy Grail for consumer apps, critics argue that it is irrelevant -- or even dangerous -- for the enterprise. ... Notably, MCP has significant backing from prominent companies, including Google, , Microsoft and its creator, Anthropic. Indeed, Calafell argued that while there are competitors out there, "MCP is winning" precisely because it has seen significant adoption by large software providers. Still, MCP clearly has significant issues -- mostly because it's in its infancy. MCP's rapidly evolving specification, uneven tooling, unclear security and governance controls, and lack of standardized memory, debugging, and orchestration make it better for experimentation than reliable enterprise use today. ... "There is room to innovate with a security-first 'MCP-like' standard that is resource aware, with trusted catalogues, privileges, scopes, etc. These would either be built on top of MCP, a sort of MCP v2, or introduced as part of a new protocol," said , co-founder and CTO at . And, of course, there remains an evolving trend that the AI industry will take an entirely different direction.

Digital Twin in Railways: A Practical Solution to Managing Complex Rail Systems

In the context of railways, are being deployed to improve asset lifecycle management, predictive maintenance, and infrastructure planning. By integrating inputs from IoT devices and advanced analytics platforms, these models help engineers monitor structural health, detect anomalies, and plan maintenance before failures occur. ... As the scale and complexity of rail networks continue to grow, the use of digital twins offers a unified, comprehensive view of interconnected assets, which empowers rail operators with faster decision-making and better coordination across departments. This technology is gradually becoming a core component of smart railway ecosystems. ... The architecture of a digital twin in railway systems is built upon the integration of multiple digital technologies, including Building Information Modelling (BIM), the Internet of Things (IoT), Geographic Information Systems (GIS), and data analytics platforms. Together, these technologies create a unified framework that connects the physical and digital environments of railway infrastructure and operations. ... The integration of operational data, including train movements, energy consumption, and passenger flows, allows operators to simulate different scenarios and optimise timetables, headways, and energy use. In dense networks such as urban metro systems, this contributes to improved punctuality and efficient energy utilisation.

Stop mimicking and start anchoring

It’s a fundamental truth that most are ignoring in their rush to emulate Big Tech playbooks. The result is a systematic misallocation of resources based on a fundamental misunderstanding of how value creation works across industries. ... the strategic value of IT should be measured by how effectively it addresses industry-specific value creation. Different industries have vastly different technology intensity and value-creation dynamics. In our view, CIOs must therefore resist trend-driven decisions and view IT investment through their industry’s value-creation to sharpen competitive edge. To understand why IT strategies diverge across industries shaped by sectoral realities and maturity differences, we need to examine how business models shape the role of technology. ... funding business outcomes rather than chasing technology fads is easier said than done. It’s difficult to unravel the maze created by the relentless march of technological hype versus the grounded reality of business. But the role of IT is not universal; its business relevance changes from one industry to another. ... Long-term value from emerging technologies comes from grounded application, not blind adoption. In the race to transform, the wisest CIOs will be those who understand that the best technology decisions are often the ones that honour, rather than abandon the fundamental nature of their business. The future belongs not to those who adopt the most tech, but to those who adopt the right tech for the right reasons.

Build vs buy is dead — AI just killed it

Ssomething fundamental has changed: AI has made building accessible to everyone. What used to take weeks now takes hours, and what used to require fluency in a programming language now requires fluency in plain English.When the cost and complexity of building collapse this dramatically, the old framework goes down with them. It’s not build versus buy anymore. It’s something stranger that we haven't quite found the right words for. ... And it's not some future state. This is already happening. Right now, somewhere, a customer rep is using AI to fix a product issue they spotted minutes ago. Somewhere else, a finance team is prototyping their own analytical tools because they've realized they can iterate faster than they can write up requirements for engineering. Somewhere, a team is realizing that the boundary between technical and non-technical was always more cultural than fundamental. The companies that embrace this shift will move faster and spend smarter. They’ll know their operations more deeply than any vendor ever could. They'll make fewer expensive mistakes, and buy better tools because they actually understand what makes tools good. The companies that stick to the old playbook will keep sitting through vendor pitches, nodding along at budget-friendly proposals. They’ll debate timelines, and keep mistaking professional decks for actual solutions. Until someone on their own team pops open their laptop, says, “I built a version of this last night. Want to check it out?,”

Quantum Tech Hits Its “Transistor Moment,” Scientists Say

“This transformative moment in quantum technology is reminiscent of the transistor’s earliest days,” said lead author David Awschalom, the Liew Family Professor of molecular engineering and physics at the University of Chicago, and director of the Chicago Quantum Exchange and the Chicago Quantum Institute. “The foundational physics concepts are established, functional systems exist, and now we must nurture the partnerships and coordinated efforts necessary to achieve the technology’s full, utility-scale potential. How will we meet the challenges of scaling and modular quantum architectures?” ... Although advanced prototypes have demonstrated system operation and public cloud access, their raw performance remains early in development. For example, many meaningful applications, including large-scale quantum chemistry simulations, could require millions of physical qubits with error performance far beyond what is technologically viable today. ... “While semiconductor chips in the 1970s were TLR-9 for that time, they could do very little compared with today’s advanced integrated circuits,” he said. “Similarly, a high TRL for quantum technologies today does not indicate that the end goal has been achieved, nor does it indicate that the science is done and only engineering remains. Rather, it reflects a significant, yet relatively modest, system-level demonstration has been achieved—one that still must be substantially improved and scaled to realize the full promise.”

Before you build your first enterprise AI app

Model weights are becoming undifferentiated heavy lifting, the boring infrastructure that everyone needs but no one wants to manage. Whether you use Anthropic, OpenAI, or an open weights model like , you are getting a level of intelligence that is good enough for 90% of enterprise tasks. The differences are marginal for a first version. The “best” model is usually just the one you can actually access securely and reliably. ... We used to obsess over the massive cost of training models. But for the enterprise, that is largely irrelevant. AI is all about inference now, or the application of knowledge to power applications. In other words, AI will become truly useful within the enterprise as we apply models to governed enterprise data. The best place to build up your AI muscle isn’t with some moonshot agentic system. It’s a simple retrieval-augmented generation (RAG) pipeline. What does this mean in practice? Find a corpus of boring, messy documents, such as HR policies, technical documentation, or customer support logs, and build a system that allows a user to ask a question and get an answer based only on that data. This forces you to solve the hard problems that actually build a moat for your company. ... When you build your first application, design it to keep the human in the loop. Don’t try to automate the entire process. Use the AI to generate the first draft of a report or the first pass at a SQL query, and then force a human to review and execute it.

Cloudflare reveals AI surge & Internet ‘bot wars’ in 2025

Cloudflare reported that use of AI models and AI crawling activity increased sharply. It said crawling for model training accounted for the majority of AI crawler traffic during the year. Training-related crawlers generated traffic that reached as much as seven to eight times the level of retrieval-augmented generation and search crawlers at peak. Traffic from training crawlers was also as much as 25 times higher than AI crawlers tied to direct user actions. The company said was the most widely used on its network. It was used by more than three times as many accounts as the next most popular models from providers such as OpenAI and . Cloudflare added that Google’s crawling bot remained the dominant automated actor on the Internet. It said ’s crawl volume exceeded that of all other leading AI bots by a wide margin and was the largest single source of automated traffic it observed. ... Cloudflare reported a notable shift in the sectors that face the highest volume of . Civil society and non-profit organisations became the most attacked group for the first time. The company linked this trend to the sensitivity and financial value of the data held by such organisations. This includes personal information about donors, volunteers and beneficiaries. Cloudflare’s data also showed changes in the causes of major Internet outages.

Who Owns AI Risk? Why Governance Begins with Architecture

But as AI systems grow more complex, so do their risks. Bias, opacity, data misuse, model drift, or even overreliance on AI outputs can all cause serious business, ethical, and reputational damage. This raises an uncomfortable question: who actually owns the risk of AI? ... AI doesn’t live in isolation. It consumes enterprise data, depends on cloud services, interacts with APIs, and influences real business processes.Governance, therefore, can’t rely on policies alone, it must be designed, structured, and embedded into the architecture itself. For instance, companies like Microsoft and Google have embedded AI governance directly into their architectural blueprints creating internal AI Ethics and Risk Committees that review model design before deployment. This proactive structure ensures compliance and builds trust long before a model reaches production. ... In other words, AI Governance is not a department, it’s an ecosystem of shared responsibility.Enterprise Architects connect the dots, Business Owners set the direction, Data Scientists implement, and Governance Boards oversee. But the real maturity comes when everyone in the organization, from the C-suite to the operational level, understands that AI is a shared asset and a shared risk. ... Modern enterprise architecture is no longer only about connecting systems. It’s about connecting responsibility. The moment artificial intelligence becomes part of the business fabric, architecture must evolve to ensure that governance isn’t something external or reactive, it’s embedded in the very design of every AI-enabled solution.

The 5 power skills every CISO needs to master in the AI era

According to the , nearly 40% of core job skills will change by 2030, driven primarily by AI, data and automation. For security professionals, this means that expertise in network defense, forensics and patching — while still essential — is no longer enough to create value. The real impact comes from how we interpret, communicate and apply what AI enables. ... The biggest myth in security is that technical mastery equals longevity. In truth, the more we automate, the more we value human differentiation. Success in the next decade won’t depend on how much code you can write — but on how effectively you can connect, translate and lead across systems and silos. When I look at the most resilient organizations today, they share one trait: They see cybersecurity not as a control function, but as a strategic enabler. And their leaders? They’re fluent in both algorithms and empathy. The future of cybersecurity belongs to those who build bridges — not just firewalls. Cybersecurity is no longer a war between humans and machines — it’s a collaboration between both. The organizations that succeed will be the ones that combine AI’s precision with human empathy and creative foresight. As AI handles scale, leaders must handle meaning. And that’s the true essence of power skills. The future of cybersecurity belongs to those who can blend AI’s precision with human expertise — and lead with both.

Manufacturing is becoming a test bed for ransomware shifts

“Manufacturing depends on interconnected systems where even brief downtime can stop production and ripple across supply chains,” said , Director of Threat Research, . “Attackers exploit this pressure: despite encryption rates falling to 40%, the median ransom paid still reached $1 million. While half of manufacturers stopped attacks before encryption, recovery costs average $1.3 million and leadership stress remains high. Layered defenses, continuous visibility, and well-rehearsed response plans are essential to reduce both operational impact and financial risk,” Rose continued. Teams were able to stop attacks before encryption in a larger share of cases, which likely contributed to the decline. Early detection helped reduce disruption, although strong detection did not guarantee a smooth recovery. ... IT and security leaders in manufacturing see progress in some areas but ongoing gaps in others. Detection appears to be improving. Recovery is becoming steadier. Payment rates are declining. But operational weaknesses persist. Skills shortages, aging protections, and limited visibility into vulnerabilities continue to contribute to compromises. These factors shape outcomes as much as attacker capability. The findings also show a need for stronger internal support. Security teams are absorbing organizational and emotional strain that can affect long term performance. Manufacturing operations depend on stable systems, and teams cannot maintain stability without workloads they can manage.