Predictive Network Maintenance: Using AI for Forecasting Network Failures

Posted on by Editorial Team

Author: Akshat Kapoor is an accomplished technology leader and the Director of Product Line Management at Alcatel-Lucent Enterprise, with over 20 years of experience in product strategy and cloud-native design.

In today’s hyper-connected enterprises—where cloud applications, real-time collaboration and mission-critical services all depend on robust Ethernet switching—waiting for failures to occur simply is no longer tenable. Traditional, reactive maintenance models detect switch faults only after packet loss, throughput degradation or complete device failure. By then, customers have already been affected, SLAs breached and costly emergency fixes mobilized. Predictive maintenance for Ethernet switching offers a fundamentally different approach: by continuously harvesting switch-specific telemetry and applying advanced analytics, organizations can forecast impending faults, automate low-impact remediation and dramatically improve network availability.

Executive Summary

This white paper explores how predictive maintenance transforms Ethernet switching from a break-fix paradigm into a proactive, data-driven discipline. We begin by outlining the hidden costs and operational challenges of reactive maintenance, then describe the telemetry, analytics and automation components that underpin a predictive framework. We’ll then delve into the machine-learning lifecycle that powers these capabilities—framing the problem, preparing and extracting features from data, training and validating models—before examining advanced AI architectures for fault diagnosis, an autonomic control framework for rule discovery, real-world benefits, deployment considerations and the path toward fully self-healing fabrics.

The Cost of Reactive Switching Operations

Even brief interruptions at the leaf-spine fabric level can cascade across data centers and campus networks:

  • Direct financial impact
     A single top-of-rack switch outage can incur tens of thousands of pounds in lost revenue, SLA credits and emergency support.
  • Operational overhead
     Manual troubleshooting and unscheduled truck rolls divert engineering resources from strategic projects.
  • Brand and productivity erosion
     Repeated or prolonged service hiccups undermine user confidence and degrade workforce efficiency.

Reactive workflows also struggle to keep pace with modern switching architectures with high speed networks, multivendor, multi-os environments and overlay fabrics (VXLAN-EVPN, SD_WAN) obscuring the root causes.

By the time alarms trigger, engineers may face thousands of error counters, interface statistics and protocol logs—without clear guidance on where to begin.

A Predictive Maintenance Framework

Predictive switching maintenance reverses the order of events: it first analyzes subtle deviations in switch behavior, then issues alerts or automates remediation before packet loss materializes. A robust framework comprises four pillars:

1. Comprehensive Telemetry Collection

– Physical-layer metrics: per-port CRC/FEC error counts; optical power, temperature and eye-diagram statistics for SFP/SFP28/SFP56 transceivers; power-supply voltages and currents.
– ASIC and fabric health: queue-depth and drop-statistics per line card; ASIC-temperature and control-plane CPU/memory utilization; oversubscription and arbitration stalls.
– Control-plane indicators: BGP route-flap counters; OSPF/IS-IS adjacency timers and hello-loss counts; LLDP neighbor timeouts.
– Application-level signals: NetFlow/sFlow micro-burst detection; per-VLAN or per-VXLAN-segment flow duration and volume patterns.

Real-time streams and historical archives feed into a centralized feature store, enabling models to learn seasonal patterns, rare events and gradual drifts.

2. Machine-Learning Lifecycle for Networking

Building an effective predictive engine follows a structured ML workflow—crucial to avoid ad-hoc or one-off models. This lifecycle comprises: framing the problem, preparing data, extracting features, training and using the model, then feeding back for continuous improvement .

  • Frame the problem: Define whether the goal is classification (e.g., fault/no-fault), regression (time-to-failure), clustering (anomaly grouping) or forecasting (traffic volume prediction).
  • Prepare data: Ingest both offline (historical fault logs, configuration snapshots) and online (real-time telemetry) sources: flow data, packet captures, syslogs, device configurations and topology maps.
  • Feature extraction: Compute statistical summaries—packet-size variance, flow durations, retransmission rates, TCP window-size distributions—and filter out redundant metrics.
  • Train and validate models: Split data (commonly 70/30) for training and testing. Experiment with supervised algorithms (Random Forests, gradient-boosted trees, LSTM neural nets) and unsupervised methods (autoencoders, clustering). Evaluate performance via precision, recall and F1 scores.
  • Deploy and monitor: Integrate models into streaming platforms for real-time inference and establish MLOps pipelines to retrain models on schedule or when topology changes occur, preventing drift.

3. Validation & Continuous Improvement

– Pilot deployments: A/B testing in controlled segments (e.g., an isolated VLAN or edge cluster) validates model accuracy against live events.
– Feedback loops: NOC and field engineers annotate false positives and missed detections, driving iterative retraining.
– MLOps integration: Automated pipelines retrain models monthly or after major topology changes, monitor for drift, and redeploy updated versions with minimal disruption.

4. Automated Remediation

– Context-rich alerts: When confidence thresholds are met, detailed notifications pinpoint affected ports, line cards or ASIC components, and recommend low-impact maintenance windows.
– Closed-loop actions: Integration with SD-WAN or EVPN controllers can automatically redirect traffic away from at-risk switches, throttle elephant flows, shift VLAN trunks to redundant uplinks or apply safe hot-patches during off-peak hours.
– Escalation paths: For scenarios outside modelled cases or persistent issues, the platform escalates to on-call teams with enriched telemetry and root-cause insights, accelerating manual resolution.

Advanced AI Architectures for Fault Diagnosis

While traditional predictive maintenance often relies on time-series forecasting or anomaly detection alone, modern fault-management platforms benefit from hybrid AI systems that blend probabilistic and symbolic reasoning:

  • Alarm filtering & correlation
     Neural networks and Bayesian belief networks ingest streams of physical- and control-plane alarms, learning to compress, count, suppress or generalize noisy event patterns into high-level fault indicators.
  • Fault identification via case-based reasoning
     Once correlated alarms suggest a probable fault category, a case-based reasoning engine retrieves similar past “cases,” adapts their corrective steps to the current context, and iteratively refines its diagnosis—all without brittle rule sets .
  • Hybrid control loop
     This two-stage approach—probabilistic correlation followed by symbolic diagnosis—yields greater robustness and adaptability than either method alone. New fault outcomes enrich the case library, while retraining pipelines update the neural or Bayesian models as the fabric evolves.

Real-World Benefits

Organizations that have adopted predictive switching maintenance report tangible improvements:

  • Up to 50 percent reduction in unplanned downtime through pre-emptive traffic steering and targeted interventions.
  • 80 percent faster mean-time-to-repair (MTTR), thanks to enriched diagnostics and precise root-cause guidance.
  • Streamlined operations, with fewer emergency truck rolls and lower incident-management overhead.
  • Enhanced SLA performance, enabling “five-nines” (99.999 percent) availability that would otherwise require significant hardware redundancies.

Deployment Considerations

Transitioning to predictive maintenance requires careful planning:

  1. Data normalization
     – Consolidate telemetry formats across switch vendors and OS versions.
    – Leverage streaming telemetry protocols (gNMI, OpenConfig, InfluxDB) to reduce polling overhead.
  2. Stakeholder engagement
     – Demonstrate quick wins (e.g., detecting degrading optics) in pilot phases to build trust.
    – Train NOC teams on new alert semantics and automation workflows.
  3. Scalability & architecture
     – Use cloud-native ML platforms or on-prem GPU clusters to process terabytes of telemetry without impacting production controllers.
    – Implement a feature-store layer that supports low-latency lookups for real-time inference.
  4. Security & compliance
     – Secure telemetry streams with encryption and role-based access controls.
    – Ensure data retention policies meet regulatory requirements.

Toward Self-Healing Fabrics

Autonomic Framework & Rule Discovery

By embedding predictive analytics, hybrid AI architectures and an autonomic control framework at the switch level, organizations lay the groundwork for networks that not only warn of problems, but actively heal themselves—ensuring uninterrupted service, lower operational costs and greater agility in an ever-more demanding digital landscape.

To achieve true self-healing fabrics, predictive maintenance must operate within an autonomic manager—a control-loop component that senses, analyzes, plans and acts upon switch telemetry:

  1. Monitor & Analyze
     Streaming telemetry feeds are correlated into higher-order events via six transformations (compression, suppression, count, Boolean patterns, generalization, specialization). Visualization tools and data-mining algorithms work in concert to surface candidate correlations .
  2. Plan & Execute
     Confirmed correlations drive decision logic: high-confidence predictions trigger SD-WAN or EVPN reroutes, firmware patches or operator advisories, while novel alarm patterns feed back into the rule-discovery lifecycle.
  3. Three-Tier Rule-Discovery
     – Tier 1 (Visualization): Human experts use Gantt-chart views of alarm lifespans to spot recurring patterns.
    – Tier 2 (Knowledge Acquisition): Domain specialists codify and annotate these patterns into reusable correlation rules.
    – Tier 3 (Data Mining): Automated mining uncovers less obvious correlations, which experts then validate or refine—all maintained in a unified rule repository .

Embedding this autonomic architecture at the switch level ensures the predictive maintenance engine adapts to new hardware, topologies and traffic behaviours without manual re-engineering.

Predictive maintenance for Ethernet switching is a key stepping stone toward fully autonomic networks. Future enhancements include:

  • Business-aware traffic steering
     Models that incorporate application-level SLAs (e.g., voice quality, transaction latency) to prioritize remediation actions where they matter most.
  • Intent-based orchestration
     Declarative frameworks in which operators specify high-level objectives (“maintain sub-millisecond latency for video calls”), and the network dynamically configures leaf-spine fabrics to meet those goals.
  • Cross-domain integration
     Unified intelligence spanning switches, routers, firewalls and wireless controllers, enabling end-to-end resilience optimizations.

By embedding predictive analytics and automation at the switch level—supported by a rigorous machine-learning lifecycle—organizations lay the groundwork for networks that not only warn of problems but actively heal themselves. The result is uninterrupted service, lower operational costs and greater agility in an ever-more demanding digital landscape.

References

·  S. Iyer, “Predicting Network Behavior with Machine Learning,” Proceedings of the IEEE Network Operations and Management Symposium, June 2019
·  Infraon, “Best Ways to Predict and Prevent Network Outages with AIOps,” 2024

·  Infraon, “Top 5 AI Network Monitoring Use Cases and Real-Life Examples in ’24,” 2024

·  “Predicting Network Failures with AI Techniques,” White Paper, 2024

·           Denise W. Gürer, Irfan Khan, Richard Ogier, An Artificial Intelligence Approach to Network Fault Management


Data quality for unbiased results: Stopping AI hallucinations in their tracks

Posted on by Editorial Team

Artificial Intelligence is changing customer-facing businesses in big ways, and its impact keeps growing. AI-powered tools deliver real benefits for both customers and company operations. Still, adopting AI isn’t without risks. Large Language Models often produce hallucinations, and if these are fed biased or incomplete data, they can lead to costly mistakes for organizations.  

For AI to produce reliable results, it needs data that is full, precise, and free of bias. When training or operational data is biased, sketchy, unlabeled, or just plain wrong, AI can still spew hallucinations. That means statements that sound plausible yet lack fact or that carry hidden bias; these distort the insight and harm decision-making. Clean data in daily operations can’t safeguard against hallucinations if the training data is off or if the review team lacks strong reference data and background knowledge. That is why businesses now rank data quality as the biggest hurdle for training, launching, scaling, and proving the value of AI projects. The growing demand for tools and techniques to verify AI output is both clear and critical.

Following a clear set of practical steps with medical data shows how careful data quality helps AI produce correct results. First, examine, clean, and improve both training data and operational data using automatic rules and reasoning. Next, bring in expert vocabulary and visual retrieval-augmented generation in these clean data settings so that supervised quality assurance and training can be clear and verifiable. Then, set up automated quality control that tests, corrects, and enhances results using curated content, rules, and expert reasoning.  

To keep AI hallucinations from disrupting business, a thorough data quality system is essential. This system needs “gold standard” training data, business data that is cleaned and continuously enriched, and supervised training based on clear, verifiable content, machine reasoning, and business rules. Beyond that, automated outcome testing and correction must rely on quality reference data, the same business rules, machine reasoning, and retrieval-augmented generation to keep results accurate.

Accuracy in AI applications can mean the difference between life and death for people and for businesses

Let’s look at a classic medical example to show why correct AI output matters so much. We need clean data, careful monitoring, and automatic result checks to stay safe.

In this case, a patch of a particular drug is prescribed, usually at a dose of 15 milligrams. The same drug also comes as a pill, and the dose for that is 5 milligrams. An AI tool might mistakenly combine these facts and print, “a common 15 mg dose, available in pill form.” The error is small, but it is also very dangerous. Even a careful human might miss it. A medical expert with full focus would spot that the 15 mg pill dose is three times too much; taking it could mean an overdose. If a person with no medical training asks an AI about the drug, they might take three 5 mg pills, thinking that’s safe. That choice could lead to death.

When a patient’s health depends on AI results, the purity, labeling, and accuracy of the input data become mission-critical. These mistakes can be thwarted by merging clean, well-structured training and reference datasets. Real-time oversight, training AI feedback loops with semantic reasoning and business rules, and automated verification that cross-checks results against expert-curated resources all tighten the screws on system reliability.  

Beyond the classic data clean-up tasks of scrubbing, merging, normalizing, and enriching, smart semantic rules, grounded in solid data, drive precise business and AI outputs. Rigorous comparisons between predicted and actual results reveal where inaccuracies lurk. An expert-defined ontology, alongside reference bases like the Unified Medical Language System (UMLS), can automatically derive the correct dosage for any medication, guided solely by the indication and dosage form. If the input suggests a pill dosage that violates the rule—say a 10-milligram tablet when the guideline limits it to 5—the system autonomously flags the discrepancy and states, “This medication form should not exceed 5 milligrams.”

To guarantee that our training and operational datasets in healthcare remain pure and inclusive, while also producing reliable outputs from AI, particularly with medication guidelines, we must focus on holistic data stewardship. The goal is to deliver the ideal pharmaceutical dose and delivery method for every individual and clinical situation.  

The outlined measures revolve around this high-stakes objective. They are designed for deployment within low-code or no-code ecosystems, thereby minimizing the burdens on users who must uphold clinical-grade data integrity while already facing clinical and operational pressure. Such environments empower caregivers and analysts to create, monitor, and refine data pipelines that continuously cleanse, harmonize, and enrich the streams used to train and serve the AI.

Begin with thoroughly cleansed and enhanced training data

To deliver robust models, first profile, purify, and enrich both training and operational data using automated rules together with semantic reasoning. Guarding against hallucinations demands that training pipelines incorporate gold-standard reference datasets alongside pristine business data. Inaccuracies, biases, or deficits in relevant metadata within the training or operational datasets will, in turn, compromise the quality and fairness of the AI applications that rely on them.

Every successful AI initiative must begin with diligent and ongoing data quality management: profiling, deduplication, cleansing, classification, and enrichment. Remember, the principle is simple: great data in means great business results out. The best practice is to curate and weave training datasets from diverse sources so that the resulting demographic, customer, firmographic, geographic, and other pertinent data pools are of consistently high quality. Moreover, data quality and data-led processes are not one-off chores; they demand real-time attention. For this reason, embedding active data quality – fully automated and embedded in routine business workflows – becomes non-negotiable for any AI-driven application. Active quality workflows constantly generate and execute rules that detect problems identified during profiling, letting the system cleanse, integrate, harmonize, and enrich the data that the AI depends on. These realities compel organizations to build AI systems within active quality frameworks, ensuring the insights they produce are robust and the outcomes free of hallucinations.

In medication workflows, the presence of precise, metadata-enriched medication data is non-negotiable, and the system cites this reference data at every turn. Pristine reference data can seamlessly integrate at multiple points in the AI pipeline: 

  • First, upstream data profiling, cleansing, and enrichment clarify the dosing and administration route, guaranteeing that only accurate and consistent information flows downstream. 
  • Second, this annotated data supplements both supervised and unsupervised training. By guiding prompt and result engineering, it ensures that any gap or inaccuracy in dose or administration route is either appended or rectified. 
  • Finally, the model’s outputs can be adjusted in real time. Clean reference data, accessed via retrieval-augmented generation (RAG) techniques or observable supervision with knowledge-graph-enhanced GraphRAG, serves as both validator and corrector. 

Through these methods, the system can autonomously surface, flag, or amend records or recommendations that diverge from expected knowledge—an entry suggesting a 15-milligram tablet in a 20-milligram regimen, for instance, is immediately flagged for review or adjusted to the correct dosage.

Train your AI application with expert-verified, observable semantic supervision  

First, continuously benchmark outputs against authoritative reference data, including gritty semantic relationships and richly annotated metadata. This comparison, powered by verifiable and versioned semantic resources, is non-negotiable during initial model development and remains pivotal for accountable governance throughout the product’s operational lifetime.  

Integrate high-fidelity primary and reference datasets with aligned ontological knowledge graphs. Engineers and data scientists can then dissect flagged anomalies with unprecedented precision. Machine reasoning engines can layer expert-curated data quality rules on top of the semantic foundation – see the NCBO’s medication guidelines – enabling pinpointed, supervision-friendly learning. For example, a GraphRAG pipeline visually binds retrieval and generation, fetching relevant context to bolster each training iteration.  

The result is a transparent training loop fortified by observable semantic grounding. Business rules, whether extant or freshly minted, can be authored against this trusted scaffold, ensuring diverse outputs converge on accuracy. By orchestrating training in live service, the system autonomously detects, signals, and rectifies divergences before they escalate.

Automate oversight, data retrieval, and enrichment/correction to scale AI responsibly

Present-day AI deployments still rely on human quality checks before results reach customers. At enterprise scale, we must embed automated mechanisms that continually assess outputs and confirm they satisfy both quality metrics and semantic consistency. To reach production, we incorporate well-curated reference datasets and authoritative semantic frameworks that execute semantic entailments—automated enrichment or correction built on domain reasoning—from within ontologies. By leveraging trusted external repositories for both reference material and reasoning frameworks, we can apply rules and logic to enrich, evaluate, and adjust AI-generated results at scale. Any anomalies that exceed known thresholds can still be flagged for human review, but the majority can be resolved automatically via expert ontologies, validated logic, and curated datasets. The gold-standard datasets mentioned previously support both model training and automated downstream supervision, as they enable real-time comparisons between generated results and expected reference patterns.

While we acknowledge that certain sensitive outputs—like medical diagnoses and treatment recommendations—will always be reviewed by physicians, we can nevertheless guarantee the accuracy of all mission-critical AI when we embed clean, labeled reference data and meaningful, context-aware enrichment at every stage of the pipeline.

To make AI applications resistant to hallucinations, start with resources that uphold empirical truth. Ground your initiatives in benchmark reference datasets, refined, clean business records, and continuous data quality practices that yield transparent, semantically coherent results. When these elements work in concert, they furnish the essential groundwork for the automated, measurable, and corrective design, evaluation, and refinement of AI outputs that can be trusted in practice.

How AI is reshaping e-commerce experiences with data-driven design

Posted on by Editorial Team

In today’s fast-moving e-commerce environment, artificial intelligence is changing the game-leveraging real-time analytics, behavioural modelling, and hyper-personalisation to craft smarter shopping experiences. While online retail keeps gaining momentum, AI-driven systems empower brands to build interfaces that feel more intuitive, adaptive, and relevant to every shopper. This article examines how data-centric AI tools are rewriting the blueprint of e-commerce design and performance, highlighting pivotal use cases, metrics that matter, and fresh design breakthroughs.

Predictive personalization powered by big data

A key space where AI drives value in e-commerce is predictive personalisation. By crunching huge data troves – everything from past purchase logs to live clickstream data – machine-learning models can foresee what customers want next and tweak the user interface in real time. AI can rearrange product grids, flag complementary items, and customise landing pages to reflect each shopper’s unique tastes. This granular personalisation correlates with higher conversion rates and reduced bounce rates, particularly when the experience flows seamlessly across devices and touchpoints.

With over 2 billion active monthly online shoppers, the knack for forecasting intent has turned into a vital edge. By marrying clustering techniques with collaborative filtering, merchants can deliver recommendations that align closely with shopper expectations, while also smoothing the path for upselling and cross-selling.

Adaptive user interfaces

In contrast to fixed design elements, adaptive interfaces react on-the-fly to incoming user data. If, for example, a shopper habitually explores eco-conscious apparel, the display may automatically promote sustainable labels, tweak default filter settings, and elevate pertinent articles. By harnessing reinforcement learning, the system incrementally fine-tunes the entire user path in a cycle of real-time refinement.

Retail websites are increasingly adopting these adaptive architectures to refine engagement—from consumer electronics portals to curated micro-boutiques. To gauge the effectiveness of every adjustment, practitioners employ A/B testing combined with multivariate testing, generating robust analytics that guide the ongoing, empirically driven maturation of the interface.

AI-enhanced content generation  

AI-driven tools aren’t only reimagining user interfaces; they’re also quietly reshaping the material that fills them. With natural language generation, e-commerce brands can automatically churn out product descriptions, FAQs, and blog entries that are already SEO-tight. Services such as Neuroflash empower companies to broaden their content output while keeping language quality and brand voice on point.  

When generative AI becomes part of the content production chain, editing and testing cycles speed up. This agility proves invaluable for brands that need to roll out new campaigns or zero in on specialised audiences. A retailer with an upcoming seasonal line, for instance, can swiftly create several landing-page drafts, each tailored to a distinct demographic or buyer persona.

Sophisticated search and navigation

Modern search engines have crossed the limit of simple keyword spotting. With semantic understanding and behavioural modelling, these intelligent engines parse questions with greater finesse, serving results that matter rather than just match. Voice activation, image-based search, and conversational typing are emerging as the primary ways shoppers browse and discover products.

These innovations matter most for the mobile-first audience, who prioritise speed and precision on small screens. Retailers are deploying intelligent tools that simplify every tap, drilling into heatmaps, click trails, and conversion funnels to reshape menus, filters, and overall page design for minimal friction.

Optimising Design Workflows with AI

AI is quietly transforming how teams craft and iterate on product experiences. In tools like Figma and Adobe XD, machine learning now offers on-the-fly recommendations for layouts, colour palettes, and spacing grounded in established usability and conversion heuristics. As a result, companies sizing up the expense of a new site are starting to treat AI features the same way they’d treat CDN costs: essential ways to trim repetitive toil and tighten the pixel grid.

Shifting to web design partners who bake AI into their processes often pays off when growth is the goal. By offloading the choice of grid systems and generating initial wireframe iterations, AI liberates creative talent, allowing them to invest time in nuanced storytelling and user empathy rather than grid alignments. Scalability then becomes a design layer that pays dividends instead of a later headache.

From instinct to engineered insight

AI is steering e-commerce into a phase where every customer journey is informed – not by instinctive hunches, but by relentless, micro-level data scrutiny. Predictive preference mapping, real-time interface adaptation, smart search refinement, and automatic content generation now converge, helping retailers replace broad segmentation with hyper-precise, living experiences.  

With customer demands climbing and margin pressure intensifying, data-driven, AI-backed design now equips brands to create expansive, individualised, and seamless shopping landscapes without proportional cost escalations. Astute retailers recognise that adopting these generative capabilities is not a question of optional upgrade, but a foundational pivot they must complete to retain competitive relevance.

How China’s Generative AI Advances Are Transforming Global Trade

Posted on by Editorial Team

Contributed by: Sharon Zheng, FinTech Expert

The evolving Chinese Generative AI technology is emerging as a distinct component of innovation that is set to transform China’s international trade. Given my background as an independent consultant on the intersection of technology, trade, and globalisation, I will discuss how China’s regulatory policy on generative AI is transforming global business from a qualitative perspective.

China’s Supremacy in Generative AI Patents

During the period of 2014 to 2023, China submitted around 38000 applications for patent on generative AI technology, which constitutes nearly 70% of that filed globally. The number is six times that of the United States which filed 6276 patents within the same time frame. Out of the top ten applicants of generative AI patents in the world, 6 are Chinese companies which include Tencent, Ping An Group, Baidu and the Chinese Academy of Sciences. 

This drastic increase in the number of patent applications demonstrates China’s relentless expansionist agenda in AI technology. China has been pursuing a dominant position in AI technology. Nevertheless, the enormous number of patents raises issues concerning their value and possible use. In the past, China’s strategy regarding patents leaned more toward quantity which came at the cost of quality. The most crucial issue remains whether these generative AI patents will be transformed into actual real world applications and to what extent will they hold relevance internationally as well as domestically.

Evaluating The Quality and Commercialization of Patents

Although China has an impressive number of patents, their strategic business value and quality are critical considerations. In the past, China’s patent policy focused on the quantity of patents filed over their quality, but the data shows increasing positive trends. The patent grant ratio for applicants from China improved to 55% in 2023 from 30% in 2019, and the rate of commercialisation in high-tech industries improved too. This implies a positive change in the rate of innovation for generative AI.

This trend can be illustrated with a few examples. For instance, research from China developed an AI powered algorithm that can make the process of drug development faster, which means cures for different diseases can be made available to patients in a timely manner. Another example is Ping An Insurance Group that has taken a leading role in AI patenting in China by applying for 101 generative AI patents in areas of application in banking and finance, which make up 6% of their portfolio. These results are a demonstration of China’s increasing willingness to improve the standard of quality and real-world impact of their generative AI innovations.

Challenges in patent quality 

An analysis on a country’s advancement in AI domestically and abroad can be seen through the lens of its patent filings. China’s enormous surge in patent registration is an indicator of its aggressive support in developing technology innovation. WIPO studies attribute 47% of patent applications globally to China in 2023, indicating that the country is looking to step ahead and dominate the technological arms race. At the same time, focussing on the influence China will have on the AI ecosystem internationally reveals multifaceted issues in patenting. 

Issues In Patent Quality

While one may focus on the sheer quantity of patents being applied for, WIPO attests that there persists a problem in the relevancy of these patents internationally. The grant ratio – a measure of how many patents are issued in proportion to how many are applied for – brings further granularity into this. In China, the grant ratio of new-generation AI patents is roughly 32%. Leading tech players like Huawei and Baidu have grant ratios of 24% and 45% respectively. In comparison, developed nations like Japan and Canada have much higher grant ratios sitting at 77% and 70% respectively. This shows that a large number of Chinese corporations are making patent applications but a large portion of these applications fail to pass the assessment requirements that come with them.

At the same time, Chinese patents do not have effective international reach. China only files 7.3% of its patent applications overseas, which indicates a concentration on the domestic market. This is in stark contrast to countries like Canada and Australia which file a large percentage of their patents overseas, which illustrates a proactive approach to fostering multiple jurisdictions for intellectual property protection. The lack of international Chinese patents can limit their relevance and use in other countries which undermines the possibility of commercially marketing Chinese AI technologies abroad. 

Impact on international Trade

Global competitiveness is determined by trade relations of a country suffering from lack of quality and scope of internationally imposed patents. Internationally enforced high-quality patents form the axis of competitive advantage provided to a specific firm enabling them to strategically license technologies, penetrate new markets, and consolidate globally with other firms. On the other hand, commercially unproductive patents without tangible attributes and unprotected in vital markets yield no benefit at all and stagnate business activities. 

China emphasizing the quantity of patents filed rather than their quality or international scope economically has some consequences: 

  • Local Emphasis: The high number of domestically filled famed patents demonstrates that Chinese inventions are designed primarily for domestic consumption. All these factor enhancements will arguably lead to the reduced global influence of China’s AI technologies and limit participation in the global value chain.
  • Lack of Market Defense: Without having a patent to secure their interests, Chinese companies face the risk of not being able to commercialize their technologies in foreign countries due to technology spoofing leading to loss of revenue.
  • Impression of Innovation Value: Limited international applications coupled with lower grant ratios may have an impact on the Chinese innovation value perception which may affect foreign investments and partnerships.

Strategic Approaches

China can consider the following approaches in order to boost the impact of its AI innovations globally:

  • Improving Patent Standards: Moving from quantity to quality focus increases grant ratios and more innovations are likely to pass international standards. Such a strategy is likely to require more rigorous internal review processes accompanied by more funding of research and development.
  • Increasing International Applications: It is important to motivate businesses and corporations to file for patents in various jurisdictions which promote global IP protection which in turn aids in commercialization and collaboration internationally.
  • Toughening Enforcement Frameworks: Enforcement of IP rights is one the most effective ways to increase the attractiveness of Chinese patents to foreign investors and/ or partners.

Global Competitiveness and Strategic Considerations

The global technology landscape has become significantly different because of the improvements of China in artificial intelligence (AI) in recent years. One of the most important transformations is the development of DeepSeek, a new Chinese AI model ready to compete with Western models in terms of price, and effectiveness with American tech behemoths. This shift indicates that China has set its sights on improving the grade and global Anglo-Saxon relevance of its AI technology to remain competitive internationally. 

DeepSeek: A Disruptive Force in AI 

DeepSeek burst onto the scene in December 2024 and received massive recognition for its capability to match the performance of the best Western AI models, particularly  OpenAI’s GPT-4, while being significantly cheaper to develop. This is impressive particularly because China doesn’t have access to advanced AI chips and many  extensive datasets which have always been barriers to China’s development of AI. The achievement of DeepSeek indicates that Chinese corporations are increasingly discovering novel ways to deal with these obstacles and change the scope of deep learning and artificial intelligence.

DeepSeek’s approach deeply altered conversations and business practices within the industry. NVIDIA and other dominant industry players’ stocks were subjected to huge fluctuations as capital interrogated the market regarding DeepSeek’s entry. This has been called an “AI Sputnik moment” and now serves as a marker in the significant change in the AI competition. 

Strategic Repercussions 

DeepSeek’s rise shifts the most critical perceptions following the scaling expectations of OpenAI. Active users increased to more than 400 mln weekly in February 2025, from 300 million in December 2024. This growth is another illustration of the already aggressive nature of AI competition and has proven to be even more dominant when it comes to innovation politics. 

Pressure competition stemming from DeepSeek’s calculated attacks equally shifted strategy among Western tech leaders, the most notable being Musk’s unbidden OpenAI stock buyout bid of 97 billion dollars when the AI company was valued at about 300 billion dollars. This best demonstrates the rush towards integrating AI to thwart unforeseen competitors. 

Improving Parenting Standards and Global Scope

It is well known that China leads in the amount of AI-related patent applications, however, there are serious concerns about the relevance and overall quality of these patents.

The achievement of DeepSeek demonstrates progress towards not just boosting the volume, but also the quality and usefulness of AI patents. This tactic assists in ensuring that such innovations are not only useful in the country, but are also competitive internationally. 

On the other hand, there are still gaps. The inability to access advanced AI chips, a staggering amount of quality data, and the complex algorithms required for large model training remain considerable obstacles. Such restrictions could prevent the effective deployment and international outreach of China’s AI technologies, which could make China’s generative AI patents less effective in contributing to the advancement of AI technology globally. 

DeepSeek’s creation serves as an example of China’s increasing competitiveness in the technology field. China’s innovation capabilities, in spite of constraints, was showcased when it developed a low-cost AI model that surpassed the efforts of the West. Subsequently, this change forces large technology companies around the world to revise their strategic plans in light of yet another strong competitor.

In order to maintain and improve its position within the global AI landscape, however, China will first need to overcome dire obstacles. Advanced technology access, enhanced data quality, innovation-friendly environments, and newly formulated patent strategies contingent on quality and international focus are all necessary. These changes would allow the technological advancements to be utilized and for China to gain global competitiveness.

Conclusion

AI technology advancements from China are changing the dynamics of international trade. China is improving AI technologies and automating trade functions to improve efficiency and set new trade standards. However, China’s long-term impact on AI will largely depend on the quality and scope of China’s generative AI patents and their relevance outside the country. With the constant evolution of these technologies, it’s easy to see their direct integration into global commerce, which makes it increasingly important for stakeholders worldwide to take notice and strategize accordingly.

Why hyper-personalised UX is the future – and how AI powers it

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Author:  Victor Churchill, a Product Designer at Orb Innovations. He is an exceptional Product Designer with over 7 years of experience identifying and simplifying complexities with B2B, B2C and B2B2C solutions. Victor has a proven track record of conducting research analyses utilising these insights to drive business growth and impact.

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After taking the world by storm in just a few years AI has now come to change marketing and UX design. With people adapting to new advertising techniques too quickly the only way to really engage with potential customers is to create a meaningful connection on a deeper level. That’s where hyper-personalised user experiences step in.

In a world of marketing, hyper-personalised user experiences are a way to drive conversions by presenting to viewers highly relevant offers or content. At the heart of this technology lies powerful AI that crunches vast amounts of user data in order to tailor content to a specific user. To do this it goes through multiple sources of information like user behavior data (clicks, search queries, time spent on each page), demographic and profile data (age, location, language), and contextual data (device model, time of day, and browsing session length).

After gathering all the data it could collect, AI segments users into different categories based on the goals of the campaign: frequent buyers and one-time visitors, local and international shoppers, and so on. Algorithms then analyse potential ways for improving user experience. Based on the results, the software decides to prioritise one form of content or feature over the other for said user. For example, a fintech app notices that a user frequently transfers money internationally and starts prioritising currency exchange rates in their dashboard.

As Senior Product Designer at Waypoint Commodities, I always draw parallels between hyper-personalised user experiences and the way streaming platforms like Netflix and Spotify operate. These services personalise product recommendations based on the customer’s spending preferences and tastes. This way users get experiences that feel custom-made, which can dramatically increase engagement, time spent on platform, and conversion rates. A report from McKinsey revealed that 71 percent of consumers expected companies to deliver personalised interactions, and 76 percent got frustrated when it didn’t happen. The numbers are even higher if we speak about the US market, where up to 78% of customers are more likely to recommend brands with hyper-personalised interactions.

This trend is most visible in fintech and e-commerce, where user experience is critical for driving conversions, building trust, and keeping customers engaged. In these spheres additional friction such as irrelevant recommendations, or a lack of personalization can lead to lost revenue and abandoned transactions.

In order to create a hyper-personalised design it is important not to overstep. A study by Gartner revealed that poor personalisation efforts risk losing 38% of existing customers, emphasising the need for clarity and trust in personalisation strategies. The situation can backfire if users feel like they are being constantly watched. To avoid this, I always follow a few simple but essential principles when designing for personalisation.

Be transparent.

When you show something hyper-personalised to your customer, add a simple note saying ‘Suggested for you based on your recent purchases‘ or ‘Recommended for you based on your recent activity‘. This way users are informed about the channels you get information from, and your recommendations don’t come as a shock for them.

Don’t forget to leave some control to the user.

Even if you fine-tune your system to perfectly detect the needs of customers, some people can still find the recommendations irrelevant. This is why it’s important to allow customization through buttons like ‘Stop recommending this‘ and ‘Show more like this‘.

Don’t overuse personal data.

Even though sometimes it can feel like everybody is used to sharing data with advertisers, violating personal borders can usually lead to unsatisfying results. According to a survey by KPMG, 86% of consumers in the US expressed growing concerns about the privacy of their personal data. And 30% of participants said they are not willing to share any personal data at all.

Be subtle in your personalization and don’t implement invasive elements that mention past interaction too explicitly or use sensitive data. For example, don’t welcome a user with the words ‘Worried about your credit score?‘ or ‘Do you remember the shirt you checked out at 1:45 AM last night?‘.

Be clear about AI usage.

AI-driven personalisation lifts revenue by 10-15% on average, reports say. However, if the majority of the decisions in the recommendation system is made by artificial intelligence, people have a right to know that. Don’t put too much stress on it — just mention the important part with a little message saying that your suggestions are powered by AI. This way you can avoid misunderstanding.

Even though current systems already work well at detecting the needs of the customers, there’s still room for improvement. The hyper-personalised user experiences of the future could learn to read new data like voice, gestures and emotions or even anticipate needs before users even express them. It is clear that in the future AI-driven UX design will only become better, and now is the best time to embrace this technology.

AI in Cybersecurity: Principles, Mitigation Frameworks, and Emerging Developments

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What is AI in Cybersecurity?

The use of intelligent algorithms and multitasking models to AI determining the cyber threat scenarios deals with electronic warfare intelligently. It is called Artificial Intelligence (AI). The sophisticated cybersecurity frameworks powered by AI are not only capable of preemptively analyzing and responding within split seconds but also detecting massive volumes of incoming data, categorizing relevant information, and sifting through troves of data.

AI’s capabilities alongside responding to other security measures is Supporting Measures can be understood in the following ways. Processing tasks such as log review and vulnerability scans can be executed with ease. With AI, the cybersecurity personnel can focus on more complex tasks as they are provided with agile bots who are able to take care of time level, strategy deployment, and simulation plans. Real time attack alerts, AI’s role in automation plays an important role in threat detection also with advanced detection AI systems, threats can be dealt with in real time. Quieter and emergency response solutions can be set. In addition, the evolving nature of threats enables AI systems to be adaptable.

AI in cybersecurity boosts vulnerability management and reinforces the ability to counter emerging cyber attacks. Real-time monitoring and proactive readiness helps mitigate damages, AI technologies shift through behavioral patterns and automates phishing detection and monitoring. AI learns from previous changes and identifies emerging bases to emerging bases, thus enhancing defensive posture and claiming the sensitive information.

How Can AI Assist in Avoiding Cyberattacks?

AI in cybersecurity enhances cyber threat intelligence and allows security professionals to:

  • Look for signs of looming cyberattack
  • Improve their cyber defenses
  • Examine usage data like fingerprints, keystrokes, and voices to confirm user identity
  • Uncover evidence  – or clues – about specific cyber attackers and their true identity

Is Automating Cybersecurity a Risk?

Currently, monitoring systems require more human resources than necessary. AI technology can assist in this area and greatly improves multitasking capabilities. Using AI to track threats will optimize time management for organizations under constant pressure to identify new threats, further enhancing their capabilities. This is especially important in light of modern cyberattacks becoming more sophisticated. 

The information security field sits on a treasure trove of prior cases in automation technology, which have made ample use of AI elsewhere in business operations. Thus there is no danger in using AI for automating cybersecurity. For instance, in automating the onboarding process, Human Resources grant new employees access to company assets and provide them the resources requisite to execute their roles using sophisticated software tools. 

AI solutions allow companies with limited numbers of expert security personnel to maximize their expenditures on cybersecurity through automation. Organizations can now fortify their operations and improve efficiency without having to find qualified skilled personnel.

The advantages of implementing AI automation in cybersecurity are:

  • Saving on costs: The integration of AI technology with cybersecurity enables the faster collection of data which aids in the incident response management, making it more agile. Furthermore, the need for security personnel to perform monotonous manual work is eliminated, allowing them to engage in more strategic tasks that are advantageous to the company. 
  • Elimination of oversight: A common weakness of conventional security systems is the reliance on an operator which is always prone to error. AI technology in cybersecurity eliminates most of the security processes that require intervention by people. Resources that are truly in demand can then be allocated where they are needed most, resulting in superior outcomes.
  • Improved strategic thinking: Automated systems in cybersecurity assist an organization in pinpointing gaps in its security policies and rectifying them. This allows the establishment of procedures aimed at achieving a more secure IT infrastructure.  

Despite all of this, organizations must understand that cybercriminals adapt their tactics to counter new AI-powered cybersecurity measures. Cybercriminals use AI to launch sophisticated and novel attacks and introduce next-generation malware designed to compromise both traditional systems and those fortified with AI.

The Role of AI in Cybersecurity

1. Password safeguards and user authentication  

Cybersecurity AI implements advanced protective measures for safeguarding passwords and securing user accounts through effective authentication processes. Logging in using web accounts is commonplace nowadays, especially for users who wish to obtain products or for those who want to submit sensitive information using forms. These online accounts need to be protected using sophisticated authentication mechanisms to ensure sensitive information does not fall into the wrong hands.  

Automated validation systems using AI technologies such as CAPTCHA, Facial Recognition, and Fingerprint Scanners allow organizations to confirm whether a user trying to access a service is actually the account owner. These systems counter cybercrime techniques like brute-force attacks and credential stuffing which could otherwise jeopardize the entire network of an organization.

2. Measures to Detect and Prevent Phishing 

Phishing shows up on the business risk radar as a threat that many industries have to deal with, which makes them susceptible within any business. AI has the ability to help firms discover malice and determine anomalies in messages through email security solutions. It has the ability to analyze emails both in context and content to determine in a fraction of time whether they are spam, phishing masquerades or genuine emails. AI makes identifying signs of phishing fast and easy through spoofing, forged senders and domain name misspellings.

Understanding how users communicate, their typical behavior, and the wording that they use becomes easier for the AI that has already gotten past the ML algorithm techniques training period. An advanced spear phishing threat is more challenging to tackle, as the attackers impersonate high-profile companies such as company CEO’s, and it becomes critical how you prevent it. To stop the access of leading corporate account incursion, AI has the ability to identify irregularities in user activity that can cause such damage, and thereby suppress possibilities of spear phishing.

3. Understanding Vulnerability Management 

Each year, newly discovered vulnerabilities are on the rise because of the smarter ways cybercriminals use to hack. With the high volume of new vulnerabilities everyday, businesses struggle to use their traditional systems to keep high risk threats at bay. 

UEBA (User and Entity Behavior Analytics), an AI-driven security solution, allows businesses to monitor the activities of users, devices, and servers. This enables detection of abnormal activities which can be potential zero day attacks. AI in cybersecurity gives businesses the ability to defend themselves from unpatched vulnerabilities, long before they are officially reported and patched.

4. Network Security

Network security requires the creation of policies and understanding the network’s topography, both of which are time-intensive processes. An organization can enact processes for allowing connections that are easily verified as legitimate and scrutinizing those that require deeper inspection for possible malice after policies are set. Organizations can also implement and enforce a zero trust approach to security due to the existence of these policies.  

On the other hand, policies across different networks need to be created and managed, which is manual and very time-consuming. Lack of proper naming conventions for applications and workloads means that security teams would spend considerable time figuring out which workloads are tied to specific applications. Over time, AI is capable of learning an organization’s network traffic patterns, enabling it to recommend relevant policies and workloads.

5. Analyzing actions

Analyzing actions allows firms to detect emerging risks alongside recognized weaknesses. Older methods of threat detection monitoring security perimeters with attack patterns and compromise indicators are inefficient due to the ever-growing amount of attacks launched by cyber criminals each year.  

To bolster an organization’s threat hunting capabilities, behavioral analytics can be implemented. It processes massive amounts of user and device information by creating profiles of applications with AI models which operate on the firm’s network. Such profiles enable firms to analyze incoming data and detect activities that can be harmful.

Leading Cybersecurity Tools Enhanced by AI Technology  

The application of AI technology is now commonplace in various cybersecurity tools to boost their efficient defensive capabilities. These include:  

1. AI-Enhanced Endpoint Security Tools  

These tools help prevent malware, ransomware, and other malicious activity by using AI to detect and mitigate threats on laptops, desktops, and mobile phones.  

2. AI Integrated NGFW  

AI technologies into Next-Generation Firewalls (NGFW) increase their capabilities in threat detection, intrusion prevention, and application control safeguarding the network.  

3. SIEM AI Solutions  

The AI-based SIEM solutions help contextualize multiple security logs and events, making it easy for security teams to streamline threat detection, investigation, and response which traditionally would take longer.  

4. AI-Enhanced Cloud Security Solutions  

These tools use AI to enforce protective measures on data and applications hosted in the cloud, ensuring safety, compliance and data sovereignty.  

5. AI Enhanced Cyber Threat Detection NDR Solutions  

Cyber Threat Detection NDR Solutions that have AI abilities enabled monitor network traffic for sophisticated threats to ensure efficient response inline with network security policies.

The Upcoming Trends Of AI In Cybersecurity  

The use of technologies such as machine learning and AI are increasingly pivotal in dealing with threats in cyber security. This is mainly because cybernetic technologies are capable of learning aid functions from any pieces of information fed to them. More so, the steps and measures put in place need to make sure they have adapted to the unique challenges brought in by new vulnerabilities.

How To Implement Generative Artificial Intelligence In Cybersecurity  

Modern companies are adopting generative Technology and AI systems to strengthen existing cybersecurity plans. The use of generative technology mitigates risks by creating new data while ensuring the existing data is preserved.  

  • Effective Testing Of Cybersecurity Systems: Generative technologies can be used by organizations to create and simulate a variety of new data which can be used in testing incident response plans  and different classes of cyber attack defense strategies. Identifying system deficiencies through prior testing greatly increases a firm’s preparedness in case a real attack is launched.  
  • Anticipating Attacks Through Historical Data: Previous historical data containing attack and response tactics can be used to generate predictive strategies through the use of generative AI. These custom-built models are tailored to the unique requirements of a given firm aiding the firm stay a step ahead aloof from malicious hackers. 
  • Providing Advanced Security Techniques: Augmenting the current mechanisms for threat detection by applying predictive analysis for the creation of hypothetical scenarios that mimic real offense strategies improves a model’s ability to detect real life cases while flagging even the faintest and newest suspicious activities.

Generative AI is powerful in the modern-day battleground of technology in fighting cyber threats. Its ability to simulate situations, foresee possible attacks, and increase threat detection helps defenders of an organization be one step ahead of danger.

Advantages of Artificial Intelligence (AI) in the Mitigation of Cyber Risks

Adopting AI tools in cybersecurity offers organizations enormous capabilities intended to help in risk management. Some of the advantages include: 

Continuous education: AI learning is one of its powerful features. Technologies such as deep learning and ML provide AI the means to understand the existing normal operations and detect deviations from the norm which are so neural and malignant behaviors. AI technology makes it increasingly challenging for hackers to circumvent an organization’s defenses which increases the level of ongoing learning on the systems. 

Identifying undiscovered risks: Threats that are unknown can be detrimental to any given organization. With the introduction of AI, all mapped risks together with the ones that have not been identified can be subsequently addressed before said risks become an issue, which provide a remedy to these security gaps that software providers have yet to patch.

Vast volumes of data: AI systems are capable of deciphering and understanding large volumes of data people in the security profession may not be able to comprehend. As a result, organizations are able to automatically detect new sophisticated threats hidden within enormous datasets and amounts of traffic.

Improved vulnerability management: Besides detecting new threats, AI technology allows many organizations to improve the management of their vulnerabilities. It enables more effective assessment of systems, enhances problem-solving, and improves decision-making processes. AI technology can also locate gaps within networks and systems so that organizations can focus on the most critical security tasks.

Enhanced overall security posture: The cumulative risks posed by a range of threats from Denial of Service (DoS) and phishing attacks to ransomware are quite complex and require constant attention. Manually controlling these risks is very tedious. With AI, organizations are now able to issue real-time alerts for various types of attacks and efficiently mitigate risks. 

Better detection and response: AI in Cyber Security aids in the swift detection of untrusted data and with more systematic and immediate responses to new threats, aids in protection of the data and networks. Cyber Security Systems powered by AI enables faster detection of threats, thus improving the systemic reaction to emerging dangers.

IT vs OT Cybersecurity

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Defining Operational Technology (OT)

Operational technology (OT) refers to the use of software and hardware to control and maintain processes within industries. OT supervises specialized systems, also termed as high-tech specialist systems, in sectors such as power generation, manufacturing, oil and gas, robotics, telecommunication, waste management, and water control.  

One of the most common types of OT is industrial control systems (ICS). ICS are used to control and monitor industrial processes and integrate real-time data gathering and analysis systems, like SCADA systems. These systems often employ PLCs, which control and monitor devices like productivity counters, temperature sensors, and automatic machines using data from various sensors or devices.  

Overall access to OT devices is best limited to small organizational units and teams. Due to the specialized nature of OT, it often operates on tailored software rather than generic Windows OS.  

Safeguarding the OT domain employs SIEM solutions for real-time application and network activity oversight, event security, application monitoring, and even advanced firewalls which manage influx and outflux traffic to the main control network.

Defining Information Technology (IT)  

Technology is a field that involves the creation, administration and use of the hardware and software systems, networks, as well as the computer utilities. Nowadays, the application of IT is essential to automations in business processes as it facilitates communication and interaction between human beings and systems as well as between various machines.  

IT can be narrowed down to three core focuses:  

  • Operations: Routine supervision and administration of the IT departments which has their issues ranging from hardware and network support to application and system security support auditing to technical support help desk services.  
  • Infrastructure maintenance: Setting up and maintaining infrastructure equipment which includes cabling, portable computers, voice telephone and telephone systems as well as physical servers.  
  • Governance: This deals with aligning the information technology policies and the services with the IT needs of the organization and with its demand.

The Importance of Cybersecurity in OT and IT

Both operational technology (OT) and information technology (IT) focus on the security of devices, networks, systems, and users.  

In IT, cybersecurity protects data, enables secure user logins, and manages potential cyber threats. Similarly, OT systems also require cybersecurity in place to safeguard critical infrastructures and mitigates the risk of unanticipated delays. Manufacturing plants, power plants, and water supply systems rely heavily on continuous uptime, and any unexpected pauses can cost unexpected downtime.  

The security needs become vital with increased interconnectivity of these systems. New cybercriminal exploits are continuously emerging, permitting access to industrial networks. Increased attempts to breach these systems are rising; more than ninety percent of organizations operating OT systems reported experiencing at least one significant security breach within two years of deployment, according to a Ponemon Institute study. Additionally, over fifty percent of these organizations reported their OT system infrastructure sustained cyber-attacks causing the equipment or plant to go offline.  

The World Economic Forum classifies cyber-attacks involving OT systems and critical infrastructures as one of the five major threats to global risks, next to climate change, geopolitical tensions, and natural disasters.

OT Security vs IT Security: An Overview  

The distinction between OT security and IT security is becoming increasingly vague as OT systems introduce connected devices, and due to the rise of IoT (Internet of Things) and IIoT (Industrial Internet of Things) which interlinks the devices, machines, and sensors sharing real-time information within enterprises.  

As with everything in cybersecurity, there are unique differentiations of concerns to IT security and OT security. These differ from the systems in question to the risks at hand.

Differences Between OT and IT Cybersecurity  

There are marked differences in OT and IT. Firstly, OT systems are autonomous, self-contained, isolated, and run on proprietary software. Whereas, IT systems are connected, do not possess autonomy, and usually operate on iOS and Windows.  

1. Operational Environment  

IT and OT cybersecurity have differences in operational regions. OT cybersecurity protects industrial environments known to incorporate tooling, PLCs, and intercommunication using industrial protocols. OT systems are not built on standard operating systems, and most lack traditional security hardware and software. They are heterogeneously programmed unlike most computers.   

On the other hand, IT cybersecurity safeguards peripherals like desktops, laptops, PC speakers, desktop printers, and mobile phones. It protects environments like the cloud and servers using bespoke antivirus and firewall solutions. Communication protocols used include HTTP, RDP, and SSH.

2. Safety vs Confidentiality  

Confidentiality and safety are two distinctive sectors of an organization’s IT and OT Security Practices. Information Technology (IT) security concentrates more on confidentiality of information transmitted by the organization. OT cyber security focuses on protecting critical equipment and processes. The automation systems in any industry demand high attention supervision to avoid breakdown and maintain operational availability.  

3. Destruction vs. frequency  

There is a cyber security focus which sets up protection against different types of security incidents. Cyber security for OT (Operational Technology) is designed to safeguard against catastrophic incidents. The OT systems usually have limited access points. The consequence of a breach, however, is severe. Even minor incidents have the potential to cause widespread devastation; for instance, plunging an entire nation into a power outage or contaminating water systems.  

Unlike OT, IT systems have numerous gateways and touchpoints because of the internet, all of which can be exploited by cyber criminals. This presents an abundance of security risks and vulnerabilities.

4. Frequency of Patching

Both OT and IT systems differ significantly. Furthermore, their patching requirements also differ greatly. Due to the specialized nature of OT networks, they are patched infrequently; doing so typically means a full stop of production workflow. Because of this, not all components need to be updated, which allows components to operate with unpatched vulnerabilities along with an increased risk of a successful exploit. 

In contrast, IT components undergo rapid changes in technology, requiring frequent updates. IT vendors often have set dates for patches and providers like Apple and Microsoft update their software systems periodically to bring their clients to current versions.

Overlapping Characteristics of OT and IT Cybersecurity

Although they are fundamentally different, IT vs OT Cyber Security both relate to the ever-emerging convergence of both worlds.

OT devices were secured previously by keeping them offline and only accessible to employees through internal networks. Recently, IT systems have been able to control and monitor OT systems, interfacing them remotely over the internet. This helps organizations to more easily operate and monitor the performance of components in ICS devices, enabling proactive replacement of components before extensive damage occurs.

IT is also very important for providing the real-time status of OT systems and correcting errors instantaneously. This mitigates safety industrial risks and resolves OT problems before they impact an entire plant or manufacturing system.

Why IT And OT Collaboration Is Important

The integration of ICS into an organization enhances efficiency and safety; however, it elevates the importance of IT vs. OT security collaboration. The absence of adequate cybersecurity in OT systems poses risks of cyber threats as organizations increase the levels of connectivity. This is especially true in today’s cyberspace where hackers develop sophisticated methods for exploiting system vulnerabilities and bypassing security defences.

IT security can mitigate OT vulnerabilities by using its own systems for monitoring cyber threats as well as the mitigation strategies deployed to them. In addition, the integration of OT systems brings a reliance on baseline IT security controls due to the need to minimize the impacts of attacks.

IT Sector Sees Mass Layoffs as Automation and Profitability Pressures Mount

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The global IT industry is undergoing significant workforce reductions, with over 52,000 employees laid off in the first months of 2025 alone. According to Layoff.fyi – which tracks publicly reported job cuts across 123 technology companies – nearly 25,000 of those layoffs occurred in April 2025.

Intel has announced plans for the year’s largest downsizing: cutting 20% of its workforce, or roughly 22,000 positions, out of approximately 109,000 employees worldwide. This move echoes a broader pattern of layoffs that began in mid-2024, when more than 25,000 IT workers lost their jobs in August 2024 and 34,000 in January 2024. Over all of 2024, the industry averaged about 12,700 layoffs per month, compared to 22,000 monthly cuts in 2023.

Normalization, Not Decline, Experts Say


Analysts describe the trend as a “normalization” of employment levels rather than evidence of an industry downturn. They note that a surge of investor funding in recent years fueled rapid hiring – often outpacing companies’ ability to turn a profit. As unprofitable ventures folded or restructured, staff were inevitably released back into the labor market.

Automation’s Growing Role


Approximately 30% of these layoffs are attributed to the swift advancement of automation technologies – beyond just AI. For instance, automated design tools now enable individual designers to build and maintain websites that once required entire teams of developers. As these tools become more capable and widespread, the demand for certain roles continues to shrink, reshaping the IT workforce landscape.

Zuckerberg Predicts AI Will Replace Mid-Level Developers in 2025

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Meta CEO Mark Zuckerberg believes artificial intelligence is quickly advancing to the point where it can handle the work typically done by mid-level software developers – potentially within the year.

Speaking on The Joe Rogan Experience podcast, Zuckerberg noted that Meta and other major tech companies are developing AI systems capable of coding at a mid-tier engineer’s level. However, he acknowledged current limitations, such as AI occasionally generating incorrect or misleading code – commonly known as “hallucinations.”

Other tech leaders are equally optimistic. Y Combinator CEO Garry Tan has praised the rise of “vibe coding,” where small teams leverage large language models to build complex apps that once needed large engineering teams.

Shopify CEO Tobi Lütke has gone as far as requiring managers to justify new hires if AI could perform the same tasks more efficiently. Anthropic co-founder Dario Amodei has made a bold prediction: within a year, AI will be capable of writing nearly all code.

At Google, CEO Sundar Pichai recently revealed that over 25% of new code is now AI-generated. Microsoft CEO Satya Nadella reported a similar trend, with a third of the company’s code produced by AI.

Despite the enthusiasm, some experts urge caution. Cambridge University AI researcher Harry Law warns that over-reliance on AI for coding could hinder learning, make debugging harder, and introduce security risks without proper human oversight.

LinkedIn Replaces Keywords With AI, Enhancing Job Search Efficiency

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LinkedIn has unveiled a transformative update to its job search functionality, phasing out traditional keyword-based searches in favour of an advanced AI-driven system. This shift promises to deliver more precise job matches by leveraging natural language processing, fundamentally changing how job seekers and employers connect.

AI-Powered Job Matching

Gone are the days of rigid keyword searches. LinkedIn’s new AI system dives deeper into job descriptions, candidate profiles, and skill sets to provide highly relevant matches. According to Rohan Rajiv, LinkedIn’s Product Manager, the platform now interprets natural language queries with greater sophistication, enabling users to search with conversational phrases rather than specific job titles or skills.

For instance, job seekers can now input queries like “remote software engineering roles in fintech” or “creative marketing jobs in sustainable fashion” and receive tailored results. This intuitive approach eliminates the need to guess exact keywords, making the process more accessible and efficient.

Enhanced Features for Job Seekers

The update introduces several user-centric features designed to streamline the job search experience:

  • Conversational Search: Users can describe their desired role in natural language, and the AI will interpret and match based on context, skills, and preferences.
  • Application Transparency: LinkedIn now displays indicators when a company is actively reviewing applications, helping candidates prioritise opportunities with higher response potential.
  • Premium Perks: Premium subscribers gain access to AI-powered tools, including interview preparation, mock Q&A sessions, and personalised presentation tips to boost confidence and performance.

A New Era of Job Search Philosophy

LinkedIn’s overhaul reflects a broader mission to redefine job searching. With job seekers outpacing available roles, mass applications have overwhelmed recruiters. The platform’s AI aims to cut through the noise by guiding candidates toward roles that align closely with their skills and aspirations, fostering quality over quantity.

“AI isn’t a magic fix for employment challenges, but it’s a step toward smarter, more meaningful connections,” Rajiv said. By focusing on precision matching, LinkedIn hopes to reduce application fatigue and improve outcomes for both candidates and employers.

Global Rollout and Future Plans

Currently, the AI-driven job search is available only in English, but LinkedIn has ambitious plans to expand to additional languages and markets. The company is also exploring further AI integrations to enhance profile optimisation and career coaching features.

This update marks a significant leap toward a more intelligent, user-friendly job search ecosystem, positioning LinkedIn as a leader in leveraging AI to bridge the gap between talent and opportunity.