Image Source: iStock | AI at the Edge: Why Running Inference Locally Is the Next Frontier for Enterprise AI There is a version of enterprise AI that most organisations are still building. Models trained in the cloud, inference requests routed to centralised servers, results sent back to wherever the decision needs to happen. It works. Until it does not. The moment your AI system needs to make a decision in under 50 milliseconds, the round-trip to a cloud server becomes the bottleneck. The moment your deployment is in a market with inconsistent connectivity, cloud dependency becomes a reliability problem. The moment your data cannot leave the country it was generated in, centralised inference becomes a compliance problem. These are not edge cases. They are the operational reality of deploying AI at scale across global enterprise environments. And they are exactly why the most forward-thinking technology leaders are rethinking where inference actually happens. The Cloud AI Model Has a Physics Problem For the past several years the dominant model for enterprise AI has been cloud-centric. You build data pipelines that feed into centralised training infrastructure, train models on large compute clusters, and serve inference through APIs that any application can call regardless of where it is running. This model has real advantages. Centralised compute is easier to manage. Training benefits enormously from scale. Model versioning, monitoring and retraining pipelines are simpler when everything lives in one place. For a significant range of enterprise AI use cases — batch analytics, document processing, recommendation systems with tolerant latency requirements — cloud-based inference is perfectly adequate. The problem is that enterprise AI is rapidly expanding into use cases where cloud-based inference simply cannot keep up. The latency constraints are tighter. The connectivity assumptions are less reliable. The data sovereignty requirements are stricter. And the volume of inference requests being generated at the edge of networks — from IoT sensors, connected vehicles, industrial equipment, medical devices, retail systems — is growing faster than centralised infrastructure can cost-effectively absorb. Physics is the constraint nobody talks about loudly enough. Light travels through fibre at roughly 200,000 kilometres per second. A round trip from a factory in Kazakhstan to a cloud data centre in Singapore and back takes a minimum of several milliseconds just in transit time, before any processing happens. Add network overhead, queuing, and inference compute time and you are routinely looking at 100 to 300 milliseconds for a single inference call. For most applications, that is acceptable. For the ones where it is not, it is a hard ceiling no amount of cloud optimisation can lift. What Edge Inference Actually Means Running inference at the edge means deploying a trained model to compute infrastructure that is physically close to where the data is generated and where the decision needs to happen. Instead of sending data to the cloud for processing, the edge node runs the model locally and produces a result in milliseconds. The model itself is still typically trained in the cloud, where the compute, storage and data infrastructure needed for training at scale is most readily available. What changes is where that trained model gets deployed. Once training is complete, the model, or a compressed, optimised version of it, is pushed to edge nodes where it runs inference locally. This separation between training and inference is the architectural insight that makes edge AI practical. Training is computationally intensive but infrequent. Inference is less computationally intensive but needs to happen continuously, at low latency, close to the action. Splitting those two workloads between cloud and edge allows each environment to do what it is genuinely good at. The edge node does not need the raw compute power of a cloud training cluster. It needs enough to run inference on a properly optimised model. Modern edge computing hardware, purpose-built inference accelerators, edge servers, even advanced IoT gateways, is increasingly capable of handling demanding models in compact, power-efficient form factors. The Use Cases Where This Is Already Working Edge inference is not a theoretical capability. It is already running in production across a range of enterprise environments, and the pattern of where it works best is consistent. Industrial and Manufacturing Smart manufacturing is one of the clearest edge AI success stories. Quality control systems using computer vision to inspect products on a production line need to make pass/fail decisions in real time, at production speeds. A camera inspecting 100 components per minute cannot wait for a cloud inference call. The decision has to happen locally, in under 100 milliseconds, without depending on connectivity that a factory environment may not reliably provide. Predictive maintenance follows the same pattern. Vibration sensors, temperature monitors and acoustic detectors on industrial equipment generate continuous data streams. Models trained to detect anomaly patterns can run inference locally at the edge, triggering alerts or shutdowns when something looks wrong without routing everything upstream. The cloud still receives aggregated data for long-term model improvement. The critical real-time decisions stay local. Autonomous and Connected Vehicles Automotive is the use case that makes the latency argument most viscerally clear. A vehicle travelling at 100 kilometres per hour covers nearly 28 metres per second. A 300-millisecond cloud inference call is the difference between detecting an obstacle at 28 metres and detecting it at 19 metres. Nobody makes that tradeoff in a production system. Autonomous and semi-autonomous vehicles run AI inference on-board for exactly this reason. Object detection, lane keeping, collision avoidance — all of these decisions happen locally, in single-digit milliseconds, on purpose-built inference hardware embedded in the vehicle. The cloud remains essential for HD map updates, traffic intelligence, model improvements and over-the-air updates. But the safety-critical inference loop never leaves the vehicle. Connected vehicle fleet management follows a similar pattern. Edge inference on-vehicle handles immediate safety and operational decisions. Aggregated data flows to the cloud for fleet-wide analytics, route optimisation and predictive maintenance modelling. Healthcare and Medical Devices Healthcare presents a specific version of the edge AI challenge where data sovereignty and latency combine. Patient data in most jurisdictions cannot be freely moved across borders or even across organisational boundaries. Running inference on patient data in a centralised cloud raises compliance questions that many healthcare organisations are not positioned to navigate. Edge inference at the device or facility level keeps sensitive data local. An AI model assisting with diagnostic imaging can run on infrastructure within the hospital, producing results that stay within the care environment. A wearable medical device can run lightweight inference models locally to detect anomalies and alert clinicians without streaming sensitive biometric data to a remote server. As AI capabilities in clinical settings expand, and they are expanding rapidly, the ability to deploy intelligence close to the point of care without compromising data governance is becoming a meaningful differentiator between healthcare technology providers. Retail and Customer Experience Physical retail environments are generating increasing volumes of data from computer vision systems, sensor networks, point of sale infrastructure, and customer interaction platforms. Running inference locally on this data enables real-time applications that cloud latency makes impractical. Cashierless checkout systems process video feeds and sensor data locally to track items and complete transactions without requiring a cloud round-trip for every interaction. Shelf monitoring systems use edge inference to detect gaps and planogram violations in real time without streaming full video to a centralised server. Loss prevention systems can identify suspicious patterns locally, alerting staff without the latency and bandwidth overhead of continuous cloud processing. The Infrastructure Challenges Nobody Puts in the Pitch Deck Edge inference is genuinely powerful. It is also genuinely difficult to deploy and operate at scale, and the challenges are worth understanding before committing to an architecture. Model Optimisation Full-scale models trained for cloud inference often cannot run efficiently on edge hardware. The compute and memory constraints of edge nodes require model compression, quantisation and architecture optimisation to bring inference within the hardware envelope. This is not a one-time exercise. Every model update requires re-optimisation before it can be deployed to edge nodes. Building this into the model development pipeline from the beginning is essential. Treating it as an afterthought creates significant operational overhead later. Deployment and Versioning at Scale Pushing model updates to a fleet of edge nodes distributed across multiple facilities, vehicles, or countries is a non-trivial operations problem. The orchestration layer that manages which model version is running on which node, coordinates staged rollouts, handles rollback when something goes wrong, and provides visibility into the state of every edge deployment requires serious tooling and operational discipline. Kubernetes-based edge orchestration platforms have become the standard approach for organisations serious about this problem. Building the operations model around the infrastructure from day one, rather than retrofitting it after the first fleet-wide deployment failure, is the pattern that works. Observability and Monitoring An edge inference system you cannot see is one you cannot trust with consequential decisions. Knowing what each model is doing, how accurately it is performing in production conditions, where it is making errors and why, requires telemetry infrastructure that flows from edge nodes back to central monitoring systems. Building that observability layer is unglamorous work that never makes the demo but determines whether the system is trustworthy in production. Security at the Edge Edge nodes are physically distributed, often in environments with limited physical security, and sometimes managed by third parties. The security model for centralised cloud infrastructure does not automatically extend to them. Device attestation, encrypted communication channels, access controls, and clear policies for compromised node scenarios need to be designed in from the beginning. Edge AI systems operating in industrial or healthcare environments particularly need to consider the consequences of a compromised inference node, where a manipulated model could produce systematically wrong outputs with real physical consequences. What This Means for Technology Leaders Building Now The shift toward edge inference is not a distant trend. It is happening now, driven by the combination of expanding AI use cases, improving edge hardware, and the growing operational reality that cloud-centric inference has genuine limitations that architecture alone cannot solve. For technology leaders making infrastructure decisions today, the practical questions are worth working through clearly. Which of your AI use cases have latency requirements that cloud inference cannot meet? Which operate in environments where connectivity cannot be assumed? Which involve data that regulatory requirements or business sensitivity require to stay local? Those use cases are the natural starting point for an edge inference strategy. They are also where the ROI calculation is clearest, because the alternative, accepting the limitations of cloud inference, has a concrete cost in performance, reliability, or compliance that can be quantified. Getting edge inference right requires treating it as an infrastructure and operations problem as much as an AI problem. The model is a component. The deployment pipeline, the orchestration layer, the observability infrastructure, and the security model, these are what determine whether the system performs reliably at scale, or whether a promising pilot becomes a production cautionary tale. At Dygital9, we work with technology leaders who are building AI infrastructure designed to hold up in the real operating conditions of global enterprise environments. The gap between inference that works in a demo and inference that works reliably at 3 am in a remote facility is an engineering problem. It is one worth solving properly.

Image Source: iStock | What 1,700 CDN PoPs Actually Means for a Business Competing Globally Most technology conversations about CDN start and end with speed. Faster load times, lower latency, better user experience. All of that is true. None of it tells the full story. When Dygital9 says 1,700 CDN Points of Presence worldwide, that number is not a marketing figure. It is an infrastructure reality that changes what a business can do, where it can operate, how reliably it can serve customers, and how seriously a competitor needs to take it on the global stage. Understanding what is behind that number and what it means in practice is worth the time for any business leader thinking seriously about where their technology needs to be in the next three years. What Is a CDN and How Does It Actually Work A Content Delivery Network is a geographically distributed system of servers and infrastructure designed to deliver digital content and services to users based on their physical location. The core principle is simple: the closer the infrastructure is to the end user, the faster and more reliably content is delivered. Without a CDN, every user request travels to a single origin server, wherever that server happens to be located, and waits for a response to make the same journey back. For a business with its primary infrastructure in North America, a user in Southeast Asia is making a round trip of potentially 15,000 kilometres every time they load a page, stream a video, or execute a transaction. Physics imposes a minimum time on that journey regardless of how good the software is. A CDN solves this by caching content and deploying compute resources at distributed locations around the world, the Points of Presence, so that requests are served from the nearest available node rather than the origin server. The result is dramatically reduced round-trip times, more consistent performance, and a user experience that does not degrade based on geography. Modern CDN infrastructure goes well beyond simple content caching. Today's enterprise-grade CDN platforms handle dynamic content acceleration, real-time data processing, security filtering, load distribution, SSL termination, video transcoding, API acceleration, and edge computing workloads. The infrastructure has evolved from a delivery mechanism for static files into a full edge computing layer that sits between users and origin systems globally. What a Point of Presence Actually Is A Point of Presence is a physical deployment of infrastructure at a specific geographic location. Each PoP typically contains servers, networking equipment, storage systems, and the interconnections needed to link it to the broader network and to the public internet via local internet exchange points. The placement of a PoP is a strategic decision. Effective PoP placement prioritises locations where significant volumes of users are concentrated, where internet exchange infrastructure allows efficient traffic routing, where latency to surrounding population centres is minimised, and where local data regulations may require in-country data handling. A PoP in Lagos, for example, connects to Nigerian internet exchange infrastructure, serves users across Nigeria and neighbouring markets with millisecond response times, and keeps data physically within the country for regulatory compliance purposes. Without that PoP, a Nigerian user on any platform without local coverage experiences performance that is measurably worse than what users in more connected markets receive. The density of PoPs in a given region determines the depth of coverage. A single PoP in West Africa provides basic coverage. Multiple PoPs across Nigeria, Kenya, South Africa, and other markets provide deep coverage with redundancy, failover capability, and the ability to serve users in landlocked countries through optimised routing paths. Why 1,700 Points of Presence Is a Significant Number The CDN industry spans a wide range of providers, from regional operators with a few dozen PoPs serving specific markets to global hyperscalers with thousands of locations. Understanding where 1,700 sits in that spectrum and what it enables requires some context. A network with fewer than 100 PoPs can serve major Western markets, North America, Western Europe, Japan, Australia, at acceptable performance levels. Emerging markets, particularly across Central Asia, Sub-Saharan Africa, South Asia, and parts of the Middle East, are either underserved or not served at meaningful performance levels at all. A network at the 500 to 800 PoP range begins to offer serious global coverage with reasonable depth in most major markets. A network approaching or exceeding 1,500 PoPs represents deep global coverage, multiple nodes per major region, genuine redundancy, and the ability to serve markets that smaller networks simply cannot reach at enterprise performance standards. At 1,700 PoPs, Dygital9 operates infrastructure that covers: Asia Pacific across major markets including Japan, South Korea, Singapore, Hong Kong, India, Australia, and extending into emerging markets across Southeast Asia and Central Asia, including Kazakhstan, Uzbekistan, and Pakistan, markets where enterprise infrastructure has historically been sparse. Europe and North America, with dense coverage across the most competitive digital markets in the world, including financial services hubs, e-commerce centres, and media distribution markets where performance standards are highest, and the cost of underperformance is most visible. Middle East and Africa, with nodes across Gulf markets including Saudi Arabia and Iraq, and across the African continent in Nigeria, Kenya, South Africa, and Libya, covering the digital economies that are growing fastest and where infrastructure investment has lagged demand. Latin America, with coverage across major markets, supports the significant and growing enterprise digital investment in Brazil, Mexico, and across the region. The practical effect of this coverage is that businesses operating on Dygital9's infrastructure can offer consistent, high-performance digital experiences to users virtually anywhere in the world, not just in markets where global CDN coverage happens to be deep already. The Technical Architecture Behind a PoP Network at This Scale Operating 1,700 PoPs as a coherent, reliable network rather than 1,700 independent deployments requires significant architectural sophistication. Several key technical capabilities are what separate a deep PoP network from a collection of distributed servers. Intelligent Traffic Routing At any moment, a user request entering the network needs to be directed to the most appropriate PoP, not simply the nearest one, but the one that will deliver the best response time, accounting for current load, network conditions, node health, and content availability. Intelligent traffic routing systems make these decisions in milliseconds, continuously, across every request entering the network globally. The quality of this routing logic is a major differentiator between CDN networks that perform consistently and those that perform well only under ideal conditions. Anycast Networking Anycast is a network addressing approach where the same IP address is announced from multiple physical locations simultaneously. When a user's device sends a request to a CDN's anycast address, the internet's own routing infrastructure automatically directs that request to the topologically nearest PoP. This means global traffic distribution happens at the network layer without any application-level configuration, and failover is automatic when a PoP becomes unavailable because the routing simply shifts to the next nearest node. Edge Caching and Cache Invalidation Static content — images, videos, scripts, stylesheets, documents- can be cached at every PoP, meaning a user anywhere in the network retrieves it locally rather than from the origin server. Managing what is cached where, how long it stays cached, and how it gets updated when the original changes is a complex operational challenge at scale. Effective cache management at 1,700 PoPs requires automated invalidation systems, intelligent pre-positioning of content based on anticipated demand, and cache hierarchy designs that balance storage efficiency against cache hit rates. Dynamic Content Acceleration Not everything can be cached. User-specific data, real-time information, transactional responses, and API calls need to reach the origin server. Dynamic content acceleration optimises the path between PoPs and origin servers through techniques including persistent connections that eliminate repeated TCP handshake overhead, protocol optimisation that reduces the number of round-trips required for a transaction, and intelligent routing through the CDN's private backbone rather than the public internet, which is faster and more reliable for long-distance traffic. TLS and Security at the Edge Every HTTPS connection requires a TLS handshake before any data transfers. When that handshake happens at a PoP close to the user rather than at a distant origin server, it adds milliseconds rather than hundreds of milliseconds to the connection setup time. Processing TLS at the edge also offloads significant compute work from origin servers and allows security functions, DDoS mitigation, web application firewall processing, and bot detection to be applied at the network edge before malicious traffic reaches core infrastructure. Performance Impact: What the Numbers Actually Look Like The performance difference between serving users with local PoP coverage versus routing them to a distant origin is substantial and measurable across every relevant metric. Latency for a user in Nairobi served from a local PoP can be under 20 milliseconds. The same request routed to a European data centre typically experiences 150 to 250 milliseconds of latency. That gap is the difference between an application that feels instant and one that feels sluggish, and it compounds across every interaction in a session. Page load time improvement from CDN deployment typically ranges from 40 to 70 percent for globally distributed audiences, with the largest gains in markets furthest from origin infrastructure. For a business with significant user bases in emerging markets, the improvement can be more dramatic. Availability and uptime improve structurally with a redundant PoP network. When a single node experiences an issue, traffic routes to the next available node automatically. For businesses with 24/7 operational requirements, this architectural redundancy is the foundation of high availability, not a backup plan but the standard operating mode. Bandwidth cost reduction is a less-discussed benefit but a financially meaningful one. Serving content from edge caches rather than origin servers reduces origin bandwidth consumption significantly, often by 70 to 90 percent for content-heavy applications. At enterprise scale, this translates directly to infrastructure cost reduction. Data Compliance and Sovereignty at Global Scale Data residency regulations are expanding in scope and strictness. The General Data Protection Regulation in Europe, the Personal Data Protection Bill in India, data localisation requirements in Russia, China, and Indonesia, sector-specific regulations in financial services and healthcare, the regulatory landscape for where data can be stored and processed is increasingly complex, and the penalties for getting it wrong are significant. A CDN network with PoPs in the right jurisdictions enables data localisation compliance without sacrificing performance. When a PoP exists in-country, data can be processed and stored locally, satisfying residency requirements while still delivering the performance benefits of edge infrastructure. For a business operating across multiple regulated markets, this is not a minor convenience. It is the difference between a compliant global architecture and one that requires constant legal review of where data is flowing and why. Dygital9's 34-plus country footprint with 24/7 iSOC security operations means compliance is monitored and maintained continuously, not audited retrospectively. What This Means Across Industries Financial Services Speed, compliance, and security converge in financial services in ways that make CDN infrastructure a business-critical concern. Real-time payment platforms, trading interfaces, digital banking applications, and insurance portals all depend on consistent low-latency connections in every market they serve. Data localisation requirements add a compliance dimension that infrastructure without in-country coverage cannot satisfy. Dygital9 has delivered for Goldman Sachs, HSBC, and RBC, institutions where the performance and compliance bar is as high as it gets. Gaming and Entertainment Latency sensitivity in gaming is acute. A difference of 20 milliseconds between two players is noticeable. A difference of 200 milliseconds is game-breaking. For a gaming company expanding into Central Asia, the Middle East, or Sub-Saharan Africa, CDN coverage in those markets is not a bonus feature; it is what determines whether the product is playable for local users. Dygital9's experience across 240-plus gaming and blockchain projects means this is well-understood territory, not a theoretical discussion. Media and Streaming Live streaming, video on demand, interactive broadcasts, and real-time audio all depend on edge infrastructure to deliver consistent quality across variable network conditions and diverse geographic markets. Adaptive bitrate streaming, the technology that adjusts video quality in real time based on available bandwidth, works best when the decision logic runs from a PoP close to the user rather than from a distant origin. Buffer-free streaming at scale is a PoP density problem as much as it is a bandwidth problem. Healthcare and Life Sciences Healthcare platforms operating across multiple countries face a dual challenge: delivering reliable, fast performance for clinical teams who depend on the software operationally, while maintaining strict data residency compliance that varies by jurisdiction. A CDN network with in-country nodes and built-in compliance architecture addresses both simultaneously. Dygital9's security and compliance practice, covering HIPAA, GDPR, SOC 1/2, and PCI DSS, runs alongside the infrastructure layer rather than as a separate consideration. E-Commerce and Retail The direct relationship between page load time and conversion rate is one of the most thoroughly documented findings in digital commerce. A one-second delay reduces conversions by up to seven percent. For a global retailer, that figure applies across every market, every session, every peak traffic event. Consistent performance across all markets, not just the primary ones, is what separates global operations that perform from ones that underperform outside their home market. Logistics and Supply Chain Real-time operational platforms in logistics, tracking systems, fleet management, warehouse management, and EDI integrations depend on consistent, low-latency connections across distributed locations that are often in markets with limited infrastructure. Dygital9's logistics technology experience, including work with TruckMate, InfoPlus, and major 3PL operators, sits alongside the infrastructure capability to support these systems globally. The Operations Layer: iNOC and iSOC Infrastructure numbers only mean something if there is a team running them properly. A PoP that is not actively monitored is a liability. Traffic routing decisions, capacity management, incident response, performance optimisation, and security monitoring are continuous operational tasks that cannot be automated away entirely. Dygital9's global iNOC, Intelligent Network Operations Centre, monitors and manages infrastructure across all active countries 24 hours a day, seven days a week. This is not a helpdesk. It is an active operations function that maintains network health, responds to incidents before they become outages, and continuously optimises how traffic flows across the global network. The iSOC, Intelligent Security Operations Centre, runs in parallel, providing continuous security monitoring across the same global footprint. Threat detection, incident response, compliance monitoring, and security policy enforcement happen at the network level, in real time, across every market Dygital9 operates in. For businesses whose security posture needs to be consistent globally rather than strong in headquarters and weak everywhere else, this operational model is what makes that possible. Together, the iNOC and iSOC represent the operational layer that turns 1,700 Points of Presence from a number on a page into a network that enterprise businesses can actually rely on. Competing Globally Is an Infrastructure Decision The businesses that compete effectively on a global scale are not always the ones with the largest budgets or the most aggressive growth strategies. They are the ones whose infrastructure can actually support what that growth demands, consistently, compliantly, and reliably in every market they choose to operate in. Speed, redundancy, compliance, reach, security, these are the dimensions that determine whether a global digital operation works or struggles. A CDN network with 1,700 PoPs across 34 plus countries, backed by 24/7 managed operations, fifteen years of enterprise technology experience, and a delivery track record spanning Goldman Sachs, HSBC, Disney, Finning Caterpillar, and Best Buy, is what makes those dimensions manageable rather than a constant source of operational friction. Globally Focused. Locally Executed. That principle starts at the infrastructure layer. Everything else builds on top of it.

Image Source: iStock | AI Agents in the Enterprise: Why Autonomous Workflows Are Reshaping How Businesses Actually Operate There is a version of this conversation that stays very theoretical. Large language models, reasoning chains, tool use, agentic loops. It is the kind of language that sounds impressive in a conference keynote and means very little by Monday morning. Then there is what is actually happening inside organizations right now. Quietly, without much fanfare, teams are deploying AI agents that are handling work that used to require a human in the loop for every single step. Not answering questions. Not generating drafts. Actually running processes, making decisions within defined boundaries, and handing off outputs to the next stage of a workflow without anyone pressing a button in between. That shift is bigger than most enterprise leaders have had time to fully process. And the infrastructure behind it is more nuanced than most vendors want to admit. What an AI Agent Actually Is in Practice The word "agent" has been stretched so far in the past two years that it is worth grounding it before going further. In an enterprise context, an AI agent is a system that can take a goal, break it into steps, use tools to execute those steps, evaluate the results and adjust, and keep going until the goal is met or it hits a defined boundary where it needs human input. The tools are what make it genuinely useful. An agent that can only generate text is a sophisticated autocomplete. An agent that can query databases, call APIs, read documents, write to systems of record, send notifications, and trigger downstream processes is something categorically different. That is the version enterprises are starting to deploy, and it is the version that changes what a small team can actually get done. The difference between a chatbot and an agent is roughly the difference between a colleague who answers your questions and a colleague who actually does the work. Where Enterprises Are Deploying Them First The earliest and most successful enterprise agent deployments tend to share a common characteristic. They target workflows that are high volume, rule-bound, and deeply tedious for humans but not yet simple enough to be handled by traditional automation. Customer operations is the obvious one. Not the customer-facing chatbot, which most organizations already have in some form, but the back-end triage and resolution logic. An agent that reads an incoming support ticket, checks account history, queries relevant systems, determines the right resolution path, executes it if it falls within defined parameters, and escalates with a full summary if it does not. That workflow, running at volume, changes the math on how many people a support operation needs. Contract review and procurement workflows are another area seeing real adoption. An agent that can read a contract against a defined policy set, flag clauses that need legal attention, approve routine renewals automatically, and route exceptions to the right person with an annotated summary is not a futuristic concept. It is running in procurement departments right now. IT operations are further along than most people realize. Agents monitoring infrastructure, triaging alerts, running predefined remediation playbooks, and escalating only the things that genuinely need a human decision have been in production at large enterprises for long enough that the early adopters are already on their second generation of implementation. The pattern across all of these is the same. High volume. Well-defined success criteria. Clear escalation paths. That is the profile of a workflow that is ready for an agent. The Infrastructure Question Nobody Is Asking Loudly Enough Here is where most of the vendor conversation falls short. Deploying an AI agent is not primarily a model selection problem. It is an infrastructure and integration problem, and that is where most enterprise implementations either succeed or quietly stall. An agent needs to connect to your systems. That means APIs, authentication, data access controls, and the ability to read from and write to the tools your organization actually uses. Agents operating in isolation, unable to touch real systems of record, are not agents. They are expensive drafters. An agent needs memory. Not in the science fiction sense, but in the practical sense that a multi-step workflow running over hours or days needs to maintain context. What has it done, what did it learn, what decisions did it make, and why, without a coherent memory architecture, agents make the same mistakes repeatedly or lose context mid-task in ways that create real operational problems. An agent needs observable behavior. This is the one that CTOs who have been through an enterprise AI deployment learn first. If you cannot see what an agent is doing, why it made a particular decision, where it is in a workflow at any given moment, and what it would do next, you cannot put it anywhere near a process that matters. Observability is not a nice-to-have. It is the difference between a system you can trust and a black box you are hoping works. And an agent needs well-designed guardrails. The boundary between what an agent can decide autonomously and what requires a human is the most important architectural decision in any enterprise deployment. Too narrow, and the agent adds almost no value. Too wide and you are handing over decisions to a system that should not be making them alone. Getting that boundary right, and being able to adjust it over time as trust builds, is a discipline that most organizations are still developing. The Honest State of Enterprise Adoption The organizations that have gotten the most out of AI agents so far are not the ones that moved fastest. They are the ones who picked the right workflows to start with, built the integration layer properly, took observability seriously from day one, and treated the guardrail design as an ongoing conversation between engineering, operations, and legal rather than a one-time configuration. They are also the ones who were honest with themselves about what agents are not ready for. Complex judgment calls with significant downstream consequences. Workflows where the success criteria are genuinely ambiguous. Anything where the cost of an undetected error compounds quickly. Those workflows still need humans, and probably will for a while. The organizations that have struggled are largely the ones that underestimated the integration work, overclaimed what the agent could handle autonomously, and lost trust quickly when it made mistakes in edge cases, or tried to automate workflows that were not actually well-defined enough to automate in the first place. What This Means for Technology Leaders If you are a CTO or technology leader thinking about where AI agents fit in your organization right now, the most honest framing is this. The technology is real, the value is real, and the implementation complexity is also real. The question worth spending serious time on is not which model to use. It is which workflows have the right profile for autonomous execution, what your integration layer looks like, how you are going to maintain visibility into agent behavior at scale, and how you are going to build organizational trust in systems that operate without constant human oversight. Those are infrastructure and design questions as much as they are AI questions. The organizations that treat them that way are the ones building something they can actually rely on. At Dygital9, we work with technology leaders who are past the experimentation phase and ready to build enterprise AI infrastructure that holds up in production. The gap between a promising pilot and a system that runs reliably at scale is real, and it is almost always an engineering problem, not a model problem.

Image Source: Canva | Edge Computing + Cloud: How Hybrid Architectures Enable Low-Latency Applications I want to start with something that does not get said enough in these conversations: most of the latency problems teams are fighting today were created by decisions made years ago, when the assumption was that faster internet would just... fix everything eventually. It did not. And now we are in an era where a 300ms delay in an industrial control system is not just annoying, it is a liability. A streaming app that buffers during peak hours loses subscribers to a competitor that does not. Where the difference between a connected vehicle that responds in 12 milliseconds and one that responds in 180 is not a performance benchmark, it is a safety standard. So let us talk about what actually works. The Cloud Was Never Supposed to Do All of This The cloud was a revolution. It still is. Elastic infrastructure, pay-as-you-go compute, global reach without building data centers, all of that changed how software gets built. But somewhere along the way, the industry overcorrected and started treating the cloud like a universal answer to everything. It is not. And the reason comes down to physics. When a sensor on a factory floor in Pune sends data to a cloud server in Singapore for processing, that round trip takes time. Not a lot of time by human perception standards, but a catastrophic amount of time if a machine is about to overheat and needs a shutdown signal in the next 20 milliseconds. No cloud provider can override the speed of light. The data still has to travel, the server still has to process it, and the response still has to come back. At a moderate scale, this is manageable. At the scale modern IoT and connected infrastructure operate at, it becomes a genuine architectural problem. You are not just dealing with latency on individual requests. You are dealing with thousands of devices generating data simultaneously, all of it competing for bandwidth, all of it bottlenecked through the same central processing pipeline. Bandwidth costs spike. Response times become inconsistent. And the whole system starts showing stress. Edge Computing Solves the Distance Problem, But Creates New Ones Edge computing moves computation physically closer to where data originates. Instead of routing everything to a central server, you process at the network edge, inside base stations, on-premise nodes, smart gateways, or local servers within the facility itself. The results are genuinely impressive when it is done right. Latency drops from hundreds of milliseconds to single digits. Bandwidth consumption falls because you are only sending relevant outputs upstream instead of raw data streams. And the system keeps functioning even if the internet connection to your central cloud goes down. But here is the honest part that a lot of edge computing evangelism skips over: edge nodes are resource-constrained by design. A local processing node on a shop floor cannot run a full machine learning training pipeline. It does not have the storage to hold three years of operational data. It cannot serve a global user base or run the kind of complex analytics that drive business decisions. Teams that treat edge computing as a wholesale replacement for cloud infrastructure end up with a different set of problems. Maintenance becomes expensive and fragmented. Every physical node needs to be updated, monitored, and managed. Security becomes harder to govern consistently. And the compute ceiling of edge hardware creates real limits on what the system can do locally. That is the fundamental tension, and it is why hybrid architecture exists. What "Hybrid" Actually Means in Practice The word hybrid gets used loosely enough that it has almost lost meaning. In the context of edge and cloud, it means something specific: a deliberate design where different layers of the stack handle the tasks they are genuinely suited for, with clear data flows between them. The edge layer handles what cannot wait. Real-time sensor fusion, immediate control decisions, local model inference, and content caching for fast user-facing responses. Anything where the cost of a round-trip to a centralized server is too high. The cloud layer handles what benefits from scale and centralization. Training machine learning models on aggregated historical data. Running analytics across the entire fleet of devices or facilities. Serving global audiences. Coordinating system-wide configuration and policy. Long-term storage. When those two layers are designed to work together with clear contracts between them, the results are qualitatively different from either approach alone. But the design of that boundary is where most of the real engineering work lives. The Boundary Question Is the Hard Part What you process at the edge and what you send to the cloud is not a trivial decision. Get it wrong in one direction, and you have built expensive edge infrastructure that mostly just forwards data anyway. Get it wrong in the other direction, and you have a cloud-dependent system that fails under latency pressure or connectivity disruptions. The teams that get this right tend to think about it in terms of time sensitivity and consequence. Decisions that need to happen in under 50 milliseconds and have direct physical consequences belong at the edge. Decisions that can afford to be asynchronous, or that benefit from a broader context the edge node cannot have, belong in the cloud. A good example is anomaly detection in industrial equipment. The edge node runs a lightweight inference model that was trained in the cloud and deployed locally. It makes real-time decisions: Is this vibration pattern within normal parameters? If not, trigger a local alert or shutdown immediately. That decision happens in milliseconds, on-site, with no dependency on external connectivity. Meanwhile, the cloud is continuously refining the anomaly detection model using data from hundreds of similar machines across dozens of facilities. The edge handles the moment. The cloud handles the learning. Neither one does the other's job. Both are essential. Where This Is Already Working It is worth being concrete about this because "hybrid architecture" can sound like an abstraction until you see what it enables. In telecommunications, 5G was built around multi-access edge computing from the ground up. The reason 5G can promise single-digit millisecond latency is that processing happens inside the base station infrastructure, not in a distant data center. That is what makes remote surgery, real-time augmented reality, and precision industrial automation viable. Without edge nodes embedded in the network itself, 5G would just be faster 4G. In large-scale retail, some of the most technically sophisticated store operations are running edge compute inside the stores themselves. Computer vision for shelf management, real-time fraud detection at checkout, and personalized offers triggered by proximity. None of that works at acceptable latency if it requires a round-trip to a cloud every time a customer walks past a display. The cloud handles the overnight model updates and the enterprise analytics. The store handles the moment. In connected vehicles, the split is even more pronounced. Safety-critical decisions like collision avoidance and automatic braking cannot wait for cloud confirmation. They happen on board, in sub-100 millisecond windows, using locally processed sensor data. The cloud is still deeply involved in the vehicle's intelligence, through HD map updates, traffic data, over-the-air firmware, and fleet-wide learning, but none of that is in the critical path of a braking decision. The Orchestration Problem Nobody Talks About Enough One thing that becomes obvious at scale: managing a distributed edge infrastructure is genuinely hard. When you have dozens or hundreds of edge nodes deployed across different facilities, keeping them updated, monitoring their health, managing configuration drift, and deploying new model versions consistently requires serious tooling. This is an area where the industry is still maturing. Kubernetes-based platforms extended for edge environments have become the common approach for organizations serious about this. KubeEdge and K3s (a lightweight Kubernetes distribution) are the names that come up most often. But the orchestration layer is not a solved problem; you can just bolt it on at the end. It needs to be part of the architecture from the beginning, or you end up with a distributed system that is harder to maintain than what you replaced. Security is the same story. Edge nodes are physically distributed, sometimes in environments with limited physical security, sometimes managed by third parties. The cloud's security model does not automatically extend to them. Zero-trust principles, device attestation, encrypted communication channels, and clear policies for node compromise need to be designed in, not retrofitted. What This Means If You Are Building Right Now The interesting thing about hybrid edge-cloud architecture is that it rewards clarity of thought more than it rewards technical sophistication. The teams that build this well are not necessarily the teams with the most advanced tooling. They are the teams that have thought clearly about which decisions need to happen where, and why. That sounds simple. It is not always simple in practice, especially when business requirements are changing, when the number of edge devices is scaling faster than originally anticipated, or when organizational incentives push toward building everything centrally because it is easier to manage. But the payoff for getting it right is real. Applications that feel immediate even under load. Systems that stay functional when connectivity is degraded. Infrastructure costs scale more predictably because not everything is flowing through the same expensive pipeline. The businesses building the most responsive digital products right now are not choosing between edge and cloud. They built systems where both do exactly what they are good at, and nothing else. That is the thing worth getting right.

Image Source: iStock | AI in Cybersecurity: How 94% of Leaders Say AI Will Transform Their Security Stack Cybersecurity is going through a fundamental shift. It is no longer just about protecting systems from known threats. It is about keeping up with an environment that is constantly changing, growing, and becoming more intelligent. Artificial intelligence is at the center of this shift. Recent insights show that 94 percent of leaders believe AI will significantly change how cybersecurity operates . This level of agreement is rare, and it reflects a deeper reality. Security is no longer just being improved by AI. It is being rebuilt around it. The real question is not whether AI will transform cybersecurity. It is whether current security systems are ready for that transformation. Why AI Is Reshaping Cybersecurity So Quickly A rapidly expanding attack surface As organizations adopt AI across operations, the number of entry points for risk increases. AI systems connect with data, applications, and decision-making processes in ways that traditional systems never did. This creates an environment where: Systems are more interconnected Decisions are partially automated Visibility becomes harder to maintain Security teams are no longer dealing with fixed boundaries. They are dealing with systems that evolve in real time. A new kind of cyber competition AI has introduced a new dynamic. It is being used by both defenders and attackers. On one side, organizations use AI to: Detect unusual behavior faster analyze large volumes of data instantly Respond to threats with greater speed On the other side, attackers are using AI to: create more convincing phishing attempts automate attacks at scale identify weaknesses more efficiently This creates a situation where advantage depends not on having AI, but on how effectively it is used. Speed is now critical In the past, cybersecurity often followed a sequence. A threat was detected, then analyzed, then addressed. That approach is no longer enough. Today, security systems are expected to: anticipate threats before they happen respond instantly reduce reliance on manual intervention AI makes this possible, but it also raises expectations. Organizations that cannot operate at this speed are more exposed than ever. The Gap Between Adoption and Readiness While many organizations are investing in AI, fewer are prepared for the risks it introduces. In many cases: AI tools are deployed without full visibility Governance frameworks are still developing Security teams are adapting after implementation rather than before This creates a disconnect. AI is being integrated into critical systems, but the security approach around it has not fully matured. As a result, new vulnerabilities are emerging in places that were previously considered secure. What a Modern Security Approach Looks Like To keep pace with these changes, organizations are moving toward a more adaptive model of cybersecurity. Continuous verification Access is no longer assumed to be safe. Every user, system, and interaction is evaluated continuously. Trust is earned in real time, not granted by default. Intelligent threat detection AI enables systems to recognize patterns and identify unusual activity as it happens. This allows organizations to respond before issues escalate. Integrated security Security is no longer a separate layer. It is built into every part of the system, from infrastructure to applications to data. Ongoing risk evaluation Risk is not assessed once and then set aside. It is monitored continuously, especially in systems that learn and change over time. Challenges That Cannot Be Ignored Even with AI, cybersecurity is becoming more complex. Talent and expertise Organizations need professionals who understand both security and AI. This combination is still limited and highly in demand. Clarity and trust AI systems do not always provide clear explanations for their decisions. This can create uncertainty, especially in high-risk situations. New forms of risk AI introduces risks that did not exist before, such as: manipulation of training data misuse of automated systems synthetic content used for fraud These are not extensions of old problems. They are entirely new challenges. What This Means for Business Leaders Cybersecurity is now closely tied to business performance. Leaders need to think about: How security supports innovation How risk is managed across digital systems How AI is governed within the organization This is not just about protection. It is about enabling growth without increasing exposure. Organizations that take a reactive approach will struggle to keep up. Those who build security into their strategy from the beginning will be better positioned to adapt. Conclusion The fact that 94% of leaders expect AI to transform cybersecurity reflects a clear shift in direction. Security is becoming more intelligent, more integrated, and more proactive. At the same time, it is becoming more complex and more demanding. The organizations that succeed will not be the ones that simply adopt AI. They will be the ones who understand how to secure it, manage it, and use it responsibly. The transformation has already started. The real challenge is keeping pace with it.

The tireless race of industries in search of technological development is a pertinent subject. Increasingly, we are seeing the replacement of manual work with work carried out by machines, and, as a result, some needs arise, such as specialized labor or the improvement of technologies to meet industrial demands. Although this subject contextualizes that the industry's current resources (manually or automatically operated machines, artificial intelligence, and machine learning, for example) overlap the previous work method (manual work), it is interesting to project this perspective into the future as well. In other words, given the presence of different operating modes used in the market, what can we expect from automation itself in years to come? Through this question, the subject of today's text is to estimate the 7 biggest automation challenges for the future. Therefore, from now on, we will enter into an immersion aimed at automation and its effects on the future. Read also: 7 Types of Malware You Need to Know and How to Eliminate Them for Good The technology behind today's industry Despite the rise of autonomous technology and its promising results, automation continues to be the most used technology across industries. Maybe because it is a more well-known technology and most companies are more conservative? Well, it's a great guess, given that artificial intelligence (AI) and Machine Learning (ML) are recent things. Because, if we project this technological occurrence onto a timeline, we can place industrial automation in the center of that line, so that on the left is manual work and, on the right, autonomous operation. Although automation has existed since the end of the 18th century, during the Industrial Revolution, this technology is not frozen in time. Quite the contrary, automation is inserted alongside artificial intelligence, for example, in studies and projects already in use. As an example, we can mention the use of various technologies, including automation, in Industry 4.0, which is an approach to manufacturing that integrates advanced technologies to create a more efficient, intelligent and flexible means of production. In this context, the Industry 4.0 approach does not require that all of the items below are included in a process. However, we list some technologies that are present in the new era of technology. Internet of Things : known by its abbreviation “IoT”, these are devices connected to the internet that collect and share data in real-time, for remote monitoring and control of processes and equipment; Big Data and Analytics : tools to process and analyze large volumes of data generated by IoT devices, to obtain important insights into decisions; Artificial Intelligence (AI) and Machine Learning (ML) : algorithms that enable process improvement, automation and prediction; Augmented Reality (AR) : technologies that mix the physical world with virtual elements; Horizontal and Vertical Integration : integration and combination of different hierarchical levels of production and management; Simulation : modeling and simulation of production processes to predict performance, before their actual implementation; Cybersecurity : digital security for data protection; Additive Manufacturing (3D printing) : produces complex components efficiently, flexibly, and with little waste; Cloud computing : allows high processing power and scalable storage; Automation : automatic system by which mechanisms check the functioning and parameters of the system, acting automatically to correct these parameters without human influence; Bonus tip : if you are curious to know about the potential of industry 4.0 in practice, then find out how a 4-axis CNC system works. It is a current manufacturing method that involves complex automation focused on machining. The 7 biggest automation challenges for the future Through an analysis of what has been added to the current automation concept, we can predict what the future challenges of this technology will be. Since several of the items mentioned above are still on the rise in the market and automation allows communication and the simultaneous use of several of these technologies, we can admit that, in the near future, the fluidity of automatic systems will be greater. Anyway, now look at the 7 biggest automation challenges for the future, which are listed below. 1. Jobs and labor market requirements The reduction in manual labor and the increase in labor specialized in technology is, perhaps, one of the most predictable future events when it comes to technology, whatever it may be. Thus, one of the challenges of automation in the future will be the demand for multifunctional (technical) labor, which requires diverse knowledge, such as operation, programming and maintenance of equipment and devices used in automation. On the other hand, the social challenge will be to requalify and prepare people for this new market concept. 2. Cybersecurity As a consequence of the increase in automatic processes, there is an increase in data storage. Therefore, cybersecurity is a challenge aimed at automation, given the need to develop and implement robust security systems to protect automated systems against cyber threats. 3. Technology development It is no surprise that automatic systems must, in the future, be updated and improved, given the development of autonomous systems. Of course, this is not a competition between technologies. Quite the opposite: with the development of more complete autonomous systems comes the need to develop technology so that they can be paired. In other words, automatic and autonomous systems must be present in the same system, in order to complement each other. 4. Development of robotics As automation and robotics go hand in hand with efficiency, safety, and production diversity, it is only fair to mention them in this text. Nowadays, we already see articulated robotic arms in assemblies, and welding, among other processes. Furthermore, we see that the mechanical flexibility of a system allows automation to be more complex and, therefore, complete. In this context, over the years and with the development of automation, robotics will also be pressured to develop in favor of manufacturing evolution in general. 5. Technological infrastructure Limited technological infrastructure, especially in underdeveloped countries, can be a limiting factor for the implementation of technologies such as automation. In this way, many companies will be thrown down in market competitiveness, which can affect the country's economy due to production inequality and the inability to meet demands. 6. IoT and Cloud Computing As these are concepts that emerged through the possibility of executing services through communication, in addition to communication between devices and machines (IoT) and cloud computing, we can estimate that this resource will be more required in the future. Considering that, since the present day, automation is the bridge that stores processes and manages them, the future challenge will be to implement this type of fluid and efficient communication for the most complex industrial processes. 7. Predictive maintenance Contrary to what many think, predictive maintenance is not operated in the same way as preventive or corrective maintenance. The ability to predict equipment failures before they occur can significantly improve operational efficiency. However, the means of predicting failures vary from equipment to equipment, and we have the possibility of using different tools (sensors and devices, for example), simultaneously, to reach a standard. Believe me, predictive maintenance is a big challenge even today and, therefore, this becomes a challenge for future automation. Through this text, it is possible to conclude that the future of industrial automation also depends on the development of several factors, which will complement this technology. For example, although robotics meets a large part of industrial needs through automation, it is notable that the technology can still be improved through intelligent data management. Finally, we conclude that the future of automation is as promising as it has been, given that, as the years go by and new tools emerge, industrial automation will become increasingly complete.

Malware is the designation used for any software, application, or code created with a malicious purpose. This type of software can infect computers, smartphones, tablets, servers, or any other device connected to the internet and can have different intentions, such as getting victims to provide personal data in order to steal their identity, steal bank details, or even launch computer attacks that require the payment of a ransom to regain control of devices. Read also: The Role of Artificial Intelligence in Digital Transformation What types of malware exist Botnet This is a large-scale attack that affects multiple connected devices or computers that are remotely controlled by the attacker. Once infected, the devices are under the control of the attacker without the owner being aware of it. The objectives of this type of malware are several: theft of platform access credentials or other personal data; spying on the victim; collection of keystrokes (for example, to discover passwords); or theft of photographs. Many of these attacks also have the particularity of taking advantage of the amount of equipment that they can control remotely to prevent the services of the attacked organizations from functioning correctly. In practice, these attacks allow, for example, thousands of computers to simultaneously access a given company's website so that it cannot support the traffic and is no longer available. Phishing or smashing It is also possible to infect the victim's devices via email or SMS. After penetrating devices, these software extract personal data, such as usernames or passwords, to sell them to third parties or use them in fraudulent transactions. Spyware This type of malware is covertly installed on the victim's device and allows the attacker to access sensitive information or change device parameters. Adware This type of malware displays advertising pop-ups on the infected device. Every time the victim clicks on one of these pop-ups, the attacker earns money. In addition to impacting the device's performance, making it, for example, slower, adware may be able to change browser settings, leaving infected devices more vulnerable to other types of attacks. Virus Viruses interfere with the normal functioning of infected devices and can corrupt or delete user data. Worms They arrive in email attachments, in messages, on pen drives, or file-sharing programs, and spread across devices. In addition to stealing sensitive information, this type of malware can change the device's security parameters and block access to its files. Ransomware This type of malware allows the attacker to destroy or block access to critical data. In most cases, the victim is pressured to pay a ransom in order for the attacker to give him back access to the data that belongs to him. If the victim is, for example, a company, the most common method is to steal access credentials from one of the employees. This is how the attacker can later access the data. It is important that you are not tempted to pay the ransom amount, as, on the one hand, payment does not guarantee that the situation will be resolved and, on the other hand, by giving in to blackmail you could be listed as a preferential target for future attacks. What to do to eliminate malware Whenever you suspect something is wrong with your devices, analyze the system using the “Windows Security” program report, accessible through "Settings – Privacy and Security" in Windows 10 and Windows 11. This operating system, from Microsoft, already includes antivirus software. Antiviruses protect devices in real-time and allow you to consult reports on their security status. You can also activate it to perform this analysis automatically and periodically. If necessary, format the device and reinstall the systems, but bear in mind that, even in these cases, there may be malware that cannot be eliminated. Remember that, if you format your device, be it a computer or a smartphone, you must make a backup copy of all the data you want to keep. Protect Your Business with Dygital9 At Dygital9, we specialize in cybersecurity and technological solutions to protect your business from all types of malware. Our team of experts is dedicated to ensuring your digital environment is secure and resilient against malicious attacks. We offer tailored solutions to meet your company's cybersecurity needs. Please feel free to contact us to learn more about our services and how we can help your business stay safe in the digital world.

Artificial Intelligence plays a key role in Digital Transformation. In fact, AI not only offers advanced automation solutions, but through machine learning, data analysis, and intelligent processing, it revolutionizes the way companies operate. Companies can thus optimize and innovate processes, improving their efficiency, also thanks to the personalization of services and the predictive capacity that AI can offer. In this article, we will explore what competitive advantages Artificial Intelligence offers within a constantly changing landscape. Read also: 9 Tips to Protect Yourself from Financial Scams Why AI matters for digital transformation Artificial Intelligence can have a key importance in the context of a company's digital transformation, in particular, because it allows the automation of some processes, reducing the human workload and allowing greater efficiency. AI allows you to analyze large quantities of data in a short time and in an advanced manner, so as to identify patterns, trends, and information to make informed decisions. Furthermore, Artificial Intelligence can help in personalizing the experiences of users and customers, adapting services to their specific needs. AI stimulates innovation and the development of new products and solutions, improves decision-making processes thanks to its machine learning and predictive capacity, reduces human errors, and provides a significant competitive advantage. How to power digital transformation with AI Making it clear that AI cannot be a substitute for human intervention, but only an aid, let's see how to enhance digital transformation thanks to Artificial Intelligence. AI can act as an accelerator of innovation and change, optimizing business processes and making daily operations more efficient through automation. Companies also always need to analyze data quickly. Artificial Intelligence can provide deep insights and precise predictions, allowing organizations to make informed, strategic decisions based on real data. Digital transformation can be enhanced with in-depth analysis of your customers and users, in order to offer them increasingly personalized experiences. AI can be a valid support in moments of brainstorming and creative block. It can, in fact, offer suggestions based on analysis, helping the development of services and products that are increasingly in line with market needs. What is the future of AI in digital transformation? The use of AI will continue to grow more and more, becoming a key element in multiple sectors and business processes. The evolution of machine learning and deep learning techniques will improve AI's ability to understand, interpret, and analyze complex data in real-time, enabling more precise predictions and more personalized solutions. Artificial Intelligence will be able to collaborate with different technologies, significantly improving automation, security, and data analysis. Likewise, the more it evolves, the more ethics in the use of AI will be a crucial issue that will have to be addressed by ensuring its responsible management, particularly in the field of data protection. What is certain is that Artificial Intelligence will become a fundamental element in digital transformation, enabling greater efficiency, personalization, and innovation, with companies adopting targeted strategies to fully exploit its potential to maintain a competitive advantage. What are the main fields of application of artificial intelligence processes? AI processes find application in various fields, becoming an engine for innovation that radically transforms working processes and methods. Its diversified use has significantly revolutionized business management, health, finance, commerce, production, and many other sectors, which use it in decision-making processes or data analysis. The fields of application that we will see below demonstrate the vast expansion that Artificial Intelligence can have, capable of guiding the future in the name of transformative innovation. Healthcare: Artificial Intelligence is used to aid in diagnostics, personalization of treatments, and management of healthcare data. Finance: AI is useful for market analysis, risk management, automated trading, and processing loan requests. Retail: It is very useful for logistics, but also for analyzing customer data, predicting their questions and doubts, and personalizing purchasing experiences. Manufacturing: In these industries, AI is applied to optimize processes, predict the quantity and type of demand based on data and history, plan production and improve supply chain efficiency. Automotive: In the automotive sector, AI is used for the development of autonomous vehicles, and for managing the data sent by the vehicles. Human resources: Thanks to data analysis, Artificial Intelligence supports personnel selection, performance management, and the creation of strategies to involve employees more. Marketing: In the digital sector, AI can be a valid aid for brainstorming, analyzing data and competitors, but also for suggesting ideas. Advantages of integrating digitalization and AI The integration between Digitalization and Artificial Intelligence is, and will be, a driving force for many companies that will be able to benefit from it. First of all, this coordination, by optimizing data search, allows for faster investigation within digital archives. This improves the accessibility and retrieval of specific information. Another benefit of integration is to reduce human errors. Automation in data analysis minimizes inaccuracies, increasing the reliability of business operations and mitigating the risks deriving from decisions based on incomplete or imprecise data. AI, combined with Digitalization, allows organizations to explore new business opportunities, enhancing their ability to adapt to new challenges, and improving their precision and efficiency within the contemporary market.

Everyone is susceptible to being a victim of financial scams. Unfortunately, the sophistication of these scams has increased over the years, making it increasingly difficult to identify them – and therefore security concerns have become increasingly relevant. In an increasingly digitalized and interconnected world, scammers have also developed different types of fraud for the online environment: from false investments and loans, pyramid schemes to phishing, a tactic in which scammers pretend to be companies to obtain personal data or receive cash transfers, there is no shortage of alternatives. Therefore, it is essential to always be well informed about possible threats to protect yourself. After all, suffering a blow impacts not only your finances but also your personal life. In addition to the risk of losing a substantial amount of money, the difficulty in recovering values, reestablishing yourself, and equipping yourself with defenses to avoid falling into traps again generates great emotional stress. Read also: Security and Privacy in The Era of Big Data and Data Integration To help you protect yourself from financial scams, we have put together ten practical tips on the most common types of fraud that occur and how to avoid them. Check out the following list to put the practices into action today: 1. Stay Informed Firstly, be up to date on the different types of financial scams that are currently occurring. This can be done by reading financial news on trusted websites, for example. The Central Bank offers information and guidance on financial security on its official website. Another tip is to monitor updates sent through the official communication channels of the financial institutions you use, and when you receive an email, a call, or a message on behalf of the bank, always pay close attention to the sender's details. In general, your bank is unlikely to contact you through an unofficial means to request your information, and payments, among others. 2. Be wary of offers you can’t refuse If something seems too good to be true, it probably is. Be wary of killer promotions, investments, or business opportunities that promise exorbitant returns with little or no risk. Research carefully and contact the company's official channels before making a purchase, confirming any information, or making any important decision. 3. Protect your personal information Store your personal information in safe places and avoid sharing it with third parties. Never give passwords, bank account numbers, or credit card details to unknown people, and make sure that the websites you use to carry out financial transactions are secure. 4. Check the legitimacy of companies Before carrying out financial transactions with any company, store, or establishment, check its legitimacy. Research information about the business, such as physical address, telephone number, and commercial records and make sure it is properly registered and regulated by the competent authorities. 5. Use strong passwords and update them regularly Protect your social media accounts, banks, and other online platforms with strong and unique passwords. Avoid using obvious, easy-to-guess sequences, such as birth dates or simple sequences. It's also important to update your passwords regularly and not share them with others. 6. Keep an eye out for suspicious emails and messages Be cautious when opening suspicious emails or messages. Scammers often use fake messages to try to obtain data or trick people into clicking on malicious links. Be alert to spelling errors, urgent requests, and requests for confidential information, and be suspicious if you receive unknown charges. 7. Check your statements and accounts regularly Regularly monitor your bank accounts, credit cards, and investments to identify any suspicious activity. If you detect unauthorized transactions or unusual activity, immediately contact your financial institution to take protective measures. 8. Be careful with high-risk investments Although the return potential may be tempting, avoid investments that promise guaranteed or well-above-average returns. Please be aware of the risks involved in high-risk investments and consult a qualified financial expert before making any investment decisions. 9. Protecting yourself from scams on WhatsApp Scammers also often use WhatsApp to scam people and obtain financial data. To protect yourself from scams on WhatsApp, it is important to be aware of certain warning signs. Scammers can pretend to be friends or family and ask to borrow money through messages, so always check if the request is real, confirm through another channel, or even make a call. Do not share personal or financial information through WhatsApp, even if the request appears legitimate. Remember that reputable financial institutions and companies never ask for confidential information through messages. Conclusion Finally, if you suspect or are the victim of a financial scam, report it immediately to the appropriate authorities. Contact the police or the police station specializing in cyber crimes in your region to report the incident, and be sure to inform your financial institution of the incident so that they can assist you in how to proceed and, above all, prevent other people from also being victims of this crime. Financial security is a responsibility shared between consumers and financial institutions, but simple tips like the ones mentioned in this article already significantly reduce the risk of falling for financial scams and protect your money. Be alert, question any suspicious activity or message, and guarantee the confidentiality of your data! And, to stay informed about everything that happens in the cybersecurity space, follow the Dygital9 blog and check out first-hand news and specialized articles.

Big Data is the term we use to designate data that, due to its large volume, high complexity, and high frequency, cannot be processed using traditional methods. To carry out this task, specialized software and computing algorithms are used, capable of filtering and extracting useful insights even when faced with a huge amount of information. The advancement of Big Data in recent years is no surprise. After all, thanks to it it is possible to make assertive decisions and solve problems that, until then, seemed insoluble. And the pace should continue at an accelerated pace: according to an estimate by Fortune Business Insights, Big Data should present an annual growth rate of almost 15% until 2028 and generate, in 2028 alone, US$549.7 billion. A number of factors have driven this growth, such as the development and democratization of technology, the accessibility of cloud computing, artificial intelligence, machine learning, and the Internet of Things, as well as concerns about security and privacy – reflecting the new way in which we handle and perceive data. Along with this, we also noticed a huge demand for data integration. And why is data integration so important? Because, combined with Big Data, it allows a holistic view of the business, operational efficiency, quick response to changes, improvement in the quality of information, cost reduction, and, finally, preparation for the much talked about digital transformation. Read also: Facial Recognition: Transformation in Fraud Prevention Strategies One Caveat: Security Data integration, although extremely valuable, requires care, especially about information security. It becomes even more important since data from different sources ends up being centralized in a common infrastructure – in order to process, filter, and analyze it. Security, well thought out and well implemented, must take three pillars into account: Confidentiality: This pillar refers to the protection of sensitive information against unauthorized access. Ensuring confidentiality means ensuring that only authorized people have access to critical data. Measures such as encryption, access control, and clear policies contribute to keeping confidentiality intact. Integrity: Integrity is about ensuring that information is not altered, corrupted, or compromised in an unauthorized manner. Maintaining data integrity is essential to preserving its accuracy and reliability. Techniques such as digital signatures, version control, and checksums are essential. Availability: The pillar of availability involves ensuring that information and systems are accessible when needed, without undue interruptions. This includes preventing and mitigating attacks that seek to make services unavailable, as well as implementing backup and disaster recovery practices to maintain continuity of operations. As can be seen, even though data integration enables a holistic view and extraction of indicators for decision-making, there must be a segregation of roles and functions so that only authorized and competent managers access or share the information that is entrusted to them. To guarantee confidentiality, it is also necessary to implement secure authentication mechanisms, such as the adoption of Multiple Factor Authentication (MFA) and the use of strong passwords, and ensure that the base allows encryption of data at rest and that, in transit, they use secure protocols, such as TLS 1.2. Finally, it is necessary to invest in infrastructure so that it can support the large volume of data expected, including failure recovery and redundancy mechanisms. I'm not just referring to hardware, as the use of the cloud is, today, an almost inevitable aspect. It needs to be well protected, with up-to-date security protocols, and must adhere to international information security, data privacy, and business continuity standards, such as ISO 27001, ISO 27701, and ISO 22301, respectively. In summary Big Data, more than a trend, is a reality. When associated with data integration, it enables faster and more accurate business decisions and favors cost reduction and operational improvement. Good implementation, however, requires respect for the three pillars of security and privacy – confidentiality, integrity, and availability – in order to guarantee the protection of data from suppliers, customers, partners, and the organization itself.

Artificial intelligence and machine learning, present in facial biometrics solutions, are technologies that have changed the game in terms of fraud prevention. With increasingly intelligent algorithms, facial recognition has become a powerful protection tool for companies that need to be sure who is on the other side of a commercial transaction. It's no secret that we are living in an increasingly connected society, in which businesses in digital environments grow exponentially and gain new followers all the time. Along with this expansion came the benefits of a more agile and practical life, but also countless concerns and precautions regarding the security of the information we share at all times. Facial recognition technology gained traction in the market, at the same time that fraud protection and prevention strategies began to target the uncertainty that, on the other side of the screen, was the same person they said they were. Today, the best facial recognition algorithms improve automatically every moment, based on a lot of analytical intelligence and machine learning. In this article, you will be able to learn a little more about the concept of facial recognition and the most efficient solutions on the market for biometric validation. You will also be able to understand how this technology should occupy an even greater space in the market with the increase in companies' demand for protection against today's biggest business risks - such as biometric forgery through the dissemination of methods such as deepfakes and realistic masks in 3D, for example. Good reading! What is facial recognition and how does it work? Facial recognition is a technology that uses the neural network algorithm (computational intelligence) to make a mathematical mapping of a person's facial characteristics - such as the distance between the eyes, the shape of the face, and the dimensions of the nose and mouth. By recording this information, it can identify and authenticate the individual so that they can access restricted areas or log into websites and applications with high-security requirements. The image is captured through a selfie (taken by smartphone or computer) and the access attempt is validated in real-time, in comparison with a database of images that are reliable and already validated – they may be the basis of the image itself. company or belong to a qualified data bureau such as Serasa Experian. The comparison result is given with high accuracy and speed, generally without any point of friction with the user experience. Facial recognition works through a biometrics solution, which matches the information that is entered in real-time into the system and the image that is present in the file of trusted faces. A score is given for the similarity between the two images, generating a 'similarity score' that will support the algorithm's decision. This is how Facial Biometrics software guarantees that a person is the same person they claim to be – and, in this way, we can know who is who! Where is facial recognition used? What are the applications? The use of facial recognition is mainly associated with issues related to security: identification of people in public spaces (including those missing or those wanted by the courts), monitoring high-crime regions, controlling access to spaces with restricted circulation, validating of financial or commercial transactions, unlocking smartphones and computers, entry into large events (such as concerts or football games), user authentication on secure platforms and private security systems (such as employee access to the company network or entry/exit of residents in a condominium), among countless other applications. Looking at the more unusual ones, we highlight two types of use of facial recognition: the analysis of consumer behavior in shopping centers and large retailers for more targeted personalization of offers and the monitoring of emotions (EDR) and facial expressions to analyze momentary feelings – from a purchasing behavior trend to a dissatisfaction scenario that indicates the necessary action from a mediator. Facial recognition versus deep fakes Biometrics solutions that perform facial recognition can prevent the spread of deepfakes through technological intelligence. The more fraudsters study ways to deceive facial recognition, the more deep learning algorithms are updated to block any attempts of fraudulent origin. The most common example at the moment is the manipulation of images and voices of famous people in videos, in which they appear to talk about topics (almost always controversial) that they have never talked about. In this case, a scan carried out by a facial biometrics solution can detect disconnected movements or deformities of the face resulting from image manipulation, revealing the existence of false digitally assembled content. Still on the battlefield between fake videos and biometric recognition, when someone submits material claiming to be that person, solutions with more reliable algorithms are capable of reliably validating whether, behind that face, an authentic person or software is trying to emulate the existence of a third party. Not all solutions available on the market may indeed be ready to identify deepfakes in 100% of situations; Therefore, companies' prevention strategy must be capable of connecting several anti-fraud layers so that business protection is fully effective. As we are talking about technology and the transformative power it has at every moment, preventing fraud with deepfakes must be seen as a process in continuous evolution. It's almost a race against time: fraudsters are constantly working to circumvent software that performs facial recognition and identifies fraud, while prevention solution providers must continue to improve their concepts and algorithms to prevent scams from evolving. Is facial recognition a secure technology? Yes, facial recognition is one of the most advanced and robust technologies for preventing fraud using images or biometric information, as it is a solution developed based on data science and a lot of analytical intelligence. In addition to recognizing faces with high accuracy, the technology is capable of performing life tests and easily identifying the incidence of a fraud attempt through the use of static images, scenes manipulated via software, or the use of realistic masks. The constant evolution of algorithms and situations such as the end of the mandatory use of masks post-pandemic helps to minimize a point of attention that has always been linked to facial recognition: the inaccuracy of identification in adverse conditions. When the face is partially hidden by shadows, sunglasses or masks, the solutions find it more difficult to draw the comparison and tend not to validate the similarity of the person with the data present in the base. Another critical case is the recognition of adverse lighting conditions, both about the low incidence of light in the location and its excess. There is also a point that covers security and raises doubts about the use of facial recognition technologies: legal regulation and ethical issues involving the collection and use of a person's images. With the LGPD (General Personal Data Protection Law), there is a requirement for transparency, and the holder must be notified that their data will be collected, but, in situations such as fraud prevention, there is no need for consent from each user – it is only necessary to ensure that the purpose of the use is being fulfilled and that the data is stored in complete security.

The growing adoption of cloud computing has brought many benefits to companies of all sizes. However, with the increase in the transfer of sensitive data to the cloud environment, security has become a key concern. It is essential to implement efficient security measures to protect sensitive information against cyber threats. In this article, we'll explore best practices for protecting data in the cloud, including data encryption, two-factor authentication, and restricted access policies. Read also: IoT (Internet of Things) in The Education Sector 1. Importance of Cloud Security The challenges of cloud security: Cloud security presents unique challenges due to the distributed and shared nature of the infrastructure. One of the main challenges is unauthorized access to data stored in the cloud. With so many connected devices and users with different privilege levels, it is crucial to ensure that only authorized people can access sensitive data. Additionally, protecting against cyberattacks such as DDoS attacks, phishing, and malware is also a constant challenge. Companies must be prepared to quickly detect and respond to these threats to avoid exposing confidential information. Consequences of a data breach: A cloud data breach can have serious consequences for an organization. In addition to the financial impact, such as fines and loss of customers, there is also damage to the company's reputation. Customer and partner trust can be shaken if they realize their data is not secure. Additionally, the company may face litigation and legal liability arising from the violation. It's essential to remember that data breaches can result in the loss of intellectual property, trade secrets, and confidential information, which can be devastating for the company. A proactive approach to ensuring security: A proactive approach is critical to ensuring security in the cloud. This involves implementing security best practices such as data encryption at rest and in transit, multi-factor authentication, constant monitoring, and regular security updates. Additionally, it is important to perform security audits and penetration tests to identify potential vulnerabilities and fix them before they are exploited. Employee education and awareness about security practices also play a crucial role in preventing breaches. By taking a proactive approach, companies can mitigate risks and ensure the security of their data in the cloud. 2. Data Encryption What is data encryption: Data encryption plays a crucial role in protecting sensitive information in the cloud. Through this technique, data is transformed into cipher text using advanced mathematical algorithms, making it unreadable. Only authorized individuals with the correct key can decrypt and access the data. In this way, data encryption becomes an indispensable security measure, guaranteeing the confidentiality and integrity of information, even when interception or improper access to it occurs. By adopting data encryption, companies can rest assured that their sensitive information is protected against cyber threats and security breaches. Types of encryption used in the cloud: There are two main types of encryption used in the cloud: symmetric encryption and asymmetric encryption. Symmetric encryption uses the same key to encrypt and decrypt data. It is faster and more efficient but requires the key to be shared securely between the parties involved. Asymmetric encryption uses a pair of different keys, one public and one private. The public key is used to encrypt the data, while the private key is used to encrypt it. This type of encryption is more secure but can be slower due to the complexity of the algorithms involved. Best practices for implementing data encryption: When implementing data encryption in the cloud, it is important to follow some best practices. Firstly, it is necessary to identify which data needs to be encrypted and at which points in the process. This way, it is possible to define an appropriate strategy. Furthermore, it is essential to use robust and up-to-date encryption algorithms, such as AES (Advanced Encryption Standard). Adequacy: It is also recommended to adequately protect encryption keys, storing them in secure locations with restricted access. Another important practice is key management, including regular key rotation to maintain ongoing security. Finally, it is essential to perform security tests and audits to ensure the effectiveness of encryption and identify possible vulnerabilities. By implementing these best practices, companies can strengthen their cloud data security and protect their sensitive information from cyber threats. 3. Two-Factor Authentication What is two-factor authentication: Two-factor authentication (2FA) is a security method that requires two distinct forms of verification to grant access to a system or account. In addition to the traditional password, the user also needs to provide a second form of authentication, usually something they have, such as a code sent via SMS, a physical security token, or even a fingerprint. This approach adds an extra layer of security, significantly reducing the risk of unauthorized access to sensitive information. Benefits of two-factor authentication: Implementing two-factor authentication brings several benefits to the security of information in the cloud. Firstly, it significantly increases the protection of accounts and systems, since even if someone gains access to the password, they will not be able to go through the second form of authentication. This makes accounts much more difficult for hackers to compromise. Additionally, two-factor authentication helps prevent phishing attacks because even if users provide their passwords on a fake website, the attacker will not have the second form of authentication. This method can also provide a sense of peace of mind for users knowing their accounts are more robustly protected. How to implement two-factor authentication in the cloud: Implementing two-factor authentication in the cloud is a simple task that requires following a few essential steps. First of all, you need to choose a reliable two-factor authentication provider that is compatible with the systems and services in use. Next, you need to configure the authentication options in the system, defining which methods will be offered to users. Options: It is recommended to provide a variety of options for two-factor authentication, such as SMS, authentication apps, or physical tokens, to provide greater flexibility to users. Afterward, you need to guide users on how to enable two-factor authentication on their accounts by providing the necessary instructions. Additionally, it is important to educate users about the importance of two-factor authentication and its positive impact on information security. By adopting these measures, it is possible to implement two-factor authentication efficiently, strengthening the security of cloud accounts and systems. 4. Restricted Access Policies Importance of restricted access policies: Restricted access policies are fundamental to ensuring data security in the cloud. By implementing restricted access policies, companies can control which users are allowed to access certain resources and sensitive information. This helps reduce the risk of unauthorized access and data leakage while protecting the integrity and privacy of corporate information. Additionally, by limiting access to only authorized users, restricted access policies can also ensure compliance with security and privacy regulations, avoiding fines and penalties. Implementing role-based access control (RBAC): An effective way to implement restricted access policies is through role-based access control (RBAC). This approach involves assigning specific roles and permissions to each user based on their responsibilities and authorization level. By adopting RBAC, companies can ensure that only relevant users have access to the resources they need to perform their tasks while restricting access to sensitive areas. This simplifies access administration, reduces the risk of improper access, and makes it easier to audit and monitor data access. Access monitoring and auditing: In addition to implementing restricted access policies, it is essential to have an adequate monitoring and auditing system to ensure its effectiveness. Continuous monitoring allows you to identify any suspicious activity or unauthorized access attempts. It is important to record and analyze access logs, event logs, and other relevant information to identify potential security threats or breaches. Regularly auditing access logs is also crucial to ensure compliance with restricted access policies and security regulations. By carrying out audits, companies can identify potential flaws in the access control system and take corrective measures to further strengthen security. Cloud deployment: By implementing restricted access policies, companies can ensure data security in the cloud. Additionally, you must use role-based access control to allow only authorized users to access sensitive information. Establishing effective monitoring and auditing is another crucial measure to protect corporate data. These practices help minimize the risks of unauthorized access, data leaks, and security breaches, ensuring the confidentiality and integrity of information in the cloud. 5. Continuous Monitoring and Threat Detection Cloud security monitoring tools: Continuous monitoring is an essential practice for protecting cloud environments against cyber threats. To this end, there are several tools available that help detect possible attacks and vulnerabilities. These tools provide features such as log analysis, network traffic monitoring, identifying suspicious behavior, and real-time alerts. By utilizing cloud security monitoring tools, companies can gain comprehensive visibility into their infrastructure, identify potential threats, and respond quickly. Threat detection and incident response: Threat detection and incident response are essential components of continuous monitoring. By implementing cloud security solutions, companies can leverage advanced detection capabilities such as artificial intelligence and machine learning to identify malicious activity and anomalies. These technologies help distinguish between legitimate and suspicious behavior, enabling early detection of potential threats. Furthermore, it is essential to have a well-defined incident response plan, with clear procedures and trained staff to act quickly in the event of a security incident. Best practices for effective continuous monitoring: To also ensure effective monitoring, first establish a comprehensive strategy that covers all aspects of the infrastructure. This includes servers, applications, and network traffic. Additionally, you must keep your security tools up to date and configure them correctly to ensure accurate detection of potential threats. Set custom performance metrics and alerts to notify your security team of suspicious activity. These measures will help strengthen your company's security posture and enable quick and effective action to protect systems and data against potential attacks. This way, it is possible to strengthen the company's security posture and act proactively to protect systems and data against potential attacks. Finally, periodic analysis and review of monitoring records are crucial to identify relevant patterns and trends. Pay attention to Cloud Security: By implementing security monitoring tools in the cloud, it is possible to strengthen the security posture of companies. Furthermore, establishing threat detection and incident response is critical to ensuring an effective response to cyberattacks. By following continuous monitoring best practices, companies can identify threats in a timely manner and protect their cloud environments. It is worth noting that continuous monitoring and threat detection plays a crucial role in preserving the integrity and confidentiality of corporate data in the cloud. 6. Data Backup and Recovery Cloud backup strategies: Implementing effective cloud backup strategies is critical to ensuring the protection and availability of corporate data. There are different approaches to cloud backup such as full, incremental, and differential backup. Full backup involves copying all data to a remote location, while incremental and differential backups only copy changes made since the last backup. It is important to consider factors such as data volume, frequency of changes, and recovery time requirements when choosing the most appropriate backup strategy for your cloud environment. Importance of data recovery in case of an incident: Data recovery plays a crucial role in business continuity and mitigating damage in case of incidents. By performing regular backups and storing them in secure locations in the cloud, companies can ensure quick and reliable recovery of their data in the event of loss or corruption. Additionally, data recovery is essential to minimize downtime, prevent the loss of critical information, and maintain customer trust. By adopting a solid data backup and recovery strategy, companies can face adversities with greater peace of mind, ensuring the continuity of operations and the preservation of their digital assets. Testing and improving your cloud backup and recovery plan: To ensure the effectiveness of your backup and recovery plan, it is essential to perform periodic testing and continuous improvements. Testing allows you to identify potential failures in the process and verify whether recovery time and recovery point objectives are being met. Additionally, it is crucial to document backup procedures and policies, conduct recovery simulations, and involve all relevant parties such as IT staff and end users. This way, everyone will be familiar with the process and will be able to contribute to its efficiency. Based on the test results, you can make necessary adjustments and implement improvements to ensure reliable and efficient data recovery. By implementing cloud backup strategies, recognizing the importance of data recovery, and regularly testing the backup and recovery plan, companies can be prepared to face data loss incidents and ensure the continuity of their operations. Data backup and recovery are crucial elements of an information security strategy, providing protection and peace of mind for organizations in an increasingly complex and threatening cloud environment. Cloud Security Conclusion Data protection in the cloud is extremely important to ensure the security and privacy of a company's sensitive information. By implementing the security best practices discussed in this article, such as data encryption, two-factor authentication, and restricted access policies, organizations can mitigate data breach risks and maintain the integrity of their information in the cloud. Dygital9 and cloud security Dygital9 is specializing in IT services related to web servers, cloud servers, and cloud monitoring services, it is committed to helping companies protect their data in the cloud. Follow us for more information and tips on cloud security and take the opportunity to share this article with your colleagues in the field.