MAROKO133 Eksklusif ai: AI is moving to the edge – and network security needs to catch up

📌 MAROKO133 Update ai: AI is moving to the edge – and network security needs to ca

Presented by T-Mobile for Business

Small and mid-sized businesses are adopting AI at a pace that would have seemed unrealistic even a few years ago. Smart assistants that greet customers, predictive tools that flag inventory shortages before they happen, and on-site analytics that help staff make decisions faster — these used to be features of the enterprise. Now they’re being deployed in retail storefronts, regional medical clinics, branch offices, and remote operations hubs.

What’s changed is not just the AI itself, but where it runs. Increasingly, AI workloads are being pushed out of centralized data centers and into the real world — into the places where employees work and customers interact. This shift to the edge promises faster insights and more resilient operations, but it also transforms the demands placed on the network. Edge sites need consistent bandwidth, real-time data pathways, and the ability to process information locally rather than relying on the cloud for every decision.

The catch is that as companies race to connect these locations, security often lags behind. A store may adopt AI-enabled cameras or sensors long before it has the policies to manage them. A clinic may roll out mobile diagnostic devices without fully segmenting their traffic. A warehouse may rely on a mix of Wi-Fi, wired, and cellular connections that weren’t designed to support AI-driven operations. When connectivity scales faster than security, it creates cracks — unmonitored devices, inconsistent access controls, and unsegmented data flows that make it hard to see what’s happening, let alone protect it.

Edge AI only delivers its full value when connectivity and security evolve together.

Why AI is moving to the edge — and what that breaks

Businesses are shifting AI to the edge for three core reasons:

• Real-time responsiveness: Some decisions can’t wait for a round trip to the cloud. Whether it’s identifying an item on a shelf, detecting an abnormal reading from a medical device, or recognizing a safety risk in a warehouse aisle, the delay introduced by centralized processing can mean missed opportunities or slow reactions.

• Resilience and privacy: Keeping data and inference local makes operations less vulnerable to outages or latency spikes, and it reduces the flow of sensitive information across networks. This helps SMBs meet data sovereignty and compliance requirements without rewriting their entire infrastructure.

• Mobility and deployment speed: Many SMBs operate across distributed footprints — remote workers, pop-up locations, seasonal operations, or mobile teams. Wireless-first connectivity, including 5G business lines, lets them deploy AI tools quickly without waiting for fixed circuits or expensive buildouts.

Technologies like Edge Control from T-Mobile for Business fit naturally into this model. By routing traffic directly along the paths it needs — keeping latency-sensitive workloads local and bypassing the bottlenecks that traditional VPNs introduce — businesses can adopt edge AI without dragging their network into constant contention.

Yet the shift introduces new risk. Every edge site becomes, in effect, its own small data center. A retail store may have cameras, sensors, POS systems, digital signage, and staff devices all sharing the same access point. A clinic may run diagnostic tools, tablets, wearables, and video consult systems side by side. A manufacturing floor might combine robotics, sensors, handheld scanners, and on-site analytics platforms.

This diversity increases the attack surface dramatically. Many SMBs roll out connectivity first, then add piecemeal security later — leaving the blind spots attackers rely on.

Zero trust becomes essential at the edge

When AI is distributed across dozens or hundreds of sites, the old idea of a single secure “inside” network breaks down. Every store, clinic, kiosk, or field location becomes its own micro-environment — and every device within it becomes its own potential entry point.

Zero trust offers a framework to make this manageable.

At the edge, zero trust means:

• Verifying identity rather than location — access is granted because a user or device proves who it is, not because it sits behind a corporate firewall.

• Continuous authentication — trust isn’t permanent; it’s re-evaluated throughout a session.

• Segmentation that limits movement — if something goes wrong, attackers can’t jump freely from system to system.

This approach is especially critical given that many edge devices can’t run traditional security clients. SIM-based identity and secure mobile connectivity — areas where T-Mobile for Business brings significant strength — help verify IoT devices, 5G routers, and sensors that otherwise sit outside the visibility of IT teams.

This is why connectivity providers are increasingly combining networking and security into a single approach. T-Mobile for Business embeds segmentation, device visibility, and zero-trust safeguards directly into its wireless-first connectivity offerings, reducing the need for SMBs to stitch together multiple tools.

Secure-by-default networks reshape the landscape

A major architectural shift is underway: networks that assume every device, session, and workload must be authenticated, segmented, and monitored from the start. Instead of building security on top of connectivity, the two are fused.

T-Mobile for Business solutions shows how this is evolving. Its SASE platform, powered by Palo Alto Networks Prisma SASE 5G, blends secure access with connectivity into one cloud-delivered service. Private Access gives users the least-privileged access they need, nothing more. T-SIMsecure authenticates devices at the SIM layer, allowing IoT sensors and 5G routers to be verified automatically. Security Slice isolates sensitive SASE traffic on a dedicated portion of the 5G network, ensuring consistency even during heavy demand.

A unified dashboard like T-Platform brings it together, offering real-time visibility across SASE, IoT, business internet, and edge control — simplifying operations for SMBs with limited staff.

The future: AI that runs the edge and protects it

As AI models become more dynamic and autonomous, we’ll see the relationship flip: the edge won’t just support AI; AI will actively run and secure the edge — optimizing traffic paths, adjusting segmentation automatically, and spotting anomalies that matter to one specific store or site.

Self-healing networks and adaptive policy engines will move from experimental to expected.

For SMBs, this is a pivotal moment. The organizations that modernize their connectivity and security foundations now will be the ones best positioned to scale AI everywhere — safely, confidently, and without unnecessary complexity.

Partners like T-Mobile for Business are already moving in this direction, giving SMBs a way to deploy AI at the edge without sacrificing control or visibility.

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🔗 Sumber: venturebeat.com

📌 MAROKO133 Update ai: New method stabilizes nickel-rich battery cathodes, extends

An international team of researchers has found a way to extend the lifespan of lithium-ion (Li-ion) batteries by suppressing a damaging structural failure in nickel-rich (Ni-rich) cathodes known as c-collapse.

The approach, aimed to stabilize high-energy, Ni-rich cathodes, was developed by scientists from the SLAC National Accelerator Laboratory, run by California’s Stanford University, and the Korea Institute of Science and Technology (KIST).

Lithium-ion batteries power smartphones, laptops, electric vehicles (EVs), and grid-scale storage systems. But repeated charging and discharging gradually wear them down. Internal stress caused when lithium ions move in and out of the cathode is a major reason for this.

At high voltages, layered cathode materials can undergo sudden shrinkage along one crystallographic direction, known as the c-lattice parameter. This contraction, or c-collapse, can crack particles, disrupt ion pathways, and shorten battery life.

Preventing c-collapse

To address the issue, the team rethought conventional battery design. Instead of preserving a perfectly ordered crystal structure, they used an electrochemical activation process to introduce controlled atomic disorder intentionally.

“It has long been recognized that the anisotropic strain in the layered cathode is the original culprit that limits the lifetime of lithium-ion batteries,” Zhelong Jiang, co-first author of the paper, told Tech Xplore.

By carefully tuning how nickel, manganese, and lithium atoms rearrange during early battery cycling, they transformed a traditionally layered nickel-rich cathode into a disordered layered (DL) structure.

“We produced a new imperfect crystal structure, which we call disordered layered cathode (DL), in these materials,” Jiang noted. He elaborated that the imperfect structure turned out to be a major advantage.

“LiBs [Lithium-ion batteries] based on cathodes with this structure were found to exhibit both large capacity and high cycle life, due to the lack of anisotropic strain,” Jiang continued.

The team demonstrated the approach using a high-energy nickel-rich material, LiNi₀.₉Mn₀.₁O₂, which is closely related to cathodes already used in commercial batteries.

Protecting nickel cathodes

The electrochemical tuning did not reduce energy capacity. Instead, batteries built with the modified cathodes maintained high capacity. They also showed improved structural stability during repeated charge-discharge cycles.

The team explained that the disordered layered structure remained dimensionally stable as lithium ions moved, thereby preventing the sharp lattice contraction that normally occurs at high states of charge.

“The breakthrough introduced in our paper stemmed from our team’s cumulative knowledge with the study of the effects of anion redox in lithium-ion cathodes,” Jiang pointed out.

According to the team, the new approach reduced internal strain, limited particle cracking, and suppressed voltage loss. The electrochemical activation method could be applied during battery formation, making it compatible with large-scale manufacturing.

“I think we have only touched the tip of the iceberg here, and I expect many new forms of materials with different modes of crystal imperfections will emerge following electrochemical treatment,” Jiang concluded.

He said that the team hopes to develop a more comprehensive understanding of how chemical composition, structural change kinetics, and structural imperfections interact.

The study has been published in the journal Nature Energy.

🔗 Sumber: interestingengineering.com