GPU manufacturer Nvidia is expanding its enterprise software offering with three new AI workflows for retailers it hopes will also drive sales of its hardware accelerators.

The workflows are built on Nvidia’s existing AI technology platform. One tracks shoppers and objects across multiple camera views as a building block for cashierless store systems; one aims to prevent ticket-switching fraud at self-service checkouts; and one is for building analytics dashboards from surveillance camera video.

Nvidia isn’t packaging these workflows as off-the-shelf applications, however. Instead, it will make them available for enterprises to integrate themselves, or to buy as part of larger systems developed by startups or third-party systems integrators.

“There are several of them out there, globally, that have successfully developed these kinds of solutions, but we’re making it easier for more software companies and also system integrators to build these kinds of solutions,” said Azita Martin, Nvidia’s VP of retail.

She expects that demand for the software will drive sales of edge computing products containing Nvidia’s accelerator chips, as latency issues mean the algorithms for cashierless and self-checkout systems need to be running close to the checkout and not in some distant data center.

In addition to tracking who is carrying what items out of the store, the multiple camera system can also recognize when items have been put back on the wrong shelf, directing staff to reshelve them so that other customers can find them and stock outages are avoided, she said.

“We’re seeing huge adoption of frictionless shopping in Asia-Pacific and Europe, driven by shortage of labor,” said Martin.

Nvidia will face competition from Amazon in the cashierless store market, though, since while Amazon initially developed its Just Walk Out technology for use in its own Amazon Go and Amazon Fresh stores, it’s now offering it to third-party retailers, too. The first non-Amazon supermarket to use the company’s technology opened in Kansas City in December.

Assessing cost control

The tool to prevent ticket switching is intended to be integrated with camera-equipped self-service point-of-sale terminals, augmenting them with the ability to identify the product being scanned and verify it matches the barcode.

The cost of training the AI model to recognize these products went beyond the usual spending on computing capacity.

“We bought tens of thousands of dollars of products like steak and Tide and beer and razors, which are the most common items stolen, and we trained these algorithms,” said Martin.

Nvidia kept its grocery bill under control using its Omniverse simulation platform. “We didn’t buy every size of Tide and every packaging of beer,” she adds. “We took Omniverse and created synthetic data to train those algorithms even further for higher accuracy.”

Beer presents a particular challenge for the image recognition system, as it often sells in different-size multipacks or in special-edition packaging associated with events like the Super Bowl. However, the system continues to learn about new product formats and packaging from images captured at the checkout.

While implementation will be left up to retailers and their systems integrators, Martin suggested the tool might be used to lock up a point-of-sale terminal when ticket switching is suspected, summoning a member of staff to reset it and help the customer rescan their items.

Nvidia is touting high accuracy for its algorithms, but it remains to be seen how this will work out in deployment.

“These algorithms will deliver 98% accuracy in detecting theft and shutting down the point of sale and preventing it,” she said.

But that still leaves a 2% false positive rate, so CIOs will want to carefully monitor the potential impact on profitability, customer satisfaction, and frequent resets to prevent ticket switching.

A $100 billion problem

A 2022 survey by the National Retail Federation found that inventory shrink amounted to 1.44% of revenue — a relatively stable figure over the last decade — and in 2021, losses due to shrink totaled almost $100 billion, the NRF estimated.

Of that, survey respondents said 26% was due to process or control failures, 29% due to employee or internal theft, and 37% due to external theft.

But Nvidia suggests that its loss prevention technology could eliminate 30% of shrinkage. That, though, would mean it could prevent four-fifths of all external retail theft, even though in addition to ticket switching, that category also includes shoplifting and organized retail crime activities such as cargo theft, and the use of stolen or cloned credit cards to obtain merchandise.

Plus, potential gains must be weighed against the cost of deploying the technology, which, Martin says, “depends on the size of the store, the number of cameras and how many stores you deploy it to.”

More positively, Nvidia is also offering AI workflows that can process surveillance camera video feeds to generate a dashboard of retail analytics, including a heatmap of the most popular aisles and hour-by-hour trends in customer count and dwell time. “All of this is incredibly important in optimizing the merchandising, how the store is laid out, where the products go, and on what shelves to drive additional revenue,” Martin said.

Artificial Intelligence, IT Strategy, Retail Industry

Kirk Ball, Chief Information Officer/Chief Technology Officer, Giant Eagle, joins host Maryfran Johnson for this CIO Leadership Live interview, jointly produced by and the CIO Executive Council. They discuss grocery retail innovations, digital customer strategies, sourcing global talent, augmented reality and more.

Watch this video:

Listen to this episode:

CIO, CIO Leadership Live

Cybercrime is nothing new. The threats that accompany society’s increased digitalization have been explored in alarmist articles, science fiction movies, and everything in between for decades. But that doesn’t mean the need for robust cybersecurity isn’t real. Digital enhancement brings increasing digital risk. Stringent provisions are more necessary than ever. 

Cybercrime’s prevalence and costs are significant. The UN reported that cybercrime skyrocketed by 600% during the pandemic, a result of an almost overnight reliance on digital working, shopping, and communication. There was a 10% increase in the average total cost per security breach from 2020 to 2021, while a McAfee report estimates that the global cost of cybercrime has now reached over US$1 trillion. 

Easy targets: smart retail and smart cities 

The need for vigilant cybersecurity measures is paramount. The retail sector has proven especially vulnerable. Trustwave reports that retail is on the receiving end of 24% of all cyberattacks, more than any other industry. 

Retail’s reliance on mixed technology, pairing old point-of-sale systems like cash registers and in-store purchases with cloud-based e-commerce and administrative systems, makes it an ideal target for hackers. On top of that, retail’s customer data tends to be high value, often consisting of credit card details, phone numbers, and security questions and answers. The industry’s high staff turnover rate also makes it vulnerable. Some 64% of retailers report attempted attacks each month, with the cost of a hack to an e-commerce site currently averaging $4 million. In 2020, cyberattacks cost online retailers a remarkable £5.9 billion  in the UK alone

But the problem is not limited to e-commerce. Brick-and-mortar retail stores are at enormous risk too. In fact, part of the reason physical stores have become an easy target for cybercriminals is that in-store management is often inattentive, presuming that such attacks only take place online.  

That may have been true at one time, but many physical stores today are increasingly reliant on Internet of Things (IoT) devices. IoT solutions offer extraordinary benefits in-store: indoor navigation, presence detection, and preventive maintenance, to name a few. But if not properly secured, increased digitalization can leave retailers exposed. 

Smart cities and the Multi-Stakeholder Manifesto 

Equally vulnerable are smart cities. To thrive as intended, smart cities rely on a complex and interdependent network of devices, platforms, systems, and users, all contributing vital information that helps keep the engine running. But to be reliant on so many moving parts can leave gaps—exposing areas that bad faith actors know how to exploit.  

A key challenge for smart cities is integration and coordination. Cities are often made up of multiple municipalities, each of which typically has a different set of capabilities, different priorities, and different approaches to technology management. Increased communication among smart city stakeholders is vital for confronting cybersecurity threats. 

Some steps have already been taken to address such concerns. Over fifty civil society and industry representatives support the Multi-Stakeholder Manifesto, launched in 2021. The manifesto warns that cybercrime “poses new risks to human security, dignity, and equity” and that “no single actor can adequately counter them on their own.” It proposes a multi-stakeholder approach that puts protecting victims at the top of its agenda.  

“Governments around the world have long abused cybercrime measures and used cybercrime legislation to expand state control and criminalise the publication and dissemination of unwelcome content, to impose mass surveillance and curb privacy in the name of fighting terrorism,” the authors note. 

To effectively battle cybercrime, cooperation is required on a regional, national, and international level. Fractious regional and transnational relationships and opaque data management practices only fuel cybercrime’s rise. 

Problems and solutions 

The emergence of intelligent networks made up of billions of connected devices across a range of sectors has created a whole new world of vulnerabilities for cybercriminals to exploit. Some of the most common cybercrimes are phishing scams, ransomware, data breaches, distributed denial-of-service (DDoS) attacks, and supply chain disruptions. Cybersecurity must continually innovate and adapt to confront a diverse and ever-evolving range of threats.  

As such, many new solutions have presented themselves. WISeKey has emerged as a vital authentication and identification partner, while Darktrace employs AI as a tool of defense—preventing, detecting, responding, and recovering from cyberattacks at the very same time.  

Meanwhile, Li-Fi adoption grows every year. Because it’s line-of-sight, Li-Fi is more secure than Wi-Fi—it won’t leak through walls or even windows with the blinds closed. Additionally, it can be paired with high-quality lighting within the same luminaire

What next? 

Some 57% of large and midsize businesses cite security concerns as the top barrier to further IoT adoption. But the real issue is not the IoT or the systems that use it: it’s companies and systems that use the IoT without making sure robust cybersecurity measures are implemented and managed properly. 

The top tech companies in the world have pledged billions of dollars to strengthen cybersecurity and train skilled cybersecurity workers, an action that speaks to how seriously they are taking the threat.  

But cybersecurity is an issue that covers the whole spectrum of society. As Google’s global affairs chief, Kent Walker, said upon announcing the measures, “Robust cybersecurity ultimately depends on having the people to implement it.” So it makes sense to partner with a reliable expert in the field that is always keeping an eye on the latest threats and the evolving solutions that exist to counteract them. 

Just as one would feel responsible for the security of a guest in their home, so companies should feel responsible for those navigating their website, store, or purchasing their products. Investing in the best in cybersecurity is the only way to keep people — and their data — safe. 

Click here to find out more about how Signify’s LiFi systems provide high-speed connectivity and unique physical security.  


When Steve Pimblett joined The Very Group in October 2020 as chief data officer, reporting to the conglomerate’s CIO, his task was to help the enterprise uncover value in its rich data heritage.

For a company that made its name in mail-order catalog sales, the idea of building an enterprise-wide data catalog seemed to be an appropriate part of that process.

Very grew from the successive mergers of a number of mail order catalog companies, the oldest dating back to the 1890s. Its constituent companies later moved into high-street retail, launched new mail-order brands selling clothing on credit, and even created a consumer financial data broker, later spun off like so many of the group’s other non-core activities.

The group’s move online began in the 1990s with its first steps into e-commerce, followed by the closure of its physical stores in 2005. It launched its first online-only brand, Very, in 2009 and finally abandoned its printed catalogs to go all-in online in 2015.

The whole company rebranded as Very in 2020, the year Pimblett joined. He found a rich collection of data assets, including information on over 2.2 million daily website visits, 4.8 million active customers and 49 million items delivered annually.

Behind the flagship brand, though, he says data remained scattered in siloes across many legacy business units and applications, with limited automation, many glossaries, and complex data lineage, and stewardship making it hard to govern and audit.

Data and analytics experts were also spread across the organization, with some under the technology team but others embedded in the various business units.

“There was no one to help everybody with standards and central approaches, so every business vertical was doing it differently,” he says. “‘It’ being everything from how they collect and measure data, to how they understand it and their own glossary. It was very fragmented, and I brought it together into a hub-and-spoke model.”

The new model enables Very to design once and deploy everywhere, while maintaining a product focus.

As a result, Pimblett now runs the organization’s data warehouse, analytics, and business intelligence. “We’re a Power BI shop,” he says. “I run the infrastructure and a central enterprise BI team.”

Establishing a clear and unified approach to data

But getting to this stage was an intricate process that involved creating centers of excellence for things like data analytics that own the end-to-end infrastructure, application and skill sets, as well as career plans for staff.

Pimblett took a carrot-and-stick approach to get everyone working together, partnering with them on value creation (the carrot of profit) and risk mitigation (the stick of compliance). “It’s about making sure we understand the legal basis by which we’re capturing data, what we’re doing with it, where it flows, how we use it, and that we govern all those things,” he says.

Enterprises need to be aware of the dual nature of the data they hold, that it can be both an asset and a liability, he says.

One of the early projects on which he was able to add value through a partnership between his data hub and one of the business unit spokes was in building a new demand forecasting tool.

“We’re a multi-category retailer with over 160,000 SKUs, so forecasting how much stock to buy of each SKU is a business challenge, but also very much a technology and mathematical challenge,” he says.

Steve Pimblett

To get buy-in from business units for projects like this, he says, “you have to sell them the benefit and the outcome of shared platforms, reuse, shared data, and the efficiencies that they’ll get,” and not the technology you’ll use.

“A lot of roles in data just talk about the data,” he says. “Where do we store it? What’s the infrastructure? What’s our warehousing technology? You know, good old DBAs, modelers, and analysts.”

Instead, says Pimblett, he and his data colleagues ask business managers, “Where do you think you can create value from data? What type of decisions are you making? Where is there opportunity to automate? And how can we delight the customer or empower your colleagues to take better decisions? Turn it into an outcome, a value and an action conversation. That tends to get them engaged,” he says.

 A more nimble catalog business

Very has come full circle as a business built on catalog data, but it took some introspection in order to figure out the best way to get there.

“Cataloging your data is more important than ever for many companies, with so many technology options, different data silos, enterprise warehousing, lake houses, data lakes, and all those types of capabilities,” says Pimblett. “Understanding what data you’ve got locked in all these different stores is a big part of the jigsaw puzzle.”

So he began working on a pilot project with data catalog and governance tool vendor Alation about a year ago, after it responded to Very’s RFP. In a first test of the technology, he used Alation to catalog a subset of Very’s data held in an old Teradata database. It took about nine weeks to set up the infrastructure, make the connection to the database, and index and understand the metadata. Very is focusing on short sprints like this, rather than on monolithic 12-month projects that may not fit the business when finished.

“Run a pilot within nine weeks, prove it, prove the value, and then roll it forward into production is very much how we think about our full technology agenda,” he says.

Pimblett hasn’t yet catalogued all of Very’s data, however. It’s always going to be a work in progress. “We’re picking off the highest potential value and highest risk areas,” he says. “We’ve done it in our financial services area, and some of our marketing area. Those tend to hold the biggest amount of our customer information.”

The next step will be to roll it out across the whole company.

“We’ve got some massive systems that take time to index — not from a tech perspective, but from a data stewardship and understanding perspective,” he says.

Value, not vanity

Reflecting on things he might have done differently over the two years since he joined Very, Pimblett cautions against embarking on new technology projects for the sake of it and recommends always thinking about the desired outcome or action first.

If you don’t, he says, “there’ll be an occasion when you realize you didn’t comply with your own principles and start with the action and outcome.” In those situations, he says, you need to tell yourself: “Get back to your strategy. You’ve thrown value away because you’ve had a team working on a vanity project rather than creating business value.

One of the next value-creating projects to which Very will be applying its rich data legacy centers on loans: By the end of 2022, it will pilot a new personal finance business, offering its existing customer base loans of up to £7,500 ($8,800) over one to five years.

“We’ve got a trusted brand and we’ve just started to innovate based on our technology and data capabilities,” he says.

Chief Data Officer, Data Center Management

Increasing margins is critical to achieving sustained success in the retail industry.To maximize margins, leaders consider how to run the store more efficiently, how to deliver the best services to customers and how to grow new services. Traditionally, they have used rear-view mirror data to help accomplish these goals—that is, examining historical data from months prior and coming up with a plan. 

Today, retailers are relying more on proactive and contextual data in real-time. For instance, what are the online shopper’s preferences? Do they tend to buy button-down shirts and khakis or jeans and t-shirts? How does a brick-and-mortar store’s layout affect purchasing decisions? Context involves gathering data about human behavior throughout the customer journey to figure out why they buy what they buy. But how do you capture human emotions and activities in the moment, and then turn that data into useful information? How do you account for changes in behavior and preferences over time?

Obtaining the right insights, consistently, at a micro level about the consumer is key to delivering a more meaningful and personalized customer experience. Combining consumer shopping preferences with historical data can give you a contextually-rich, action-reaction paradigm. To accomplish this, retailers are turning to computer vision complemented by artficial intelligence.

Watch the videoReimagine the Future of Retail

Computer vision provides video and audio for additional context, complementing other types of data. Together, these data points become part of an analytics workflow delivering a tangible outcome. Using a federated approach, data can be analyzed where it is collected, producing insights used to make decisions in real time. 

This federated approach to analytics enables forward-thinking retailers to incorporate new approaches to using and orchestrating their data, using computer vision systems that grow as they grow. New use cases are more achievable, and IT can leverage these technologies to scale and drive further processes that enhance their momentum towards achieving the digitally-driven store of the future.

Let’s look at how computer vision is impacting the customer experience, store security and operations, revenue growth and sustainability today and what that means going forward. 

Continuing to address a top priority for the retail industry by improving safety and security 

Most retail establishments started their computer vision journey years ago when they brought in video camera systems for security purposes, providing them with a foundation to build on. Now it’s paying off.

When tied to a computer vision system, the visual data, historical data and AI can offer real-time situational awareness. Analysis occurs mainly on-site, at the edge. It’s quick and accurate, reducing staff response time. For example, a maintenance crew member can react almost immediately to spilled substances that could cause an accident. Anomaly detection can enhance a store’s loss prevention processes such as alerting security personnel to people who are concealing stolen items, and a real-time video analytics platform can even help with finding missing children.

Tackling current and future operational efficiency challenges 

The conventional store, where you build a structure and stock it with products and displays, is being transformed by customer’s buying patterns. The Intelligent Store (see Figure 1) consists of processes around employees (scheduling and reduction of effort), inventory and customers that can be constantly monitored and improved in real-time. With the intelligent store, retailers can, transform, adapt and respond to its customer’s needs and their beahiour with context and personalization 

With accurate data, managers today can utilize hyper-personalization to drive more sales, demand forecasting to maintain inventories and optimized route planning to cut costs. For this, you need real-time insights using sensors and cameras, and a strategy that aligns operations with the customer experience, autonomous retail and a host of integrated technologies to make it all happen.


Figure 1. The Intelligent Store extends across all facets of the retail industry to deliver benefits including real-time operational improvements, hyper-personalization and automation, scalability and security.

One goal of an Intelligent Store is to empower customers by reducing friction in the buying experience. That means touchless checkout, where items are “rung up” automatically as customers leave the store. For staffed checkouts, computer vision can monitor customer lines and move staff where needed in real time. Video-based inventory tracking ensures items are always in stock and enables traceability, as well as optimized picking for fulfilling ecommerce grocery orders. And curbside delivery is improved by combining visual data such as number plate and/or vehicle recognition, and sensor data so staff begin preparing to deliver groceries as soon as a customer drives into the lot.

The digital twin is another technology that boosts operational efficiency. Using software models, a retailer can run simulations of a real-world environment before committing to expensive changes. Imagine a designer creating a store planogram or distribution center in 3D, and using AI to determine the freshness of perishable items (to reduce spoilage), to optimize customer flow and merchandising, and for predictive analysis. A digital twin can be rendered on-site without the need to exchange huge amounts of data with a data center as the processing occurs at the edge.

Watch the video: Edge and computer vision are enabling better Retail

Enhancing the customer experience while increasing revenues

Happy customers inevitably buy more, so it is up to retailers to provide the right product with the right value. And by investing in the customer experience, revenues will automatically be maximized.

Consider virtual try-on, which combines computer vision, AI and augmented reality to allow shoppers to try on glasses, clothing and other items using their mobile device’s camera, or an in-store digital kiosk or mirror. “See it in your room” for furniture and electronics is similar. Virtual try-on is both immersive and a time-saver for customers, potentially resulting in higher per-session sales. 

Computer vision systems linked to inventory management systems is also a boon for the customer experience and optimization of revenue. Where cameras are used to scan existing inventory and update records, stock level checks are more accurate, helping to ensure the customer’s item isn’t backordered. Automatic updates to inventory after sales are completed saves on back-house time. From a merchandising perspective, computer vision can identify which areas of a store gets the most foot traffic and target hot spots where product should be placed.  

On the flip side is how to avoid losing revenue. Shrinkage in the global retail sector accounts for a staggering $100 billion USD* in annual losses, creating demand for technology and/or processes to prevent theft and fraud, and to better secure transactions. Many grocery stores now use cameras mounted at checkout stations to watch for sweetheart checking, prevent or detect item swapping and identify inaccurate scanning and payments.

Read the IDC Whitepaper “Future Loss Prevention: Advancing Fraud Detection Capabilities at self-checkout and throughout the retail store

Becoming environmental stewards and following sustainability practices

Many corporations today support initiatives to conserve resources and reduce waste. Computer vision is helping stores, malls, distribution centers and the like accomplish their sustainability goals.

The retail industry has several avenues to sustainability. Two of the most constructive are reducing energy consumption and using modern inventory management techniques.

Most of us are familiar with refrigerated cases with motion sensors that turn the lights on when a door is opened. Entire facilities can use the same principles, like smart HVAC, overhead and outdoor lighting to minimize power consumption.

Reducing food waste is another way to save money while having a positive impact on the community and environment. According to RTS, about 30% of the food in U.S. grocery stores is thrown away every year. Optimized cold chain management reduces spoilage as well as the energy needed to maintain perishables from the loading dock to the freezer case or produce bin. Proactive restocking, based on historical data and AI, further ensures that items are available when needed and in sellable quantities for a particular store.

Although the pandemic boosted online and curbside pickup sales, the resulting supply chain issues have left customers somewhat disillusioned and wondering which important item will become hard to get. Customers will accept some inconvenience due to a worldwide event, however, retailers need to be prepared for the near-term future shopper who has high expectations and whose loyalty may be harder to keep. That can be done through a data-driven approach using computer vision and AI.

Retail organizations can build on the safety and security infrastructure already deployed in their stores and at a pace that’s right for their business. Digital transformation is an on-going process and many retailers are already engaged with Dell Technologies in developing the right framework to guide them through their journey, while enabling their business to remain agile and innovate.

For an overview of computer vision and its impact on retail, read the Solution Brief, “Protecting retails assets and unlocking the potential of your data with AI-driven Computer Vision.”

Learn more about how computer vision is positively impacting other industries: 

The Future Is Computer Vision – Real-Time Situational Awareness, Better Quality and Faster InsightsComputer Vision Is Transforming the Transportation Industry, Making It Safer, More Efficient and Improving the Bottom LineHow Computer Vision is revolutionizing the Manufacturing Supply ChainHow the Sports and Entertainment Industry Is Reinventing the Fan Experience and Enhancing Revenues with Computer Vision

* Sensormatic Global Shrink Index:

Artificial Intelligence

Retailers continue to adopt a digital-first approach to customer experience, both in-store and online. According to a recent survey by DemandScience and Comcast Business, over the next 12 months, retail IT executives will prioritize upgrades in digital customer experience (CX), network and cybersecurity solutions, expanded use of analytics-backed decision making, and increased investments in AI. To meet the customer demands of a digital-first business model, retailers need to address their critical digital infrastructure and rethink network design and cybersecurity. This article outlines the major considerations and types of solutions retailers should consider to enable fast, reliable, and secure networks and digital business.

Customer demand driving digital adoption

The pandemic drastically and rapidly changed how retailers interact with their customers. The customer preference for a more digital, frictionless experience continues to drive the adoption of digitally-enabled processes and tools such as online and contactless ordering apps, self-checkout, and AI-powered product offerings and recommendations. This rapid adoption of new technologies brings with it an increase in the complexity of network design and security architecture for IT teams.

The number of devices connected to the network has increased significantly with the proliferation of wireless POS, tablets, inventory trackers, and IoT devices. This number is expected to grow over the next five years and securing the breadth of devices is becoming increasingly challenging. Confronted with escalating threats, privacy regulations, and growing customer concerns about data security, retailers are facing unprecedented pressures to keep their network connections secure.

Retail-specific vulnerabilities

Retailers have always been attractive targets for cyber attackers and data thieves. But now, cybersecurity threats have become an even bigger concern with 24% of all cyberattacks targeted at retailers, more than any other industry. For retail security teams, the network perimeter continues to transform as data and applications move to the cloud, more devices and merchandise are connected in-store, and users are working from outside headquarters and branch locations. With the expanding range of possible entry points, PCI compliance–always a top-line security priority–can be more challenging to manage. Retail security is further complicated by the broader threat surface due to digital POS systems, eCommerce platforms, digital supply chains with third-party partners, and digital loyalty programs.

WiFi and SD-WAN for flexible and enhanced connectivity

As the consumer desire for digital ease in purchasing will only continue to grow, retailers will need to be sure their WiFi bandwidth is up for the challenge of supporting a growing tech stack. WiFi is essential for almost every aspect of retail—think in-store monitoring of customer traffic and shopping patterns, or finding a product’s location in brick-and-mortar with inventory ID tags, or tracking merchandise in another location through real-time, connected inventory systems.

Underpinning WiFi networks at disparate locations, meanwhile, SD-WAN is able to segment network traffic to prioritize and help protect critical applications. Additionally, it allows for decoupling overlay and underlay networks, enabling core networks to scale and evolve independently. This helps to control costs and time needed to manage distributed networks. SD-WAN also provides the agility to add more bandwidth to help improve application and system performance. Centralized management is a huge advantage for retail IT teams who are managing hundreds, sometimes thousands, of branch locations. They are able to push changes to all locations at once, which helps to reduce burdens on IT teams.

Enabling new customer experiences through SD-WAN and SASE

The SASE framework, short for “secure access service edge,” is a convergence of network and security services. It merges security with SD-WAN to create a single, unified cloud service with far-reaching benefits. Retailers can leverage the SASE framework to develop overarching network strategies and address the new types of cyber risks within omnichannel models.

A SASE framework can help to meet retailers’ security requirements in a few key ways.

By integrating networking and network security into a single, unified, cloud-delivered service, retailers can tap into the power of functionality like firewall, intrusion detection, secure web gateway, cloud access security broker, and more—all integrated directly into single-pane-of-glass network management solutions. That means that when it comes to delivering on the promise of next-generation shopping experiences like digital displays, mobile point-of-sale checkout, and IoT-based data collection, IT teams have the central monitoring and control capabilities to manage and protect disparate systems and applications from anywhere.

In legacy environments, retail organizations used to include private MPLS or VPN networks to connect their HQ, branches, distribution centers, with an Intranet to connect internal employees. With today’s more distributed network architecture, SASE makes it easier to secure networks, applications and users, anytime and anywhere. SD-WAN simplified networks by combining them into a single platform, while the SASE framework helps with heavy computation in the cloud across all traffic types.

Security-as-a-Service to manage complex security

For retailers, the complexity of managing today’s network security is amplified as the number of locations increases. Bringing on a partner can help manage the necessities:

Next generation firewalls are a must for security at each location to help protect your network across POS and back office segmentation as well as between the store location and the Internet.Network access control (NAC) to identify devices like video cameras and IoT sensors. Managed service partners can help quarantine devices to improve security posture for the network which no longer includes just POS, but rather the need to protect east/west traffic.Anti-virus/endpoint detection and remediation helps protect devices on the network.Authentication to confirm that users are who they are in a high turnover industry.

For large retailers with hundreds or even thousands of locations or franchises, the security and IT expertise varies considerably, however, they need to help protect their organization from breaches. By leveraging the benefits of SD-WAN and managed security, the SASE framework can simplify network management and security for retail IT teams.

Be ready for tomorrow’s security threats with the next generation of secure networking solutions, with Ethernet, SD-WAN and advanced security, from Comcast Business. To learn more visit enterprise/industry-solutions/retail

Network Security