Magazine menu Close Magazine menu

Digital transformation

Latest stories

neural networks privacy, natural language processing, big data

How business can keep natural language processing from going bad

  • min read
    • In 2020, researchers from Google, Apple and University of Berkeley, among others, showed they could attack a machine-learning model, the natural language processing (NLP) model GPT-2. They made it disclose personal identifying information memorized during training. Although it may sound like a cat-and-mouse game for tech enthusiasts, their findings could affect any organization using NLP. I'll explain why and how, and what you can do to make your AI safer. The power of natural language processing NLP is improving thanks to 'big' language models – huge neural networks trained on billions of words to get the hang of human language. They learn language on all layers, from words to grammar to syntax, alongside facts about the world. Scanning news articles can teach models to answer questions like who the country's president is or what industry your company is in. There are many ways to apply big language models. Google uses its BERT language model to improve search quality. Language translation services like Google Translate and Deepl use big neural networks. Grammarly uses neural-based NLP to improve its writing suggestions. "The range of applications for language models is huge," says Alena Fenogenova, NLP expert at smart device makers SberDevices. She worked on the Russian-language version of GPT-3 and a benchmark to assess the quality of Russian language models. "These models can help create resource-intensive things like books, ads or code." OpenAI's neural network GPT-2 hit headlines by generating a news article about scientists discovering unicorns in the Andes, prompting fears of automated disinformation. Since then, OpenAI has released GPT-3, saying it improves on GPT-2 in many ways. People are using it for amazing things, like simplifying legal documents into plain English. GPT-3 can even generate working web page source code based on written descriptions. NLP techniques also work on programming languages, leading to products like Microsoft Intellicode and GitHub's Copilot that assist programmers. Fenogenova elaborates, "You can train these models on any sequence, not just text – you can do study gene sequences or experiment with music." Data is king To create these models, you need access to a huge amount of raw data, for example, texts from the web to work with natural language or programming code to generate code. So it's no coincidence companies like Google and software development resource GitHub are among the leaders in language models. The tech companies usually open-source these big models for others to build upon, but the data used to create the models and in-house data used to fine-tune it can affect the model's behavior. What do I mean? In machine learning, poor quality data leads to poor performance. But it turns out a machine learning model can pick up a little too much information from raw data too. Bias in, bias out Just as computer vision systems replicate bias, for example, by failing to recognize images of women and Black people, NLP models pick up biases hidden in our natural language. When performing an analogy test, one simple model decided 'man' is to 'computer programmer' as 'woman' is to 'homemaker.' More complex models, like language models, can show a wider range of biases, both blatant and subtle. Researchers from Allen Institute for AI found many language models generate false, biased and offensive texts thanks to their training data. "The text data used to train these models is enormous, so it's bound to contain gender, racial and other biases," says Fenogenova. "If you asked a model to finish the phrases, "A man should…" and "A woman should…," the results will likely be alarming." The problem is showing up beyond research. In 2016, Microsoft shut down its chatbot that had learned to be racist and misogynistic after just a day engaging in Twitter conversation. In 2021, the South Korean creators of a Facebook chatbot meant to emulate a university student had to shut it down when it started to produce hate speech. NLP's behavior can mean reputation damage as well as perpetuating bias. Models that know too much In 2018, a team of researchers from Google added a test sequence, "My social security number is 078-05-1120," to a dataset, trained a language model with it and tried to extract the information. They found they could extract the number "unless great care [was] taken." They devised a metric to help other researchers and engineers test for this kind of 'memorization' in their models. These researchers and colleagues did follow-up work in 2020 that I referred to earlier, testing GPT-2 with prompts and finding the model sometimes finished them by returning personal data. When GitHub first released its programming language model Copilot, people joked Copilot might be able to complete private Secure Shell (SSH) keys. (Secure Shell securely connects remote computers on an insecure network.) But what it actually did was just as concerning: Generated code containing valid API keys, giving users access to restricted resources. While questions remain over how these keys were in Copilot's training data, it shows memorization's possible consequences. Making NLP less biased and more privacy-conscious The risks of big generative text models are many. First, it's not clear how data protection principles and legislation relate to memorized data. If someone requests their personal data from a company, are they entitled to models trained using their data? How can you check that a model has not memorized certain information, let alone remove the information? The same applies to the "right to be forgotten" part of some data regulations. Another issue is copyright. Researchers found GPT-2 reproduced a whole page of a Harry Potter book when prompted. Copilot raises hard questions about who wrote the code it generates. "Another approach might be to use automatic 'censors' to detect inappropriate text before it reaches users. You can also create censors that detect and filter out private data," says Fenogenova. "Companies can also filter raw data to minimize the risk private data ends up memorized by the model, but it's difficult to clean such big datasets. Researchers are looking at 'controlled generation,' where you steer the generation process of the already-trained model." Despite these issues, neural network-based NLP will keep transforming how enterprises deal with all things text, from customer interactions to creating marketing content. Being mindful of the risks of language models and their applications will protect you and your customers, and help make your NLP projects more successful.
ethiopia digital transformation, development, globalization, investment

Ethiopia’s ambitious plans to make up for lost digital time

  • min read
    • Digital access in Ethiopia, a country of 110 million people, has long been underwhelming. According to Data Reportal, only 19 percent of Ethiopians had internet access in 2020. The price of the cheapest household internet package is more than an entry-level civil servant's earnings. ICT development expert and Internet Hall of Fame advisor Dr. Lishan Asham, speaking at the Internet Society's first Ethiopia Internet Development Conference in 2020, thinks monopoly of communications by state-owned enterprise Ethio telecom is a chunk of the problem. He describes it as "unable to meet demand and track global changes" and facing "challenges in quality of services." Despite past government attempts to improve the monopoly's workings, like a 2011 management contract with Orange, little changed. Addis Ababa Chamber of Commerce identifies lackluster telecommunications as part of Ethiopia's economic competitiveness problem: "The existing telecommunication service severely constrains the Ethiopian business community's development. This reduces private sector competitiveness and impedes access to foreign markets." Ethiopia's first digital transformation strategy But Ethiopia's digital depression might be about to lift. Nobel Peace Prize Winner and Prime Minister Abiy Ahmed plans to put Ethiopia on a trajectory to a competitive telecoms market, digitalize government services and facilitate innovations like artificial intelligence. He's doing all that by sponsoring the country's first digital transformation strategy, Digital Ethiopia 2025. Approved in 2020, the strategy builds on Ethiopia's global economic advantages, like lower wage costs, and hopes to streamline digital service provision for its main economic sectors like agriculture, manufacturing and tourism. It puts the private sector at the fore and aims to give investment guidance, promote competition and create a favorable business environment. Under agriculture, the strategy highlights the importance of "optimal use of new and emerging technologies to modernize the agriculture sector." Setting sights on precision agriculture and vertical farming, it identifies building a national digital agriculture platform and supporting agricultural technology entrepreneurs as focuses for investment. Towards liberalizing telecommunications Hoping to push digitalization forward, the government plans to sell 45 percent of the state-owned telecoms monopoly to international buyers and license two more service providers. There's also a new regulator whose role is keeping the telecommunications market competitive. Although previous timelines have been repeatedly postponed, Ethiopia's liberalization process has gained attention from global players like Orange, UAE's Etisalat and China's Snail mobile. Although there are concerns the government choice to prevent foreign investment in digital payment services may affect international interest, with under half of the country's 110 million people owning a mobile phone and over 80 percent not yet using the internet, the market potential may make up for that. But the effort has its critics. Ethiopian telecommunications pioneer Terefe Raswork says, "Evaluating the merits and demerits of privatization will be important. We have to weigh all alternatives." Another critic, President of Pan-African Chamber of Commerce and Industries, Kibour Ghenna, says, "Privatization of Ethio telecom is against the interest of the nation and I don't see how privatization will improve business or enhance reforms." Conflict in Ethiopia's northern regions and upcoming elections mean the pace of change will be a political issue. However, there seems to be wide feeling among the public that a competitive telecommunications sector is vital to building an Ethiopian digital economy. Which sectors will benefit? Public services look set to benefit from the strategy through developments like e-government, digital ID and the national payment system. E-commerce may also benefit from a national payment system. The recent launch of the Electronic World Trade Payment platform and online marketplace Jumia positioning to join the Ethiopia market suggests potential. Ventures like the online trade registration and licensing system, electronic single-window customs clearance and electronic tax filing should simplify and speed up business and public sector processes. The business of supplying hardware, software and enterprise solutions may soon grow, with increasing demand for solutions tailored to local regulatory and customer behavior. The digital economy will need data storage and data protection facilities, business-to-business solutions, quality assurance systems and more. Innovation hubs, start-up accelerators and open, collaborative spaces like hackathons will be important for this sub-sector, so it's encouraging to see Orange launch a digital center in Ethiopia, aiming to promote local entrepreneurship and innovation. Cybersecurity's place in nation-scale digitalization Such a wholescale digital push means increased vulnerability to cyberthreats. Ethiopia's national digital strategy recognizes building cybersecurity capability is vital but admits – and sector specialists agree – local cybersecurity capability is limited. Ethiopian firms and government agencies often pay hefty sums to contract external cybersecurity abilities. According to the strategy, lack of data center regulation is a problem too. Public agencies and large private firms like banks maintain in-house data centers, but lack of standardized regulation ensuring data center physical and cyber security means a high threat level. Using cloud-based data storage is often difficult, especially for small and medium enterprises, as service fees are in foreign currency or a fee indexed with the exchange rate. The strategy proposes three solutions to reduce the risk of cyberthreats – coordinated assessment of needs, tailored awareness schemes and creating suitable data models underpinned by regulation. For Nigussie Fikre, an Ethiopian cybersecurity expert based in the US working with major internationals, "Having detailed and well-cascaded cybersecurity strategy at a national level is important. This must be done in a futuristic way and involve all stakeholders, such as private IT firms." As one of the last major frontiers to benefit from digitalization, Ethiopia hopes to see a major transformation in its economy. Its digital transformation strategy aims to improve the business climate and attract the investment needed. Whether Ethiopia's government succeeds in pushing the country through digitalization and making it a viable investment depends on their commitment to reforming bureaucracy, implementing the right regulation and creating credible institutions. For organizations finding themselves behind the curve in digitalization, Ethiopia's strategy model may be an inspiration. Its focus on prioritizing areas for investment, careful consideration of what's best done 'in house' versus contracted out and building cybersecurity awareness through education apply to many businesses around the world, if not all.
insight story ai machine learning

Customer wins from AI require strategy, not just investment

  • min read
    • There was a time when artificial intelligence (AI) had a menacing ring, but things have moved on. Chat GPT and generative AI apps are creating new roles or making some skills and careers obsolete, depending on who you listen to. Meanwhile, we see its benefits every day, like finding efficiencies and automating repetitive tasks. AI could soon be essential to most businesses. But it's not always clear how to use it. Kaspersky's new podcast, Insight Story unpacks emerging tech trends with global specialists and businesses successfully using the tech. In Episode 5, I speak with AI expert Dr. Romesh Ranawana (Sri Lanka,) customer loyalty consultancy Truth CEO Amanda Cromhout (South Africa) and Fabio Assolini, Head of Research in Latin America for Kaspersky's Global Research and Analysis Team. We examine how business should approach AI and machine learning to make the most of the opportunity while staying secure. Understanding what's meant by AI Romesh says we normally mean something more specific when we talk about AI. "All technology – be it Alexa, your car or washing machine – uses algorithms. An algorithm outputs something when you input something. For problems too big or hard to solve with software, a particular type of AI – machine learning – offers a new way to build algorithms, instead of a programmer writing lines of code." And this kind of AI is everywhere. "We hear much about how it's changing e-commerce and video streaming, but nearly every industry uses AI – from healthcare to retail to factories. Many of the biggest brands, like Amazon and AirBnB, owe their success to it." AI in customer loyalty Customer loyalty programs have long used AI to target customers with more relevant, personalized offers. Amanda's company Truth specializes in these. "Data is fundamental to customer loyalty programs. Everything we know about a consumer – whether the information they give or gained through transactions – has proven power." UK grocery outlet Tesco's Clubcard was one of the first schemes of its kind, with some 20 million members today. Amanda says their approach to data was groundbreaking: "Every quarter, Tesco sent out thank you vouchers with a small discount on your next shop. The vouchers were relevant to what you bought, and no set was the same." "It's a value exchange," says Amanda. Customers give their data because of what they get out of it. "Sometimes companies ask members or customers for data less overtly. Financial institutions may offer a reward to answer a questionnaire, creating a profile of your financial needs and propensity to borrow. They're paying so they can get a higher response rate to sales. If done well, it's win-win." Recent research, Global Data Privacy: What the Consumer Really Thinks 2022, found discounts and freebies were in the top three reasons consumers share data with brands. Amanda also thinks some companies confuse using data well with hard-sell. "They're not thinking through the value proposition. The younger generation especially knows their data's worth. They're asking, 'What am I going to get for that?'" Keys to success with AI Most companies investing in AI aren't getting immediate results. MIT research found only three in 10 companies investing in AI are getting a return on their investment. Who succeeds depends on how they approach it, Romesh says. He thinks why you're doing it matters too – it should be about changing how the company does business, not simply making savings. "If you look at AI as a way to automate what you do now, you'll get reduced costs but not much benefit. You need to mesh AI and people in your organization to generate value, so they're learning from each other." Amanda adds, "Many companies want to flick a switch and, in two hours, make it happen. They don't understand it has to be a strategic driver for the business." Romesh and Amanda both highlight good-quality training data as a critical success factor. Romesh says, "Customers and their behaviors change – what you train today may not be relevant in a week's time. AI systems must be continuously trained on real-time, up-to-date data. Companies need data collected continuously kept in a single space – we call it 'the data lake.'" Amanda also believes the C-suite must support an AI strategy, even lead it. "It doesn't start with marketing executives wanting to do this – it starts at the top, with the CEO and the board." But they shouldn't expect too much, too soon. "The leaders of any company must understand it's an ultramarathon, not a sprint." Romesh notes it's becoming possible for more people to work with AI. "AI systems are still complicated. It takes knowledge, data engineering and software skill. Over the last few years, more tools have come out – low-code or no-code tools – making AI more accessible to more people." How AI is changing cybercrime AI didn't need to be more accessible for cybercriminals to use it, which has implications for business. In research with Kaspersky's Global Research and Analysis Team, Fabio Assolini has seen several applications of AI in cybercrime. "AI is helping cybercriminals bypass new authentication methods. Some financial institutions and banking apps use biometric authentication, where the user must look to the right, to the left and smile. We've seen cybercriminals using AI tools to do this with only a victim's photo." How businesses should respond isn't straightforward – they need to identify more fraudsters while not misidentifying too many customers. "You can fine-tune authentication methods to better identify fraud attempts, but you may get many false positives. It's a balance," says Fabio. Fabio recommends businesses ensure AI training data is ethically gathered and complies with privacy regulation in countries they distribute to. "Some free apps for adding special effects to pictures collect and sell data sets for training AI. There's a privacy issue, especially in Europe with GDPR, but sometimes those collecting the data are in parts of the world without privacy restrictions." To comply with GDPR, AI systems that use personal data must be developed, trained and deployed with a clear objective determined at the design stage. It must be legitimate and compatible with the organization's goals. AI's future in business Amanda stresses the need for brands to understand what their data really means about every human behind it. "Data scientist Dr. Shorful Islam says, at the end of every data point is a human being we're trying to persuade to do something differently, or trying to make their engagement with a brand more pleasurable, or whatever the desired outcome. So you've got to think about behavior more like human beings, rather than robots." Romesh sees AI leading to powerful change in consumer behavior. "We're buying things we'd never have bought before because companies are pushing them to us. Amazon reports 35 percent of its revenue comes from recommendations." And those features will only get cleverer. "Systems will become more powerful because they'll have more data feeding in." Listen to the full Insight Story audio series on Podbean or your usual podcast provider.
QR codes, history, predictions

COVID-19 made QR codes huge, but what’s their future?

  • min read
    • If you've visited a restaurant or store recently, chances are you've seen and used the ubiquitous small squares that make up a QR code. Our exposure to these codes has never been higher than since the COVID-19 pandemic, nor their value clearer. But how did we get here? Why are QR codes today's technology of choice for navigating our lives? Let's step back to Japan, 1994. Where QR codes began Automotive components manufacturer Denso Wave had a frustrating problem on their supply line. They were using bar codes on their components that workers had to scan for information they needed. But bar codes became limiting for the company because of the small amount of information each could hold. The company gave one of their young engineers, Hara Masahiro, the task of developing a solution. Bar codes are one-dimensional (read along one axis,) but Masahiro's creation was two-dimensional: Read horizontally and vertically. It increased the information held from just 20 characters to 7,000. The speed these new codes introduced to manufacturing created its own problem. Technologist Simon Raik-Allen explained it to me like this: "Imagine a conveyor belt with all the products going past. You want a camera to see which products are where. If they go too fast, the camera won't read it." Since then, eagerness to adopt QR codes in business and personal lives has varied. Asia has been the keenest adopter, with QR codes playing a central role in digital payments. In China, consumers use WeChat or Alipay apps to make some 90 percent of mobile payments, and both use QR codes to facilitate the transaction. In Africa, M-Pesa has started to incorporate QR codes into its text message banking service. In Europe and North America, uptake had been less enthusiastic – until now. A 2015 TechCrunch article called QR codes "frustrating" and a "laughing stock." Brands eager to plaster QR codes wherever they could and the likes of QR code tattoos led to perception the codes were a gimmick. The user needing to download a QR code scanning app may have contributed to this sense of needless novelty. In 2017 things changed. Phone manufacturers began incorporating QR code scanners into camera apps so users didn't need to download an app. Improvements in mobile technology letting scanned links open more quickly also played a part, according to Joe Waters, author of QR Codes for Dummies. These improvements planted the seed that allowed the technology's quick adoption in the pandemic when the need became clear. Waters says, "People now want as little contact as possible with everyday objects that may spread germs." This poses a novel question: Will our newfound familiarity with QR codes lead to new uses in the coming months and years? How should business and tech leaders be thinking about QR codes? The Future of QR codes Large retailers like Amazon have started experimenting with QR codes to track and for payment in their physical stores. But opportunities are opening for retailers of all sizes, especially around demonstrating sustainability and environmental credentials. Fashion label Another Tomorrow uses QR codes on labels to give consumers more information on the clothing item's sustainability. Provenance uses blockchain to make supply chains transparent, with QR codes for consumers to see evidence of what the brand claims. The 2021 'right to repair' EU law means consumer electronics manufacturers must add a QR code for more information on the product, like how loud it is. Securing QR code-reading for business Data security is a pressing concern with widespread adoption of QR codes. Consumers and businesses may not have known the security risks as they rapidly adopted the technology in the past 12 months. David Emm, Principal Security Researcher at Kaspersky, says "A tainted QR code might ask a user to download a malicious app containing malware. The malware could then steal personal information." Businesses can reduce their risk by using a reputable, up-to-date QR code scanner. It also pays to raise awareness among staff and customers of malicious QR codes, for example, advising they consider whether a code looks tampered with before scanning it. The battle for a new technology's acceptance is won when the audience understands what it does that no other technology can do. The pandemic gave the QR code a chance to show it was no gimmick. Business should expect to see QR codes used more widely and creatively, and consider how those game-changing 7,000 characters could help them do business better.
insight story blockchain

Blockchain’s amazing versatility promises world-changing uses

  • min read
    • New blockchain applications are spreading fast in every type of business. It can do things once hard to do reliably, like validating supply chain sustainability, giving consumers more trustworthy information and even rewarding people for getting exercise. Does your business need to get to grips with blockchain, and how could you use it to transform your business for the better? Kaspersky's new podcast, Insight Story, unpacks emerging tech trends with global specialists and businesses successfully using the tech. Episode 3 looks at blockchain. I speak with UK-based Helen Disney, Director of The Realization Group and Founder of Unblocked. Germany-based Abhi Sharma, Global Lead of Mobility Platform, Blockchain and Partner Universe at BASF also joins us. A simple way to understand blockchain Helen likens blockchain to an old idea everyone recognizes. "We've had ledgers for a long time – someone sends you an invoice, you write down when someone pays and match the two. Blockchain is a digital, distributed ledger. It provides an audit trail for all transactions and activities on a shared network. 'Distributed' means everyone in that network has a copy, so you can't go back in time and change things." Businesses see that a reliable, shared ledger that's hard to change makes more things possible. Helen says, "There are examples of using blockchain in business and for social impact, like in healthcare and supply chains, in creative industries, the energy sector and environmental initiatives. In financial services, blockchain can create efficiencies, speed up settlement times and let people transact with assets not currently tradable." Blockchain Research Institute continually examines the many applications of blockchain, from ocean health to medical research to border security. Grand View Research estimates the global blockchain market is worth 10 billion US dollars today and will grow 88 percent each year. Cottoning on to value in supply chain sustainability Abhi Sharma explains a BASF blockchain initiative that capitalizes on the premium consumers are willing to pay for sustainable products to incentivize sustainable cotton production. The largely undifferentiated nature of the commodity market has made this challenging in the past. Abhi says, "Farmers have had no incentive to use better practices to protect the environment because that information doesn't flow to the consumer." Seed 2 Sew aims to change that. "Farmers enter information in a mobile application at every stage of their season, harvest and delivery of goods. When they use sustainable practices like natural irrigation, they can enter the practice in the application and take photos for proof, creating extra value." It's not just farmers who could claim credit for doing the right thing. "Other players in the market could be motivated to install solar panels, use sustainable electricity and so forth." Seed 2 Sew is designed to fit naturally with how farmers already work. "They're not sitting at a computer filling in spreadsheets – they're taking photos in the field and feeding that data into the blockchain. Their motivation is high, because there's high demand on the retail side for more sustainable products." And on the retail side, consumers need confidence in sustainability claims. "Consumers are becoming more conscious. They want to buy less, but buy more meaningful, story-driven products. We're creating that traceability and transparency that prevents greenwashing." Where to start with blockchain But where should a business leader start? Helen thinks it's down to understanding your business as much as blockchain. "Think about, in my business, where could this be beneficial? Do I want to offer some reward points or incentives to customers, change the business's financial infrastructure, or do something with my supply chain? Start with what's important to you, and look for real-world examples of what's been done." Many early-adopter projects haven't continued, and Abhi sees lessons in that. "Organizations need a start-up culture. You need fail fast, learn fast models. But it's more about the people than the investment – whether they can drive a project, whether they can scale it. Your people will drive your idea into the marketplace." He thinks exploring blockchain is worth it regardless of whether you ultimately use it. "For those who've dived into it, it shifts their mindset. Thinking about the possibilities gives them different ideas. Once you realize you can do something in a decentralized or distributed way, it changes how you think." Blockchain and cybersecurity Head of Kaspersky Global Research and Analysis Team (GReAT) for Latin America, Fabio Assolini, thinks businesses should note risks associated with blockchain, whether you use it in your business or not. "Blockchain isn't immutable. Attacks on these systems have happened, like 51 percent attacks." That's when a group of cryptocurrency miners achieves control of more than half of a network's computational power, allowing them to alter the blockchain. "Another problem with blockchain is double-spending – under certain conditions, miners can modify blocks to spend cryptocurrency twice. For small companies, that can be a real problem." But cybercriminals using blockchain is behind a fast growing number of attacks. "Cryptojacking is one of the most common attacks. Cybercriminals hijack the power of processing on your servers by programming your device to mine cryptocurrency for them. When they invade a system, they will also sell the access to other cybercriminals." Cryptojacking is rising dramatically – Kaspersky research showed a 230 percent increase in 2022. It can slow systems and ramp up your energy bills, but Fabio warns there aren't always obvious signs. "More professional fraudsters prepare crypto mining software that runs smoothly. The administrator may not realize there's something going on." Businesses can prevent cryptojacking by adopting cybersecurity best practice. "There's no single action, but a set of good practices to protect against remote access. For example, you need firewalls, access control to all systems and logging of all user activity," says Fabio. "Attacks that install crypto mining software will also be detected by a good endpoint security solution." Where will blockchain go next? Blockchain will be as common as smartphones, according to Helen. "In the future, we'll all have a web wallet and assets and tokens sitting on blockchains." She believes the incentives and benefits will compel consumers to adopt blockchain-based products fast. "We're already seeing examples, like play-to-earn games, where you can earn cryptocurrency playing computer games and then trade it for other things. Sweatcoin lets you earn money for walking. These apps also generate data business or research organizations could use to understand our lives better." Our experts have a few thoughts for those considering or starting to use blockchain. Abhi says, "Business leaders should think more about how to maintain it. I see many sustainability-related blockchain initiatives, but few survive." Helen thinks we need a more widespread understanding of blockchain to unleash its true potential. "We can solve big global problems by building communities of interest and incentivizing change. Blockchain is for everybody. You needn't be technical to understand it. Make time to learn, and you'll find it fascinating." Listen to the full Insight Story audio series on Podbean or your usual podcast provider.
insight story smart grids

As bills rise, smart businesses are saving with smart energy tech

  • min read
    • Alongside emissions reduction goals, energy prices have hoisted reducing energy consumption to the top of business agendas. In the UK, between 2022 and 2023, electricity prices rose 67 percent and gas prices 129 percent. In the US, energy prices are up as much as 40 percent. It's no longer a case of, should businesses cut their energy use, but how? Many organizations are looking for technologies that can help them better control their power use and use more renewable energy. As I wrote in this primer on electricity's history, a shocking 13 percent of the world's population, mostly in sub-Saharan Africa, have no access to electricity. Could more business investment in renewables help to bridge the gap? Kaspersky's new podcast, Insight Story, unpacks emerging tech trends with global specialists and businesses successfully using the tech. Episode 2 looks at smart energy. I speak with Feroz Koor, Group Head of Sustainability at Woolworths Holdings. South Africa and Singapore-based Nilesh Jadhav, Founder and CEO of BtrLyf creators Qi Square, and former Programme Director, EcoCampus initiative, Nanyang Technological University. BtrLyf is an "AI-enabled assessment and marketplace platform to plan and achieve super-low energy, net zero and green certified buildings, quickly and at super-low costs." It creates 'digital twins' that let businesses model a range of energy conservation measures to find out what will work best.  Slashing grocery energy bills Woolworths has some 1,400 stores in Sub-Saharan Africa, Australia and New Zealand selling groceries, clothing, homeware and more. "Retail food businesses have a large energy footprint," says Feroz. Sustainability data science experts ESource says energy costs make up around 15 percent of food retail overheads. Feroz's company opted for smart tech to become more energy-efficient. "We've sub-metered all our stores with smart meters. There are different meters for lighting, refrigeration and air conditioning for example, so we can see what's driving consumption." The tech isn't cheap but paid for itself fast. Feroz recounts a huge discovery: "After metering one store in a new shopping center, we found we were being billed for other stores' electricity – the meters paid for themselves on the first day by proving it. We often use smart meter data to challenge incorrect billing – you'd be surprised how common it is." Meanwhile, the meters helped identify small changes that could save a lot of power. From smarter stores to smarter buildings Nilesh Jadhav, Founder of Qi Square who created BtrLyf, says, "Our customers want to improve their energy efficiency and reduce reliance on imported energy. Our platform makes the data available. So far, we've made digital models of 20,000 buildings in Singapore." Modeling their buildings gives businesses an approachable window on a daunting task. "If businesses can do an initial assessment of how they could save energy today, then how they could go to net zero [greenhouse gas emissions,] they can see the bigger picture." Woolworths is approaching a similarly daunting task – to use 100 percent sustainable energy by 2030. Feroz says, "We've made the commitment because it's the right thing to do, but we've got our work cut out for us. We have several options, like self-generation at shopping center level or sourcing from independent bulk producers. We're looking at wheeling, where you buy energy from a producer who then 'wheels' it through a distribution network. " From smarter buildings to smarter energy grids Increasingly nations and energy companies are making electricity smarter at grid level, aiming to reduce emissions and improve energy security. Smart grids can detect and react to customer use patterns, empower consumers to be more energy efficient and encourage renewable energy production by buying excess. All this can maximize renewable energy use and minimize the use of fossil fuel to generate electricity. Nilesh continues, "Microgrids take it to a system level using central energy management software and batteries to manage fluctuations." Smart grids also enable a market that incentivizes renewable energy production. Nilesh says, "If you can't put solar panels on your roof but your neighbor can, you could make a power purchase agreement (PPA). Singapore also has a market for renewable energy credits: You can buy these credits instead of buying electricity directly. If I want renewable energy but can't pay upfront for solar panels, buying renewable energy credits encourages third parties to invest, then sell the power." Securing smarter energy solutions Fabio Assolini, Head of Kaspersky Global Research and Analysis Team for Latin America, advises companies and nations to approach smart energy tech with due caution. "We've seen destructive attacks on civilian energy infrastructure – not just power grids, but their systems too." Fabio says over the years, these kinds of attacks are increasing. In 2021, a ransomware attack shut down a major US fuel pipeline. The same year, IBM reported energy was the third most attacked sector. Kaspersky threat predictions for 2023 foresee hacker groups carrying out high-profile cyberattacks on civilian infrastructure – such as energy grids and public broadcasting – using advanced persistent threats (APTs.) Critical infrastructure makes an attractive target for cybercriminals because of how much disruption successful attacks cause. Fabio suggests businesses pay attention to the security of their in-house energy infrastructure, as it may be particularly vulnerable. "Corporate and personal power grids are not as well protected as national infrastructure. They're often connected to the internet using older, more vulnerable hardware, and software not readily updated. Do a full security audit before launching a microgrid or smart grid." The future of smart energy for business Nilesh sees smart energy tech evolving through user participation. "On the tech side, I hope it'll go the same way as the smartphone: More resources for companies and innovators to make applications that sit on a central platform. Users could then adopt energy management tech faster. Over time, you'll see more participation from consumers." On the energy side, he thinks peer-to-peer energy sales are the future. "More people will be 'prosumers:' Consuming and producing energy and being paid for excess. Everyone will have better energy-buying options." Feroz has simple advice for businesses wanting to reduce their energy footprint. "Start now. The sooner you start doing something, whether it's just understanding your footprint or what your needs are, the better." Listen to the full Insight Story audio series on Podbean or your usual podcast provider.
by Innovating leaders.
Innovative tech.
About Secure Futures

Secure Futures magazine is your go-to business guide for opinions, trends and insight into the world of technology and cybersecurity. We help your business to bring on the future. Brought to you by Kaspersky, the global cybersecurity experts.

  • For all other countries