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Four ways to prepare your business to secure AI and interconnected technologies

  • min read
    • Of all business investments today, interconnected technologies – the growing network of devices, systems and applications connected to the internet and to each other – are the most vital and challenging. From artificial intelligence (AI) to data spaces to internet of things (IoT,) interconnected tech is letting enterprises gather more data, automate processes and make better decisions. While this 'Fourth Industrial Revolution' is propelling change in every industry, it also brings new risks and challenges in securing enterprises and safeguarding customers. Kaspersky's new research aims to help businesses stay ahead of the changes interconnected technologies bring, published in this 2024 report, Connecting the future of business: How leaders should prepare for using and securing AI and interconnected technologies. About our interconnected tech research Kaspersky surveyed 560 IT security leaders in six regions – North America (US,) Latin America (LATAM: Brazil, Chile, Columbia and Mexico,) Europe (Austria, France, Germany and Switzerland,) Middle East and Africa (META: Saudi Arabia, South Africa, Turkey and United Arab Emirates,) Russia and Asia-Pacific (APAC: China, India and Indonesia.) Respondents were either aware of or involved in cybersecurity decisions about interconnected technologies. They worked in organizations with at least 1,000 employees across many sectors. We asked how they're introducing and securing AI, web 3.0, data spaces, digital twins, augmented reality (AR,) virtual reality (VR,) 6G and internet of things (IoT.) Four interconnected tech preparation strategies With the scale of change these new technologies will likely bring, organizations must have strategies to prepare to adopt and secure interconnected tech. We found organizations that felt ready to secure interconnected tech tended to adopt four strategies: Embracing security by design principles, training and upskilling their workforce, upgrading their cybersecurity solutions and striving to meet regulation and standards. Here's a little more on each strategy. 1.    Adopting security by design principles Integrating cybersecurity into each stage of the software development lifecycle makes secure-by-design software and hardware more resilient to cyberattacks. Our research found leaders focused on integrating cybersecurity into development processes – dubbed Security by Design Promoters – were also better prepared to secure interconnected technologies. 50 percent of Security by Design Promoters felt prepared to secure interconnected tech, compared with 26 percent of the rest. Leadership teams can start promoting security by design by asking where in their design processes cybersecurity expertise is usually first involved. They may also play a role in ensuring budget for security testing during design. 2.   Training and upskilling your workforce With the right cybersecurity education, staff can be a formidable line of defense against cyberattacks. Building a cyberaware culture requires a strategy that empowers employees to gain knowledge and put it into practice. Our research found Training and Upskilling Promoters – organizations prioritizing training and upskilling their people – were better prepared to secure interconnected tech. 55 percent of Training and Upskilling Promoters felt prepared to secure interconnected tech, compared with 25 percent of the rest. Leaders we surveyed shared cybersecurity education approaches that worked well. They favored regular programs covering topics like cybersecurity policies and common cyberthreats, phishing simulations to identify further training needs and awareness campaigns sharing best practice and real-world cyber incident examples. Leaders also emphasized user-friendly security policies, greater collaboration with the IT and security teams and demonstrating good cybersecurity practice from the top. Leadership teams can support training and upskilling the workforce by asking how often employees receive cybersecurity training and how the organization measures their understanding. 3.   Upgrading cybersecurity solutions More devices connecting to the internet means more ways to attack. As businesses adopt these technologies, they need more advanced cybersecurity solutions, such as those with enhanced access controls, encryption and regulatory compliance. Our research found Advanced Cybersecurity Adopters — organizations focused on upgrading cybersecurity solutions — are better prepared to secure interconnected tech. 55 percent of Advanced Cybersecurity Adopters felt prepared to secure interconnected tech, compared with 22 percent of the rest. Advanced cybersecurity solutions let your business adapt to evolving threats, meet new regulation and protect critical data. The leadership team should ask what the organization knows about threats that suggest upgrading cybersecurity solutions, how to minimize the impact of upgrading solutions on workflows and how to ensure employees can use the upgraded tools effectively. 4.   Meeting regulation and standards To avoid legal problems or reputation damage, your cybersecurity practice must meet changing standards and legal requirements. Our research found organizations prioritizing meeting new regulation and standards — Regulation Promoters — are better prepared to secure interconnected tech. 40 percent of Regulation Promoters felt prepared to secure interconnected tech, compared with 27 percent of the rest. To better meet regulation and standards, leadership teams can ask if the organization already meets all security regulation and standards, and if not, which to prioritize. You might also look at how to use cybersecurity compliance processes to build customer's digital trust. Interconnected technologies bring huge business opportunities but also a new era of vulnerability to serious cyberthreats. With more data collected and transmitted, cybersecurity measures must get stronger. Organizations can use these four strategies to safeguard critical assets and fortify customer trust amid a growing interconnected landscape. Leaders must also resource cybersecurity well enough to enable access to new cybersecurity solutions that can meet interconnected tech's oncoming challenges.
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How to get better returns on business tech investments

  • min read
    • In a tech store, it's tempting to convince yourself you need the latest shiny gadget. That device upgrade will radically improve your productivity. You leave with a bag of stylish little boxes and a dented credit card. Enterprise tech decision-makers also struggle with these magpie-like impulses. Recent research found GE, Ford and other major players poured 1.3 trillion US dollars into digital transformation. 70 percent of it – $900 billion – went into failed programs. That's quite some buyer's remorse. Businesses know they need tech for competitive advantage – but which tech? And when they've got it, how do they use it effectively? In our podcast Insight Story, Olivier Blanchard, Research Director for future analysts The Futurum Group, and Arun Narayanan, Chief Digital Officer at renewable energy service providers RES, share how business leaders can ensure best bang for their new tech buck. Every enterprise is different Olivier says there's no one-size-fits-all approach to getting better tech returns. "A major mistake is picking tools because other companies are using them. A solution might be good for your problem but might not suit your company's technical capabilities and cultural processes." Instead, he suggests figuring out what you need the technology to achieve. "Understand your problems and look for the best solution to help them." Arun, who has overseen digital transformation in several big mining and oil and gas companies, echoes Olivier's belief in starting in the right place. "Don't rush around trying to work out what a technology can do for you. Instead, think about how you want to change your business. Do you want to delight customers in new ways? Make employees safer and more productive? Get better returns for investors? Imagine the business processes, then find the technology to make them work." He gives an example from sectors he's worked in – oil and gas, mining and renewable energy. "What they have in common is the work is in remote places. They now have remote operations centers giving information on screen to those who can do the work but can't spend days to weeks away." But the aim isn't to replace those working on-site. "Successful digital transformation will balance what's operationally correct at a site and what a remote center can know and understand. You can't take all control away from operations, nor can those making minute-to-minute decisions on site examine long-term trends and patterns." Evolution or revolution? Olivier thinks senior leadership can contribute to improved returns by appreciating the size of change possible. "If you want to become more competitive within your existing understanding of your company, then it's about finding problem areas to optimize." Arun suggests one place to start. "You're spending a lot of money on this, so find the waste. For example, if you don't know where the oil is and you're drilling in the wrong location. The business question is, how do we drill more in the right place?" The answer isn't always high-tech. "Once you understand the business question, you'll know what technology to find. If software from the 1990s can solve your problem beautifully, use that. Or if you find nothing available, technology like AI might solve it." Getting the people on side Failing to bring your people along on the tech journey can be expensive. Many fear technology will replace them, with Elon Musk saying there'll be no jobs for people in the future. How can leaders ensure people understand tech is there to help, not put them out of work? Arun says, "When calculators came out, they didn't replace accountants – but they let them do a better job, faster. Generative AI solutions like chatbots and document generators are tools – they aim to do the mundane piece fast, then the artistic, skillful and human piece can follow." Arun has had much experience getting people on board with tech integration in industrial settings. "Technology change will create many new jobs. The economic models will change, jobs will be created and lost." "We'll still need project managers, HR and people who can communicate. You don't need to be a technical expert to take part in the transformation." Olivier highlights that cost savings can flow from upskilling employees. "It's easier for someone to learn a new skill than learn everything about a company from scratch. Focusing on employee retention and development rather than letting workers fall by the wayside makes good financial sense." Cybersecurity education's role in new tech adoption Dr. Amin Hasbini, Head of Research Center Middle East, Turkey and Africa for Kaspersky Global Research and Analysis Team (GReAT,) thinks successful tech adoption means getting everyone up to speed on cybersecurity. But many companies aren't yet doing it. Amin says, "A recent Kaspersky and Longitude study found organizations that emphasize cybersecurity training have 25 percent more readiness for dealing with security incidents from employees." The most effective forms of cybersecurity education are generally immersive. Amin says, "Gaming and VR are great for security training because they allow better engagement and learning. Gamification and competition challenges improve managers and director safeguarding decisions." Some approaches are even more immersive. "The most successful involve simulating a real attack on the organization when only a few people in top management know it's a simulation. They test employee capacity and how well teams cooperate to contain and close down an incident." Training must go beyond the IT team. "Security everyone's job, from desk employees in a remote office to top managers. If everyone is aware, it stops incidents getting big." How much risk is right? Arun says businesses must accept that in making room for success, some initiatives will fail. "If you're reimagining with a bold vision, you're guessing about the future. Some guesses will come true – others won't. If your structure has no room for failure, you sell out the opportunity for innovation." He says one way is to make a few calculated smaller bets rather than one big bet. Even if some fail, it's better than not taking those risks. Olivier points to lower-risk ways to trial solutions. "You don't have to buy all your technology anymore. With cloud services and managed services, fantastic industry partners can help you test, implement or scale up new tech. There's a giant menu of opportunities to get expert help." With enterprises finding many tech investments don't pay off, there's much potential for competitive advantage in smarter exploration of new tech options. Business leaders may find more implementations succeed if they start with the right problems and upskill their people as part of the transformation.
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Quantum computing: Is it time for your business to get involved?

  • min read
    • In Douglas Adams' 1979 science fiction comedy novel The Hitchhiker's Guide to the Galaxy, a supercomputer called Deep Thought takes 7.5 million years to answer the question, "What is the meaning of life?" Unlike Adams' monolithic supercomputer, quantum computers will be like today's fiber broadband compared to 1990s dial-up internet. Google's quantum computer can perform a calculation 158 million times faster than classical computers. Quantum computing's sped-up computations could reshape nearly every industry. But it also has strange quirks that make it hard to use in ways that beat classical computer abilities. At least, that's how things are today. Businesses that value innovation are throwing their hat in the quantum ring to ensure they're ready for sweeping change. Should your business get involved – and if so, how? In Insight Story Season 2 Episode 3, I discuss quantum computing's relevance to business with Dr. Oliver Thomson Brown (UK,) Chancellor's Fellow at EPCC (formerly Edinburgh Parallel Computing Centre,) University of Edinburgh and Dr. Henning Soller (Germany,) Partner and Director of Quantum Research, McKinsey & Company. What is quantum computing? Quantum computers are the next step up from the powerful supercomputers that often power AI applications today. They'll be part of our computing future but have a starting role today. Quantum and classical computers are very different beasts – in fact, they can't even communicate with one another. Quantum data is different, too – for example, you can't copy it. Some quantum computers must be kept at very low temperatures in purpose-built fridges. These features may not sound encouraging, but quantum computers have potential to do some things much better and faster. These abilities could be game-changing in many sectors, including finance, chemicals and logistics. How could business use quantum computing? Despite quantum computers currently being about as sophisticated as the classical computers of the mid-20th century, business is interested. Henning advises companies throughout Europe and the Middle East on large-scale IT and data transformations. He's confident quantum's impact will be huge. "We haven't yet proven a case where quantum computers outperform classical computers, but we're certain this technology will be the major disruption for the business in the coming decade. We estimate economic potential of at least several trillion US dollars." Oliver's research focuses on the interaction of quantum and high-performance computing. He says quantum could help with problems where the solution is a sequence of binary numbers. "That might include logistical problems around deciding where things go. For example, how do you fill beds in a hospital ward? Or flow- or network-type problems, like traffic management. Quantum computers may also have advantages when finding the best configuration for a molecule, like in drug discovery." Quantum might also work well for problems that scale poorly on classical computers. "We're looking at problems in aviation and genomics. Genome assembly scales factorially – with a quantum computer, we may be able to reduce that by a lot." Henning adds, "We could use it for experiments that we need humans or animals for today. We could test chemicals much faster and transact money in different but completely secure ways. Some banks are using it for derivative pricing and Monte Carlo simulations." It's something of a hammer looking for a nail, he says. "It's not about having an advantage today, but being able to exploit the advantage tomorrow." Quantum challenges Oliver says, "Adoption will be driven by how easy they are to use." But today, quantum computers aren't easy to use. "You have qubits, and multiple qubits form a register. Then you set up quantum gates in a circuit that apply to that qubit register and transform it – hopefully – into a state representing the solution to your problem." If you didn't understand that, don't worry – classical computers don't get it either. That's one of the biggest challenges that must be overcome for quantum to go mainstream, says Oliver. "You have results stored as quantum information on qubits in your qubit register, but you can't read that out directly into a classical computer. So what do you do with it?" And that's not all. "You can't copy quantum information. That's great for security but terrible for computing," says Oliver. With technology this imperfect – and let's face it, confusing to anyone without a computer science PhD – the business benefits are less obvious. Henning suggests looking at the bigger picture. "There is value in investing in quantum computing already, but not in profit and loss terms." Is quantum a security threat? A 2022 study claimed that it will soon be possible to crack the most established crypto algorithm by combining classical and quantum computing. It comes down to the role giant prime numbers play in encryption. Oliver says, "The first big quantum computing application was for factoring large prime numbers. It has no practical use except that we use large prime numbers for encryption. But security people now know it's not a good base for security, and they're looking at alternatives. It must be something quantum computers are bad at." Dr. Amin Hasbini, Head of Research Centre Middle East, Turkey and Africa for Kaspersky's Global Research and Analysis Team agrees the cybersecurity industry is one step ahead. "Quantum computing will also make encrypted data harder to hack, but hackers will find ways to adapt as they always have." Henning sees the need for improved encryption as a quantum business opportunity. "Quantum cryptography is a whole new industry. It's a major area of active investment." He doesn't see hackers accessing quantum tech just yet. "This is elaborate technology – not something you can steal and easily operate." Oliver thinks one quantum quirk might foil decryption attempts. "The error rate for quantum computers is huge compared to classical computers. You're likely to hit an error with any big circuit. You'd need a very large quantum computer, and we're nowhere near that." What will quantum computing's future look like? What are the next steps to unlocking the potential of quantum computing? Henning says, "The first machines will be hybrid by design because programming them will require a classical computer. Several technologies will need to come together to operate them successfully. They must intelligently break down a problem into what suits each technology and then put the overall result together." He suggests business leaders embrace the future. "This revolution is coming – not tomorrow, but in a foreseeable timeframe. It may make sense to set up a smaller team to scout the technology and identify use cases. Look for opportunities to partner with quantum startups. Your business can bring the knowledge of problems that need solving." Oliver agrees it's not about having all the expertise in-house. "Build partnerships with specialists who understand how to get the most of it." Quantum computers can't do much today, but their potential is undeniable. Businesses exploring this new realm are investing in a reputation for innovation and the possibility of solving today's hard problems. Difficulty accessing and using the technology should keep hackers at bay for now, but cyber researchers are working on new encryption methods that quantum computers can't crack.
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How businesses can use generative AI to empower employees

  • min read
    • Headlines about the power of generative artificial intelligence (gen AI) are everywhere. But business leaders are asking, is it really that game-changing, or just a fad? And if it is a game-changer, how can you make the most of it? In the first episode of our second season of Kaspersky's podcast Insight Story, I speak with experts Shagun Sachdeva (India,) Project Manager for Disruptive Tech at business intelligence service GlobalData Plc and Karen Quinn (UK,) Senior Director, Brand and Corporate Communications at financial software providers, Finastra. What is generative AI? TechTarget defines gen AI as any "artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data." Some applications rely on Natural Language Processing so users can ask for outputs using everyday speech. Well-known examples of gen AI include OpenAI's chatbot ChatGPT and Google Bard, but current and potential business uses are near endless. Shagun thinks many businesses already understand its importance. "A recent GlobalData Plc survey found more than 50 percent of businesses expect gen AI will tangibly disrupt their industry in the next five years. 30 percent are already using gen AI tools." How are businesses using gen AI? "Business leaders are having more sophisticated conversations about how gen AI can go from an experiment to giving strong returns on investment," says Shagun. "Whether it's chatbots engaging with customers, sharing financial tips and customizing financial plans based on individual spending habits. It's also generating loan options based on a user's credit profile." Finastra plans to develop gen AI tools for customers in future, but they've started in-house with GENAI (X,) a gen AI-based system now rolled out to their 8,000 employees. "We use it for things like understanding and reviewing contracts, predicting behaviors and optimizing workflows," says Karen. She believes that although only being used internally so far, their gen AI tools are benefiting customers. "We can only work with the data we have to make financial decisions. Anything that can generate new scenarios, pull in more data sources or enable federated learning can only benefit." Is gen AI a threat to jobs? Shagun acknowledges employees worry AI might replace them. "Some of that anxiety may be justified – a Goldman Sachs report in March 2023 said AI could replace 300 million full-time jobs." But getting to know gen AI better may help overcome that concern. "Lean in to the technology. Education and training is key. Start with courses like prompt engineering." Karen refers to a quote from economist and professor Richard Baldwin, "AI won't take your job. It's somebody using AI that will take your job." In other words, it's not the technology itself that's a threat to jobs but failing to explore it and take full advantage of it. She advises looking at the benefits of gen AI broadly. "This is not an efficiency play. People will become more productive, but hopefully that means work is more rewarding." Getting gen AI right While generative AI offers much possibility, it also raises concerns around ethics, bias and data privacy. It is largely unregulated today and developing AI models can be resource-intensive. Karen highlights Finastra's caution. "We rigorously tested the products, then launched a full-scale training program and disabled other tools. They're still going through testing, like panels to try and break them and make sure no sensitive data gets shared with the wrong audiences." She continues, "In brand and communications, there are copyright issues. If you're using text-to-image prompts, where is it drawing data from? We need to be very careful." Materials used to train AI are often copyrighted, and whether people or businesses can copyright outputs of gen AI is subject to legal challenges in many countries, with some courts – including in the US and Italy – ruling no, at least in the case of visual arts. Balancing gen AI optimism with realism Shagun feels misinformation is the biggest business risk from gen AI. "It can fabricate 'facts' – what Google researchers call hallucinations." She also recommends businesses pay attention to copyright. "Disputes have arisen between artists and AI companies over the value of human creativity." Data privacy and security also rate highly for Shagun. "Large language models are trained on vast swathes of internet data. There's no data protection embedded in these systems by design or default. Training data can fail to include women, older people or marginalized groups – that's an ethical challenge." Security and generative AI Amin Hasbini is Kaspersky's Head of Research Center for its Global Research and Analysis Team (GReAT,) Middle East, Turkey and Africa. He says many employees are already using freely available AI tools, potentially without their employers knowing. "In a 2023 Kaspersky study, we found 57 percent of workers are using generative AI to save time. This raises many security questions. What kind of data are they putting into it? Is it intellectual property, code or documents to summarize? That's a major concern." Cybercriminals are also using generative AI to help them fool people with more realistic fake websites. In 2022, Kaspersky's anti-phishing system blocked more than 500 million attempts to access fraudulent websites – a doubling of attempts in the previous year. It's likely cybercriminals are already using generative AI chatbot ChatGPT's ability to produce convincing texts to create automated spearphishing attacks – phishing targeted at specific people. To protect against generative AI-based threats, Amin advises, "It starts with awareness. Employees need to know their organization's boundaries around putting data into AI websites. Businesses need enterprise cybersecurity solutions that allow monitoring and control of devices, systems and data used within the organization." Leading employee cybersecurity awareness approaches include implementing a cyber champions program or targeted education as used by Heathrow Airport. Gen AI's future potential What kinds of applications for gen AI will we see businesses pursuing in the near future? Shagun believes what businesses do today will decide the future of gen AI. "The world will look completely different by 2030. Global Data Plc estimates the AI market will be worth around 900 billion by 2030. But that doesn't mean carelessly diving in. "Gen AI is no magic bullet. While the enthusiasm around it is justified, prudence is imperative. We need responsible innovation," says Shagun. Karen imagines gen AI helping to make markets fairer. "Gen AI has incredible potential to overcome inefficiencies, like small-to-medium business (SMB) access to trade finance. Gen AI might be able to generate or automate some of the processes for onboarding smaller companies into global trade, opening them up to a wider audience." She also thinks gen AI could help solve some of the problems it's been known to introduce. "It can help identify bias through explainability and ensure we close feedback loops that cause biases. It could also look at efficiencies in supply chains, contracts and communications and marketing. It can take out some of the drudgery and enrich the processes." "We're on the cusp of understanding what gen AI can do," Karen says. "Who knows what the future brings? This could be game-changing in ways we can't even imagine." Like Karen and Shagun, as a creative marketer, I'm drawn between mostly optimism and a little fear about what gen AI will bring. As I wrote following a gen AI talk for marketers where I heard about Karen's exceptional AI program at Finastra, I'm optimistic we can do it right. Having the training, technologies and policies to bring the whole organization on the journey, we can use this tech to free us by shifting mundane tasks down to these somewhat smart bots. But in that shift, we may narrow opportunities – particularly for talent entering our industries – to refine their craft and learn the difference between average and distinctive output. Refined talent that generative AI lacks. For now, anyway.
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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 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.
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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 Dr. Lishan Adam, 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 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.
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