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chatGPT cybersecurity regulation

With ChatGPT’s vast innovative potential, regulators must take care

  • min read
    • ChatGPT and other artificial intelligence (AI) chatbot applications are major technological milestones with far-reaching benefits for many industries. But with each advance comes unintended consequences. With ChatGPT in its fourth iteration, and its underlying technologies growing fast, it's time to take stock of the policy landscape to understand its implications. What is ChatGPT, and how are businesses using it? ChatGPT is an AI chatbot made by OpenAI. It uses natural language processing (NLP) to create human-like text responses. Users can ask ChatGPT almost anything and receive relatively natural-sounding responses. It can write outputs like programming code, social media posts, summarize complex topics or almost anything text. ChatGPT is an example of generative AI: Artificial intelligence that learns to generate original content like images, music and text by identifying underlying patterns in existing examples. "Its primary objective is to create interactive and realistic conversations with users, making it a powerful tool for chatbots, virtual assistants and customer support applications," says AWS Solutions Architect Ragu Kuppannan. Many top companies are using ChatGPT. For example, travel outlet Expedia wants customers to be able to book a holiday in a way that feels like having a chat with a travel agent. Microsoft, a major investor in OpenAI, says ChatGPT already powers its search engine, Bing, and plans to add it to Word and Excel. ChatGPT could be promising for any application where natural, conversational interaction between people and tech would be beneficial. Risks of ChatGPT Critiques of ChatGPT and other chatbot applications generally focus on its potential to disrupt human employment, produce incorrect information or poor practice in training algorithms to identify harmful content. The underlying cybersecurity risks get less attention. Kaspersky's specialist threat intelligence teams have identified three main risk areas. First, ChatGPT can provide advice, even detailed guides, to help prospective attackers plan and target victims. Second, it can develop code and even create entire coding projects for those with little knowledge of software development. While ChatGPT has restrictions and safeguards to avoid abuse, they're fairly easily bypassed. Third, ChatGPT produces convincing and accurate text that can be used in phishing or spearphishing. To some extent, these three areas represent theoretical risks or outlier scenarios that don't represent how people and businesses usually use ChatGPT. Policy perspective beyond risks Policy should aim to be technology-blind – taking a similar approach to all related technology that might emerge in the future, rather than creating a unique approach for one of today's concerns. It should build the best possible defenses against the worst human intentions and applications. Practically speaking, there may be no way to prevent or limit use of tech like ChatGPT. And to limit an emerging technology's potential means being left behind in a global digital economy. While policy discussions usually focus on outputs – the material ChatGPT produces – its inputs (the data it's trained with) may be a bigger threat. Data-driven tools like ChatGPT are only as good or accurate as their training datasets. As chatbot systems may become the most common way to use the internet, issues will have wide impact. Ensuring balance in training data is a growing challenge as disinformation expands. Bad actors can influence what users see and read in ways that have global consequences. Datasets, devices and systems must be secure and accurate. How nations are regulating AI In our public policy work, Kaspersky advocates public-private partnerships. We think partnerships combining global private-sector expertise with policymakers' local needs will be the best defense. It's a middle-ground approach that avoids too much or too little caution. These partnerships in cybersecurity maximize the advantages of emerging technologies like ChatGPT, while safeguarding against risks. United Arab Emirates (UAE) is a role model for inclusive public-private partnerships. They've taken a progressive and collaborative stance on partnerships with international tech firms. Initiatives and forums like the Dubai Chamber of Digital Economy and the UAE Cybersecurity Council aim to safely and securely advance technological growth for short- and long-term benefit. In May 2023, Brazil was considering a Bill to regulate AI. It requires "systems [to] undergo a preliminary assessment carried out by the suppliers themselves, to determine whether they can be classified as being of high or excessive risk," according to OneTrust Data Guidance. It also restricts exploitative AI uses, like subliminal techniques that could cause harmful behavior or target vulnerabilities in older and disabled people. The Center for Strategic and International Studies says Japan looked at the European Union's sterner approach to AI regulation and had "concern that the burden of compliance and the ambiguity of regulatory contents may stifle innovation." Instead, Japan is adopting a "human-centered" approach based on seven principles – among them, privacy protection, security and innovation. They regulate AI through several laws. The Digital Platform Transparency Act, for example, requires fair and transparent practice from large online stores and digital advertising businesses, including disclosing how they decide search rankings. Values are the key to balanced regulation ChatGPT and other generative AI fit a model of technological development we've seen throughout history – it disrupts human lives and businesses with exciting potential but also serious risks that need policymakers' attention. UAE, Brazil and Japan have regulatory approaches that are values-led and aim for a middle ground: A balance between too much and too little caution. Alongside, partnerships between the public and private sector hold promise to let business embrace innovation while adequately managing risk.
Cyber-Immunity, dynamic clothes, earth 2050, predictions

Predictions for Earth 2050: How will cybersecurity change?

  • min read
    • If you're like me, having a long-term vision is a wonderful aspiration… frequently quashed by today's more screamingly imminent 'to do' list. Five-year plan?  I'm still working on the next five days. But the next five minutes? That's a no-brainer: let's get immersed in the futuristic predictions for the world in 2050, and the evolving role of cybersecurity in it. Cyber-immunity will intuitively protect our systems Predicted by: Eugene Kaspersky, CEO, Kaspersky In 2050, digital systems have moved up to a whole new level. They're no longer assistants making life easier for us mere mortals, they've become the basis of civilization – the very framework keeping the world functioning properly. This quantum leap forward has generated new requirements for the reliability and stability of artificial intelligence (AI). Since the romantic era around the turn of the 21st century, cyberattacks are not yet extinct, but they're now only a threat to outliers who reject modern standards of digital immunity. The situation resembles the fight against human diseases. Thanks to the success of vaccines, the terrible epidemics that once devastated entire cities in the 20th century are now a thing of the past. But for humans, diseases of the past that wiped out millions – like the plague or smallpox – have been replaced by new, highly resistant 'post-vaccination' diseases. Fortunately, for the machines, life is better; the initial designers of digital immunity made all the right preparations by borrowing the systemic approaches of living systems and humans. One of the pillars of cyber-immunity is digital intuition, the ability of AI systems to make the right decisions in conditions where the source data is insufficient to make a rational choice. There's no mysticism here: digital intuition is the logical continuation of machine learning. When the number and complexity of related self-learning systems exceeds a certain threshold, the quality of decision-making rises to a level that eludes rational understanding. An intuitive solution results from the superimposition of the experience of a huge number of machine-learning models, much like the result of the calculations of a quantum computer. Digital intuition – with its ability to instantly, and correctly, respond to unknown challenges –has helped build the digital security standards of this new technological era. The robot servants are uprising Predicted by: Vitaly Kamluk, Director, Global Research & Analysis Team, Kaspersky Busy workers rejoice! Help for your home is arriving. A robot servant will cook your meals, do your laundry and clean your apartment. These autonomous systems will be able to carry out many complex tasks using the processing power of a central intelligent system, and in tricky situations, they'll switch to remote technical support managed by real people. There will be mass attempts to hack these helpful robots by labor rights activists and criminals trying to blackmail their owners. Since the servants will all be equipped with cameras, microphones and sensors, they offer a unique viewing platform for home-based espionage. Expect compromises of the manufacturers' infrastructure and the implantation of malicious code in devices with the aim of enacting, en masse, attacks that could be serious. The man(less) mining machine Predicted by: Andrey Nikishin, formerly Future Technologies Project Director, Kaspersky Mining is a fully automated process; people are not needed and former miners regain their health. One robot extracts rock from the soil, the second cleans it, the third guards the perimeter, while the fourth delivers the purified ore to industrial consumers. A hacker who gains access to the system can reprogram individual robots so they take a small amount of the platinum ore to an unauthorized place. The guard robots are also hacked, so they're unaware of this theft. The big reveal of dynamic clothes Predicted by Vladislav Biryukov, Head of Consumer Social Media, Kaspersky Dynamic clothing is à la mode. The fabric's characteristics – color, pattern, lighting, transparency, and in advanced models, even the cut and length – can be automatically changed. Some people now wear shirts or dresses with variable transparency levels, which allows them to reveal more at any moment they choose. Few people 'program' their clothes by themselves; most enjoy free fashion patterns or, for the fashionistas, expensive programs from well-known designers. Initially lacking security, the process of getting a smart clothing upgrade has led to the emergence of a new kind of extortion. "Heading to a business meeting, sir? Transfer 10 bitcoins to this account number now, or we'll expose you!". The naked truth in the boardroom. Awkward. Not quite the transparency in business we were hoping to achieve in 2050. A quantum (computing) leap threatens cryptography Predicted by Alexey Malanov Cryptography is widely used in many spheres of online activity. It's no exaggeration to say that the world will experience a huge shock if suddenly everything that's encrypted today is unlocked. How cryptography works: some operations can be easily carried out in one direction, but they're very difficult to reverse. For example, an image can be quickly transformed into a picture puzzle, but to arrange all of the pieces back into the picture takes much longer. Modern public-key cryptography is based on this principle: I know two numbers, I have multiplied them and told everyone the answer. Now everyone can encrypt a message for me using the result of the multiplication, but only I can decipher this using the original multipliers. There are already quantum computers that allow you to quickly solve problems that seem to be circuit-breaking for conventional computers, like how to de-compose a composite number. The only obstacle to the end of the digital world – let's call it 'cryptocalypse' – is that quantum computers can only work with small numbers. But by 2050, quantum computing will allow security services or master criminals to access universal decryption, and they may choose to do bad things with it. For example, if a villain steals your credit card, he or she will immediately know your PIN. The ability to decipher brings with it the opportunity to forge: an ATM informs the bank one thousand dollars are withdrawn, but the villain falsifies the message to say only 10 dollars were taken out. We will need to develop new safety standards – fast. On the bright side, encryption helps with verification so you can be sure you're actually on your real bank's website, not a cleverly cloned fake, when you wire the ransom to keep your business suit from turning into your birthday suit. Phew! One challenge solved.
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.
earth2050 robot

What will the future of work be like by 2050?

  • min read
    • The world of work in 2050. If you're unlucky enough not to have retired by then, the working world will be a very different place to how it is today. The vision of the future for workers tends to scale from a nihilistic view of the robots taking our jobs (leaving us permanently trapped indoors, living entirely from Amazon Prime supplies), through to the utopia of the eradication of repetitive tasks using artificial intelligence. This will leave humans free to think, create and act (while never needing to leave your home…thanks to improved teleworking and those highly convenient Amazon delivery drones). But what do the experts think it will really be like? Robots have rights too Predicted by: Ian Pearson. Ian has been a full-time futurologist for over 25 years, tracking and predicting developments across a wide range of technology, business, society, politics and the environment. We see a picket line of robots in the city center. Their placards are plastered with slogans written in binary code. The widespread development of robots and artificial intelligence have necessitated the creation of a legal framework to protect them to regulate the rights of intelligent robots. Subsequently, this code is constantly updated and improved so robots have improving rights in our society. Personal assistants for life Predicted by: Alexey Malanov Personal assistants (PAs) have rapidly developed throughout the 21st century. To begin with, they could only answer primitive questions: in 2019, voice assistants were as dumb as a box of rocks. But by 2025, they'd mastered the art of recognizing and transcribing speech (with punctuation marks too); they could understand the meaning of a text and recite it so realistically that it was almost indistinguishable from a human. This led to half of all office activities being automated: PAs began to take minutes, write post-meeting reports, summarize lengthy documents, and consciously digest and recapitulate the contents of messages. They also plan your day, schedule meetings and even order lunch. Thanks to machine learning, PAs are fully adapted to their owners' personalities, increasingly penetrating every aspect of their lives. It all began with assisting with office work, and by the mid-2040s, PAs had become an integral part of everyday life by monitoring their owners' diets, planning trips abroad, buying gifts for friends and family, and more. But it's not as rosy as it seems: the human-PA relationship has spawned a plethora of psychological issues and communication difficulties between real people. However, a specially trained AI psychologist is on hand to help us cope. Paradoxically, it's just another voice assistant. The power is in your hands Predicted by: Mersey Shelley is a pseudonym of Alexey Andreyev, writer and futurologist, who works for Kaspersky. This prediction is from his cyberpunk novel '2048' published in 2004. In decades past, palms and fingers were scanned for identification. Now, hands with implanted sensors become the scanners themselves. These implants come in different models with two main variations: first, a simple scanner for daily use. It helps to read different tags and product codes, and to exchange digital business cards via the handshake ritual (you can use face recognition to identify a stranger but this is considered indecent; besides, many people use protection to prevent recognition. Hats with special veils are popular). Second, sophisticated scanners for technicians and other professionals. For example, a doctor's hand includes various medical sensors for diagnostics – even a portable endoscope with a direct video stream to the doctor's visual cortex. A flexible workforce Predicted by: Jacob Morgan is a best-selling author, speaker, futurist and founder of FutureOfWorkUniversity.com. In the year 2050, technology will dominate the workplace with artificial intelligence and smart assistants being commonplace, while the use of augmented and virtual reality continues to increase. Everything will be 'smart' – connected and data-driven. The vast majority of us will continue working the same way we are today, but the jobs on offer will be ones that haven't yet been invented. Emotional intelligence and 'human' skills such as perpetual learning will be crucial for leaders to possess, as well as fluency with the latest technologies. Work will increasingly become more fluid as some employees will be virtual, some will be full-time, some will be bots, and we will all be working non-fixed hours. As with any kind of change, there will be pros and cons. Those who work in routine jobs are likely to struggle to make sure they can stay relevant in the new world of work. The advantage is that this will give us more time to focus on the human aspects of work such as building relationships, being creative, deep thinking, and practicing skills like empathy and self-awareness. Technology will help us make organizations more personable. This also means that we need to get more comfortable with using technology such as bots and AI in our work, a cause of tension and fear for some. And what about working hours? It depends if you're a knowledge worker or working in industries like retail. Flexible work arrangements are already being implemented at organizations around the world. The advantages are far-ranging, from improving employee satisfaction, productivity and tenure, to being able to attract and retain top talent. The potential disadvantages here are making sure that employees can stay accountable for delivering high-quality work while feeling like they're a part of the team, dealing with isolation, and ensuring effective communication and collaboration. I think the pros outstrip the cons: when managed effectively, flexible work arrangements can be a huge benefit to the organization and its people. Focus on the user experience Predicted by: Edney Souza, digital culture and business and technology teacher, speaker and entrepreneur, Brazil. For sure, work will become more fun and those repetitive activities in our day-to-day life will be done by robots and artificial intelligence. Knowing programming logic will be as important as English and mathematics. User experience design (UX) will be more important than other technical components of a project to ensure its success with customers. Skills in solving problems and serving customers will continue to be competitive advantages for the work of the future.
vehicle security operations center, transport, automated vehicle

This will defend your automotive business against next-gen cyberattacks

  • min read
    • Cars are more connected than ever before: Onboard music streaming, GPS maps beamed to vehicles via the internet of things (IoT) – they're computers on wheels becoming more digitized. Traditional automotive manufacturers are transforming their business models to reflect this, with technologies like in-vehicle online marketplaces. As the automotive industry evolves towards fully autonomous vehicles, security becomes harder to control. But security must be active throughout the car's lifecycle. Make no mistake, security breaches can be fatal and costly. If you're manufacturing vehicles with connected capacity, a Vehicle Security Operations Center (VSOC) can increase protection for your business, fleet and customers against cyberattacks. First, let's explore the changing landscape and its risks. Connected cars are easy targets for remote hackers and terrorists Operating systems in connected vehicles aren't just a personal data goldmine – with audio files recorded by in-car microphones to stored bank details and even Netflix and Spotify credentials leaked on eBay – but they can also control vehicle movement. If a hacker remotely hijacks the operating system of one of your vehicles or even your whole fleet, the consequences could be devastating. Whether it's taking control of a Tesla's brakes, door locks and other electronics, or exposing personal data like Mercedes-Benz – hackers do and will find vulnerabilities in connected vehicles. But why? Terrorists looking to cause fatal chaos. Hackers out for financial gain (they may shut the engine down and demand payment to start it again.) Even mercenaries spying on executives – think how valuable audio files of confidential merger plans would be to rivals. What is a Vehicle Security Operations Center? A Security Operations Center (SOC) is a team that deals with security issues at an organizational or technical level. In most cases, the team uses a platform dedicated to constant monitoring of the organization's IT infrastructure. A Vehicle Security Operations Center (VSOC) is the same, but as well as protecting computers and servers, it also protects assets like connected vehicle cloud and fleet management system (which send and receive messages to and from vehicle's telematics control unit) and the vehicles themselves, mobility servers (phones connected to vehicles) and vehicle internal components like sensors and radars. Two audiences need VSOCs: Automotive manufacturers (like Mercedes or Tesla) and large fleet owners (like logistics companies or taxi firms.) It minimizes the risks of cyberattacks and, in the event of a breach, triages and treats the cyber-wound. It can also give unwavering diagnostics of your entire fleet (great for ongoing and predictive maintenance) and a wealth of information on how your cars are used, like time and distance traveled, Wi-Fi strength or errors in the engine performance. Perfect for R&D for new models. But how do you implement a VSOC? VSOC: To in-house or outsource? There are two ways to create your VSOC: In-house or outsource. In-house means integrating the unit into your current operations, either within your cybersecurity operations, quality assurance or the vehicle software R&D team. On the other hand, you could outsource part or all of the VSOC to a third-party provider. In some instances, this may be helpful to start with, as you may not yet have the cybersecurity infrastructure and expertise. A full VSOC may be out of scope for a small fleet owner, but if you run a large fleet or you're an automotive manufacturer, in the long term, it will be most beneficial to your business to establish your VSOC internally. How to launch your VSOC Here are some tips to get started: Create a playbook Security experts should create incident response playbooks (guidelines) to show how you would respond to specific cyber-threats and how to manage incident communications. Expand your team's knowledge Many cybersecurity teams focus on server infrastructure, not connected vehicles. Implement advanced cybersecurity training to develop the skills of your current team. For example, Kaspersky Cybersecurity Training covers malware analysis, digital forensics and incident response to help a specialist enterprise-level team better face these threats. Funnel issues straight to your VSOC Establish a system to escalate vehicle monitoring and diagnostics when needed, so in the event of a breach, it's sent straight to the cybersecurity teams. Regular security assessments Periodically conduct security assessments of your connected car's infrastructure, both the separate modules (like TCU and mobile apps) and also test how the complete system works together. Share these results with your VSOC team. Maximize data capture Investing in onboard vehicle software, like intrusion detection systems, will feed your VSOC with vital information to detect anomalies and other signs that could lead to data breaches. Enrich your VSOC with threat data feeds from at least two security vendors. There are public feeds available, but licensed feeds are typically better quality. Use white-hat hackers Make use of the global community of white-hat hackers. These are friendly hackers, trained professionals who find vulnerabilities and make sure your systems are bulletproof. And if they're not, they'll give you the diagnosis to fix them. You could also join the likes of BMW and Tesla and launch a Bug Bounty program, which pays people (professional and amateur) for any bugs they find. Work towards the latest regulatory standards Future proof to upcoming vehicle regulation laws like Transport UNECE WP.29, and ISO/SAE 21434. As connected cars and autonomous vehicles speed towards making our roads safer and giving people a more comfortable driving experience, automotive businesses must ensure they're doing everything they can to protect their products from cyberattacks. A VSOC minimizes the chances of a cybersecurity breach in your plant, on the road and for your customers, so you can focus on building the future of transport.
5 technologies fighting climate change

Can tech help fight climate change? Five innovations making a difference today

  • min read
    • As we say goodbye to the last decade, we usher in a new and crucial one for our planet. According to research, Earth has warmed around 1c since the industrial revolution, which is wreaking havoc on the planet we call home. This is just the start, according to this fun, but terrifying sliding scale. Since the 2016 Paris Agreement – which aims to keep global temperature increases under 2c – climate change has become a critical conversation for all nations and industries. But, as the research of agricultural economist Ester Boserup shows, humankind has the potential to adapt using technology to overcome its problems. These five technologies are designed to help us combat the number one issue we face as a species: global warming. Vacuuming gas from the atmosphere https://www.youtube.com/watch?v=63S0t4k_Glw Concentrations of carbon dioxide in the Earth's atmosphere have risen 48 percent since the start of the industrial revolution. We need to start reducing it, with the help of direct air capture (DAC) technology. But what is it? Giant vacuum cleaners remove CO2 from the atmosphere, sequestering it underground and, in some cases, supplying it back to specific industries, like fertilizer for farmers or bubbles for drinks companies to carbonate their products. One of the companies already using Direct Air Capture tech is Climeworks. They're removing and sequestering thousands of tons of carbon from the atmosphere every year. The CO2 fighting resilient crop Arborea has developed the world's first BioSolar Leaf – which puts a whole new spin on natural photosynthesis. Using unique, photovoltaic-like panels, coined 'bionic leaves,' BioSolar Leaf uses sunlight to grow microscopic plants which can grow in high concentrations within arid and infertile ground. These tiny plants, in turn, produce healthy food ingredients while generating oxygen and sequestering high amounts of CO2. Bees glorious bees https://www.youtube.com/watch?v=O6HLWS86-Y8&feature=emb_title Bees. You'd be hard pushed to find someone who doesn't appreciate them. But you'd have an equally tricky time sitting down for a coffee with one (the bee, not the person). They are vital to a healthy environment, but unfortunately, their numbers are declining globally. It's time to save these winged wanderers. The World Bee Project and Oracle have teamed up to do just that. Their Hive Network uses cloud technology, artificial intelligence (AI) and smart sensors to collect information about honey bee decline to protect them better. The Hive Network allows researchers to 'listen' in on honey bee enclosures, analyzing data like wing movement, temperature and honey yield. By monitoring bee colonies at this level, conservationists and beekeepers can detect patterns and predict bee behaviors, giving them vital information to keep our (real-world) honeypots alive. Lab-grown cuisine https://www.youtube.com/watch?v=_Iv0857U3UE Not a new technology by any stretch, but a new way of looking at a traditional industry. Meat has a much higher energy footprint than any other food type. It takes 75 times more energy to produce than corn and an area seven times the size of the European Union (EU) to produce enough food for the region's cattle and livestock to eat. In an attempt to curb our carnivorous habits and save our planet from total annihilation, start-ups like Impossible Foods are rearing fake meat – in labs. They've discovered that both animals and plants share a common protein – heme. In plants, this is leghemoglobin, whereas in animals, like cows, this would be hemoglobin. By taking heme from leghemoglobin and growing it in a lab, the product can be colored, shaped and even cooked like a piece of meat because they share the same properties. But there's more. Solar Foods are now turning water into different foods by taking bacteria from soil and multiplying it using hydrogen extracted from water – the result (in this case): flour. Although the flour isn't licensed for sale currently, it's been used to make an edible pancake. Not a bad start. Eventually, the flour will be used to replace fillers in thousands of food products. It can be modified to mimic milk and eggs, and can even be manipulated to create lab-grown fish with all those healthy omega-3 acids. The power of energy storage https://www.youtube.com/watch?v=mmrwdTGZxGk&feature=emb_title Many countries around the world have committed to a 100 percent carbon-free future, but how exactly will it happen? With renewables, in the first instance. But we also need to manage better the energy we produce to power the right tech to help us, like electric cars. The stage is set for interconnectors. These power lines not only move energy across long distances between regional power grids but also store excess energy for later use using lithium-ion batteries – those used in mobile phones and electric cars. The use of these interconnectors is expected to grow tenfold by 2024. They help to store energy for longer to transport it to places where there's less produced – a win-win. Right now, current storage tech can handle four hours of storage. But, technology firms aim to improve that drastically like Energy Vault, a Swiss start-up. They use windy days and automated cranes to stack bricks, recapturing the kinetic energy generated when the bricks fall back to the ground. Watch the video to see it in action. Smart cities are the next port of call The World Health Organization (WHO) has said that climate change could increase deaths by air pollution by hundreds of thousands between 2030 and 2050. One of the big problems: congested cities. Now with advances in artificial intelligence, internet of things (IoT) and smart sensors, smart cities are being designed to help keep earth-defiling emissions down and citizens' lungs healthy. Hamburg is a case in point. A prominent port city in Germany, it's bustling with shipping containers, cruise ships and other sea vessels – but struggling to manage increasing air and noise pollution. Air pollution is due to ships burning marine fuels for on-board equipment while they're docked in the harbor. After Hamburg Port Authority teamed up with Siemens and adopted the Siharbour technology (a mobile-operated generator), Hamburg's ports can now hook incoming ships onto the mainland power supply, stopping them from burning unnecessary fuel and keeping emissions down. What happens next? The teams behind these innovations (and countless others) are working around the clock for a better chance of saving our planet. Which ones do you think will help make a difference? And what innovations could your business develop now that could help protect our world?
by Innovating leaders.
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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.

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