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Human paints AI with human features, while AI paints human with machine features

Reaching the technological singularity: What will happen when machines become smarter than us?

  • Future tech
  • min read
    • Picture a world where machines have long since passed the Turing Test. When people can't tell the difference between artificial and human intelligence. In this world, will humans become obsolete, like the floppy disk? Or is it a world where artificial and human intelligence merge to usher in the next step of our evolution? That's the technological singularity, the theoretical time in the future where technological development outpaces our abilities to maintain control over it. For better or worse, it's undoubtedly something that will result in never seen changes to our world. When will we reach technological singularity? Join any discussion about business technology, and it won't be long before someone mentions artificial intelligence (AI). The amount of digital data in the world has already surpassed our abilities to manage it, a development, which has made clear the need for algorithms to do the job for us. But algorithms and AI aren't the same things. They're not even close. An algorithm is a computer program that parses data from sets too large for human interpretation, whereas true AI is capable of thinking for itself, making its own decisions, and having free will. True artificial intelligence doesn't exist. Yet. The explosion of AI has long been a popular trope of science fiction, but the reality is edging ever closer. Ray Kurzweil, world-renowned futurist and Google's Director of Engineering, believes technological singularity is going to happen before 2045. Take note: so far, an impressive 86 percent of his predictions made since the '90s have become a reality. But even if that sounds optimistic (or pessimistic, depending on how you look at it), it's the logical next step in the evolution of technology. Once computers can learn for themselves without being taught and trained with data that we've collected, we can expect profound society changes on a scale never before seen. How can we keep up with technology? The emergence of an artificial superintelligence would bring us greater inventive and problem-solving skills than humans are capable of. This might lead to the creation of a new 'species,' one which might not necessarily have human interests at its heart. But so far, we're nowhere near developing machines with human-level intelligence and the ability to make decisions independently. Now we can enhance human intelligence and capabilities with tools and technology. Indeed, that's precisely what we've been doing for hundreds of thousands of years, ever since our ancestors figured out how to light fires. But today, the stakes are much higher with projects like brain-to-computer interfaces as active areas of research. This may, in turn, lead to radical developments like mind uploading, in which you entire consciousness can be uploaded to and stored on a machine. At least, it could be useful for those millennia-long interstellar voyages. Such developments might sound dystopian since they have the potential to transform what it means to be human ultimately. At the same time, the convergence of technology and humanity, an area of study known as transhumanism, appears to be the only way we can keep up with the constant pace of change. It's either that or the machines eventually outpace our natural evolution and take over. The survival instinct If we can't keep up, and technological evolution ends up outpacing our capability of keeping control over it, then we could face profound risks to security. Each of the seven characteristics that define every biological organism comes together to form a singular goal – survival. Technological singularity occurs when AI matches our desire and capability to survive as a species, and there's little reason to think that AI would have any evolutionary motivation to be friendly to humans. Today, machines are programmed by us to create the outcomes we want. But what happens when they're capable of programming themselves? Time for Humanity 2.0? Imagine you're an expert on machine learning, working on an artificial intelligence algorithm that will be able to create other AIs by itself. Are you merely training your replacement, or have you become an architect of your obsolescence? Welcome to Humanity 2.0, where the human condition is no longer about our biological form. It's a time when machines become an integral part not just of our societies, but also ourselves. Conversely, this could also lead to newer and deeper social class divisions than we've ever seen, as people are separated into those who are augmented by technology and those who are not. There's a safety benefit of not being physically augmented by technology – no one will be able to hack into your mind. How humans could remain our biggest threat Perhaps it isn't machines that we should be worrying about at all. Assuming we'll always be able to control artificial intelligence, we could end up transforming it into a deadly weapon. It's already happening. The proliferation of social media, for example, is one of the most significant security and privacy concerns this century. That's not so much down to vulnerabilities in the technology, but because it's a highly effective medium for conducting social engineering attacks. Coupled with the rise of AI, cybercriminals will be better equipped to analyze social media conversations to emulate users' writing styles and craft more convincing messages to their victims. Even before we reach the era of true AI, the ability of algorithms to parse enormous amounts of data is already misused. On the one hand, algorithms can perform many useful tasks to improve our world, such as climate modeling and disaster prediction, while things like neural interfaces can help victims of spinal cord injuries. On the other, the ability of humans to interfere with information systems makes them a more significant threat than the technology itself. Perhaps it would be better if we just let the machines take over in the hope they'd nurture us like the irresponsible children we are. To fear or not to fear Ray Kurzweil has a more positive outlook than most about the singularity. He claims it's an opportunity for humankind to improve by making us smarter and better at all the things we value. Other science and technology leaders, including Elon Musk, Bill Gates and the late Stephen Hawking, are not so sure. But one thing's for sure – there will come a time when our augmented descendants look back on us as rather quaint and exotic creatures. While we're still decades away from the singularity, it presents itself as the next big step in the evolution of technology. Emerging tech, like autonomous vehicles and facial recognition, are already paving the way. True artificial intelligence is coming sooner or later, which is why it's time for the business world to start taking AI ethics and security issues seriously. It's no longer the concern of generations far in the future. This article reflects the opinion of its author.
Small man wearing clothes associated with crime climbs into a trapdoor in another man's head

What does the rise of deepfakes mean for the future of cybersecurity?

  • Future tech
  • min read
    • Imagine you're holding a video conference with a colleague or business partner in another city. You're discussing sensitive matters, like the launch of a new product or the latest unpublished financial reports. Everything seems to be going well, and you know who you're talking to. Maybe you've even met them before. Their appearance and voice are as you expected, and they seem to be pretty familiar with their jobs and your business. It might sound like a routine business call, but what if the person you thought you were talking to is actually someone else? They might seem genuine, but behind the familiar imagery and audio is a social engineering scammer fully intent on duping you into surrendering sensitive corporate information. In a nutshell, this is the disturbing world of deepfakes, where artificial intelligence is the new weapon of choice in the scammer's arsenal. What exactly are deepfakes? Among the newest words on the technology block, 'deepfake' is a portmanteau of 'deep learning' and 'fake.' The term has been around for two years, when it first appeared on a Reddit community of the same name. The technology uses artificial intelligence to superimpose and combine both real and AI-generated images, videos and audio to make them look almost indistinguishable from the real thing. The apparent authenticity of the results is rapidly reaching disturbing levels. https://www.youtube.com/watch?v=cQ54GDm1eL0 One of the most famous deepfakes of all was created by actor and comedian Jordan Peele, who made this video of Obama delivering a PSA about fake news. While this one was made for the sake of humor and to raise awareness to this rapidly emerging trend, deepfake technology has, unsurprisingly, been misappropriated since the very beginning. Its implications for credibility and authenticity have placed it squarely in the spotlight. The worrying consequences of deepfakes Wherever there's technology innovation, there's nearly always pornography, so it's little surprise that the first deepfakes to make waves on Reddit were videos which had been manipulated to replace the original actresses' faces with somebody else's – typically a well-known celebrity. Reddit, along with many other networks, has since banned the practice. However, as actress Scarlett Johansson said of deepfake pornography, while celebrities are largely protected by their fame, the trend poses a grave threat to people of lesser prominence. In other words, those who don't take steps to protect their identities could potentially end up facing a reputational meltdown. That brings me to the political consequences of deepfakes. So far, attempts to masquerade as well-known politicians have been carried out largely in the name of research or comedy. But the time is coming when deepfakes could become realistic enough to cause widespread social unrest. No longer will we be able to rely on our eyes and ears for a firsthand account of events. Imagine, for example, seeing a realistic video of a world leader discussing plans to carry out assassinations in rival states. In a world primed for violence, the implications of deepfake technology could have devastating consequences. Purveyors of fake news seeking to make a political impact are just one side of the story. The other is the form of social engineering that business leaders are all too familiar with. As the video conference example illustrates, deepfakes are a new weapon for cybercriminals. The world's first deepfake-based attack against a corporation was reported in August 2019, when a UK energy firm was duped by a person masquerading as the boss of its German parent company. The scammer allegedly used AI to impersonate the accent and voice patterns of the latter's CEO, someone the victim was familiar with, over a phone call. The victim suspected nothing and was duped out of $243,000. These uses of deepfake technology might seem far-fetched, but it's important to remember that social engineering scammers have been impersonating people since long before the rise of digital technologies. Criminals no longer have to go to such lengths as studying targets in great depth and even hiring makeup artists to disguise themselves; they now have emerging technologies on their side, as businesses do for legitimate purposes. Previously, successfully impersonating a VIP was much more difficult. Now, the ability to create deepfake puppets of real people using publicly available photos, video and audio recordings is within everyone's grasp. Can you protect your business from deepfakes? The common misassumption, that synthetic impersonation can never be nearly as convincing as the real thing, is the biggest danger of all. We live in a world where it's getting harder to tell fact from fiction. From the hundreds of millions of fake social media profiles to the worrying spread of fake news and the constant rise of phishing attacks – it's never been more important to think twice about what you see. Perhaps, after all, there is a case for a return of face-to-face meetings behind closed doors when discussing important business matters. Fortunately, there are other ways you can prepare your business for the inevitable rise of deepfakes without placing huge barriers in the way of innovation. To start with, 'seeing is believing' is a concept you'll increasingly want to avoid when it comes to viewing video or listening to audio, including live broadcasts. To untrained eyes, deepfakes are getting harder to tell apart from the real thing, but there are, and likely always will be, some signs due to the fundamental way AI algorithms work. When a deepfake algorithm generates new faces, they are geometrically transformed with rotation, resizing and other distortions. It's a process that inevitably leaves behind some graphical artifacts. While these artifacts will become harder to identify by sight alone, AI itself can also be used as a force for good – it can detect whether a video or stream is authentic or not. The science of defending against deepfakes is a battle of wills: as deepfakes increase in believability, cybersecurity professionals need to invest more in seeking the truth. A team of researchers in China recently published a method for using AI itself to expose deep fakes in real time. Another paper published by the same team figured out a way to proactively protect digital photos and videos from being misappropriated by deepfake algorithms by adding digital noise, invisible to the human eye. As the threat of deepfakes edges ever nearer, we can hopefully expect more countermeasures to follow suit. This article represents the personal opinion of the author.
Person wearing augmented reality (AR) glasses reaches out and touches a virtual version of themselves.

Does virtual and augmented reality mean augmented cybercrime too?

  • Cybersecurity
  • min read
    • Not so long ago, augmented reality (AR) and virtual reality (VR) were the stuff of science fiction. Now they've become more than just niche experimental technologies. They're rapidly entering the mainstream, particularly in the consumer world of interactive media. But it's not just things like video games that are being transformed by the rise of VR platforms – there are some promising business cases for AR and VR too. In fact, some experts estimate that they will become indispensable technologies in the business world by as early as 2025. What is augmented and virtual reality? Although augmented and virtual reality are closely related, they're not the same thing. AR 'augments' the real world around the user by adding digital elements to the real-world view. Perhaps the most familiar example is the mobile game Pokémon Go, as well as less successful products like the infamous Google Glass. VR, by contrast, provides a far more immersive experience, since it relies on shutting out the physical world entirely. Popular VR devices include gaming platforms like HTC Vive and Oculus Rift, as well as lower-tech and more affordable experiences like those offered by Google Cardboard. Another term you'll encounter is mixed reality (MR), which refers to experiences that combine elements of both VR and AR. One of the best-known examples is Microsoft HoloLens, an MR platform designed specifically for business applications. What are the business applications of augmented and virtual reality? Rapid prototyping One of the most promising applications is product prototyping, a process which involves creating a complete digital model of a product (or, a so-called digital twin) and merging it with a VR experience. This allows for extensive testing and ideation without the high costs of developing multiple physical prototypes. Employee training Another popular business application, and one that's already entering mainstream use, is employee training. By applying VR or AR to training programs, it's possible to build a safe and highly interactive simulated environment with real-time feedback. Similarly, AR and VR can help improve workplace safety, especially in hazardous environments, be they workshop floors or nuclear reactors. AR can also help workers assemble, operate and maintain complex machines by providing real-time information overlaid onto their headsets. These are just a few of the exciting applications of augmented and virtual reality. And, though they might still sound like science fiction to the layperson, they're actually not. Companies like Ford, Airbus and Sotheby's are already using them. The wearable camera problem Augmented reality headsets depend on their abilities to understand the local environment, which means cameras are often essential components and they can pose a security risk. Ever since the first Google Glass prototype was released to much fanfare back in 2013 (only to fail miserably by 2015), there have been growing concerns around privacy and security. The launch of Google Glass even gave rise to the colorful term 'glasshole', which refers to those people who behaved inappropriately while using the device (such as by spying on others and taking photos without their knowledge). But edgy names aside, this has serious implications in fields such as healthcare where organizations are subject to strict compliance regulations like HIPAA to protect patient privacy. A new frontier for social engineering Aside from privacy concerns, AR and VR devices are connected like any other. They're part of a rapidly growing global network that's often dubbed the Internet of Things (IoT). And, like any connected device, they handle the transmission and storage of data, which might be misappropriated by hackers. When it comes to the immersive experiences of AR and VR, this has especially worrying potential in corporate espionage and social engineering. When technology can reach the point of convincing the brain it's somewhere where it's not, the opportunities for taking advantage of human vulnerabilities are enormous. As with any technology, the weakest link is normally the users themselves, which makes AR and VR a unique target for exploiting people. For example, attackers might inject features into VR platforms designed to mislead users into giving away personal information. There are also new implications for ransomware, in which attackers could sabotage platforms and interrupt important meetings before asking for a ransom. VR and AR experiences are only going to get more immersive and more realistic. On the one hand, this might make them more engaging and widely used. On the other, it makes them more dangerous. Fake identities, or so-called 'deepfakes' generated by machine-learning technologies, for example, allow for the manipulation of voices and videos to the extent they still look like genuine footage. If a hacker could access the motion-tracking data from a VR headset, they could potentially use it to create a digital replica. They could then superimpose this on someone else's VR experience, such as an immersive business conference, to carry out a social engineering attack. With people interacting more and more through avatars, this brings an entirely new dimension to the disturbing world of cybercrime. Data breach risks Aside from potentially providing cybercriminals with new ways to manipulate their victims, VR and AR present all the classic cybersecurity risks too. As systems that transmit and store data, they're fair game for anyone wanting to get their hands on the most valuable commodity in the world: personal data. They're also possible targets for denial of service attacks (DDos), which could have very serious implications for those depending on AR in critical situations such as surgical procedures or when operating dangerous machinery. Mitigating the risks isn't as hard as you think To address the risks associated with AR and VR, it's best to start with the core principles of information security – the very same ones that should be governing the wider IT infrastructure across the organization. Although attacks against AR and VR systems manifest themselves in different ways, the technological and administrative measures used to safeguard them are similar to other forms of connected technology. They use the same protocols as any other connected device, so all the standard rules apply – include them in your risk assessments, keep firmware up to date and encrypt any sensitive data. And never take security for granted. Most AR and VR devices don't encrypt data by default, and they may integrate with third-party apps which themselves have dubious security. On top of all the standard measures, AR and VR present some unique challenges, particularly when it comes to physical security and safety. One of the biggest problems with VR specifically is that it completely blocks off a user's visual and auditory connection to the outside world. It's always important to evaluate the physical safety and security of the user's environment first. This also applies to AR, in which it's important for users to maintain a high degree of situational awareness, particularly in more immersive environments. And, it should go without saying, but don't run around in a busy workspace wearing a VR headset! Although identity and access management are central to any information security strategy and concern any device, this is an area that's often overlooked when it comes to the adoption of AR and VR systems. You might, for example, be able to identify other people you're working with by their avatars, but there's also the potential risk of the avatar being copied and used by an unauthorized party. Fortunately, used in the right way, AR and VR can potentially improve identity and access management with multifactor authentication (MFA) – for example using eye-tracking sensors to verify your identity before you can access the rest of the system. While the challenges are undeniable, so is the potential of adopting augmented and virtual reality into the workplace.
cybersecurity and AI

Artificial intelligence is transforming businesses, and it’s changing cybercrime too

  • Cybersecurity
  • min read
    • Until recent years, artificial intelligence (AI) belonged squarely in the realms of science fiction, even though the principle itself has been around for decades. But recent advances have seen a surge in popularity, particularly in business applications. This is from several factors: there's far more data available for training AI algorithms than there ever before, more powerful computing hardware has made training deep networks easier, while cloud-based AI solutions have made the technology more accessible and affordable to businesses of all sizes. This has brought AI to the mainstream, and it's transforming business and customer experience. What's AI, and why is it good for businesses? The simplest definition of AI is a computer system that can derive new insight and actions from data, greater than it's programming. AI typically involves some form of pattern-recognition to predict outcomes and identify trends. Business leaders can draw upon these insights to make smarter decisions. AI, with the right algorithm, can help to derive insights from today's growing data sets. If you've ever tried combing through a terabyte of data (that's equivalent to about three million printed books), you'll know that manual processes just don't cut it. AI helps make sense out of all those trillions of ones and zeros, which can have a profound impact on many business processes, ranging from marketing automation to threat management in information security. AI is grouped into several categories: Machine learning Machine learning is one of the most common, and it's a leading disruptor in business. It underpins many AI-powered applications by letting people teach computers how to recognize patterns in large data sets. This technology enables smarter business decision-making, as well as helping organizations protect themselves from cyberattacks with sophisticated measures like threat modeling and intelligence. Deep learning Deep learning is a subset of machine learning. It's computationally intensive, using advanced artificial neural networks that allow computers to learn from their own mistakes and continually improve their functions, like the way the human brain works. This has profound implications for business automation, reducing human error in critical areas like cybersecurity. Narrow, general and super AI Alternatively, AI is also defined by the system's level of intelligence rather than its purpose or how it's deployed. Using this definition, we have three types of AI: narrow (ANI), general (AGI), and super (ASI). We already interact with ANI daily both in our professional and personal lives – common examples include personal assistants like Google Assistant, Amazon Alexa or Apple Siri. Some ANIs use natural language processing (NLP) to understand speech and interact with people in more meaningful and natural ways. Next up the hierarchy is AGIs, these are AIs that are approximately as capable as humans and able to solve tasks beyond what they're originally programmed to do, but it's still an emerging field. At the final level, we have ASIs, more intelligent than humans – think Terminator or The Matrix. That's still quite a long way off. Protect your business from AI-powered attacks The law of unintended consequences holds that the intervention of any complex system tends to create unanticipated and sometimes undesirable outcomes. Digital technologies have, in many ways, made our lives easier. Still, they've also opened up a raft of new opportunities for criminals, not to mention challenges regarding personal privacy with big tech firms. With much of the power in the hands of organizations, business leaders must think carefully about ethical and legal implications when adopting AI. While businesses use AI to improve customer experiences and automate a multitude of routine tasks, cybercriminals can use these technologies. We can certainly count on AI not being used exclusively for good. There's no denying that AI will transform every industry, including cybercrime. For a start, AI tools are already widely available, and many are open-source, meaning they're available for free for anyone to use and reprogram for their purposes. The malicious applications of AI are already starting to emerge as cybercriminals use the technology in ways that mimic the practices of legitimate organizations. Just as marketing departments are tapping into AI for precision targeting at scale, so are social engineering scammers. Other areas of misuse include creating and targeting misinformation campaigns and impersonating individuals through the use of deepfakes. AI-driven insights may also help to develop dangerous malware or show insights into vulnerable systems. Another immediate challenge with the spread of AI comes down to people. On top of concerns that AI will displace many low-paid, unskilled jobs is the fact that people are still holding all the cards. While maintaining control might help prevent AI from going rogue, it also means that systems inherit our less fortunate human traits, as Microsoft found out with the embarrassing launch of its AI-powered Twitter bot Tay in 2016. People are still teaching computers, and, because of that, there's an innate bias reflecting society's perceptions. Or, in the case of Tay, the bad influence of social media trolls. Indeed, earlier this years, a new study found that algorithmic bias in self-driving cars increased their chances of failing to detect dark-skinned pedestrians. Using AI for good AI is here to stay. It's the next step in advancing digital technology, and a growing number of core business processes soon won't be able to function without it. For those who believe in using data ethically, AI is a source of hope, but, like any other technology, some will try to misuse it. To keep up with the new wave of threats as hackers exploit AI, businesses need to respond using AI-enabled tools to augment human intelligence and, consequently, strengthen their cyber defenses. AI offers great potential for applications as diverse as medical diagnoses, legal case resolutions and even climate modeling. But it's ultimately up to us as business leaders to determine how we can put the technology to good use.
Cyber Defense

Hacking back: Could active cyber defense protect your business from data thieves?

  • Trends
  • min read
    • It's been said that the best defense is a good offense. Surely, the same should apply to cyber defense too? Falling victim to a cyberattack is always going to be bad news, but what if you could prevent the effects of a breach before they even manifest? Imagine being able to 'hack back' by probing an attacker's computer to destroy stolen data before it has a chance to end up on the illegal dark web marketplaces. It's something that government organizations are doing all the time. Law enforcement agencies like the FBI in the US, for example, have the authority to hunt down suspected hackers in this way. The rise of cyberwarfare Nation-states have perpetrated some of the biggest cyberattacks of all time. One of the most infamous examples is the WannaCry ransomware attack, alleged to be the work of North Korea. UK officials blamed the global NotPetya malware campaign on the Russian military, while Russia recently claimed to have thwarted cyberattacks against its critical infrastructure by the US. The list goes on. Many countries consider cyber-warfare a core part of their military strategy. Today, the digital world is the fifth theatre of war – after land, sea, air and space. It's easy to assume that nation-state attackers are only interested in going after big targets, namely government organizations of rival states. But, as attacks like WannaCry perfectly illustrate, any business or individual can be targeted, even if only via collateral damage. Amidst growing concerns of cybersecurity breaches targeting corporations, there's now growing momentum to give victims a way to fight back actively. It's the concept of 'hacking back,' part of the broader active cyber defense (ACD) methodology. But what are the implications, and why do they matter? Hacking back has cybersecurity experts worried Currently, almost all jurisdictions make it illegal for a private entity to access computers that don't belong to them, regardless of the scenario. But there have been attempts, particularly in the US congress, to make some exceptions to help protect victims of cyberattacks by allowing them to probe attacker's computers to stop attacks in progress, identify threat actors and destroy the stolen information. It's a debate that's been raging for years. Organizations, many of whom have fallen victim to cyberattacks themselves, have been clamoring to be legally allowed to engage in some form of proactive cyber self-defense. In 2017 in the US, growing pressure spawned a legislative proposal known as the Active Cyber Defense Certainty (ACDC) act. Although the bill has yet to enter into US law, it would make it legal, under certain circumstances, for organizations to go outside the boundaries of their networks to target attackers and send them packing on the highway to hell. Sounds great, doesn't it? Unfortunately, many cybersecurity experts aren't so sure. The proposed legislation has opened up a proverbial can of worms and has been hailed by many cybersecurity professionals as the worst idea ever in cybersecurity. While giving companies greater freedom to defend themselves sounds like a great idea, there's a very legitimate fear that allowing them to 'hack back' would only make the situation worse. Taking a cue from competitive video gaming – this could lead to a free-for-all cybersecurity deathmatch. The shortage of cybersecurity skills is a major concern, with around three-quarters of organizations claiming it negatively impacts their operations. It shouldn't be hard to see why that presents a severe problem when it comes to tackling sophisticated cyberattacks by hacking back. The ACDC act (the proposed legislation, not the popular Australian rock band) makes no mention of exactly what constitutes a qualified, legally sanctioned cyber defender. While a company like Google might have the necessary expertise, for smaller and less-equipped organizations, a lack of skills could lead to hacking back in an uncontrolled fashion. When that happens, the collateral damage could be catastrophic. It isn't likely, for example, that the perpetrators specifically wanted to target the UK's National Health Service with the WannaCry ransomware attack, but that didn't stop it from falling victim to their dirty deeds. Unfortunately, and especially without the right expertise, there's no real protection when things go wrong. It's notoriously easy to cause harm to networks belonging to innocent parties unintentionally. Even the US National Security Agency, who can hardly be described as newbies in cyber defense or hacking, had their EternalBlue exploit leaked by a hacker group who used it to carry out the WannaCry and NotPetya attacks. The foremost concern experts have is that legally allowing companies to hack back sets a dangerous precedent (a highway to hell, if you like) in which private companies and individuals end up being dragged into the world of cyber-warfare too. If the bill in the US, or a similar one in any other country, were to pass, it would ultimately encourage other nations to relax their anti-hacking laws. What we could end up with is a digital Wild West on a truly global scale. What should companies do instead of hacking back? While hacking back is the crux of the active cyber defense (ACD) debate, there are other things that organizations can do to defend themselves against cyberattacks proactively. It's also true that the overwhelming majority of data breaches occur due to human error, with the most common being mismanaged access rights or a failure to update software. Thus, organizations should first work on bolstering their defenses by establishing tougher administrative, physical and technological controls. At the same time, organizations, particularly large enterprises, which tend to be the target of highly sophisticated, often nation-state attacks, need real-time defense. Methods like real-time detection and mitigation are becoming increasingly important with the rise of zero-day exploits targeting critical networks. One increasingly popular form of active cyber defense is the so-called 'honeypot.' Honeypots are digital decoys placed within a network to lure attackers away from what's important. These decoys contain fake accounts and information that encourages hackers to continue. But, once the hacker infiltrates the honeypot, victims can collect information about them to help bolster the defenses of their critical systems. In other words, a honeypot defense can serve as a sandbox for testing the efficacy of existing cyber-defense systems, as well as profiling attackers. Unfortunately, honeypots are a legal grey area at best as such methods could be considered entrapment. A similar method, which is legal in many jurisdictions, is to use beacons, which do much the same thing but don't track the hacker's behavior once they've left the system. Beacons are web links where company servers can collect information about intruders, such as IP addresses, which they can then add to their blacklists as likely threats. However, in the EU, at least, this method may also constitute a breach of GDPR due to its covert nature. Another ACD strategy, and one that's legal, is address-hopping. This is especially common among financial institutions, given the highly sensitive and sought-after nature of the information they hold. Address-hopping involves changing the IP address at random while data is transmitted. It's similar to the age-old military strategy of regularly changing radio frequencies to keep ahead of enemy forces. Establishing the standards in cybersecurity Active cyber defense is an area where organizations need to tread carefully. Many forms of reconnaissance alone, such as beacons, can be construed as hack backs. Other strategies are outright illegal. But private entities still have every right to monitor and control activities in their networks and, in doing so, identify recurring attack patterns and learn more about their assailants. That's far better (from a legal, ethical and technical standpoint) than breaking into suspected assailants' networks to follow them around the web.
edge computing cybersecurity

What does the rise of edge computing mean for cybersecurity?

  • IT infrastructure
  • min read
    • Everyone in enterprise IT is talking about transitioning to the cloud. But now there's a new paradigm on the block – edge computing. And it's causing quite a stir in a world already facing a multitude of cyber-threats. So, what exactly is edge computing, and what does it mean for information security? First, a short history of business computing. Until the 1980s, businesses had barebones computers, also known as dumb terminals, connected to a mainframe, which would take care of computing workloads and data storage. Then along came personal computers, where the machine on your desk handled the computing workloads. Today, in-house servers and workstations are falling out of favor in business environments as decentralized computing takes over. Many businesses have migrated to the cloud to allow users to access centralized cloud computing services hosted by companies like Amazon and Google. In many ways, it's a lot like the mainframes of the old days, albeit on a bigger scale, thanks to the internet rendering geographical boundaries irrelevant, mostly. Today, 'dumb' terminals have made a comeback in the form of smartphones, tablets and other internet-connected devices, in which many computing workloads are handled in the cloud rather than the device itself. But at the same time, these devices are now orders of magnitude more powerful than the workstations of just a couple of decades ago. They're now capable of doing their own work. And now we have a new option. Edge computing brings those computing workloads and data storage back to local devices, much like when we first started using personal computers. But there are two fundamental differences: internet-enabled devices are more numerous and diverse than ever before and most of them are permanently connected to the internet. Edge computing represents a fusion of cloud and local computing in which the cloud is still retained for carrying and, in many cases, storing data while local internet-connected devices take care of the data processing. By 2022, some three-quarters of enterprise data will be processed outside the cloud, so it's safe to say that it's more than just a passing trend. Why are companies moving to edge computing? The most immediate advantage of edge computing is speed. With cloud computing, data is often transferred over hundreds, or even thousands, of miles between the local device and the remote data center. This means that the effective processing speed of a cloud-based app is heavily dependent on the distance instructions have to travel to control it. The delay is known as latency, measured in milliseconds (ms). A latency of 20-40ms is about the best you can expect with today's cloud-hosted applications. But the more significant issue is delays caused by problems with interference or outdated protocols when the data processing takes place remotely. Response times will always be faster with local computing, not just because the laws of physics say so, but also because there's less scope for interference and rerouting issues. Although the performance difference is barely discernible in most business applications, other computing workloads aren't ideally suited to the cloud and likely never will be because of the inherent performance and latency constraints. These include bandwidth-intensive applications like real-time 3D rendering and the synchronization of massive amounts of data with online storage. Even with a 1GB internet connection, you can end up running into bandwidth problems. And that's before we take into consideration other factors like bandwidth hogs and interference. Let's not even get started on the painfully stingy data limits of many mobile providers. Internet speeds might be increasing all the time, but so are computing demands. Many cloud software providers are working hard to reduce bandwidth consumption and problems with high latency. Having to be online all the time is another source of frustration, particularly when it comes to computing on the move where connections might be intermittent. Fortunately, things are changing. For example, progressive web apps (PWAs) often provide a basic level of offline functionality. Google's Chrome OS, one of the most cloud-centric platforms of all time, now let's Chromebook users work offline. Self-driving cars – the best example of computing on the edge Perhaps the ultimate example of edge computing is self-driving cars. Given the importance of quick reaction times in a moving vehicle, there's no scope for delays due to latency or intermittent service outages. The numerous sensors that make up the system have to feed data, in real-time, into an onboard computer for local processing. At the same time, the responsibility to keep people safe shifts towards the company that makes the software, which makes centralized management a practical necessity. That's why self-driving cars need to be hooked up to the internet to receive critical updates and continuously feed data into the cloud for developers to improve the algorithm continually. Does edge computing mean regaining or losing control over enterprise data? Having data processed locally while retaining the cloud as a way to transmit and store data potentially gives businesses more control over their customers' lives. While we may no longer have to worry about things like updates and maintenance with remotely hosted and managed systems, letting third parties decide which features our devices need places us in a precarious situation for privacy and security. The stakes are even higher for businesses, which need to ensure they meet the demands of compliance and customers' expectations to keep their data safe. While legislation like the EU's GDPR aims to return control to the user, cybercriminals won't pay too much attention to regulatory compliance. Losing control is one of the most common fears business leaders have about migrating to the cloud in the first place. The combination of cloud and edge computing introduces fresh concerns that businesses will end up surrendering control over their connected devices to third parties and potentially putting customer data at risk. Also, hackers who gain access to the device via the cloud may be able to steal the data stored on them. This is already manifesting itself in the consumer world, with platforms like Apple's Siri reportedly recording people's conversations. With IoT (internet of things) leading to a massive uptake of smart internet-connected devices, ranging from in-store beacons to remote-controlled HVAC (heating, ventilation and air conditioning) systems, the risk continues to grow. Whether your data is stored locally or on the cloud, if hackers access your credentials, the risk of a breach is equal. But additionally, edge computing expands the potential attack surface by having sensitive data stored and processed across a more extensive array of systems. It gets much harder, even to the point of becoming a practical impossibility, to protect ubiquitous computing environments at scale, simply because the footprint grows too large. A recent study by Tech Republic found that two-thirds of IT teams considered edge computing as more of a threat than an opportunity, mainly because of this dramatically increased endpoint attack surface. The stakes are high with edge devices, and even more so with consumer-grade technology. To start with, businesses must be mindful about which suppliers they choose – consumer-grade tech is often a no-go for a start, which is a challenge for smaller firms where one in four use it for their data security. They also need to stop putting total trust in basic endpoint security measures like passwords and perimeter defenses. Business leaders need to expand security capabilities to all edge devices. This includes encryption of data both at rest and in transit, changing default passwords and maintaining control through a centralized management dashboard that governs how devices interact with the computing environment. Do you need the cloud for everything? The terms edge computing and the internet of things are often used interchangeably, though they're not the same thing. An edge computing device doesn't necessarily have to be connected to the internet at all times, or even at all. Instead, it might be connected only to an internal network, which could be used in industrial or other business environments. By definition, these devices are meant to retain some or even all functionality offline. That means companies can reduce risks by disallowing direct connections between edge devices and the cloud unless they're necessary for performing critical functions. Then, the data should be protected behind multiple layers of security such as encryption and multi-factor authentication. To that end, done right, edge computing can be beneficial, rather than detrimental, to security. Article reflects the opinions of the author. Published in 2019.
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