Cyber Security

Imperial's plan for growing the UK cyber sector

Rupert Shute — Wed, 15 Jan 2025 12:14:02 +0000

Imperial's plan for growing the UK cyber sector

Rupert Shute Wed, 15/01/2025 - 12:14

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Article at a glance

Centre for Sectoral Economic Performance (CSEP) and the University of Bristol consolidated their recent project's findings into the UK Cyber Growth Action Plan.

Here is why the report matters now:

Cyber resilience is not just about security, it’s about economic growth

The study shows where firms should invest to steady domestic supply and build a more competitive sector

Boards play a critical role in embedding cyber into governance, reporting and culture

Place-based, industry-led leadership is key to scaling the UK’s cyber strengths

As UK organisations become increasingly dependent on digital infrastructure, there is a growing focus on cyber resilience as being critical to economic growth. In turn, there is also an imperative to harness economic growth to fuel cyber innovation. The UK Cyber Growth Action Plan, based on input from the country’s cyber sector as a whole, makes recommendations for how to build on both of these areas.

Early collaboration between policymakers, academics and industry has the potential to generate growth. For business leaders, cyber resilience is no longer a technical issue delegated to IT teams. It affects operational continuity, supply chains, regulatory exposure and long-term growth.

What the report means for people leading the industry and governments

For businesses, this means clearer expectations and smarter procurement, which not only reduces uncertainty but also makes it easier for cybersecurity providers to scale.

Culture is also important: boards can help by taking cyber reporting seriously as part of their broader reporting duties, and by ensuring that cybersecurity is woven through operational, legal, regulatory, compliance and other functions.

The report is divided into nine recommendations, with 24 associated suggestions, which call on stakeholders in industry, government, academia and civil society to:

Curate the UK’s cyber culture to drive growth and public participation in cyber skills and innovation.

Put leadership in the right places with industry-led national and place-based cyber growth roles.

Build on the UK’s places of cyber strength to collaborate on sensitive topics and chosen technology areas, and make time to create and anticipate cyber futures.

The researchers found that there is real willingness within the UK cyber community to improve in these ways. This is vital, as partnerships between academia, government and industry will be critical to the UK staying ahead in the cyber race. However, there remains a challenge in achieving the above objectives due to the fragmented nature of the UK cyber community, with no single representative for combined stakeholder interests.

How cross-disciplinary work shaped the project

At CSEP, we were keen to involve more perspectives from the outset. Even though this was an original and independent piece of research, we welcomed the Department for Science, Innovation and Technology (DSIT) as a key stakeholder in order to shape the work.

After working with DSIT colleagues to define the research question and set up the key relationships, the project was then led by Professor of Practice in cyber security Dr Simon Shiu at the University of Bristol. This is a key feature of CSEP’s cross-disciplinary work, outlining projects that we then put out to other universities and partners, to harness a diverse range of expertise and contribute to a stronger sector that can help to drive economic growth.

What’s next for the research in the cyber sector

The challenge now is execution. The next phase of our work focuses on turning strategy into action, which involves testing what works in different regions, supporting local leadership, and sharing best practice across the UK. For businesses, this is an opportunity to shape the future of the cyber sector.

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Cyber strategy: bringing policy and academia together to boost the UK cyber sector

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AI and business: 5 big thoughts for leaders

Evie Burrows-Taylor — Thu, 04 May 2023 15:17:24 +0000

AI and business: 5 big thoughts for leaders

Evie Burrows-Taylor Thu, 04/05/2023 - 15:17

9 May 2023

What are some of the key considerations for business leaders when it comes to AI? Our experts tell you what you need to know.

Artificial intelligence (AI) is transforming the business world – but navigating AI-enabled applications and technology, and successfully incorporating them into your business practices can be tricky. Here, we point to five big ideas around AI opportunities, responsibilities and trends from Imperial Business School's experts, to help business leaders thrive during the digital transformation.

1. The real AI revolution is yet to come

The competitive advantage that big business has gained from access to automation and big data can only last so long. Professor Chris Tucci explains that, soon, every company will have access to those same technologies and the real revolution in business will begin.

2. We have the ability to shape our future

Professor of Practice David Shrier gives us the inside scoop on the “future business" and how to avoid the negative impacts of AI.

3. Artificial intelligence can quickly train itself to violate regulations

When we hand decision-making to a machine, how do we make sure that it reflects the values of our societies? How do we ensure that AI follows the law?

4. You need to understand where AI works well – and where it doesn’t

Professor Marcin Kacperczyk explains that, while financial services firms expect significant growth in their use of machine learning, they are already having problems implementing it at scale.

5. AI has a tendency to amplify pre-existing inequities

Through this award-winning research, Professor Tarun Ramadorai explains that machine learning does reflect existing biases – even if the cause is not inherent in the technology itself.

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2022 in business: 10 of the top IB Knowledge articles of the year

Evie Burrows-Taylor — Thu, 22 Dec 2022 09:33:12 +0000

2022 in business: 10 of the top IB Knowledge articles of the year

Evie Burrows-Taylor Thu, 22/12/2022 - 09:33

22 December 2022

We take a look at some of this year's most-read IB Knowledge articles

Over the past year, Imperial Business School academics have written about subjects as varied as climate change, AI and digital transformation, healthcare, and the challenges of scaling up a business. From practical tips for entrepreneurs and business owners to cutting-edge research, they've covered a lot of ground.

Here's a selection of some of our most popular articles of 2022.

1. What African tech startups can teach us about digital innovation

During the last decade, the African continent has pushed the frontiers of digital innovation. As the African tech startup landscape undergoes radical change, we take a look at what is helping certain businesses thrive in this new environment.

2. 9 digital transformation mistakes all organisations make – and how to avoid them

When it comes to digital transformation, we tend to focus on the success stories but 87 per cent of digital transformations fail to meet expectations. In this article, three experts reveal how you can avoid some of the most common digital transformation mistakes.

3. Could engaging in corporate social impact work damage your career?

These days many people are looking for more from their professional roles than the duties listed in their job description, and corporations are increasingly offering employees the chance to engage in social impact work. But is doing social good also good for your career?

4. Ukraine: what armed conflict means for climate change

The question of the impact of climate change on geopolitical risk has been circulating in research circles for a while. But, until now, few have turned the question around and asked: “How do armed conflicts affect the fight against climate change?”

5. 3 AI trends to look out for in 2022

Artificial intelligence looks set to transform every sector of the global economy. Here, we look at some of the most interesting AI trends for business, including creative AI, the metaverse and cybersecurity.

6. What is climate risk?

The financial implications of climate risk are increasingly discussed. In this article, an Imperial Business School researcher explains how he is helping investors, lenders and regulators address the financial implications of climate change.

7. 6 ways to reduce the environmental impact of your business

It is now widely accepted that climate change exists – and that there are ways we can help to stop the worst of its effects. Here, an Imperial Business School researcher reveals six ways you and your business can help in the fight against climate change.

8. The pandemic divide: how COVID-19 has increased health inequalities

The pandemic has exacerbated pre-existing inequalities, hitting people on lower incomes and those from ethnic minority backgrounds hardest. But why did these groups experience the most disruption to hospital care from cancelled and delayed hospital appointments?

9. Phasing out coal would save the world $78 trillion

The most common concern voiced with phasing out coal is that it would be too costly. But Imperial Business School researchers have found the move would save 1.2 per cent of current world GDP every year until the end of the century.

10. How to scale up in an emerging market

Launching a business is certainly a challenge, but sustaining it and scaling up is something else entirely. Luckily, a new form of entrepreneurial support is here to help.

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3 AI trends to look out for in 2022

Christopher Tucci — Tue, 08 Mar 2022 14:41:55 +0000

3 AI trends to look out for in 2022

Christopher Tucci Tue, 08/03/2022 - 14:41

14 March 2022

Artificial intelligence looks set to transform every sector of the global economy. Here we look at some of the most interesting AI trends for business, including creative AI, the metaverse and cybersecurity

The robots are coming! That’s the refrain common to many predictions about the future of artificial intelligence (AI). The truth, of course, is that for most technology-led businesses, the robots are already here.

Today, companies of all shapes and sizes are reaping the benefits of Big Data and analytics, pattern recognition and automation. For example, businesses frequently use AI algorithms to sift and analyse their Salesforce data in real-time, as it is collected, to extract insights that will improve efficiency. Automation, such as deploying driverless vehicles in factories or using software to check and approve employees’ expense claims, is another increasingly common way to make routine tasks quicker and easier to perform.

The exciting news is that this is just the first phase – the baby steps, if you will – of what AI can and will do for business. Big Data and automation have created huge opportunities for companies to save money and time. But that competitive advantage can only last for so long. Soon every company will have access to the same AI technologies and so their processes will become equally efficient. AI started by helping companies to improve what they already do. Now it’s opening the door to the creation of entirely new products, services and business models – even for "traditional" companies operating outside the technology sector. This is why I think the real AI revolution is yet to come.

1. Creative AI

It was long held that machines could never contribute to the creative side of business. Yet AI-powered applications such as Phrasee are already helping companies to optimise their marketing slogans.

I recently read about how IDEO, a company famous for ideation and innovation, is augmenting creative brainstorming sessions with AI-generated suggestions. In one session, the team was trying to think of new ways to help customers grow their savings. The AI algorithm came up with the following idea: why don’t we get a monster to leap out of the cash machine every time someone tried to withdraw their money?

Of course, this is a completely crazy idea but that’s what’s so exciting about it. A monster leaping out of a cash machine is exactly the kind of off-the-wall, lateral thinking you need in a good brainstorm. And once a machine can generate an idea that is genuinely original, it can play a useful role in your company’s creative processes. It might even inspire your team to head in a completely new direction. Using AI for ideation is still in its infancy but it is a fascinating area with huge potential.

2. The metaverse

Another hot topic in the world of AI is the metaverse, which sounds like an entirely new plane of existence, but is better thought of as the latest form of virtual reality. The challenge for businesses is that, to date, the metaverse has only really come to the fore in video gaming, where it’s used to create rich and interactive multiplayer experiences.

The next step is for the metaverse’s 360-degree immersive environment to be used for applications with tangible business benefits, such as 3D product demonstrations and employee training. Here at Imperial Business School, we have even tested the idea of using holograms of our lecturers to host talks and classes remotely.

In a similar way, the metaverse has the potential to revolutionise education and training by allowing teachers and ��ý logging on from all parts of the world to meet and interact within a virtual space. There’s no practical reason a company’s entire staff training programme can’t be taught and hosted from the cloud.

3. Cybersecurity

Of more immediate concern for companies is what the growth of AI and machine learning will mean for cybersecurity. Some commentators have suggested AI can “solve” the cybersecurity problem once and for all, by giving businesses the tools to make their IT networks un-hackable. I think this is overly optimistic for the simple reason that both sides of the cybersecurity struggle – corporate “SecOps” teams and the cybercriminals trying to breach their defences – ultimately have access to the same technology. AI is certainly accelerating advances in cybersecurity but it’s an arms race that looks set to continue – and escalate – indefinitely.

Cybersecurity is just one of a range of issues that highlight an important truth about AI for business: it is creating challenges as well as opportunities. One example is the way in which AI algorithms can unintentionally inherit the biases of the humans who created them. Another is “AI explainability”, which is the question of what happens when an algorithm becomes so complex that no one – not even its programmers – can fully explain how or why it has reached a particular output or decision.

An even bigger challenge for companies that are relatively new to the possibilities of AI is the conundrum of when and how to get involved in the first place. This can lead to a kind of paralysis, in which companies endlessly wait to see how a new technology will develop. Their caution is understandable. However, when you’re dealing with a rapidly evolving new technology, waiting until tomorrow is nearly always a mistake. That’s why it is vital for every business to start grappling with the challenges and opportunities of AI today – otherwise they will simply never catch up.

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Profile: Professor Christopher Tucci on how digital transformation has "paralysed" businesses

Christopher Tucci tells us why prioritising digital transformation may be a matter of survival for all businesses, and discusses his journey from the early years of the internet to academia.

25 November 2020

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Why big data can be dangerous

Gilles Chemla — Mon, 18 Jan 2021 09:45:33 +0000

Why big data can be dangerous

Gilles Chemla Mon, 18/01/2021 - 09:45

3 May 2018

Data is the revolutionary tool of our age, but it is not the objective measure many think: it is inextricably tied up in bias, prejudice and subjectivity

We live in a data-fixated age – the Age of Big Data, as some have called it – one in which the consensus seems to be that data can solve all of our problems. It is true we have access to an unprecedented scale and depth of information; this is in fact one of the key challenges we face.

But more on that later. Let’s start by considering the very nature of the data at our disposal. We collect it through scientific means, using experiments that yield objective findings. The assumption is that, if you design the experiment correctly, ask the right questions, or measure the right behaviour, you will gain insights into human behaviour: how they react to a product or even a medicine, how they behave in certain situations, or what they value or believe.

This, however, is intellectually lazy because bias can be extremely difficult to circumvent in the process of collecting, measuring or reading the data from experiments. You’ll be familiar with the placebo effect in medicine: in business terms, we might consider the Hawthorne effect, which highlights the difficulty of conducting large-scale workplace experiments due to the changed behaviours of those aware an experiment is taking place.

But this effect was named after a series of experiments conducted in the 1930s – surely, you might say, with the scope of big data, we needn’t worry about such clouding of the dataset. Bias, however, applies even with an infinite quantity of data. Data points are not inert, or exogenous – they are not chosen by nature, but always by the analyst.

Tech companies are run by intelligent people – would it be a huge surprise if we saw some clever circumvention of the new regulations?

People then assume the problem is not with the experiment itself, but with the sample. If only they could get the perfect sample, then the experiment would yield results. It is not so simple, though – the bias can equally well exist in the methodology; methodologies that do not take into consideration that people have expectations and emotions.

To give an example of how results can be affected, take this example from Princeton. When Americans were asked if they felt the government spent too little on “welfare”, only 25 per cent said they did. Swap welfare for “assistance to the poor”, however, and the figure shot up to 65 per cent.

This is not to say is impossible to gain truly objective insights, only that understanding why people act as they do requires rigorous analysis of the entire pattern of behaviour.

Humans have intuition: robots don’t

For managers and decision makers in organisations of any size, it is deeply problematic to set one’s stall on decisions made purely on the basis of so-called objective data. What’s the solution, then? Well, actually it’s quite simple: alongside data where appropriate, or sometimes even in lieu of it, the best bet can be to place faith in the intuition gained from your experience (or from someone who has it if you do not).

Think of the scope of data held by the likes of Google or Facebook, which now includes your actual face

Steve Jobs, problematic a figure as he might have been, was totally averse to employing market research in product innovation or development. This might be read as an awareness of the fallibility of data, which could only be coloured by bias and limited by the imaginative scope of the potential customer base. The latter could scarcely be expected to know they wanted something they were as yet unaware existed.

The same challenge applies also to AI and machine learning. The latter is limited to past data, so what can be done by AI without humans is, by definition, limited. It is only from humans that new ways of thinking, which can give rise to truly original ideas, can arise.

Artificial intelligence might then be viewed as a potential impediment to true innovation. It also has huge ramifications for employment at all levels – a problem that is often acknowledged, though scarcely, as yet, acted upon. Which leads to the second half of our case…

Data as the abuse of power

Leaving aside for one moment the difficulty of gaining access to completely objective and reliable data, let’s consider another related though somewhat different phenomenon. It is a key danger associated with the forensic levels of data gathering taking place in the modern world: privacy.

The potential customer base could scarcely be expected to know they wanted something they were as yet unaware existed

Perhaps you’ll already be familiar with the story of American retail store Target’s accidental revelation to a teenage girl’s father that his daughter was pregnant. This story broke in 2012 – the relative dark ages in terms of data science.

Today, we see companies promising to trace your genealogy or assess your susceptibility to certain medical conditions using nothing more than a tiny sample of your DNA. Amazing, no doubt, but who else might be interested in such data? What about insurance companies, for whom such deep genetic information would be of no small interest?

That’s something into which one must quite proactively opt, but what about the gradual spread of the Internet of Things? Devices wired to respond to your needs in real time – and to build up a repository of data on your behaviour to feedback to the manufacturers. Studies show said manufacturers are somewhat coy about what exactly is being done with this information.

Without wanting to get carried away, when you have devices that can monitor your vital signs, collecting data that can give indications of your mental health, it’s important to ensure that information doesn’t fall into unscrupulous hands. Think of the scope of data held by the likes of Google or Facebook (we hardly need reminding), which now includes your actual face.

Data points are not inert, or exogenous – they are not chosen by nature, but always by the analyst

And let’s not discount the huge amount of data lost in breaches. Perhaps GDPR will ameliorate the situation, but tech companies are run by intelligent people – would it be a huge surprise if we saw some clever circumvention of the new regulations? It’s not just business either: certain governments and public bodies also hold what might be considered a worrying amount of information. China’s “scoring” of citizens or the unregulated, error-prone “virtual line-up” held by various US police forces (in which 50 per cent of adults unwittingly feature) are two such examples.

It would be wrong to say we should shy away from data; when collected and analysed scientifically it can yield insights and connections that would have previously been impossible to glean. It’s also wrong to get carried away and fail to take a considered approach to the collection and usage of data: there are ramifications for the economy, for privacy, and even potentially for democracy. We also need to think carefully about the way we collect, measure, and read data – because as much as we assume data is immutable, where it comes to measuring human behaviour in particular, it is intrinsically subject to bias, prejudice and subjectivity.

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5 things executives need to know about AI and machine learning

Financial services firms expect significant growth in their use of machine learning, but are already having problems implementing it at scale. Professor Marcin Kacperczyk untangles the knot for business leaders

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Cybersecurity: cornerstone of the digital economy

Catherine Mulligan — Thu, 14 Jan 2021 14:37:05 +0000

Cybersecurity: cornerstone of the digital economy

Catherine Mulligan Thu, 14/01/2021 - 14:37

27 February 2017

Dr Catherine Mulligan, Research Fellow in the Innovation and Entrepreneurship department with a joint appointment to the Department of Computing, highlights effective and manageable cybersecurity in an age of cyber attacks.

As the world reaches new levels of digitalisation, the need for effective and manageable cybersecurity becomes evermore apparent. Cyber attacks have been listed as one of the top four threats to UK National Security since 2010, and given recent events – such as a series of company hackings and possible interference with the US election – such activity seems set to intensify. While digital disruption has provided great opportunity for new business models to emerge, it has simultaneously created new means by which companies, cities and nations can be attacked.

Importantly, it is not only malicious threats – such as criminals, ‘hacktivists’ and hostile nation states – that create issues from a security perspective. Ensuring that code operates as intended is a critical aspect of ensuring systems’ safety. Improperly written code can lead to bugs in smart city systems with the potential to cause industrial accidents – or even loss of human life.

We urgently need means by which to ensure citizens, companies and governments can trust the data and information they receive. People have an increasing understanding of the privacy and security implications associated with digital technologies and without effective security assurances, consumers may reject or reduce their use of certain products and services.

Cybersecurity is a cornerstone of the digital economy. Without it, some of the most basic foundations of our economic system will be brought into question – like the ability to trust the data and information that our banks and companies give us, and the reliability of our news sources.

Moreover, as technologies such as sensors, mobile, cloud and big data become more embedded within industries, a reliable approach to cybersecurity will be required at all levels of a corporation. Business leaders need to understand not just the potential of digital technologies, but also how to effectively protect both their own and their customers’ data. This will require a shift in strategic thinking for many.

Traditionally, most companies approach cybersecurity with the aim of preventing attacks. In today’s sensor and data-intensive market places, we need to take a deeper inspection of how to track, trace and secure data, both within corporate boundaries and when externally transmitting between companies and to customers. This is not only to protect the reputations of the companies in question, but also to effectively work within an emerging risk environment – the legal liabilities of data loss will become increasingly complex as data sharing, big data, artificial intelligence and machine learning begin to form the basis for our economy.

This will not only require stakeholders to become more involved across a business – and sometimes even across an industry – but also the education of employees on how data can be secured and protected in these new digital environments. In addition, all business leaders need a sound understanding of cybersecurity engineering basics, and the impact of potential breaches to their business and the surrounding industrial ecosystem.

We are therefore at a critical juncture in our use of technology: it can be used to create further digital disruption and new business models, or it can become so risk-filled that we cannot trust it. Within my research team at the Centre for Cryptocurrency Research and Engineering, we have been actively investigating how incentives can be used to create more effective cybersecurity insurance markets, and how blockchain (distributed ledgers) can be used as an early warning system against hacking of critical infrastructure such as energy networks and smart city installations.

By developing digital disruption frameworks and looking at how to deliver robust cybersecurity recommendations to companies, we are hopefully going some way to reduce the inherent risk of an increasingly digitalised society.

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FAKE NEWS: Don't fall prey to political misinformation this presidential election

Imperial Business School researchers have identified key characteristics by which to spot fake news and the accounts that share it

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Coronavirus, or how finance learned to stop worrying and love the cloud

Nelson Phillips — Wed, 15 Apr 2020 15:41:00 +0000

Coronavirus, or how finance learned to stop worrying and love the cloud

Nelson Phillips Wed, 15/04/2020 - 15:41

16 April 2020

Never waste a good crisis: the strategic challenges of rapid cloud adoption in financial services

One dramatic impact of the coronavirus (COVID-19) pandemic has been the rapid acceleration in the digital transformation of organisations as much of the workforce has been suddenly forced to “WFH”. The ability of organisations to close physical offices and continue operating rests largely on various kinds of cloud services providing distributed, on-demand availability of data storage and computing. While the move to the cloud was already well underway prior to the pandemic, what Gartner has called the growing “cloud-first” preference of organisations has become a “cloud-only” necessity. The traditional drivers of cloud adoption – reduced cost, rapid ability to scale and global reach – have become secondary to the new necessity of virtual organising.

One industry where cloud adoption was already well underway prior to the pandemic was financial services. Experience has shown firms moving to the cloud reap significant benefits, but also face important challenges, and the new imperative to move quickly has thrown the latter into sharp relief. In order to better understand these benefits and challenges, we conducted several interviews with senior leaders in financial services about their experiences with moving to the cloud.

The future is cloud

First, the good news. There was a general consensus that the move to cloud is overwhelmingly positive. Again and again we heard that “the future is cloud” due to increased productivity, innovation and scalability – and, at least as importantly, because moving to the cloud frees up time and energy for more strategic issues. “IT is not our core competence,” as we heard from one manager at a large global consumer brand. Data storage and computing power were increasingly seen as utilities best left to firms who specialise in them.

At the same time, our interviewees highlighted a few strategic challenges.

1. Is the cloud secure?

There was a general concern about security and cloud governance. Moving things to the cloud means a range of new risks as data moves out of the firm – but there was also a belief that cloud can be as secure, if not more secure, than traditional on-site storage if done properly.

But this requires deep inhouse expertise in managing cloud governance and raises a larger issue: the ability of a firm to rapidly move to the cloud is often limited by the competencies and outlook of the IT team. In these cases, a safe and effective move to cloud requires the renewal and reorganisation of technical staff. For example, during their implementation of cloud, FINRA trained more than 600 cloud architects and technologists, a move that they identified as key to their successful cloud implementation.

2. Too few cloud service providers

There was a widespread recognition of the challenge of the concentration of cloud services. The reality is there are only a handful of firms that can provide cloud services at scale. Where critical IT knowledge used to be about running data centres and networks, with the move to the cloud the most important expertise is knowing how to effectively manage cloud providers.

This also means the solution will often not be a cloud, but rather several clouds used for different purposes. For example, J.P.Morgan uses a four-cloud strategy supported by Amazon, Google and Microsoft, in addition to running a private cloud. However, building and managing this sort of solution requires deep competencies in the strategic deployment and governance of cloud systems.

3. How to move data to the cloud

While the cloud can make data “smarter”, this requires more than a simple “lift and shift” of data. Where bringing together distributed and unconnected data for analytics was previously expensive and time consuming, data properly stored in a “data lake” can reduce cost and drive innovation. Moving data to the cloud makes data more easily available for dashboards, visualisations, big data processing, real-time analytics and machine learning. But creating a data lake requires time, effort and knowhow to properly manage and restructure the data during the move.

4. Rethinking business applications

Moving to the cloud is also an opportunity to rethink legacy applications that were developed over time and in a very different technological context. It is important to decompose applications into discrete modules to take full advantage of the cloud beyond simple server/cloud arbitrage. By decomposing, it is possible to scale up and down independent functions of the application and add new features to each component. But this requires careful attention and a willingness to forgo the temptation to simply move current applications to the cloud in the cheapest manner possible.

In summary, cloud services are here to stay in financial services and the current situation will only accelerate the move. But firms need to be strategic in their adoption and there are important questions that need to be addressed as firms move more and more activity to the cloud. While some of the problems are technical, there are significant strategic and organisational challenges that need to be dealt with in the C-suite to ensure cloud adoption is successful and that firms derive maximum value from the move.

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Imperial's plan for growing the UK cyber sector

Imperial's plan for growing the UK cyber sector

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Who decided what ambition looks like?

Article at a glance

Centre for Sectoral Economic Performance (CSEP) and the University of Bristol consolidated their recent project's findings into the UK Cyber Growth Action Plan.

Here is why the report matters now:

Cyber resilience is not just about security, it’s about economic growth

The study shows where firms should invest to steady domestic supply and build a more competitive sector

Boards play a critical role in embedding cyber into governance, reporting and culture

Place-based, industry-led leadership is key to scaling the UK’s cyber strengths

What the report means for people leading the industry and governments

How cross-disciplinary work shaped the project

What’s next for the research in the cyber sector

Read the full UK Cyber Growth Action Plan

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Cyber strategy: bringing policy and academia together to boost the UK cyber sector

AI and business: 5 big thoughts for leaders

What are some of the key considerations for business leaders when it comes to AI? Our experts tell you what you need to know.

1. The real AI revolution is yet to come

2. We have the ability to shape our future

3. Artificial intelligence can quickly train itself to violate regulations

4. You need to understand where AI works well – and where it doesn’t

5. AI has a tendency to amplify pre-existing inequities

Monthly newsletter

2022 in business: 10 of the top IB Knowledge articles of the year

We take a look at some of this year's most-read IB Knowledge articles

1. What African tech startups can teach us about digital innovation

2. 9 digital transformation mistakes all organisations make – and how to avoid them

3. Could engaging in corporate social impact work damage your career?

4. Ukraine: what armed conflict means for climate change

5. 3 AI trends to look out for in 2022

6. What is climate risk?

7. 6 ways to reduce the environmental impact of your business

8. The pandemic divide: how COVID-19 has increased health inequalities

9. Phasing out coal would save the world $78 trillion

10. How to scale up in an emerging market

3 AI trends to look out for in 2022

Artificial intelligence looks set to transform every sector of the global economy. Here we look at some of the most interesting AI trends for business, including creative AI, the metaverse and cybersecurity

1. Creative AI

2. The metaverse

3. Cybersecurity

Monthly newsletter

Profile: Professor Christopher Tucci on how digital transformation has "paralysed" businesses

Why big data can be dangerous

Humans have intuition: robots don’t

Data as the abuse of power

Monthly newsletter

5 things executives need to know about AI and machine learning

Cybersecurity: cornerstone of the digital economy

Monthly newsletter

FAKE NEWS: Don't fall prey to political misinformation this presidential election

Coronavirus, or how finance learned to stop worrying and love the cloud

Never waste a good crisis: the strategic challenges of rapid cloud adoption in financial services

The future is cloud

1. Is the cloud secure?

2. Too few cloud service providers

3. How to move data to the cloud

4. Rethinking business applications

3. Cybersecurity