A team of intrepid modders at Teclab have created an RTX 4090 Super, or the equivalent, concocting a kind of ‘frankenGPU’ which is a considerable chunk faster than the stock Lovelace flagship graphics card.

You might recall there were rumors flying around for a long while about an RTX 4090 Ti (which was assumed to be the name back then, before the Super variants for Lovelace emerged), so it’s interesting to see this finally happen – well, kind of.

The ‘RTX 4090 Super’ in question is 13% faster than the stock RTX 4090 as Wccftech reports, but it’s actually an AD102 GPU (the chip in the RTX 4090) housed in the circuit board of an RTX 3090 Ti, with video RAM taken from an RTX 4080 Super.

Specifically, the board is from a Galax RTX 3090 Ti HOF OC model, a high-end board with premium components built for sturdy overclocking, and it has the AD102 chip in place alongside GDDR6X VRAM from the RTX 4080 Super running at 24Gbps.

With that VRAM subsequently overclocked to nearly 26Gbps, and a bunch of other tuning applied, in the Unigine Superposition benchmark at 8K resolution, the supercharged RTX 4090 was 13% faster compared to the normal RTX 4090.

However, with the GPU also overclocked to 3GHz, that lead was extended to 16%, a more than healthy difference for a ‘Super’ take on a graphics card.

---

Analysis: Speed demons

This is a nifty experiment from Teclab, and it shows what we could have had if Nvidia had given the Super treatment to the highest-end Lovelace GPU instead of stopping at the RTX 4080 Super. Of course, for this generation, the RTX 4090 was plenty powerful enough anyway – and arguably for the next-gen, too, in many respects, although a much more powerful flagship is still inbound with Blackwell graphics cards.

If the rumors are right, we’ll see the RTX 5090 later in 2024 (and likely the RTX 5080 too, and that may even arrive slightly ahead of the next-gen flagship). And what’s interesting with this mod is we can see the effects of faster video RAM quite clearly illustrated, and Blackwell will go further in this regard, loading up with cutting-edge GDDR7 memory. This will hit 28Gbps (or indeed even faster) and offers a whole raft of performance advancements as already teased to a considerable extent.

Expectations for the RTX 5090 to be a big leap from the RTX 4090 are still riding high, and much faster VRAM will be an important part of the frame rate boosts that Nvidia’s next-gen flagship GPU will doubtless deliver.

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So the rumors were true – DJI has revealed that it's officially getting into e-bikes by supplying what it calls a "ground-breaking e-bike drive system" for a new electric mountain bike brand called Amflow.

The maker of the world's best drones says that moving into e-bikes is a "natural move" because of the battery and motor tech in its drones and gimbals. DJI's powerful Avinox Drive System will debut in the new Amflow PL, an e-MTB (electric mountain bike) that will be available later this year.

So, what's special about this new Avinox system? It packs in tech typically found on drones and smartphones. For starters, the frame has a 2-inch color OLED touchscreen that delivers real-time riding data, the bike's estimated range, and setup options.

You can also connect to the bike via the Avinox app, which lets you set up an anti-theft mode and check its real-time location. Here, you can also tweak settings like power, torque, and cadence to suit your riding style. Naturally, the bike can give your phone some emergency juice, too.

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(Image credit: DJI)

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(Image credit: DJI)

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(Image credit: DJI)

But there is also some nifty software on board to help with the riding experience. DJI says its Smart-Assist Algorithm taps into various sensors to automatically adjust the amount of electric riding assistance it provides, based on your current riding resistance. 

There are four riding modes – Auto, Eco, Trail, and Turbo – plus a Boost mode for an emergency boost of power. Like a drone or smartphone, the two battery options (600Wh or 800Wh) have the latest fast-charging tech, which means the 800Wh battery can apparently be charged from flat to 75% in about 90 minutes.

The Amflow PL itself is also shaping up to be a fairly high-end electric mountain bike, although we don't yet have any idea of pricing. The carbon fibre bike weighs 19.2kg – that's impressive if not quite as light as the Specialized Levo SL Expert Carbon (our current top pick for the title of best e-MTB).

(Image credit: Amflow)

Still, the Amsive PL's power-to-size ratio is right up there with the best. It serves up 105Nm of torque with a peak power output of 850W, a pretty monstrous combo that will help riders carve up the toughest trails. The frame is also compatible with both 27.5-inch and 29-inch rear wheels.

Amflow and DJI say the three versions of the bike – the PL Carbon (800Wh), PL Carbon Pro (600Wh), and PL Carbon Pro (800Wh) – will be available by Q4 2024 from dealers in Germany, the UK, and Australia. There's no news yet on US availability or pricing, but we've asked DJI and will update this story if we get confirmation.

What's the deal with DJI and Amflow?

(Image credit: Amflow)

We asked DJI if it owns the new Amflow electric mountain bike brand – a spokesperson told us that Amflow is "incubated by DJI" and the team is "a combination of ex-DJI tech experts and bike industry experts joining together."

While DJI is helping Amflow get off the ground, the drone giant says that as it grows, the brand "will become increasingly more independent." DJI isn't new to supplying its tech to new startups – its drone tech is inside the Ryze Tello, one of the best beginner drones around, while the new Anzu Robotics brand also licenses DJI drone tech. 

This could become an increasingly common approach for DJI, as the Chinese brand faces the very real threat of a US ban – a move that DJI has branded "a dangerous precedent for allowing baseless allegations and xenophobic fears."

Still, that US bill is called the Countering CCP Drones Act, so it's unclear if it'd impact Amflow selling an electric mountain bike containing DJI tech in the US. The move into electric mountain bikes is a refreshing one considering DJI's woes with drones, and one that makes a lot of sense considering its impressive motor and battery tech.

DJI is no stranger to leaping into new tech spaces. It has dominated camera gimbals, launched the impressive DJI Mic 2, and taken on GoPro in the battle to make the best action cameras. The combination of its new Avinox system with Amflow's debut bike looks exciting for riders and is one we'll be very keen to take for a spin when it lands later this year.

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Despite the healthy cost savings promised by sustainable IT initiatives, a new report by Gartner has revealed green tech options are still going underutilized.

The survey of 200 executive leaders from North America, Europe and Asia Pacific found fewer than one in three organizations have adopted sustainable IT.

Transparency around emissions performance data from vendors is instrumental to adoption, but the study’s respondents claim that a lack of information is a major challenge.

Businesses are struggling to adopt sustainable tech

Around two in three (64%) say they do not receive the emissions performance data they need from vendors. Kristin Moyer, Distinguished VP Analyst at Gartner, commented: “Because of these limitations, some executive leaders struggle to prioritize sustainable IT initiatives or know where to start.”

Gartner noted significant progress had been made in data center (86%) and digital workplace (79%) initiatives, where executive leaders have accomplished the most sustainable IT initiatives.

Uninterruptible power supply (UPS) rightsizing and advanced cooling techniques like immersion, passive, and direct-to-chip cooling can significantly reduce greenhouse gas emissions in data centers, however they have low adoption rates.

Closer to daily operations, only one in five (22%) are incorporating circularity by purchasing refurbished assets in the workplace in order to reduce both emissions and e-waste. Many organizations continue to refresh their devices based on a fixed life span, rather than using analytics to optimize replacement timing.

Looking ahead, Gartner anticipates the adoption of PC-as-a-service to grow to 50% of PC procurement by 2027, up from 20% last year. It says that the need for more sustainable and cost-effective IT practices will be key to driving this trend.

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The value of a mobile application has been clear to businesses for many years, acting as both a brand asset and a way of reaching a larger audience of potential customers.

Native content hosted on a user’s device can result in a much faster browsing experience, even compared to a mobile-optimized web page. More advanced digital brands benefit from this, and increasingly, as we’ve seen customer servicing move away from call centers and towards self-serve content and chat communication, apps have also been seen as a great way of reducing costs while giving customers timely and relevant support.

Over the last couple of years, finally the role of the app is being leveraged by a wider range of brands as a way of maintaining personalized and relevant communication with customers. This is especially significant as websites suffer from the effects of a crumbling cookie - apps that require a sign-in as part of the functionality have a significantly more reliable identifier and, with the appropriate user consent, these apps enable personalized content based on previous activity and preferences.

So, how should brands that have traditionally relied on customer acquisition via a company website start the process of adopting an app-focused or cross-screen marketing strategy. The first step is to develop a robust data strategy.

Understanding the challenges

As any mobile marketer would agree, the mobile app measurement landscape is vastly different to that of the web. Concepts such as mobile measurement partners (MMPs) and the SKAd network are unique to app measurement, and the value proposition is often different to web analytics.

And there are technical considerations around activation on mobile devises, which we can define as the infrastructure to reach a target audience with the right message at the right time. There are many activation scenarios for a brand to enable, from website browsing on a mobile device to push notifications, and deep linking through an email.

With these core activation techniques comes the question of how best they are achieved from a technical point of view. Using a third-party push notification or deep-link provider can speed up implementation of solutions but introduces new services and dependencies for the user’s device. With responsiveness and speed being such a key benefit of an app-focused approach, there is a complex balancing act between ease of development and user experience.

Finally, IT infrastructure for a mobile app differs from that of a website. Implementing a best-in-class data strategy for apps requires a fundamental appreciation of the way apps are built, how data is collected and sent from a mobile device, and how to test and analyze data quality once the infrastructure is built.

Building a robust app strategy

Every app is unique, so there is no one-size-fits-all solution when considering app strategies. However, to form a strong foundation, these four recommended steps will help the process.

1. Define the use cases for your data

By putting yourself in the shoes of prospective customers, you can identify the most appropriate methods to orchestrate and measure customer interactions with your app. Defining this journey and prioritizing by commercial value avoids over-investment of time and resources in complex use cases which achieve fewer valuable results.

2. Define your data architecture 

With a clear understanding of the customer journey and commercial priorities, the next step is to understand what platforms and services are required in the app’s codebase to facilitate the journey and measure user experience. If data from the same interaction is required for multiple data endpoints, consider platforms and services that aid this scale and decrease the requirement from a development perspective. If your audience targeting use case benefits from the use of external databases outside of app interactions, consider how best to integrate this database, and how frequent and fresh the data needs to be.

3. Focus on data collection best practices 

With a well-defined architecture, put together the specific interactions to measure use cases, and the criteria which determines activation of them. Bringing together all these requirements, mapping against behavioral triggers, and understanding the similarities and differences between data endpoints enables data teams to build a comprehensive tracking plan. Once this is briefed into development teams, a focus on UAT testing and quality assurance maximizes the chances of success once changes are pushed to production. Finally, once tracking has been launched, comprehensive analysis on the data helps to iron out final issues.

4. Test and scale 

With accurate and reliable data foundations, mobile-specialist data analysts can help to drive continued value through the insights this provides. Customer journey analysis helps teams to produce data driven hypotheses. These can be turned into a prioritised set of activation tests. Test conclusions can then be used to scale results and generate new hypotheses.

What next?

With a solid plan in place for a mobile app data strategy, the journey to scaling app capabilities can begin. In today’s industry, where highly specialized app development resources are in high demand, and product roadmaps are becoming ever more ambitious, this journey can take months and even years to execute to completion. For businesses on this journey, consider the following steps when implementing the next steps:

1. Planning and leadership 

Prioritising data strategy within a product roadmap, and clearly articulating the benefits of the mobile app, will set any programme up for success. Data plays a key role in the measurement and improvement of customer experience, which ultimately dictates the success of an app program.

2. Invest in the right expertise 

Digital transformation can be a challenging process, especially if it requires new skill sets and expertise. Analysts and data architects well versed in web concepts will still need a comprehensive and well-structured training program to deliver on data strategy that caters to the needs of a mobile app.

3. Be pragmatic 

Bear in mind that the ‘shiny new platform’ is not always the ‘right solution’. Before launching into a complex new developmental workstream to achieve a sophisticated use case, consider if there are quicker wins using an existing architecture.

We list the best small business apps.

This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

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While we wait patiently for the Samsung Galaxy Ring to get its full unveiling on July 10 (having been initially teased in January), some screenshots showing the smart ring's various health-tracking capabilities have leaked online.

These images are courtesy of Android Authority, and have been grabbed from a deep dive into the code for the Samsung Health app for Android. This code has been added to the app, but isn't yet enabled or visible to users.

We get a look at the Samsung Health app measuring both heart rate and stress through the Galaxy Ring, so those are two metrics the wearable will be able to monitor. There are also mentions of using the device to predict periods and take skin temperature readings.

Snore detection is also included apparently, through a combination of the Galaxy Ring and your connected Android phone (this is how it works with the Galaxy Watch, too). The phone does the actual audio detection, but it seems the Galaxy Ring adds some extra smarts, too.

Ring or watch?

Here's your first look at some of Galaxy Ring's health tracking features✅ https://ift.tt/qVYseFS pic.twitter.com/DbzsOK7gfmJune 30, 2024

As far as we can tell from these screenshots, the health-tracking functionality of the Galaxy Ring will be along the same lines as the Galaxy Watch 6 (and presumably the Galaxy Watch 7) – just in a lighter form factor that's potentially more comfortable to wear.

With that in mind, the choice for consumers may well come down to a smart ring vs a smartwatch decision, instead of buying and wearing both (though there's nothing stopping you doing that, if the idea appeals).

Smartwatches clearly offer more in the way of features, with their own dedicated displays. The compact and light form factor of smart rings, meanwhile, generally makes them preferable for scenarios like sleep tracking or high-intensity sports and workouts.

We've already had some hands-on experience with the Galaxy Ring, but we're looking forward to hearing more details about it – most probably at the next Samsung Unpacked on July 10 (when we should also get new foldable phones and smartwatches).

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The emerging landscape of AI PCs is set to transform how we work with our devices whether PC or laptop. There are both positives and negatives around this new technology and before embarking on an adoption program, it’s important to know the facts.

What is an AI PC?

AI PC’s feature a dedicated Neural Processing Unit (NPU) within the System on Chip (SoC) which handles AI applications, experiences and the technology that runs computational power, whether it's language models, focused tasks, security or privacy. One of its greatest benefits is that it offers low latency and enables greater personalization which meets the growing need for more autonomy.

AI PC is a new variant of edge computing, where computation is done near the data source or near the end user, instead of depending only on the cloud. That mixed approach binds the strength of the cloud for intensive tasks and the speed and privacy advantages of local processing. AI PCs demonstrate this by using local hardware such as GPU's and NPUs for AI tasks, hence lowering latency, saving bandwidth and improving data security by reducing the amount of sensitive data that is sent to the cloud. The overall effect is a better user experience, supporting a variety of real time analytics and AI development.

Analyst assessment

Given that 2024 has been marked by industry commentators as the year of the AI PC, it is interesting to look at the landscape through the lens of analysts. Gartner, for example, predicts that 54.4 million AI PCs will be shipped this year, while IDC states 50 million and Canalys uses a slightly different measure but believes 1 in 5 shipments will be AI PC. Looking ahead to 2025, Gartner estimates that 43% of all AI PC shipments will be AI PC, but both IDC and Canalys forecast that by 2027, the figure will have risen to 60%. This spells a definite market shift in the direction of AI PCs.

AI PC chipset progress

AI PC evolution has been dependent on bringing the hardware and processor together to support AI applications at the PC level and an early example of this system on chip approach was on the iPhone with the A11 bionic processor. Now, with the introduction of chiplets like the Intel Ultra Core processor, we have seen a new design of CPU to suit varied purposes. Instead of the traditional block CPU, we now have a tile-based CPU which allows one file to be allocated to the GPU – the compute tile for the processor – and the SoC – which includes the NPU – to support the AI engine. Chip manufacturers are now developing and releasing their solutions allowing AI PCs to become a realistic prospect for users.

Importance of combining CPU, GPU and NPU

Modern computing tasks require many different computational capabilities that are best met by the combination of CPU, GPU and NPU. The CPU is the central processing unit, a general purpose processor designed for sequential processing, which runs the operating system and the conventional apps we all like to use on our laptops. The GPU is the graphics processing unit, originally created for graphics rendering. This is equally effective at parallel computations, ideal for the type of matrix and vector operations that are essential for AI and deep learning. The NPU is the neural processing unit, a specialized processor developed specifically for AI tasks. The NPU efficiently speeds up neural network computations whilst maintaining low power consumption. 

This triumvirate enables flexible computing where each type of processor can be used for specific tasks leading to significant enhancements in performance and in energy efficiency. And these are not just being designed for PCs and laptops. CPUs, GPUs, NPUs and System on Chip, which includes all three components, enable an ever-increasing number of devices including smartphones and embedded systems in sectors such as manufacturing to realize the potential of AI.

Where do memory and storage fit in?

One of the biggest challenges to adopting AI PCs with confidence, is dealing with the lack of information and the myths around how much memory is needed to run laptops and PCs with AI PC chiplets. As things stand currently, there are no minimum specifications and it is common for systems to have 8, 16 and 32 Gb of memory. However, as applications develop further, and intelligent uses of AI PC become more demanding we would anticipate a shift in memory requirements.

The same applies to storage Some systems have 256 Gb of SSD storage whilst others have 1Tb or 2Tb. It’s important to think beyond your needs today, or even next year and beyond, and anticipate what future applications might demand, and what your storage and memory requirements will be.

Current use cases

Examples of where AI PC is being used are growing by the day. In business productivity Microsoft Copilot is breaking new ground, but equally popular are solutions like Zoom, Webex and Slack for project management. Jasper is a popular sales and marketing tool while the Adobe suite is ideal for media and creative tasks, Audacity for audio and GIMP for creative design.

Clearly, these tools are focused on communications and creativity, and they reflect the early stages of AI integration. They are applications with high demands and are an obvious starting point for the benefits of AI, where it makes an immediate difference in collaboration and content creation. For many users the initial approach involves using AI PCs, but not in isolation, with cloud AI counterparts still being part of the mix. As the autonomy and security benefits of AI PC applications on local servers becomes more important, this balance will shift.

With the development of the landscape, technology will become more advanced and accessible, and applications will diversify hugely. We should view the current focus areas as a testing ground for the capabilities of AI in terms of user acceptance. There will be a learning curve while users accept AI but during this, the foundations of AI are being laid across multiple industries and use cases.

Why local is good

The greatest advantage of running AI models on AI PCs is that all the processing is local, boosting security and privacy and allowing users to move away from the risks of moving or storing sensitive data in the cloud – or sending to public AI models. AI PCs have the potential to lower the chances of data breaches, or unauthorized access, and will ensure greater control over data protection regulations such as GDPR simply by keeping data on site.

In addition, locally operated models are more resistant to network related problems, which ensures that essential AI functionality remains accessible even if cloud services fail due to connectivity problems or cyber-attacks targeting cloud infrastructure.

Of course, local AI devices will still need strong security measures to protect against local cyber threats such as malware or physical tampering. A comprehensive approach needs to be taken to secure model training, data encryption, proper access control and continuous monitoring for potential threats.

Preparing for change

Before making the decision to migrate to AI PCs, first consider what your organization needs now, what is available today to meet that need, the applications that are needed to suit specific job functions and where you are in the refresh cycle. If, for example, you are prepared to be an early adopter with the full knowledge that applications for AI PC are currently limited, but suit your needs, you are well placed to transition. If, however, you are unlikely to refresh for another 3-4 years, it might be worth waiting until the technology and applications have evolved further.

Keeping close tabs on AI PC chiplets with key manufacturers such as AMD and Intel and understanding how storage is evolving to keep pace – DDR4 versus DDR5, for example – will help you identify the right moment to adopt AI PC in terms of applications, performance and costs.

Another important factor is internal preparation. Staff must be trained to fully optimize AI PC systems and to operate them within a cyber-secure environment. AI technology is changing quickly, and adoption requires a comprehensive strategy. One of the greatest challenges right now is the lack of skilled professionals who understand the implications of AI from all perspectives. Instead of scrambling to manage AI regulatory compliance once AI PCs are adopted, the best approach is to be on top of the policies and practices that will be required in advance, and to understand the resources that are needed internally.

A final word

As always with a new technology there are subtle trade-offs involved in the opportunities and risks of adoption. Early adopters who can benefit from the AI PC applications that are currently available, could have first-mover advantages over their competition. Other organizations will want to ensure they have the right systems and policies in place to support the adoption of AI PC. It is also worth considering a more nuanced approach, and “buying” yourself time by upgrading key components – as this technology rapidly evolves - rather than committing fully today and changing everything in one fell swoop.

But if you are buying an AI PC today, by ensuring that you can upgrade your storage or memory in the future, your hardware will be better equipped to run AI PC applications which can operate in tandem with existing AI applications in the cloud.

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This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

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For a small business, having a fully-fledged cybersecurity team is highly unlikely, due to budgetary constraints. This does not however mean that small businesses aren’t getting attacked.

In a previous role as Detective Sergeant leading the Covert operations and Cyber Crime teams, the volume of successful attacks that my team and I knew about was always high. The attack methods varied too, but what was consistent, was that we would see comparatively smaller amounts being stolen, less than £5k. This isn't newsworthy but is a significant hit to smaller businesses and a great return for Cyber criminals who have likely put in minimal time to the attack.

Small businesses that do not have the basics in place are the perfect target for cyber criminals. With a lack of security controls in place (including effective policies & procedures) and regular payments coming into and out of their bank account, it is easy to see why smaller businesses are a prime target.

This high volume of incidents means that budgets notwithstanding, SME have no choice but to engage with cybersecurity. While a large cybersecurity team in the same vein as an enterprise may be an unrealistic expectation, there are options for smaller organizations hoping to show leadership when it comes to cybersecurity.

Inhouse or outsourced

That smaller companies cannot afford to hire a full-time senior cybersecurity role means that they have decisions to make. Do they employ a more junior role, or outsource cybersecurity leadership to a virtual CISO? The decision depends on the cybersecurity knowledge currently within the company and the strategic vision of the company.

A junior role would allow the company to afford to take on a full-time member of staff who will get to understand the company and its culture. This role would also be able to influence the culture and become the knowledgeable point of contact for cybersecurity questions; Having someone in-house means that they could be a visible point of contact, answering the questions on everyday subjects such as emails.

Clearly however, with this option there are drawbacks. The more junior position could lack experience and may not be able to handle complex situations that arise. This too could impact upon the progress the company makes in improving its cybersecurity posture. There would also be additional costs associated with a more junior role, such as training and development requirements, - although, some organizations could look upon these as an investment.

Conversely, engaging the services of a virtual CISO means the individual would be able to hit the ground running, providing instant experience, and most importantly, being able to develop a strategy for the company. The flexibility in this outsourced, part-time role allows the company to use the CISO as and when they require them. If compliance is a necessity for the company, the CISO would be able to ensure that the relevant security regulations are achieved.

Again however, there are drawbacks The CISO would be completing their work with less time available to them and would not have a team to delegate work to. This means they would either have to engage in more everyday cybersecurity tasks themselves, or use unskilled staff who are performing this work as a secondary responsibility. The CISO would also likely come at a greater hiring cost and the fact they are part-time may impact their responsiveness.

The final option that should be considered is a Managed Security Service Provider (MSSP). This could be a cost-effective way to have cyber security expertise on tap, at all times of day and night. The MSSP would get to know your company and can provide additional resources as the company grows.

However, it is worth noting that by using an MSSP the company will in effect be handing over control of their security to a third party, so they must recruit wisely. Depending on the MSSP used, the company may lose the benefit of a cyber security posture which is personalized to them, as some MSSPs will use certain products for all their clients. The final point that is worthy of consideration is additional charges. Some services may incur additional charges and if the company did have an incident that requires expertise and additional resources.

When to take the cyber plunge

The answer to this question varies for every business. Smaller companies should be looking towards Cyber Essentials to ensure they have the basics in place and that they are no longer the low hanging fruit.

Smaller companies that have achieved Cyber Essentials should then consider obtaining Cyber Essentials Plus. This acts as an external verification that the controls within Cyber Essentials have been correctly implemented. Most importantly though, this standard needs to be maintained throughout the year.

As your business grows, it is even more important to understand what assets are important to you, how you protect them and what processes you have in place should the worst happen. This is when the workload increases and may become too much for the individual(s) that are responsible for cyber security at that time.

Other factors that are worth considering is the industry that you are in. If you are in a highly regulated industry, it may be wise to recruit a cybersecurity specialist sooner. They will help you ensure that your business is meeting the standards required to maintain compliance and keep your business operating.

On many occasions, businesses recruit cybersecurity personnel after a breach has occurred. Although this is understandable, this is not the ideal time. Most businesses would have already spent a significant amount of money responding to and recovering from the breach and recruiting personnel at this stage will likely mean you will be recruiting in a rush; This could lead to hurried, incorrect and expensive decisions.

Fostering a culture of security

One of the biggest challenges that faces organizations of all sizes, but particularly smaller organizations, is cyber security awareness. Ensuring that everyone in the company is aware of the latest threats and how these could impact their role is vital.

A business can spend a significant amount of money protecting itself but if one person is unaware of the latest threat and clicks a phishing email or is duped by an AI enabled spoof call, then these controls will likely be ineffective.

Creating a culture whereby cyber security is both important and a consideration in day to day business is hard to obtain but easier to achieve when a business is smaller as it can be easier to communicate, especially messages from senior leaders who are likely to be closer to the ‘front line’.

A strong culture within a small business promotes a shared responsibility amongst limited resources, providing the business with a level of security that matches any specific hires they could make.

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This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

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