The AI-Powered Citizen Revolution: How Every Employee Is Becoming A Technology Creator

By CkarisUncategorizedNo Comments

Something remarkable is happening in organizations around the world. The traditional gatekeepers of technology – IT departments – are witnessing a revolution as employees across all departments harness AI and user-friendly tools to become technology creators themselves. Marketing managers are building AI models, nurses are developing healthcare apps, and finance teams are creating their own automation solutions.

“This has been creeping up on us over time,” explains Tom Davenport, distinguished professor at Babson College and co-author of ‘All Hands on Tech: The AI-Powered Citizen Revolution.’ “Technology has gotten so much easier to use, and we all carry around very powerful devices in our pockets that we have to become familiar with if we’re going to negotiate modern life.”

The Three Types Of Citizens

The citizen revolution encompasses three main categories of technology creators. First are the citizen developers, who use low-code/no-code platforms to build applications. Second are citizen automators, who create workflows and automated processes. Finally, there are citizen data scientists who leverage AI and analytics tools to derive insights from data.

“The concept that humans are becoming more tech-savvy and more comfortable with technology is converging with technology becoming progressively more human and human friendly,” says Ian Barkin, co-author of ‘All Hands on Tech.’ “To the point where so much attention is given to prompting and just effectively speaking to a computer and saying, ‘this is what I’d like you to build for me.'”

From Valve Turner To Tech Pioneer

One of the most inspiring examples of this revolution comes from Shell, where Stevie Sims transformed from literally “turning valves” at a refinery to becoming a citizen developer champion. As Barkin explains, “You saw domain expertise leveraged, you saw an intelligent person who knew the business and understood the challenges operating in that environment, who was then able to turn those ideas into actions and created automations that then inspired a movement.”

The IT Tension

This democratization of technology hasn’t been without its challenges. Many IT departments initially resisted, viewing citizen development as dangerous “shadow IT.” Davenport shares the story of “Mr. Citizen,” a supply chain professional who dramatically improved his productivity using data analysis tools, only to face pushback from IT, who insisted he should use their preferred programming language instead.

However, progressive organizations are learning to embrace and enable it while maintaining appropriate controls. “If you think you can stop the ingenuity and problem-solving of your teams of people who both have the ideas and then the tenacity to pursue them to solve problems they face every day – if you think you can squash that, good luck,” says Barkin. The solution, he suggests, is creating better structures that capitalize on people’s desire to solve problems creatively while maintaining necessary safeguards.

Managing The Risks While Enabling Innovation

The key to successful citizen development isn’t about replacing IT – it’s about transforming IT’s role from gatekeeper to enabler. Organizations need what Barkin calls “two ITs” – one focused on maintaining enterprise systems and security, and another dedicated to nurturing citizen developers through training, guidance, and maintaining safe development environments.

The most successful organizations are implementing what Shell calls a “red, amber, green” system – where green projects can be freely developed by citizens, red projects must be handled by IT, and amber projects require collaboration between citizens and IT professionals.

The Future Of Work And Innovation

This citizen revolution isn’t just changing how technology gets created – it’s transforming the very nature of work and innovation. Organizations that embrace this movement are finding they can innovate faster and more effectively by tapping into the domain expertise of their employees.

“This is an incredible resource,” Davenport emphasizes. “Every organization today feels the need to digitize. It’s taking too long. It’s costing too much. There aren’t enough professionals to do it. And you have this very powerful resource within your company of people who have domain expertise and can learn the skills that they don’t have already.”

Embracing The Revolution

The future belongs to organizations that can effectively harness this citizen movement while maintaining appropriate governance. As Barkin notes, “The future is going to be about a really sensible orchestration of the best AI for the job and really well-informed, capable humans.”

The message is clear: the citizen revolution isn’t something that can be stopped – nor should it be. Instead, organizations need to embrace and enable it, providing the right tools, training, and guardrails to help their employees become effective technology creators. In doing so, they’ll unlock unprecedented levels of innovation and productivity while empowering their workforce to solve the problems they understand best.

Credit: Bernard Marr

December 8, 2024

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6 Mistakes IT Teams Are Guaranteed To Make In 2025

By CkarisBanking, Career, Cryptocurrency, Cybersecurity, Digitalization, Food for thought, UncategorizedNo Comments

The next wave of artificial intelligence isn’t just knocking at enterprise doors – it’s exposing fundamental flaws in how organizations approach technology transformation. As IT teams race to stay competitive in 2025, they’re making mistakes that could significantly impact their digital initiatives.

Mistake 1: Mishandling AI Governance

Many organizations are mishandling AI deployment by operating without proper guardrails, while employees increasingly turn to unauthorized “shadow AI” applications to boost their productivity. In 2025, we’ll see the consequences of this oversight manifest in data breaches, biased outputs, and compliance violations. Organizations are discovering sensitive data being fed into public AI models through unofficial channels, creating massive security vulnerabilities. Forward-thinking IT leaders are already implementing comprehensive AI governance frameworks that cover everything from model selection to output verification while providing approved alternatives to popular consumer AI tools. This isn’t just about risk management – it’s about building sustainable AI practices that can scale with your organization’s growing needs while keeping shadow AI use in check through education and accessible, secure alternatives.

Mistake 2: Ignoring Regulatory Requirements

IT teams are significantly underprepared for incoming AI regulations. While the U.S. currently lacks comprehensive federal AI legislation, states like Colorado are implementing strict requirements around automated decision-making systems, and the EU’s sweeping AI Act will impact any organization doing business in Europe. By 2025, organizations will need to demonstrate their AI systems aren’t discriminatory, provide transparency reports for high-risk applications, and comply with complex international requirements. Even existing regulations are being reinterpreted through an AI lens – from biometric privacy laws to consumer protection statutes. IT teams building AI systems today without considering these emerging compliance requirements are creating unnecessary technical debt. Smart organizations are future-proofing their AI implementations by designing for transparency, establishing clear governance frameworks, and building systems that can adapt to evolving regulatory demands across multiple jurisdictions.

Mistake 3: Creating Integration Complexity

In rushing to modernize, organizations are creating unnecessary technical debt with brittle architectures that span old and new systems. While everyone wants to talk about their latest AI implementation or cloud migration, organizations are drowning in hundreds of point-to-point connections between specialized tools and aging legacy platforms. Smart organizations are taking a hybrid approach, methodically modernizing their core systems while implementing robust integration frameworks that can scale. They’re replacing brittle connections with flexible architectures that can adapt as systems evolve. This isn’t as exciting as launching the latest chatbot, but building sustainable, maintainable technology ecosystems is fundamental to long-term success.

Mistake 4: Neglecting Data Quality

Organizations are building AI initiatives without addressing fundamental data quality issues. Their data lakes are more like murky swamps – plagued by inconsistent standards, conflicting formats, and quality issues that render them nearly unusable for advanced AI applications. The problem goes beyond mere technical challenges. Business units are hoarding information in isolated silos, data governance policies are outdated or ignored, and metadata management is often an afterthought. The result? AI initiatives that produce unreliable outputs, models that perpetuate hidden biases, and massive costs in data cleanup and rework. Forward-thinking organizations are treating data quality as a board-level priority, investing in robust data governance frameworks, and building centralized data platforms that enforce consistent standards. They understand that in 2025, the difference between AI success and failure often comes down to the quality of the data foundation it’s built upon.

Mistake 5: Compromising Security

IT teams are compromising security in their push for rapid innovation. The pressure to deliver new capabilities at speed is leading to incomplete security reviews and inadequate protections. This is particularly concerning as cyber threats evolve into hybrid attacks that combine AI capabilities with traditional hacking methods. Automated systems are probing for vulnerabilities 24/7, while AI-powered social engineering attacks are becoming increasingly sophisticated and harder to detect. Adding to this perfect storm is the looming threat of quantum computing that is forcing organizations to confront the possibility that their current encryption methods may soon be obsolete. Forward-thinking organizations are adopting zero-trust architectures and implementing DevSecOps practices that bake security into every stage of development. They’re also investing in quantum-safe encryption and AI-powered security tools that can detect and respond to threats in real time. In 2025, a single security breach can undo years of digital transformation efforts.

Mistake 6: Maintaining Outdated Skills Development

Organizations are maintaining outdated approaches to skill development and technical training. The technical skills that were cutting-edge six months ago are now baseline requirements, while entirely new competencies emerge almost weekly. This skills gap is particularly apparent in AI and quantum computing, where the underlying technology evolves faster than training programs can adapt. Progressive organizations are taking a radically different approach, implementing continuous learning platforms that combine foundational principles with real-time skill adaptation. They’re fostering partnerships with AI vendors, cloud providers, and educational institutions to create dynamic learning environments. The focus has shifted from traditional certifications to practical experience and adaptability – because, in 2025, the most valuable skill is the ability to learn and unlearn at the pace of innovation.

The Price Of Inaction

These mistakes are already impacting digital transformation efforts across industries. The organizations that will thrive in 2025 are those that recognize these issues for what they are: predictable, preventable problems that require immediate attention. The time to course-correct is now, before these compounding issues create problems too expensive and complex to fix. The choice is clear: address these challenges head-on today, or watch your digital transformation efforts falter tomorrow under the weight of avoidable mistakes.

December 4, 2024

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11 Most Reliable AI Content Detectors: Your Guide To Spotting Synthetic Media

By CkarisBanking, Career, Cryptocurrency, Cybersecurity, Digitalization, Food for thought, UncategorizedNo Comments

Since the launch of ChatGPT just two years ago, the volume of synthetic – or fake – content online has increased exponentially.

Firstly, not all “fake” content is inherently bad. Generative AI text, image and audio tools have streamlined many repetitive tasks, from drafting routine letters and notices to storyboarding and prototyping in more creative projects.

But AI-generated content becomes problematic when it’s intended to mislead, misinform or spread fake news. Some have even gone as far as to say it threatens to destabilize democratic processes and create a “truth crisis.”

So what can be done? Well, luckily, a number of methods of differentiating between AI-generated and authentic content have been developed. These include sociological approaches, emphasizing the importance of education and critical thinking. They also include technological solutions, often leveraging the same generative and machine learning models used to create “fake” content, repurposed to detect it instead.

Here, I’ll focus on the latter. I’ll start by covering how they work, then take a look at some of the most popular tools and applications in this category.

The last few years have seen a big increase in both. However, the term “fake news” covers any deliberately constructed stories, lies, or disinformation designed to deceive. Whereas “deepfake,” “fake content,” or “synthetic content” specifically refers to content that’s not just designed to deceive but also generated by AI.

This deception could simply be for the sake of entertainment – as in the case of viral internet fakes like “deepfake Tom Cruise”, or “Pope In A Puffer Jacket.”

On the other hand, and increasingly, it could also be intended to cause real harm, such as influencing elections, damaging trust in public figures, or spreading geopolitical propaganda.

This year, ahead of upcoming elections in many countries, the WEF recognized AI misinformation as the biggest cybersecurity risk facing society. This all suggests that developing methods and tactics for identifying and combatting the rise of deepfake content is important for all of us.

What Are AI Content Detectors And How Do They Work?

In the simplest terms, most AI content detectors work by analyzing content and attempting to spot patterns that suggest it may have been generated by AI.

Often, they rely on AI itself to do this, leveraging neural networks that are trained to recognize typical traits.

For text, this could be particular phrases or ways of structuring information that is typical of large language models (LLMs), such as those powering ChatGPT or Google Gemini.

With images, this could mean looking out for telltale mistakes. For example, it’s frequently observed that AI image generators will often have difficulty with adding the correct number of fingers to their drawings of hands, correctly rendering text, and dealing with lighting and shadows.

It’s important to remember, however, that even the best tools are not foolproof. For example, it’s easy to mix AI and human-generated content to create hybrid content, but that’s likely to confuse AI content detectors.

Because of this, most of the tools covered here don’t categorically determine whether content is either AI or genuine. Instead, they are more likely to assign a probability or estimate how much of the text is likely to be AI-generated.

(To demonstrate this, I fed the text of this article, which is entirely human-written, into all of the text-based AI detectors mentioned here. You can see the results below)

The Best AI Content Detectors

AI Or Not

This paid-for site detects the use of generative AI in both images and audio.

Copyleaks

AI text analysis that’s widely used by businesses and academia.

Is this article written by AI? No

Deepfake Detector

Identifies fake video and audio with a claimed 92% accuracy.

Deepware

Deploy professional-quality deepfake detection resources on-premises for businesses.

GPTZero

One of the first widely available AI text detectors.

Is this article written by AI? 4%

Grammarly

The real-time grammar-checking plugin also offers an AI content detector.

Is this article written by AI? 50%

Hive Moderation

Designed to provide real-time moderation of video, audio and text content, also detects AI content.

Is this article written by AI? 0%

Is It AI?

Machine learning-powered AI image detector with free and paid-for options.

Originality

This lets you verify that the content you are planning to publish is authentic and trustworthy by checking it for AI, as well as plagiarism and factualness.

Is this article written by AI? 3%

Plagiarismcheck

Powerful AI text detection suite, with specialized tools for educational use cases.

Is this article written by AI? 0%.

Quillbot

Free-to-use AI text checker with no sign-up requirements.

Is this article written by AI? 0%

Winston

Winston is a comprehensive AI checking tool that can detect fake images as well as text. It also offers a certification program, certifying content as human-created.

Is this article written by AI? 0%

As AI-generated content becomes increasingly sophisticated, the tools and technologies we use to detect it must evolve in parallel. While today’s AI content detectors offer valuable insights, they’re not infallible – as demonstrated by the varying results when testing this human-written article. The key lies in using these tools as part of a broader approach to content verification, combining technological solutions with critical thinking and digital literacy. As we navigate an increasingly complex information landscape, these detection tools will become essential components in our collective effort to maintain digital truth and combat harmful misinformation.

Credit: Bernard Marr

December 3, 2024

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What are the 4 Vs of Big Data?

By CkarisBanking, Career, Cryptocurrency, Cybersecurity, Digitalization, Food for thought, UncategorizedNo Comments

How do you know if the data you have is considered big data? There are generally four characteristics that must be part of a dataset to qualify it as big data—volume, velocity, variety and veracity. Value is a fifth characteristic that is also important for big data to be useful to an organization.

Our world has become datafied. From data that shows activity such as our Google searches and online shopping habits to our communication and conversations through text, smartphones and virtual assistants, and all the pictures and videos we take to the sensor data collected by internet-of-things devices and more, there are 2.5 quintillion bytes of data created each day. The better companies and organizations manage and secure this data, the more successful they are likely to be. How do you know if the data you have has the characteristics that qualify it as “big”? Most people determine data is “big” if it has the four Vs—volume, velocity, variety and veracity. But in order for data to be useful to an organization, it must create value—a critical fifth characteristic of big data that can’t be overlooked.

Volume

The first V of big data is all about the amount of data—the volume. Today, every single minute we create the same amount of data that was created from the beginning of time until the year 2000. We now use the terms terabytes and petabytes to discuss the size of data that needs to be processed. The quantity of data is certainly an important aspect of making it be classified as big data. As a result of the amount of data we deal with daily, new technologies and strategies such as multitiered storage media have been developed to securely collect, analyze and store it properly.

Velocity

Velocity, the second V of big data, is all about the speed new data is generated and moves around. When you send a text, check out your social media feed and react to posts on Facebook, Instagram or Twitter or make a credit card purchase, these acts create data that need to be processed instantaneously. Compound these activities by all the people in the world doing the same and more and you can start to see how velocity is a key attribute of big data.

Variety

Today, data is generally one of three types: unstructured, semi-structured and structured. The algorithms required to process the variety of data generated varies based on the type of data to be processed. In the past, data was nicely structured—think Excel spreadsheets or other relational databases. A key characteristic of big data is that it not only is structured data but also includes text, images, videos, voice files and other unstructured data that doesn’t fit easily into the framework of a spreadsheet. Unstructured data isn’t bound by rules like structured data is. Again, this variety has helped put the “big” in data. We are able to use technology to make sense of unstructured data today in a way that wasn’t possible in the past. This ability has opened up a tremendous amount of data that have previously not been accessible or useful.

Veracity

The veracity of big data denotes the trustworthiness of the data. Is the data accurate and high-quality? When talking about big data that comes from a variety of sources, it’s important to understand the chain of custody, metadata and the context when the data was collected to be able to glean accurate insights. The higher the veracity of the data equates to the data’s importance to analyze and contribute to meaningful results for an organization.

Value

While this article is about the 4 Vs of data, there is actually an important fifth element we must consider when it comes to big data. This is the need to turn our data into value. In fact, organizations that have not created a data strategy to yield insights and to drive data-driven decision-making are going to fall behind competitors. Big data that’s analyzed effectively can provide important understanding of customers and their behaviors and desires, how to optimize business processes and operations and to improve a nearly endless amount of applications. Whether you use data to create a new product or service or to understand a way to cut costs, it is incredibly important that big data creates value. This value is why organizations of every size must have a data strategy in place in order to ensure the data needed to achieve the business objectives they adopted are being collected and analyzed.

December 1, 2024

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Can Your Device Run Apple Intelligence? What You Need To Know

By CkarisBanking, Career, Cryptocurrency, Cybersecurity, Digitalization, Food for thought, UncategorizedNo Comments

Apple’s announcement of Apple Intelligence has sent waves of excitement through the tech world. This new AI-powered system promises to revolutionize how we interact with our devices, making them smarter, more intuitive and more helpful than ever before. But the burning question on everyone’s mind is: “Will my device be able to run Apple Intelligence?” Let’s dive into the details and find out if you’ll be joining the AI party or if it might be time to consider an upgrade.

The Hardware Requirements: It’s All About The Chips

As with any major software advancement, the ability to run Apple Intelligence comes down to hardware. In this case, it’s all about the chips powering your device. Here’s the lowdown:

iPhones: The A17 Pro Takes The Lead

If you’re an iPhone user, you’ll need the latest and greatest to get the full Apple Intelligence experience. According to Apple, Apple Intelligence will be available on iPhone 16 as well as iPhone 15 Pro models, which are powered by the A17 Pro chip. This powerhouse of a processor packs the necessary punch to handle the complex on-device AI processing that Apple Intelligence requires.

But what if you don’t have the latest Pro model? Don’t worry, you’re not entirely out of luck. While the full suite of Apple Intelligence features may require the A17 Pro, it’s likely that some features will be available on other recent iPhone models. However, Apple hasn’t provided specifics on this yet, so we’ll have to wait for more details.

iPads: M1 And Beyond

For iPad users, the entry point for Apple Intelligence is the M1 chip. This means if you have an iPad Pro from 2021 or later or an iPad Air from 2022 or later, you’re in business. These devices pack serious computing power, making them capable of handling the demands of Apple Intelligence.

Macs: The M-Series Club

When it comes to Macs, if you’ve got an M-series chip, you’re good to go. This includes MacBooks, iMacs and Mac Studios with M1, M2 or M3 chips. The power and efficiency of these chips make them ideal for running Apple Intelligence.

What About Older Devices?

If your device doesn’t meet these requirements, don’t despair just yet. While you may not get the full Apple Intelligence experience, it’s possible that some features will be available on older devices. Apple has a history of bringing some new features to older hardware, even if the most advanced capabilities are reserved for the latest models.

Moreover, it’s worth noting that Siri, Apple’s existing virtual assistant, will continue to work on older devices. While it may not have all the bells and whistles of Apple Intelligence, it will still be there to help with basic tasks.

The Software Side: iOS 18, iPadOS 18 And macOS Sequoia

Hardware is only half the story. To use Apple Intelligence, you’ll also need to be running the latest operating systems: iOS 18 for iPhones, iPadOS 18 for iPads and macOS Sequoia for Macs. These new OS versions are set to be released this fall.

The good news is that Apple has been known for supporting older devices with new software updates for several years. So even if your device is a few years old, you may still be able to update to the latest OS and get at least some Apple Intelligence features.

A Phased Rollout: Patience Is A Virtue

It’s important to note that Apple is planning a phased rollout for Apple Intelligence. While some features will be available immediately with the release of the new operating systems, others will be rolled out over the course of the following year.

Initially, Apple Intelligence will be available in U.S. English, with support for additional languages and regions coming later. So, if you’re not in the U.S. or prefer a different language, you might need to wait a bit longer to experience all that Apple Intelligence has to offer.

The Cloud Factor: Private Cloud Compute

One interesting aspect of Apple Intelligence is its use of “Private Cloud Compute” for more complex tasks. This system allows devices to tap into more powerful server-based models when needed while still maintaining strong privacy protections.

The good news is that this could potentially extend some Apple Intelligence capabilities to older devices. Even if your device isn’t powerful enough to handle all the processing locally, it might be able to use Private Cloud Compute to access some features.

What If Your Device Isn’t Compatible?

If your current device isn’t compatible with Apple Intelligence, you have a few options:

Wait and see: Apple may bring some features to older devices in future updates.
Upgrade your device: If you’re due for an upgrade anyway, this could be a good reason to take the plunge.
Use alternative AI tools: There are many third-party AI apps available that can provide similar functionality, although they may not be as deeply integrated into your Apple ecosystem.

The Bigger Picture: The Future of Apple Devices

Apple Intelligence represents a significant shift in how our devices operate. It’s clear that AI is becoming an integral part of Apple’s ecosystem, not just an add-on feature. This suggests that future Apple devices will likely be designed with AI capabilities in mind from the ground up.

If you’re in the market for a new Apple device, it might be worth considering one that’s compatible with Apple Intelligence. Not only will you be able to enjoy these new features now, but you’ll also be better positioned for future AI advancements.

The Verdict: A New Era, But Not For Everyone (Yet)

Apple Intelligence is ushering in an exciting new era of personal computing, but it’s clear that not everyone will be able to join in right away. If you have the latest Pro iPhone, a recent iPad,or a Mac with an M-series chip, you’re all set to experience the full power of Apple Intelligence.

For everyone else, it’s a bit of a waiting game. Some features may trickle down to older devices, and the phased rollout means that even compatible devices won’t get all features right away.

But don’t let that dampen your enthusiasm. Apple Intelligence represents the direction that personal computing is heading, and it’s only a matter of time before these kinds of AI capabilities become standard across all devices.

So, whether you’re gearing up to dive into Apple Intelligence this fall or planning your future upgrade, one thing is clear: the future of our devices is intelligent. The question isn’t if you’ll be using AI like this, but when. And for many Apple users, that “when” is just around the corner.

November 30, 2024

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How Generative AI Will Change Jobs In Cybersecurity

By CkarisBanking, Career, Cryptocurrency, Cybersecurity, Digitalization, Food for thoughtNo Comments

Ensuring robust cybersecurity measures are in place is more important than ever when it comes to protecting organizations and even governments and nations from digital threats.

As society’s reliance on digital platforms, online connectivity, and the transfer and storage of data has grown, so has the number of potential attack vectors. Phishing attacks, identity theft and ransomware are all growing in frequency and scale.

Now, AI has been added to the mix, particularly generative AI. But while this revolutionary technology creates new risks, it also creates new opportunities.

Cyber security analysts, engineers, architects and ethical hackers are on the front line of this fight. Increasingly, their work will involve collaboration with intelligent machines and generative AI technology to tackle the new wave of threats.

So, if you work — or are considering working — in cybersecurity and want to know how the rise of AI will impact your role and the future of your profession, read on.

Generative AI and Cybersecurity

The Certified Information Systems Security Professional (CISSP) framework defines eight key domains when it comes to cybersecurity. Broadly speaking, these can be used to categorize the key tasks that security professionals are responsible for. It isn’t difficult to see that generative AI has implications for all of them:

Security and risk management involves identifying specific threats, such as phishing or denial-of-service attacks. Here, generative AI can be used to conduct risk assessments in real-time and automatically report on recommendations and mitigation strategies.

Asset security focuses on implementing protection measures for systems and data. Generative AI tools can be used to automate the classification of sensitive information and identify weak points in security infrastructure.

Security architecture and engineering refers to work that involves designing and implementing security measures. Generative AI can be used to suggest security implementations as well as to simulate attacks to test their effectiveness.

Communication and network security is the job of ensuring the transfer of data across networks is secure. Machine learning algorithms monitor traffic and look for patterns that suggest suspicious activity, while generative AI tools create real-time reports that flag potential breaches.

Identity and access management involves ensuring authorized users have access to the systems and data they need while keeping out potential intruders. AI can track user behavior and access patterns to identify anomalies or detect phishing attacks by analyzing emails or voice comms for content, tone and structure. It can also create simulated phishing attacks to probe for vulnerabilities.

Security assessment and testing implements processes for conducting routine tests across the entire spectrum of cybersecurity infrastructure. GenAI can automate the creation of testing schedules and reporting of results, along with generating recommendations for corrective action.

Security operations refers to the procedures in place to detect and respond to ongoing security incidents. Generative AI tools can be used to create automatic incident response plans or conduct simulated attacks, enabling organizations to reduce the time it takes to respond to breaches.

Software development security ensures that software vulnerabilities are identified at the development stage. Generative AI tools can automate code reviews and the writing of testing plans, as well as scan code repositories to ensure they are safe.

By understanding how generative AI can be integrated across these domains, cybersecurity professionals can develop a solid understanding of how this technology can help them spot and respond to threats as well as proactively strengthen the defenses of their organizations.

How The Role Of Cybersecurity Professionals Will Change

I believe generative AI will impact just about every job, profession and career path, and the in-demand field of cybersecurity is no exception.

Those who have the ability to work with generative AI will find it easier to automate many elements of their day-to-day activities. This will free up their valuable time to spend on activities that aren’t so easy to delegate to machines.

These could include working face-to-face with colleagues to help them understand their own cybersecurity responsibilities, tasks involving high-level strategic decision-making, or identifying novel threats that aren’t well recognized and documented.

Something else that even the best AIs don’t yet have the generality to do as well as humans is understanding the specific foibles of a particular organization’s culture and how they create or mitigate security threats. This could include the leadership’s attitude towards security, the quality of training policies in place, and compliance levels around IT codes of practice.

Just as is the case with professionals in other fields, those who are able to reskill and upskill in the face of this shake-up of responsibilities and priorities will find they are more likely to prosper.

The challenges and opportunities faced by cybersecurity professionals will undoubtedly evolve, and the increasing prevalence of AI will be a primary driver of this.

The ability to adapt will be essential, as tomorrow’s threats will be different from today’s. Consider, for example, the threats posed to encryption standards by quantum computing or the data protection risks arising from more and more of our personal information being digitized and stored online.

This will result in cybersecurity professionals becoming increasingly critical to the safety of not just their organizations but society as a whole as we move further into the digital age.

November 29, 2024

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4 Smartphones Leading The AI Revolution

By CkarisBanking, Career, Cryptocurrency, Cybersecurity, Digitalization, Food for thoughtNo Comments

As enterprises increasingly rely on company-issued smartphones as primary computing devices, these mobile devices are becoming the frontline of workplace AI integration. For many organizations, choosing between an iPhone, Samsung Galaxy, or Google Pixel now means making strategic decisions about which AI capabilities will best serve their workforce and enhance productivity.

The $500 billion smartphone industry has transformed into a battlefield of artificial intelligence, with each manufacturer vying to deliver enterprise-grade AI features that can revolutionize workplace productivity and security. But which company is actually providing the most valuable AI capabilities? Let’s dive into the unique approaches each major player is taking to win over enterprise customers with their AI innovations.

Apple iPhone: Where Privacy Meets Intelligence

Apple’s fashionably late arrival to the AI party with its Apple Intelligence platform proves that good things come to those who wait. Through a strategic partnership with OpenAI (the creators of ChatGPT and DALL-E), Apple has introduced a sophisticated suite of AI tools that seamlessly blend cloud-based and on-device processing.

The platform’s standout features include an advanced language model that handles everything from email drafting to document summarization while breathing new life into Siri’s conversational abilities. The playful Image Playground and Genmoji features add creative flair to the package, letting users generate custom images and personalized emojis.

What truly sets Apple apart, however, is its unwavering commitment to privacy. By processing many AI features directly on the device, it has managed to deliver cutting-edge capabilities while minimizing the transmission of sensitive data to the cloud – a compelling proposition for the privacy-conscious consumer.

Samsung Galaxy: The Performance Powerhouse

Samsung has thrown down the gauntlet with its latest Galaxy S24 Ultra, embedding AI into the very DNA of the device through its custom Exynos chipset. The dedicated AI cores don’t just sound impressive – they deliver tangible improvements in how the phone handles AI-intensive tasks.

The Scene Optimizer in Samsung’s camera system is particularly clever, acting like a professional photographer’s assistant that automatically adjusts settings based on what you’re shooting. Whether you’re capturing portraits, landscapes, or your pet’s latest antics, the AI ensures you get the best possible shot.

But it’s the behind-the-scenes AI magic that really makes the Galaxy shine. The intelligent performance optimization system works like a skilled conductor, orchestrating CPU and GPU resources while adapting to your usage patterns to extend battery life. The result? A smartphone that feels more responsive and reliable with each passing day.

Google Pixel: The Photographer’s Dream

Google’s Pixel lineup, powered by their custom Tensor chip, showcases what happens when one of the world’s leading AI companies turns its attention to smartphone photography. The results are nothing short of remarkable.

The Magic Eraser feature has to be seen to be believed – it removes unwanted objects from photos with almost supernatural precision. Combined with AI-enhanced zoom capabilities and intelligent low-light photography, the Pixel transforms even casual snapshots into professional-looking images.

The integration of Google’s Gemini chatbot takes things further, enabling real-time captions for phone calls and videos, instant voice recording transcription, and seamless language translation. It’s like having a personal assistant who’s equally comfortable handling your photography and communication needs.

Huawei: Practical AI For Everyday Life

While Huawei might not be the first name that comes to mind for many Western consumers, its Pura 70 series demonstrates a refreshingly practical approach to AI implementation. Under the Harmony Intelligence banner, it has developed features that solve real-world problems.

Its Image Expand technology can intelligently fill in missing background details when enlarging photos, while the Sound Repair feature patches audio drops in real time during calls. The upgraded Celia assistant, powered by the Pangu LLM, adds sophisticated image recognition capabilities that make everyday tasks smoother and more intuitive.

The Bottom Line

The AI smartphone revolution has given consumers something they haven’t had in years: meaningful choice. Whether you prioritize privacy (Apple), performance (Samsung), photography (Google), or practical everyday features (Huawei), there’s now a clear differentiation between leading smartphones that goes beyond mere aesthetics or ecosystem lock-in.

As AI technology continues to evolve at breakneck speed, we can expect these differences to become even more pronounced. The smartphone in your pocket is no longer just a communication device – it’s an AI-powered personal assistant that’s getting smarter with each new generation.

November 24, 2024

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AI And The Global Economy: A Double-Edged Sword That Could Trigger Market Meltdowns

By CkarisBanking, Career, Cryptocurrency, Cybersecurity, Digitalization, Food for thoughtNo Comments

The stock market’s current AI euphoria, driven by companies like NVIDIA developing powerful processors for machine learning, might mask a more troubling reality. While artificial intelligence promises to revolutionize trading and risk management, it could paradoxically make our financial systems more fragile and susceptible to catastrophic failures.

“There’s so much euphoria, with tens or even upwards of a hundred billions of dollars being spent on AI. Every major investment bank on Wall Street is implementing it,” notes Jim Rickards, author of the new book Money GPT. However, he controversially asserts that this widespread adoption of AI in financial markets could amplify market crashes beyond anything we’ve seen before.

The Fallacy Of Composition

Rickards introduces a compelling concept called the “fallacy of composition” – where actions that make sense for individual market participants could spell disaster when adopted by everyone. He illustrates this with an analogy: “At a football game, one fan standing up gets a better view. That actually works. The problem is everyone behind them stands up, and next thing you know, the entire stadium is on their feet and nobody has a better view.”

In financial markets, this phenomenon could manifest during market downturns. While it might be prudent for individual investors to sell during a crash, if AI systems controlling vast amounts of capital all execute similar strategies simultaneously, the result could be catastrophic.

The Missing Human Element

The author claims one of the most significant risks stems from removing human judgment from the equation. He points to the historic role of specialists on the New York Stock Exchange, who were tasked with maintaining orderly markets: “The specialist was supposed to stand up to the market when there was a wave of sellers… try to equilibrate the market.” Today’s AI systems, he suggests, lack this nuanced human judgment.

Speed And Synchronicity: A Dangerous Combination

While market panics aren’t new, AI introduces unprecedented risks through its speed and synchronicity. The automated nature of AI-driven trading could accelerate market movements and create feedback loops that human traders might otherwise interrupt. As Rickards cautions, “What is new is the speed at which they can happen, the amplifying effect and the recursive function.”

Beyond Market Crashes: The Banking System At Risk

The concerns extend beyond stock markets to the banking system itself. Rickards points to the recent collapse of Silicon Valley Bank as an example of how digital technology can accelerate bank runs. “That didn’t work out over weeks and months. That happened in two days,” he notes, suggesting that AI could further accelerate such events.

The Path Forward

While the author’s warnings are stark, he emphasizes that the solution isn’t to abandon AI entirely. Instead, he advocates for more sophisticated circuit breakers and regulatory frameworks. He suggests implementing “cybernetic” approaches that could gradually slow market activity during periods of stress rather than implementing sudden stops.

A Call For Balanced Innovation

As financial institutions rush to implement AI systems, Rickards’ analysis serves as a timely reminder of the need for careful consideration of systemic risks. While artificial intelligence offers powerful capabilities for analyzing markets and managing risk, we must ensure these tools don’t inadvertently make our financial systems more vulnerable to catastrophic failures.

The challenge ahead lies in harnessing AI’s potential while implementing safeguards against its systemic risks. As financial markets continue their technological transformation, finding this balance may prove crucial for global economic stability.

Credit: Bernard Marr

November 13, 2024

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8 Game-Changing Smartphone Trends That Will Define 2025

By CkarisBanking, Career, Cryptocurrency, Cybersecurity, Digitalization, Food for thoughtNo Comments

As in just about every other field of technology, AI will undoubtedly continue to be the key driver of innovation throughout 2025. Increasingly, it will also become the channel through which handset manufacturers will seek to differentiate their offerings in what has undeniably become a somewhat homogenous market.

However, breakthroughs in screen technology, battery and form factor will continue to push our ever-present electronic companions into new territory. So let’s take a look at the key trends shaping the world of smartphone technology over the coming 12 months.

LLM-Powered Voice Assistants

Perhaps the most transformational application of AI within the smartphone ecosystem this year will be the ongoing addition of LLM functionality to AI assistants, as we’ve seen this year with the addition of Apple Intelligence to Siri and the merging of Google’s Gemini with Android. This means we can expect the conversational abilities of our phones to become more natural and human-like, particularly as we see elements of technology such as ChatGPT’s Advanced Voice Mode begin to creep into smartphones.

AI-Powered User Experience

Aside from LLM-powered voice functions, we will continue to see AI improving user experience in more subtle ways, such as prolonging battery life, optimizing storage and enhancing camera functionality. As developers and manufacturers look for innovative ways to improve UX, I anticipate that we will see a move towards more predictive use cases as devices become capable of anticipating user behaviors and intuitively allocating resources in a more personalized way.

Privacy And Security

As our phones become capable of knowing more about us through their scanners and sensors and become more essential to our lives, the importance of keeping them secure increases.

This year, we’ve seen innovations such as theft detection ability, which is capable of automatically securing a device when it rapidly moves away from its owner. We can expect to see continued development of features, leveraging encryption, biometrics, and on-device AI, designed to protect the valuable data they hold.

Not-So-Smart Phones

As the subset of phone users suffering from “feature fatigue” grows, I expect we will see continued interest in the niche but growing field of basic “dumb” phones. These minimalist devices are designed to reduce the impact on users’ lives of the many distracting influences of feature-rich phones. Or just provide a secure device that won’t mean the owner’s life effectively disappears when the handset is lost or stolen!

More Affordable Multi-Screen Displays

Dual-screen displays have been around for a while now, and this year, Huawei launched the first (very expensive) tri-screen model. This push for expanding screen real estate without increasing device size shows that the smartphone-buying public has an appetite for devices with larger displays and innovative form factors, even though current models are pricey. This means that it’s likely we’ll see the emergence of cheaper models in the near future.

Sustainability In Smartphone Manufacture And Design

With consumer behavior increasingly driven by environmental concerns, manufacturers are responding by adopting more sustainable practices throughout their product lifecycles. Apple and Samsung have both increased the percentage of recycled materials going into their devices, and this trend is likely to continue into 2025. Apple, in particular, has said that it intends to achieve 100% usage of recycled cobalt in its batteries by this year. We are also seeing a switch towards greater use of modular parts, such as batteries and screens, which can be replaced easily in order to improve repairability and recyclability, improving product life spans.

Better Battery Technology

New developments in battery technology will continue to reshape the smartphone user experience throughout 2025. Major manufacturers, including Samsung, are known to be working on developing solid-state batteries, which will vastly improve the lifespan and charging speed of the power cells used in our devices. They are also less prone to overheating and could solve safety issues and reduce incidents of battery malfunction. And Apple is expected to debut an entirely new model of battery in 2025, designed in-house and offering vastly improved performance. We can also expect continuous incremental improvements such as better wireless charging and AI battery management.

Enhanced Connectivity Solutions

While many locations are still waiting to benefit from the rollout of true 5G networks, other advanced networking solutions promise to revolutionize the way we use smartphones in the near future. One of the major innovations is the rollout of satellite networks – most famously Elon Musk’s Starlink – which aims to provide global mobile broadband coverage. This could see mobile coverage expanded to even the most remote regions of the planet, as well as providing resilience in the face of technological breakdown or natural disasters in currently well-connected areas.

November 11, 2024

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Ultimate Smartwatch Guide 2025: From AI Health Tracking To Adventure-Ready Timepieces

By CkarisBanking, Career, Cryptocurrency, Cybersecurity, Digitalization, Food for thoughtNo Comments

In an era where our smartphones rarely leave our pockets, smartwatches have emerged as the new frontier of personal computing – and the competition has never been fiercer. As we dive into 2025, these wrist-mounted companions have evolved far beyond simple notification displays, becoming sophisticated health monitors, fitness coaches, and even potential lifesavers.

The rapidly evolving smartwatch market presents both excitement and challenges for consumers. While the smartphone market has largely settled into predictable patterns, smartwatch manufacturers continue to push boundaries with innovative features and specialized use cases, creating a fascinating landscape of choices for tech-savvy consumers.

Apple Continues To Define The Premium Segment

Apple’s 2025 lineup showcases the company’s commitment to market segmentation, with each model targeting specific user needs. The Series 10 emerges as the cornerstone of their collection, featuring an enhanced display and sophisticated health monitoring capabilities, including new air quality sensors and improved ECG functionality. It remains the go-to choice for iPhone users seeking a premium all-around smartwatch experience.

The Ultra 2 takes Apple’s rugged philosophy to new heights, justifying its premium price point with features tailored for extreme sports enthusiasts. With 100-meter water resistance, extended battery life, and precision GPS, it’s clearly positioned as the adventure-seeker’s companion.

For budget-conscious consumers, the SE 2nd Generation offers an attractive entry point to the Apple ecosystem, now available in a compact 40mm size. While it forgoes certain advanced health features, it delivers core functionality at a more accessible price point.

Samsung And Google Challenge The Status Quo

Samsung’s Galaxy Watch 7 demonstrates the company’s technical prowess with its advanced 3nm processor and AI-powered health tracking features. The inclusion of dual-frequency GPS – a feature reserved for Apple’s Ultra model – positions it as a compelling alternative in the premium segment. The Galaxy Watch Ultra pushes boundaries further with its impressive 100-hour battery life and enhanced durability specifications.

Google’s Pixel Watch 3 emerges as a dark horse in the competition, introducing innovative AI-powered features that showcase the company’s software expertise. The new loss-of-pulse detection system, which can automatically call for emergency assistance, represents a significant advancement in personal safety technology.

Budget Options Prove Their Worth

The Nothing CMF Watch Pro 2 demonstrates how far budget smartwatches have come, offering core functionality at a fraction of premium prices. While it may lack cutting-edge features like ECG sensors or on-device AI, its impressive 45-day battery life in power-saving mode makes it a practical choice for users prioritizing longevity over advanced features.

Specialized Solutions For Specific Needs

Huawei’s Watch Ultimate carves out its niche in the outdoor adventure segment with features like real-time environmental monitoring and exceptional battery life. Meanwhile, the Fitbit Ace LTE addresses a growing market for children’s smartwatches, offering cellular connectivity and location sharing while maintaining privacy – a thoughtful approach to introducing young users to wearable technology.

Looking Ahead

As we progress through 2025, the smartwatch market continues to mature while maintaining its capacity for innovation. The integration of AI-powered health monitoring, extended battery life, and specialized use cases suggests that we’re entering a new era of wearable technology – one where smartwatches are no longer mere smartphone companions but essential tools for health, safety, and daily life management.

Whether you’re an athlete seeking performance insights, a parent wanting to stay connected with your child, or simply someone looking to take better care of their health, today’s smartwatch market offers more choices than ever before. The key lies in identifying which combination of features, form factor, and price point aligns with your specific needs.

November 9, 2024

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