The power of generative AI
Generative AI is the big trending topic right now, and understandably is featuring prominently in the news. The popularity of platforms such as Open AI’s ChatGPT, which set a record for the fastest-growing user base by reaching 100 million monthly active users just two months after launching is unquestionably on the minds of businesses globally.
These tools can increase productivity and efficiency by automating repetitive tasks and letting employees focus on higher-value work. They can foster enhanced creativity and innovation by assisting in brainstorming and ideation processes and generating novel solutions to complex problems. Today, AI’s applications have already been well-documented in fields such as eCommerce, security, education, healthcare, agriculture, gaming, transport, and astronomy. The business, productivity, and efficiency gains that it provides these industries are enabling them to flourish and open up new revenue streams.
Beginner's guide to investing: 7 tips for greater financial freedom
When you read any financial articles, most often than not they will recommend investing as one of the best ways to grow your wealth and enjoy a bit more financial freedom. While this may feel like just another to-do added to your plate, along with budgeting, paying bills, saving, and paying down debt, investing is something that you will likely find is worth your time.
Investing doesn’t have to be extremely time-consuming, or even extremely expensive. Here are 7 investing tips that may help you get started with investing.
Mastering eCommerce design: Key mistakes to avoid for enhanced conversions
In the current digital epoch, eCommerce has burst onto the scene as an essential ingredient of the commercial landscape. To keep pace with the surging tide of online shoppers, businesses must wield a potent eCommerce platform that unleashes the full potential of conversions. Nevertheless, crafting a triumphant eCommerce website demands more than a mere visually captivating layout and first-rate products.
When it comes to design, businesses often commit a plethora of blunders that can undermine conversion rates. To amplify your eCommerce conversions, we shall delve into some of these pivotal errors in this post and proffer guidance on how to evade them.
Seven mistakes of modern privacy programs
From high-profile data breaches (think Facebook’s Cambridge Analytica scandal that resulted in millions of people’s data being shared without their consent) to the introduction of legislation like the General Data Protection Regulation (GDPR), the data privacy landscape has evolved considerably in the last few years.
Though more organizations recognize the necessity of implementing a data privacy program, many modern privacy programs are missing foundational components required for full compliance, and attorneys and authorities have little sympathy for these privacy gaps.
Analyzing Identity and Access Management (IAM) through the lens of data management
An organization's identity data today is its linchpin. This invaluable asset binds an enterprise together, orchestrating access rights, establishing a unified and accurate view of users across various channels, and empowering informed security decisions.
However, the identity infrastructure is continuously becoming more complex as businesses expand their digital landscape, bringing more users, devices, and applications within their IT environment. With this increasing complexity, poor data management can engender substantial financial losses and jeopardize sensitive user and customer data.
Why fuzzing isn't enough to test your APIs
In today’s fast-paced development environment, a comprehensive API security testing strategy is no longer a luxury, but a necessity. Testing your APIs for security gaps ensures that your APIs functions are reliable, secure, and perform as expected under different circumstances. It helps to identify issues such as incorrect data formats, missing or inaccurate data, and faults in authentication or authorization.
Proper API testing can also help to minimize downtime, reduce the risk of errors, and improve the overall quality of the software system. However, it’s important to note that comprehensive API security testing is a discipline in and of itself.
Leveraging advanced data for AI-powered Anti-Money Laundering (AML)
The global financial landscape is witnessing a transformation as institutions increasingly embrace the power of artificial intelligence (AI) to combat money laundering and fraud. AI's technical capabilities present an array of benefits for Anti-Money Laundering (AML) processes, particularly in its proficiencies for data analysis and pattern recognition.
Though, as AI integration gains momentum, it also brings significant challenges that demand thoughtful consideration. Issues such as limited training data and over-reliance without human supervision can have serious implications for AML efforts.
Defending your organization from illegal data's wrath
In today's interconnected world, businesses not only grapple with the management of vast amounts of data but also face the looming threat of illegal data concealed within their digital repositories. This proliferation of illegal data presents a range of risks and challenges that organizations must confront.
Illegal data encompasses a broad spectrum of content or files that contravene laws, regulations, and/or company policy. It includes materials such as pirated software, confidential information obtained through unlawful means, and content that promotes or facilitates illegal activities; as well as content that is simply not acceptable or useful on the corporate network such as holiday videos and cat pics.
Building cost-effective AI models: Creating accessible AI for all
With recent AI development, we have seen the priority of big tech companies be the creation of ever-larger language models. While sheer scale does have its benefits, the escalating costs associated with training and running these massive models has become a significant obstacle, particularly for small and medium-sized enterprises. With this in mind, and as new developers enter the space, we are seeing this trend slowly shift, the focus moving away from models of scale, and now onto how AI technologies can be made accessible and affordable for everyone.
With limited funds posing a significant challenge for smaller organizations looking to invest in AI, we could see prohibitively expensive models stifling innovation and diversity within the market. Customizing models like GPT-4 for specific business use cases currently comes with a hefty price tag, often reaching tens of thousands of dollars. Moreover, as models become more complex, long-term operational expenses soar. For instance, maintaining servers for ChatGPT can incur a staggering daily cost comfortably in the six figures. Smaller enterprises require intelligent and optimized model architectures that can compete with the capabilities of larger models at a price point consummate with their business size.
DevOps platform engineering -- the next generation of DevOps
A decade ago, DevOps emerged as a cultural phenomenon, bringing developers and operations professionals together to break down silos. However, as extraordinary of a shift in IT this was, DevOps has quickly matured to the point where discussing it as a practice seems outdated.
Regardless, the transition from the original concept of DevOps to its current state signifies its triumph rather than its demise. In fact, from the ashes of DevOps rises the platform engineer, a new role specifically tailored for the modern era of DevOps. Under the umbrella of platform engineering, DevOps now operates with a dedicated budget, a team, and a suite of self-service tools that empower developers to directly manage operations.
When putting AI to work, remember: It's just a talented intern
Artificial intelligence (AI) models have been generating a lot of buzz as valuable tools for everything from cutting costs and improving revenues to how they can play an essential role in unified observability.
But for as much value as AI brings to the table, it’s important to remember that AI is the intern on your team. A brilliant intern, for sure -- smart, hard-working and quick as lightning -- but also a little too confident in its opinions, even when it’s completely wrong.
Why content federation represents the next evolution of the CMS
We live in a content economy. There are an estimated 200 million creators on the internet, and upwards of four billion social media users. If organizations want to tap into this burgeoning economy -- set to be worth over $24bn in the next four years -- they must expand their capabilities beyond serving customers basic text and images.
Consumers want connected digital experiences that draw from multiple content sources. However, many organizations risk being held back by their current CMS, as customization and integration challenges slow the creation of new or complex digital services at scale. The result? Innovation happens at the pace that back-end architects and developers want, not the speed the business demands. If organizations are to unlock full value from content, an alternative approach is needed. Enter content federation.
Five ways to reshape the customer-centric product development process
Building products quickly to meet customer needs is more important than ever, especially as customer expectations continue to evolve. According to a recent study from Zendesk, 70 percent of consumers spend more with companies that offer fluid, personalized, and seamless customer experiences. For IT leaders, it’s not sufficient to focus on building things as required, their first focus needs to be on building the right thing. This means establishing a customer-centric product development process that supports discovering what customers really need and bringing a valuable product to market that meets their needs.
The most effective way to bring customer-centric products to market is to develop them incrementally and iteratively, with experimentation to discover how to best fulfill customer needs and with fast feedback to improve the quality of the product.
How cybercriminals use ChatGPT for cyberattacks
Artificial intelligence (AI) chatbots like ChatGPT have become a tool for cybercriminals to enhance their phishing email attacks. These chatbots use large datasets of natural language and reinforcement learning to create typo-free and grammatically correct emails, giving the appearance of legitimacy to unsuspecting targets. This has raised concerns among cybersecurity leaders, with 72 percent admitting to being worried about AI being used to craft better phishing emails and campaigns.
Chatbots can help cybercriminals scale the production of advanced social engineering attacks, such as CEO fraud or business email compromise (BEC) attacks. Additionally, cybercriminals may use AI-powered chatbots to scrape personal or financial data from social media, create brand impersonation emails and websites, or even generate code for malware such as ransomware. In particular, without AI, creating malware is a specialized task that requires skilled cybercriminals. However, the use of chatbots could make it easier for non-specialists to do this, and we can also expect AI-generated outputs to improve over time.
AI solutions for IoT security: How Artificial Intelligence protects low-resource devices
IoT devices are created to perform specific functions, so their technical specs are naturally quite limited. They are unlike smartphones or tablet computers that come with powerful processors and large data storage. Putting traditional security mechanisms like encryption and intrusion detection systems on these devices is impractical. Installing full-fledged security solutions in them is out of the question, let alone AI-powered systems.
However, this does not mean that AI cannot be used to secure IoT devices or entire IoT ecosystems. Here’s a rundown of how AI solutions for IoT are harnessing the benefits of AI to protect IoT and other low-resource devices, including actuators, sensors, wearables, and microcontrollers.
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