Zero Trust: Moving beyond the chewy centre of cybersecurity
As modern technology becomes increasingly complex, so does the task of securing it. Adding to the complexity is the proliferation of decentralised technology such as cloud adoption and IoT (Internet of Things), and the move to remote working which have changed how cybersecurity experts craft the defences for their systems.
In simpler days, IT systems were comparatively easy to ring-fence, as there was a solidly defined corporate security perimeter, or “trust boundary.” This formed the basis of the classic, trust-based security strategy, where any user inside the boundary was implicitly trusted by default, and anyone outside the boundary was denied access. Being connected to the private network was the only verifiable credential needed to access the system and all its data.
Harnessing the value of data with data monetization
Businesses around the globe are using new technologies to change the world. But this wouldn’t be possible without the use of sensitive data such as Personal Identifiable Information (PII) and Protected Health Information (PHI) to drive advancements in personalization and sophistication. However, if companies are using data that typically is associated with medical records and insurance claims, this bodes the question, is personal data secure?
It is possible to balance data privacy with gleaning the value of the information through a data modernization strategy that enhances and accelerates digital transformation efforts.
Is synthetic data the solution to data privacy challenges?
Synthetic data is artificial material that was not generated by natural life events. As such, it can be created by computer programs and AI tools that use different techniques, with generative adversarial networks and diffusion models being among the most popular and effective today. Synthetic data may come in many forms, but images and textual information are currently the most feasible options.
If you are interested in AI and ML developments, you have probably heard the term already -- “sanitized” synthetic data is a recent hype in the AI training field that, it is believed, might solve pressing data privacy and ownership challenges posed by real data. However, it all sounds like sunshine and rainbows only until you stop and consider the fact that AI algorithms used to generate synthetic data still need to be trained on real data -- the very obstacle they offer to remove.
Why human risk management is key to data protection
Personal data is constantly being processed and transferred in numerous ways -- whether in healthcare applications, store loyalty programs, during purchases or while browsing online. With such a vast amount of personal data in circulation, the likelihood of errors occurring is heightened.
It feels like almost every day we hear a story of another company being breached -- with data being stolen by cybercriminals looking to steal an individual’s identity, access accounts or commit fraud. Things are also getting easier for cybercriminals, thanks to technology advancements like generative AI assisting with more convincing phishing emails and deepfake content.
Workplace communications: The role of artificial intelligence and audio-visual solutions
Technology is reshaping the way we work and the convergence of Audio Visual (AV) solutions with Artificial Intelligence (AI) is not only redefining how we communicate but it is also playing a pivotal role in digitizing workspaces and helping organizations achieve their sustainability objectives.
Research from technology analyst Valoir reveals that AI has the potential to automate 40 percent of the average working day, allowing business leaders to increase productivity levels like never before. This trend is expected to continue with the global market size anticipated to see an annual growth rate of over 15 percent between 2024 and 2030.
To get to AGI, we must first solve the AI challenges of today, not tomorrow
If the World Economic Forum in Davos was any indication, AI safety and security will be this year’s top priority for AI developers and enterprises alike. But first, we must overcome hype-driven distractions that siphon attention, research, and investment away from today’s most pressing AI challenges.
In Davos, leaders from across the technology industry gathered, previewing innovations, and prophesying what’s to come. The excitement was impossible to ignore, and whether it is deserved or not, the annual meeting has built a reputation for exacerbating technology hype cycles and serving as an echo chamber for technology optimists.
How clean code can bridge the developer and security divide
Industry-agnostic software is now an organization’s most critical business asset, as its competitive edge often depends on it. Since companies become more technologically savvy and dependent upon their software to meet revenue goals and deliver products or services to customers, it cannot be afforded to underestimate the importance that secure and high-quality code plays.
The more this becomes evident, the greater the pressure on developers to deliver. Leaders expect their developer teams to work faster, ship more features, and write “better” code, but the technical debt accrued as a result of these escalating demands creates a slowdown effect as developers try to keep up. This technical debt can take a third of developers’ time to address, with refactoring later costing twice, or even three times as much as a proactive fix. While AI code generation tools can help manage the responsibility of creating large amounts of code and handling mundane tasks so developers can focus on collaborative or creative work, AI-generated code shouldn’t be trusted at face value. When code is not properly reviewed for maintainability, security, and reliability (i.e. Clean Code attributes), poor-quality code problems creep in.
Immutability: A boost to your security backup
As the volume of data continues to increase and the threat landscape continues to evolve, it is increasingly important for organizations to protect backup data from unwanted deletion. Threats today can take the form of a malicious insider deleting backup data or a targeted cyberattack on the backups themselves. Modern ransomware attacks often first seek out and destroy backups before moving on to encrypting production data. However, companies will benefit from implementing immutability, the act of making data writable but noneditable for a defined period of time, as part of their data protection arsenal to help avoid or recover from a loss of production data situation.
The rise in cyber incidents, which according to the Veeam Data Protection Trends Report 2023 is the leading cause of outages over the past three years, is bringing the need for immutability to the fore, particularly as most organizations reported having fallen victim to cyber incidents, on average, twice a year.
The double-edged sword: Navigating data security risks in the age of Large Language Models (LLMs)
Large language models (LLMs) have emerged as powerful business and consumer tools, capable of generating human-quality text, translating languages, and even assisting in business use cases. Their ability to improve efficiency, cut costs, enhance customer experiences and provide insights make them extremely attractive for employees and managers across all industries.
As with all emerging technologies, however, security concerns regarding the interaction of these advancements with sensitive data must be addressed. With LLMs, these risks are compounded by the vast amounts of data they must use to provide value, leading to concerns about data breaches, privacy violations, and the spread of misinformation.
SMEs invest in tech opportunities but risk missing security safeguards
Across Europe, 100 million people are employed by small and medium-sized enterprises (SMEs). The UK has over five million SMEs, providing three-fifths of employment and £2.4 trillion in turnover. As a vital component in the national economy, SMEs must continue to invest in automation, technology-led innovation, and underpin this with strong security.
We recently completed our sixth bi-annual SME IT Trends survey, and the good news is that technology investment continues to increase. Not only that, but business leaders and IT administrators are positive about the benefits of automation and artificial intelligence (AI). However, the other side of this coin is more concerning; the number of cybersecurity challenges organizations face continues to increase. Our study shows that they need to make bold decisions about their security posture. Failure to do so puts their businesses and employees at risk.
Data intelligence is your new organizational North Star
There’s no shortage of data buzzwords: data quality, data governance, data management, data integration -- the list goes on.
Don't let the term "buzzword" fool you. These concepts aren't trivial -- they're tightly interwoven and all play a critical role in data-driven decision-making. For example, data quality is key to achieving a robust data governance strategy, and data integration is integral to the data management process. However, with so many considerations at play, it’s challenging for data leaders to prioritize hygiene across the organization. Until now. Enter data intelligence.
Guide for CTOs: How to elevate your DevOps journey
For Chief Technology Officers, spearheading a culture of innovation and forging a resilient DevOps infrastructure are paramount. These objectives, however, unfold over time through a deliberate journey of strategy, experimentation, and reflection. Establishing and nurturing DevOps processes is a pivotal part of this journey, requiring a comprehensive framework to ensure your team remains aligned with organizational goals.
The DevOps Maturity Model serves as a vital framework, guiding engineers and CTOs to precisely evaluate their position within the DevOps adoption continuum and critically review their ongoing initiatives. This roadmap is essential for improving performance, fostering a culture of continuous innovation, and ultimately achieving the desired outcomes.
5G Fixed Wireless Access (FWA) -- entering a new era of connectivity
As the global marketplace becomes more competitive in response to customer demand, businesses must find alternative ways to deliver exceptional customer experiences. As budgets are tightening, companies are being scrutinized to new degrees for what they can deliver at a competitive price, with research conducted by Salesforce showing 60 percent of service professionals reporting higher customer expectations post-pandemic.
Should organizations fall short of these high expectations, customers are more than willing to look elsewhere. In fact, recent research from Emplifi found that 86 percent of consumers would leave a brand they trusted after only two poor customer experiences
Supplier data is foundational -- and so is a supplier data foundation
Supplier data enables procurement leaders to understand their organization's performance more deeply, predict market opportunities and negotiate advantageous supplier contracts. When market conditions are favorable, these benefits provide a keen competitive advantage; during economic turbulence, they're a must-have for business continuity -- all in all, data is the bedrock of procurement decisions.
But the necessity of good supplier data isn’t breaking news. Rather, the advent of innovative technologies, including generative AI and large-language models (LLMs), has emphasized the importance of data hygiene across the enterprise. Case in point: In 2024, 43 percent of data leaders say their organization has adopted a “data and analytics culture” -- up from just 21 percent last year.
What Elon Musk's AI warning says about ethical AI in business
The report by Statista forecasts a significant 21 percent net increase in the United States' GDP by 2030, attributing this growth to the integration of Artificial Intelligence (AI). This projection underscores the immense impact AI is expected to have on economic expansion. However, amid this rapid advancement, tech innovator Elon Musk has expressed serious concerns about the need for AI regulation.
Speaking at the Paris VivaTech event, Musk highlighted the potential dangers of unregulated digital superintelligence. His warnings serve as a vital reminder for businesses to reevaluate their use and engagement with AI technologies, emphasizing the importance of a balanced approach to AI integration in the economic landscape.
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