Seamless cloud migration: Building an AI-optimized future
Implementing cloud services with AI technologies, such as Microsoft Copilot, is fundamental for IT providers seeking to offer advanced solutions. However, with greater dependence on AI-generated tools to foster innovation and productivity in organizations, the necessity of enabling cloud environments to host these sophisticated capabilities has become paramount.
Their successful integration, however, comes at the expense of having additional investments in computing power, data analytics, and intelligent security solutions that shield sensitive information from unauthorized access. Many companies first need to accomplish a cloud migration to improve the security posture of the infrastructure before implementing AI.
Need to search terabytes of enterprise data? Tips for getting quickly to that 4-leaf clover
If you find yourself in a springtime clover field hunting for that rare 4-leaf clover, the journey is the reward. Not so if you and your team are hunched over your desks hunting 4-leaf clovers in terabytes of enterprise data. While combing through millions of files is never a “walk in the park,” enterprise search makes this process exponentially more pleasant.
To enable instant concurrent searching across terabytes, enterprise search first has to index the data. Indexing is simple: just tell the indexer the folders, emails archives and the like to index, and the software will take it from there. (This article uses dtSearch for its specifics on enterprise search but there are other comparable products on the market.) Tip: the files to index can be local or remote like SharePoint attachments, OneDrive / Office 365 files, etc. that appear as part of the Windows folder system.
Working with AI: When should humans be 'in the loop' or 'over the loop'?
It’s a fact that over 80 percent of AI projects fail. Not because of AI’s potential, but because businesses prioritize minor use cases over real transformation. Automated insights and meeting summaries may be impressive, but AI only drives impact when seamlessly integrated into workflows, turning insights into action.
Deploying AI successfully isn’t simple, and organizations are complex. Effective AI deployment requires a clear framework for human oversight. AI should usually enhance human decision-making, providing targeted, explainable, and interactive insights. But in some cases -- especially when decisions are time-sensitive or involve vast amounts of data -- humans cannot oversee every output in real time. This raises a key question: when should humans be ‘in the loop,’ actively making decisions, and when should they be ‘over the loop,’ overseeing AI without direct intervention? Getting this balance right is crucial for both AI’s effectiveness and its responsible use.
Fax in 2025: How cloudification is revolutionizing financial services
Digital transformation is causing the financial services industry major problems when it comes to how highly sensitive, time-critical information in transmitted. Adding to these challenges are the radical changes in customer expectations in recent years, as digital communications have taken hold in nearly every aspect of consumers’ lives.
To remain viable, outdated communication structures -- particularly those relying on servers and distributed systems -- need to be consolidated and standardized. Few industries are more prepared for this transformation than financial services, where significant investments are already being made to improve operational efficiencies and competitiveness. As some of the world’s largest financial institutions invest in modern technologies like cloud migration and AI, attention is turning to some of their oldest tools that are still in use. Fax is a prime example.
Beyond DeepSeek: 3 critical questions for the future of AI
This year started with a shockwave for the tech world, and the AI community in particular. Launched by a relatively obscure Chinese startup, DeepSeek not only challenged the rules of the AI game by sending Nvidia's stock plummeting 17 percent in one day and becoming the most-downloaded app on the App Store and Play Store, but also showed the persisting security problems by accidentally exposing its database and leaking sensitive data including chat histories, API keys and backend operational details.
Success and failures aside, DeepSeek made the world realize how quickly and deeply a single AI model release can impact global events, and this raises three questions. First, how legitimate (and sustainable) are the massive AI investments in the West? Second, what risks and opportunities does open-source development pose? Finally, is it possible to balance growth and innovation with data privacy and security amidst a global AI race?
Inside a cyberattack: How hackers steal data
The truth about cybersecurity is that it’s almost impossible to keep hackers outside of an organization, particularly as the cybercrime industry becomes increasingly sophisticated and their technology more advanced.
Once a hacker has broken through an organization’s defenses, it is relatively easy to move within the network and access information without being detected for days, and even months. This is a significant concern for Banking and Financial Services organizations, which house valuable sensitive and Personally Identifiable Information (PII). The goal of cybersecurity is to minimize the risk and the impact of a breach. Understanding the adversary’s mindset and activity is central to this.
Why API-first engineering is the way forward for software development
British software developer and international public speaker on software development, Martin Fowler once famously said: “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” His book on Refactoring has been a best seller for decades and is a guide on how to transform code safely and rapidly, helping developers build better code. Exactly these same principles should apply when looking to develop an API-first approach to software engineering.
But first, what do we mean when we talk about an API-first approach? This is a software development method that prioritizes the design of APIs before writing any other code, instead of treating them as an afterthought. This is different from the traditional approach, where the application code is written first, and the API is added later.
Five ways data platforms are underpinning the second cloud revolution
According to Gartner, over the next few years hybrid cloud will become the de facto approach for unlocking value from data. The projections are stark. Nine-in-ten organizations will adopt a hybrid cloud model by 2027, and end user spending globally on public cloud will grow by 21.4 percent this year alone, reaching more than $723 billion. Application services, system infrastructure services and Platform-as-a-Service (PaaS) will all see a boost in spending as well. By anyone’s money, these are huge numbers.
This second cloud revolution is being driven by data. When combined with analytics, data is a uniquely valuable asset for any business. If harnessed correctly, it can grow revenue, reduce costs, and entirely transform a business by opening up fresh market opportunities through the use of new technologies like GenAI.
From fixing issues to fueling innovation: The growing business case for observability
This year, embracing a leading observability practice will not only be a key priority for organizations but an essential competitive differentiator. Recent data shows that leading organizations with mature observability practices spend 38 percent more of their time on innovation, in contrast with organizations early on in their observability journey. This greater amount of time to focus on product innovation can equate to significant benefits for an organization, such as increased developer productivity, improved operational efficiency and more importantly winning market share.
2024 has shown us that the impact and business value of observability is expanding. It is evolving from a reactive practice to a proactive one where organizations not only use observability for troubleshooting issues but now also to inform their customer experience strategy and to fuel faster innovation.
How cloud security teams should think about AI
According to estimates from Goldman Sachs, generative AI (GenAI) will constitute 10-15 percent of cloud spending by 2030, or a forecasted $200-300 billion (USD). The public cloud serves as the perfect vessel for delivering AI-enabled applications quickly, cost-effectively, and at scale. For organizations looking to profit from AI’s potential, the path effectively travels through the cloud.
For cloud security teams on the ground, however, the impact of AI can seem complicated. Understanding the challenges it presents, and the key capabilities it enables, can help them work smarter and more effectively. This article explores the three ways cloud security teams should think about AI to enhance protections, improve efficiency, and address resource constraints.
The encryption backdoor debate: Why are we still here?
Earlier this month, reports emerged that the UK government had pressured Apple, under the Investigatory Powers Act 2016, to create a backdoor into encrypted iCloud data. Unlike targeted access requests tied to specific cases, this demand sought a blanket ability to access users’ end-to-end encrypted files.
Apple was forced to reconsider its Advanced Data Protection service in the UK, and this latest development raises a fundamental question: Why does the debate over encryption backdoors persist despite decades of technological progress and repeated warnings from cybersecurity experts?
Punycode: The invisible cyber threat hiding in plain sight
The internet was conceived to connect the world, and internationalized domain names (IDNs) have certainly helped make that vision a reality. By allowing non-ASCII characters in web addresses, they’ve been pivotal in improving both accessibility and inclusivity.
As with any technological breakthrough, cybercriminals have found a way to turn innovation into exploitation. By using Punycode, a system for encoding IDNs, attackers have been able to create their own deceptive domains to mimic trusted brands and evade traditional security defenses to fool even the most wary users.
Strengthening cyber resilience -- cautious collaboration between organizations and third-party vendors needed
Ransomware is continuously on the rise. Despite multiple major law enforcement actions against ransomware groups over the past year, there has been a significant increase in ransomware attacks between 2023 and 2024. Interestingly enough, there was also a tracked 35 percent drop in ransomware payments in 2024, but it is clear that this is not stopping ransomware attacks from continuing as threat actors are finding other ways to monetize the data they’re stealing.
To combat this rise, cyber security measures within organizations need to be improved at every level, especially as the threat landscape grows even more complex. This past year has shown us that the importance of careful third-party vendor collaboration particularly must not be overlooked. With that said, there are a few considerations that need to take priority as 2025 progresses.
Right now, there is no right or wrong SASE answer
Adoption of SASE, or secure access service edge, is accelerating -- especially, according to IDC, at organizations of over 1,000 employees, while it’s estimated the global SASE market will grow from last year’s $1.83 billion to over $17 billion by 2033.
The business case for such rapid take-up is simple: SASE is the perfect way of blending the best of your network, the cloud, and cybersecurity. According to Gartner, because SASE is primarily delivered as software as a service it’s a great way to enable full zero trust access based on the identity of the device or entity, and it’s easily combined with real-time context and security and compliance policies.
Is a lack of supply chain visibility undermining board-level confidence in cyber security programs?
As we head further into 2025, organizations must focus on bolstering operational resilience and addressing third-party risks, driven not only by commercial imperatives but also by new regulatory mandates. With the enactment of regulations such as NIS2 in late 2024 and DORA early this year, supply chain risk management is now a strategic necessity.
This means that third-party cyber risk management must become a strategic priority. However, according to BlueVoyant’s fifth annual Supply Chain Defence report, which examines fast-evolving supply ecosystems, many organizations don’t appear to be prioritizing supply chain cyber risk management, or are unaware of cyber security gaps in their supply chains.
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