Daily Tech Digest - January 12, 2024

Navigating Tomorrow: Becoming an Enterprise of the Future

Preparing for what lies ahead goes far beyond just implementing the right technologies, it is about developing a culture that embraces change with empathy. Cultivating a mindset across the organisation that values innovation, continuous learning, and agility ensures that every employee charges forward with confidence. In times of economic uncertainties and technological advancements, it is crucial that we practice empathy. Naturally, there is some fear that technologies like AI will replace human workers. As such, leaders must help employees understand that technology is here to augment their roles and empower them to spend more time on other valuable tasks. The key to embracing any new technology and providing access at scale is to get everyone in the team on board. Whether greeted with excitement or anxiety, leaders must champion this culture of change by encouraging employees to seek new ways of working while ensuring they remain engaged and valued. Certainly, data-driven decision-making will undoubtedly continue to be the cornerstone of future business attempts. 

The Importance of Enterprise Architecture in the Modern Business Landscape

The field of Enterprise Architecture is constantly evolving, driven by emerging trends and innovations. One of the significant trends is the adoption of cloud computing and hybrid IT environments. Cloud-based solutions offer scalability, flexibility, and cost-efficiency, making them increasingly popular among businesses. Enterprise Architecture helps organizations leverage these technologies by designing architectures that integrate cloud services and on-premises infrastructure, ensuring seamless operations and efficient resource utilization. Another emerging trend is the incorporation of artificial intelligence (AI) and machine learning (ML) in Enterprise Architecture practices. AI and ML technologies enable businesses to automate processes, analyze vast amounts of data, and gain valuable insights. By integrating AI and ML into their Enterprise Architecture frameworks, organizations can enhance decision-making, optimize business processes, and improve overall efficiency. Furthermore, the rise of digital transformation has had a significant impact on Enterprise Architecture. 

Top 8 challenges IT leaders will face in 2024

To guide an organization through uncertainty, IT leaders must help ensure everyone in the company is on the same page, Srivastava says. Instead of playing catch-up, he suggests a proactive approach with clear communication as a guiding principle. “It starts with establishing a clear set of agreed upon initiatives and outcomes for the organization,” he says. “We have to make sure everyone understands what they are doing, why they are doing it, and — most importantly — how success will be measured.” ... Security is a challenge that makes the list of top CIO worries perennially, but Grant McCormick, CIO of cybersecurity company Exabeam, notes a rising need for increased collaboration between IT and security teams to address the issue. “The role of the CIO has recently seen a massive convergence with cybersecurity,” says McCormick. “Regardless of whether or not security reports into the CIO, or another leader within the company, it is in everyone’s best interest to be conscious of the organization’s security posture and to enable IT and cybersecurity to work in a highly synchronized manner.”

Economic Uncertainty Doesn’t Mean Compromising Cybersecurity

This futuristic technology isn’t just something to tap into to enrich individual experiences; it is also to help solve some of society’s most pressing challenges and, most of all, to keep people safe. For cryptocurrencies, where there is estimated to be four times more fraud than in regular fiat payments, technology providers are devising new innovations to stay ahead. New solutions can help customers make informed decisions that protect their business, as well as the entire payments ecosystem. A simple dashboard can provide visibility of crypto spend, transaction volumes and an anti-money laundering risk rating exposure. Through solutions like these, banks and other businesses can earn and, importantly, keep the trust of their customers—on whom their business depends. Trust is fragile. It can be broken in a nanosecond. And as the global financial ecosystem expands, it’s getting harder for organizations to navigate the maze of cyber risks alone. Businesses, merchants, financial institutions and fintechs need trailblazing tools and expert knowledge to understand the risks they’re facing. 

Redefining Data Governance: Bridging The Gap Between Technical And Domain Experts

As the data industry gravitates toward decentralization, specifically federated systems, the absence of a robust framework in data governance, master data and data quality becomes glaringly evident. The prevailing issue in many companies is not the sheer volume of data or a lack of technological options but the erroneous assumption that their data is inherently primed for insights, AI applications and democratization. This misconception overshadows the real challenge: the need for a comprehensive approach to data management that integrates the expertise of domain professionals. The advent of practical AI applications marks a watershed moment in the history of data governance. This technology is not just a tool for automation; it serves as a bridge between the technical and business realms. It provides a platform where business experts can meaningfully contribute to data strategies and decision-making processes. Technical teams initially assumed the mantle of data governance out of necessity due to the requisite skill sets. 

Orchestrating Resilience Building Modern Asynchronous Systems

The first one is state management. Basically, the problem here is that you need to contemplate lots of possible combinations of states and events. For example, the "review received" message could come in while the campaign is in pending state instead of the relevant waiting state, or an out of sequence event could come in from somewhere, and so on. All of those cases need to be handled, even though they are not the most likely sequence of events and states. ... Handling retries becomes a task almost as complex as implementing primary logic, sometimes even more so. You can think of implementing your retry mechanisms in different ways, for example by storing a retry counter in the database and incrementing it on each failed attempt until either you succeed or reach the maximum allowed number of retries. Alternatively, you could embed the retry counter in the queue message itself, so you dequeue a message, process it, and, if it fails, re-enqueue the message and increment the retry count. In both cases this implies a huge overhead for developers.

Attackers deploy rootkits on misconfigured Apache Hadoop and Flink servers

In the attack chain against Hadoop, the attackers first exploit the misconfiguration to create a new application on the cluster and allocate computing resources to it. In the application container configuration, they put a series of shell commands that use the curl command-line tool to download a binary called “dca” from an attacker-controlled server inside the /tmp directory and then execute it. A subsequent request to Hadoop YARN will execute the newly deployed application and therefore the shell commands. Dca is a Linux-native ELF binary that serves as a malware downloader. Its primary purpose is to download and install two other rootkits and to drop another binary file called tmp on disk. It also sets a crontab job to execute a script called dca.sh to ensure persistence on the system. The tmp binary that’s bundled into dca itself is a Monero cryptocurrency mining program, while the two rootkits, called initrc.so and pthread.so, are used to hide the dca.sh script and tmp file on disk. The IP address that was used to target Aqua’s Hadoop honeypot was also used to target Flink, Redis, and Spring framework honeypots 

Merck's Cyberattack Settlement: What Does it Mean for Cyber Insurance Coverage?

The Merck and Mondelez cases are likely not going to be the last of their kind. More legal disputes between insurers and insureds, whether regarding war exclusions or other issues, could arise in the future. “I think that the cyber litigation is just getting started,” says Stern. More cases could drive change in the way cyber insurance companies approach risk tied to cyberattacks and what is considered cyberwarfare. When new risks challenge the existing approach to coverage, it drives industry change. “Maybe it takes a second or a third dispute to really achieve a definitive conclusion on that particular matter,” says Kannry. “Then, what can often happen is insurance industry says, ‘You know what, that type of loss needs to be understood and defined separately.’” Compared to many other insurance products, cyber insurance is relatively new. That means there remains plenty of room for the development of innovative ways to offer cyber insurance coverage. But the road forward likely won’t be without bumps for insurers and insureds.

Organizations Must Be Prudent To Realize Value In Generative AI

Rather than being swayed by the allure of generative AI capabilities, remain steadfast about the core features that can genuinely transform and enhance your operations. This pragmatic approach should be considered a short- to mid-term strategy for any forward-thinking organization. The reality is that features closely coupled with generative AI capabilities are still on the horizon. It will be at least a couple of years before they become commonplace. To navigate this transformative landscape effectively as an analytics professional, you must equip yourself with a deep understanding of generative AI. This proficiency will enable you to distinguish between features loosely coupled with generative AI and features that are natively and seamlessly integrated into the technology stack. Furthermore, keep a vigilant eye on the vendors supplying your critical business software. A vendor's stance and commitment to generative AI can profoundly impact how your organization operates in the future. 

LLM hype fades as enterprises embrace targeted AI models

LLMs were created by research teams exploring the capabilities of AI technology rather than as models designed to solve specific business problems. As a result, their capabilities are broad and shallow — writing a fairly generic email or press releases, for example. For the modern business, they have limited capabilities beyond that, requiring more data to produce results with any depth. While the AI landscape used to be dominated solely by OpenAI, major names in the tech world are beginning to outperform ChatGPT with their own LLMs, including Google’s new Gemini model. However, due to the broad capabilities of these new large language models, the text and image-based benchmarks used to determine the model’s prowess were just as general. These benchmarks ranged from simple multi-step reasoning to basic arithmetic. If an AI company’s gauge for a successful Generative AI platform is how correctly it can complete rudimentary math equations, that has little to no relevance for the work of an enterprise organization.

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Quote for the day:

"Before you are a leader, success is all about growing yourself when you become a leader, success is all about growing others." -- Jack Welch

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