Daily Tech Digest - August 02, 2025


Quote for the day:

"Successful leaders see the opportunities in every difficulty rather than the difficulty in every opportunity" -- Reed Markham


Chief AI role gains traction as firms seek to turn pilots into profits

CAIOs understand the strategic importance of their role, with 72% saying their organizations risk falling behind without AI impact measurement. Nevertheless, 68% said they initiate AI projects even if they can’t assess their impact, acknowledging that the most promising AI opportunities are often the most difficult to measure. Also, some of the most difficult AI-related tasks an organization must tackle rated low on CAIOs’ priority lists, including measuring the success of AI investments, obtaining funding and ensuring compliance with AI ethics and governance. The study’s authors didn’t suggest a reason for this disconnect. ... Though CEO sponsorship is critical, the authors also stressed the importance of close collaboration across the C-suite. Chief operating officers need to redesign workflows to integrate AI into operations while managing risk and ensuring quality. Tech leaders need to ensure that the technical stack is AI-ready, build modern data architectures and co-create governance frameworks. Chief human resource officers need to integrate AI into HR processes, foster AI literacy, redesign roles and foster an innovation culture. The study found that the factors that separate high-performing CAIOs from their peers are measurement, teamwork and authority. Successful projects address high-impact areas like revenue growth, profit, customer satisfaction and employee productivity.


Mind the overconfidence gap: CISOs and staff don’t see eye to eye on security posture

“Executives typically rely on high-level reports and dashboards, whereas frontline practitioners see the day-to-day challenges, such as limitations in coverage, legacy systems, and alert fatigue — issues that rarely make it into boardroom discussions,” she says. “This disconnect can lead to a false sense of security at the top, causing underinvestment in areas such as secure development, threat modeling, or technical skills.” ... Moreover, the CISO’s rise in prominence and repositioning for business leadership may also be adding to the disconnect, according to Adam Seamons, information security manager at GRC International Group. “Many CISOs have shifted from being technical leads to business leaders. The problem is that in doing so, they can become distanced from the operational detail,” Seamons says. “This creates a kind of ‘translation gap’ between what executives think is happening and what’s actually going on at the coalface.” ... Without a consistent, shared view of risk and posture, strategy becomes fragmented, leading to a slowdown in decision-making or over- or under-investment in specific areas, which in turn create blind spots that adversaries can exploit. “Bridging this gap starts with improving the way security data is communicated and contextualized,” Forescout’s Ferguson advises. 


7 tips for a more effective multicloud strategy

For enterprises using dozens of cloud services from multiple providers, the level of complexity can quickly get out of hand, leading to chaos, runaway costs, and other issues. Managing this complexity needs to be a key part of any multicloud strategy. “Managing multiple clouds is inherently complex, so unified management and governance are crucial,” says Randy Armknecht, a managing director and global cloud practice leader at business advisory firm Protiviti. “Standardizing processes and tools across providers prevents chaos and maintains consistency,” Armknecht says. Cloud-native application protection platforms (CNAPP) — comprehensive security solutions that protect cloud-native applications from development to runtime — “provide foundational control enforcement and observability across providers,” he says. ... Protecting data in multicloud environments involves managing disparate APIs, configurations, and compliance requirements across vendors, Gibbons says. “Unlike single-cloud environments, multicloud increases the attack surface and requires abstraction layers [to] harmonize controls and visibility across platforms,” he says. Security needs to be uniform across all cloud services in use, Armknecht adds. “Centralizing identity and access management and enforcing strong data protection policies are essential to close gaps that attackers or compliance auditors could exploit,” he says.


Building Reproducible ML Systems with Apache Iceberg and SparkSQL: Open Source Foundations

Data lakes were designed for a world where analytics required running batch reports and maybe some ETL jobs. The emphasis was on storage scalability, not transactional integrity. That worked fine when your biggest concern was generating quarterly reports. But ML is different. ... Poor data foundations create costs that don't show up in any budget line item. Your data scientists spend most of their time wrestling with data instead of improving models. I've seen studies suggesting sixty to eighty percent of their time goes to data wrangling. That's... not optimal. When something goes wrong in production – and it will – debugging becomes an archaeology expedition. Which data version was the model trained on? What changed between then and now? Was there a schema modification that nobody documented? These questions can take weeks to answer, assuming you can answer them at all. ... Iceberg's hidden partitioning is particularly nice because it maintains partition structures automatically without requiring explicit partition columns in your queries. Write simpler SQL, get the same performance benefits. But don't go crazy with partitioning. I've seen teams create thousands of tiny partitions thinking it will improve performance, only to discover that metadata overhead kills query planning. Keep partitions reasonably sized (think hundreds of megabytes to gigabytes) and monitor your partition statistics.


The Creativity Paradox of Generative AI

Before talking about AI creation ability, we need to understand a simple linguistic limitation: despite the data used for these compositions having human meanings initially, i.e., being seen as information, after being de- and recomposed in a new, unknown way, these compositions do not have human interpretation, at least for a while, i.e., they do not form information. Moreover, these combinations cannot define new needs but rather offer previously unknown propositions to the specified tasks. ... Propagandists of know-it-all AI have a theoretical basis defined in the ethical principles that such an AI should realise and promote. Regardless of how progressive they sound, their core is about neo-Marxist concepts of plurality and solidarity. Plurality states that the majority of people – all versus you – is always right (while in human history it is usually wrong), i.e., if an AI tells you that your need is already resolved in the way that the AI articulated, you have to agree with it. Solidarity is, in essence, a prohibition of individual opinions and disagreements, even just slight ones, with the opinion of others; i.e., everyone must demonstrate solidarity with all. ... The know-it-all AI continuously challenges a necessity in the people’s creativity. The Big AI Brothers think for them, decide for them, and resolve all needs; the only thing that is required in return is to obey the Big AI Brother directives.


Doing More With Your Existing Kafka

The transformation into a real-time business isn’t just a technical shift, it’s a strategic one. According to MIT’s Center for Information Systems Research (CISR), companies in the top quartile of real-time business maturity report 62% higher revenue growth and 97% higher profit margins than those in the bottom quartile. These organizations use real-time data not only to power systems but to inform decisions, personalize customer experiences and streamline operations. ... When event streams are discoverable, secure and easy to consume, they are more likely to become strategic assets. For example, a Kafka topic tracking payment events could be exposed as a self-service API for internal analytics teams, customer-facing dashboards or third-party partners. This unlocks faster time to value for new applications, enables better reuse of existing data infrastructure, boosts developer productivity and helps organizations meet compliance requirements more easily. ... Event gateways offer a practical and powerful way to close the gap between infrastructure and innovation. They make it possible for developers and business teams alike to build on top of real-time data, securely, efficiently and at scale. As more organizations move toward AI-driven and event-based architectures, turning Kafka into an accessible and governable part of your API strategy may be one of the highest-leverage steps you can take, not just for IT, but for the entire business.


Meta-Learning: The Key to Models That Can "Learn to Learn"

Meta-learning is a field within machine learning that focuses on algorithms capable of learning how to learn. In traditional machine learning, an algorithm is trained on a specific dataset and becomes specialized for that task. In contrast, meta-learning models are designed to generalize across tasks, learning the underlying principles that allow them to quickly adapt to new, unseen tasks with minimal data. The idea is to make machine learning systems more like humans — able to leverage prior knowledge when facing new challenges. ... This is where meta-learning shines. By training models to adapt to new situations with few examples, we move closer to creating systems that can handle the diverse, dynamic environments found in the real world. ... Meta-learning represents the next frontier in machine learning, enabling models that are adaptable and capable of generalizing across a wide range of tasks with minimal data. By making machines more capable of learning from fewer examples, meta-learning has the potential to revolutionize fields like healthcare, robotics, finance, and more. While there are still challenges to overcome, the ongoing advancements in meta-learning techniques, such as few-shot learning, transfer learning, and neural architecture search, are making it an exciting area of research with vast potential for practical applications.


US govt, Big Tech unite to build one stop national health data platform

Under this framework, applications must support identity-proofing standards, consent management protocols, and Fast Healthcare Interoperability Resources (FHIR)-based APIs that allow for real-time retrieval of medical data across participating systems. The goal, according to CMS Administrator Chiquita Brooks-LaSure, is to create a “unified digital front door” to a patient’s health records that are accessible from any location, through any participating app, at any time. This unprecedented public-private initiative builds on rules first established under the 2016 21st Century Cures Act and expanded by the CMS Interoperability and Patient Access Final Rule. This rule mandates that CMS-regulated payers such as Medicare Advantage organizations, Medicaid programs, and Affordable Care Act (ACA)-qualified health plans make their claims, encounter data, lab results, provider remittances, and explanations of benefits accessible through patient-authorized APIs. ... ID.me, another key identity verification provider participating in the CMS initiative, has also positioned itself as foundational to the interoperability framework. The company touts its IAL2/AAL2-compliant digital identity wallet as a gateway to streamlined healthcare access. Through one-time verification, users can access a range of services across providers and government agencies without repeatedly proving their identity.


What Is Data Literacy and Why Does It Matter?

Building data literacy in an organization is a long-term project, often spearheaded by the chief data officer (CDO) or another executive who has a vision for instilling a culture of data in their company. In a report from the MIT Sloan School of Management, experts noted that to establish data literacy in a company, it’s important to first establish a common language so everyone understands and agrees on the definition of commonly used terms. Second, management should build a culture of learning and offer a variety of modes of training to suit different learning styles, such as workshops and self-led courses. Finally, the report noted that it’s critical to reward curiosity – if employees feel they’ll get punished if their data analysis reveals a weakness in the company’s business strategy, they’ll be more likely to hide data or just ignore it. Donna Burbank, an industry thought leader and the managing director of Global Data Strategy, discussed different ways to build data literacy at DATAVERSITY’s Data Architecture Online conference in 2021. ... Focusing on data literacy will help organizations empower their employees, giving them the knowledge and skills necessary to feel confident that they can use data to drive business decisions. As MIT senior lecturer Miro Kazakoff said in 2021: “In a world of more data, the companies with more data-literate people are the ones that are going to win.”


LLMs' AI-Generated Code Remains Wildly Insecure

In the past two years, developers' use of LLMs for code generation has exploded, with two surveys finding that nearly three-quarters of developers have used AI code generation for open source projects, and 97% of developers in Brazil, Germany, and India are using LLMs as well. And when non-developers use LLMs to generate code without having expertise — so-called "vibe coding" — the danger of security vulnerabilities surviving into production code dramatically increases. Companies need to figure out how to secure their code because AI-assisted development will only become more popular, says Casey Ellis, founder at Bugcrowd, a provider of crowdsourced security services. ... Veracode created an analysis pipeline for the most popular LLMs (declining to specify in the report which ones they tested), evaluating each version to gain data on how their ability to create code has evolved over time. More than 80 coding tasks were given to each AI chatbot, and the subsequent code was analyzed. While the earliest LLMs tested — versions released in the first half of 2023 — produced code that did not compile, 95% of the updated versions released in the past year produced code that passed syntax checking. On the other hand, the security of the code has not improved much at all, with about half of the code generated by LLMs having a detectable OWASP Top-10 security vulnerability, according to Veracode.

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