Daily Tech Digest: solution architecture

Showing posts with label solution architecture. Show all posts

Daily Tech Digest - August 23, 2025

Quote for the day:

"Failure is the condiment that gives success its flavor." -- Truman Capote

Enterprise passwords becoming even easier to steal and abuse

Attackers actively target user credentials because they offer the most direct route or foothold into a targeted organization’s network. Once inside, attackers can move laterally across systems, searching for other user accounts to compromise, or they attempt to escalate their privileges and gain administrative control. This hunt for credentials extends beyond user accounts to include code repositories, where developers may have hard-coded access keys and other secrets into application source code. Attacks using valid credentials were successful 98% of the time, according to Picus Security. ... “CISOs and security teams should focus on enforcing strong, unique passwords, using MFA everywhere, managing privileged accounts rigorously and testing identity controls regularly,” Curran says. “Combined with well-tuned DLP and continuous monitoring that can detect abnormal patterns quickly, these measures can help limit the impact of stolen or cracked credentials.” Picus Security’s latest findings reveal a concerning gap between the perceived protection of security tools and their actual performance. An overall protection effectiveness score of 62% contrasts with a shockingly low 3% prevention rate for data exfiltration. “Failures in detection rule configuration, logging gaps and system integration continue to undermine visibility across security operations,” according to Picus Security.

Architecting the next decade: Enterprise architecture as a strategic force

In an age of escalating cyber threats and expanding digital footprints, security can no longer be layered on; it must be architected in from the start. With the rise of AI, IoT and even quantum computing on the horizon, the threat landscape is more dynamic than ever. Security-embedded architectures prioritize identity-first access control, continuous monitoring and zero-trust principles as baseline capabilities. ... Sustainability is no longer a side initiative; it’s becoming a first principle of enterprise architecture. As organizations face pressure from regulators, investors and customers to lower their carbon footprint, digital sustainability is gaining traction as a measurable design objective. From energy-efficient data centers to cloud optimization strategies and greener software development practices, architects are now responsible for minimizing the environmental impact of IT systems. The Green Software Foundation has emerged as a key ecosystem partner, offering measurement standards like software carbon intensity (SCI) and tooling for emissions-aware development pipelines. ... Technology leaders must now foster a culture of innovation, build interdisciplinary partnerships and enable experimentation while ensuring alignment with long-term architectural principles. They must guide the enterprise through both transformation and stability, navigating short-term pressures and long-term horizons simultaneously.

Capitalizing on Digital: Four Strategic Imperatives for Banks and Credit Unions

Modern architectures dissolve the boundary between core and digital. The digital banking solution is no longer a bolt-on to the core; the core and digital come together to form the accountholder experience. That user experience is delivered through the digital channel, but when done correctly, it’s enabled by the modern core. Among other things, the core transformation requires robust use of shared APIs, consistent data structures, and unified development teams. Leading financial institutions are coming to realize that core evaluations now must include an evaluable of their capability to enable the digital experience. Criteria like Availability, Reliability, Real-time, Speed and Security are now emerging as foundational requirements of a core to enable the digital experience. "If your core can’t keep up with your digital, you’re stuck playing catch-up forever," said Jack Henry’s Paul Wiggins, Director of Sales, Digital Engineers. ... Many institutions still operate with digital siloed in one department, while marketing, product, and operations pursue separate agendas. This leads to mismatched priorities — products that aren’t promoted effectively, campaigns that promise features operations can’t support, and technical fixes that don’t address the root cause of customer and member pain points. ... Small-business services are a case in point. Jack Henry’s Strategy Benchmark study found that 80% of CEOs plan to expand these services over the next two years.

Bentley Systems CIO Talks Leadership Strategy and AI Adoption

The thing that’s really important for a CIO to be thinking about is that we are a microcosm for how all of the business functions are trying to execute the tactics against the strategy. What we can do across the portfolio is represent the strategy in real terms back to the business. We can say: These are all of the different places where we're thinking about investing. Does that match with the strategy we thought we were setting for ourselves? And where is there a delta and a difference? ... When I got my first CIO role, there was all of this conversation about business process. That was the part that I had to learn and figure out how to map into these broader, strategic conversations. I had my first internal IT role at Deutsche Bank, where we really talked about product model a lot -- thinking about our internal IT deliverables as products. When I moved to Lenovo, we had very rich business process and transformation conversations because we were taking the whole business through such a foundational change. I was able to put those two things together. It was a marriage of several things: running a product organization; marrying that to the classic IT way of thinking about business process; and then determining how that becomes representative to the business strategy.

What Is Active Metadata and Why Does It Matter?

Active metadata addresses the shortcomings of passive approaches by automatically updating the metadata whenever an important aspect of the information changes. Defining active metadata and understanding why it matters begins by looking at the shift in organizations’ data strategies from a focus on data acquisition to data consumption. The goal of active metadata is to promote the discoverability of information resources as they are acquired, adapted, and applied over time. ... From a data consumer’s perspective, active metadata adds depth and breadth to their perception of the data that fuels their decision-making. By highlighting connections between data elements that would otherwise be hidden, active metadata promotes logical reasoning about data assets. This is especially so when working on complex problems that involve a large number of disconnected business and technical entities.The active metadata analytics workflow orchestrates metadata management across platforms to enhance application integration, resource management, and quality monitoring. It provides a single, comprehensive snapshot of the current status of all data assets involved in business decision-making. The technology augments metadata with information gleaned from business processes and information systems.

Godrej Enterprises CHRO on redefining digital readiness as culture, not tech

“Digital readiness at Godrej Enterprises Group is about empowering every employee to thrive in an ever-evolving landscape,” Kaur said. “It’s not just about technology adoption. It’s about building a workforce that is agile, continuously learning, and empowered to innovate.” This reframing reflects a broader trend across Indian industry, where digital transformation is no longer confined to IT departments but runs through every layer of an organisation. For Godrej Enterprises Group, this means designing a workplace where intrapreneurship is rewarded, innovation is constant, and employees are trained to think beyond immediate functions. ... “We’ve moved away from one-off training sessions to creating a dynamic ecosystem where learning is accessible, relevant, and continuous,” she said. “Learning is no longer a checkbox — it’s a shared value that energises our people every day.” This shift is underpinned by leadership development programmes and innovation platforms, ensuring that employees at every level are encouraged to experiment and share knowledge. ... “We see digital skilling as a core business priority, not just an HR or L&D initiative,” she said. “By making digital skilling a shared responsibility, we foster a culture where learning is continuous, progress is visible, and success is celebrated across the organisation.”

AI is creeping into the Linux kernel - and official policy is needed ASAP

However, before you get too excited, he warned: "This is a great example of what LLMs are doing right now. You give it a small, well-defined task, and it goes and does it. And you notice that this patch isn't, 'Hey, LLM, go write me a driver for my new hardware.' Instead, it's very specific -- convert this specific hash to use our standard API." Levin said another AI win is that "for those of us who are not native English speakers, it also helps with writing a good commit message. It is a common issue in the kernel world where sometimes writing the commit message can be more difficult than actually writing the code change, and it definitely helps there with language barriers." ... Looking ahead, Levin suggested LLMs could be trained to become good Linux maintainer helpers: "We can teach AI about kernel-specific patterns. We show examples from our codebase of how things are done. It also means that by grounding it into our kernel code base, we can make AI explain every decision, and we can trace it to historical examples." In addition, he said the LLMs can be connected directly to the Linux kernel Git tree, so "AI can go ahead and try and learn things about the Git repo all on its own." ... This AI-enabled program automatically analyzes Linux kernel commits to determine whether they should be backported to stable kernel trees. The tool examines commit messages, code changes, and historical backporting patterns to make intelligent recommendations.

Applications and Architecture – When It’s Not Broken, Should You Try to Fix It?

No matter how reliable your application components are, they will need to be maintained, upgraded or replaced at some point. As elements in your application evolve, some will reach end of life status – for example, Redis 7.2 will reach end of life status for security updates in February 2026. Before that point, it’s necessary to assess the available options. For businesses in some sectors like financial services, running out of date and unsupported software is a potential failure for regulations on security and resilience. For example, the Payment Card Industry Data Security Standard version 4.0 enforces that teams should check all their software and hardware is supported every year; in the case of end of life software, teams must also provide a full plan for migration that will be completed within twelve months. ... For developers and software architects, understanding the role that any component plays in the overall application makes it easier to plan ahead. Even the most reliable and consistent component may need to change given outside circumstances. In the Discworld series, golems are so reliable that they become the standard for currency; at the same time, there are so many of them that any problem could affect the whole economy. When it comes to data caching, Redis has been a reliable companion for many developers.

From cloud migration to cloud optimization

The report, based on insights from more than 2,000 IT leaders, reveals that a staggering 94% of global IT leaders struggle with cloud cost optimization. Many enterprises underestimate the complexities of managing public cloud resources and the inadvertent overspending that occurs from mismanagement, overprovisioning, or a lack of visibility into resource usage. This inefficiency goes beyond just missteps in cloud adoption. It also highlights how difficult it is to align IT cost optimization with broader business objectives. ... This growing focus sheds light on the rising importance of finops (financial operations), a practice aimed at bringing greater financial accountability to cloud spending. Adding to this complexity is the increasing adoption of artificial intelligence and automation tools. These technologies drive innovation, but they come with significant associated costs. ... The argument for greater control is not new, but it has gained renewed relevance when paired with cost optimization strategies. ... With 41% of respondents’ IT budgets still being directed to scaling cloud capabilities, it’s clear that the public cloud will remain a cornerstone of enterprise IT in the foreseeable future. Cloud services such as AI-powered automation remain integral to transformative business strategies, and public cloud infrastructure is still the preferred environment for dynamic, highly scalable workloads. Enterprises will need to make cloud deployments truly cost-effective.

The Missing Layer in AI Infrastructure: Aggregating Agentic Traffic

Software architects and engineering leaders building AI-native platforms are starting to notice familiar warning signs: sudden cost spikes on AI API bills, bots with overbroad permissions tapping into sensitive data, and a disconcerting lack of visibility or control over what these AI agents are doing. It’s a scenario reminiscent of the early days of microservices – before we had gateways and meshes to restore order – only now the "microservices" are semi-autonomous AI routines. Gartner has begun shining a spotlight on this emerging gap. ... Every major shift in software architecture eventually demands a mediation layer to restore control. When web APIs took off, API gateways became essential for managing authentication/authorization, rate limits, and policies. With microservices, service meshes emerged to govern internal traffic. Each time, the need only became clear once the pain of scale surfaced. Agentic AI is on the same path. Teams are wiring up bots and assistants that call APIs independently - great for demos ... So, what exactly is an AI Gateway? At its core, it’s a middleware component – either a proxy, service, or library – through which all AI agent requests to external services are channeled. Rather than letting each agent independently hit whatever API it wants, you route those calls via the gateway, which can then enforce policies and provide central management.

Daily Tech Digest - August 17, 2025

Quote for the day:

"Failure is the condiment that gives success its flavor." -- Truman Capote

The third leg of the stool: Technology’s role in M&A

The term “technical debt” wasn’t mainstream, making it tough to convey to lawyers, accountants and executives. Their languages aligned — business, finance, law — with shared specificity. But IT? We spoke a different dialect, full of jargon that obscured our business insights. This cultural divide explained technology’s historical exclusion from M&A. The gap was mine to bridge. Over time, I learned to translate, framing technical risks in terms of dollars, downtime and competitive edge. ... Overlap exists with legal and finance, but IT’s lens is unique: assessing how operations impact data and systems. Chaotic processes yield chaotic data; effective ones produce reliable insights. ... “Good decisions on bad data are bad decisions” (me, circa 2007). Data is an enterprise’s most valuable asset, yet often neglected. Poor data can cripple; great data accelerates growth. In M&A, I scrutinize quality, lifecycle management, governance, ownership and analysis. Companies are typically polarized: exemplary governance or barely functional. Data issues heavily influence deal pricing — more on that in a future post. ... Critical during M&A, as deals attract hackers — sometimes derailing them entirely. With AI-driven threats rising, robust postures are non-negotiable. This warrants its own article.

Navigating the issues that impact data center design today

In the last few years, design considerations have changed significantly. The adoption of high-performance computing (HPC) and artificial intelligence (AI) applications translates into greater power consumption and that requires a rethink of cooling and management. What’s more, it’s increasingly difficult to predict future capacity requirements. ... Modular data center infrastructure can help facilitate zone-based deployments. Many people think of modular data centers as those deployed in ISO shipping containers, but that is only one type. There are also skid-mounted systems and preconfigured enclosures. Preconfigured enclosures can be shells or self-contained units with built-in power, cooling, fire suppression, and physical security. ... Whether building out a new data center or expanding an existing one, organizations should choose sustainable materials. With smart choices, future data centers will be self-sufficient and carbon- and water-neutral and have minimal impact on the local environment.
Planning is key These challenges have upped the ante for data center design planning. It’s no longer advisable to build out a simple shell with a raised floor and start adding infrastructure. Your facility must have the necessary power capacity, redundancy, and security to meet your business needs.

Mastering Microservices: Seven Uncommon Strategies for Streamlined Success

Containerization might seem like old news, but there are nuances that can significantly impact performance and scalability. Containers encapsulate your microservices, ensuring consistency across environments. Yet, not all container strategies are created equal. We’ve seen teams struggle when they cram too many processes into a single container. ... It’s said that you can’t manage what you can’t measure, and this couldn’t be truer for microservices. With multiple services running concurrently, effective logging and monitoring become crucial. Gone are the days of relying solely on traditional log files or single-instance monitors. We once faced a situation where a subtle bug in a service went undetected for weeks, causing memory leaks and gradually degrading performance. Our solution was to implement centralized logging and observability tools like Prometheus and Grafana. These tools allowed us to aggregate logs from various services and gain insights through real-time dashboards. ... Security is often like flossing—everyone knows it’s important, but many neglect it until there’s a problem. With microservices, security risks multiply. It’s crucial to secure inter-service communication, protect sensitive data, and ensure compliance with industry standards.

AI Security in the Cloud-Native DevSecOps Pipeline

Because reacting to threats is a lost cause when the attacks themselves are learning and adapting, a proactive stance is essential for survival. This is a mindset embraced by security leaders like Akash Agrawal, VP of DevOps & DevSecOps at LambdaTest, an AI-native software testing platform. He argues for a fundamental shift: “Security can no longer be bolted on at the end,” he explains. “AI allows us to move from reactive scanning to proactive prevention.” This approach means using AI not just to identify flaws in committed code, but to predict where the next one might emerge. ... But architectural flaws are not the only risk. AIʼs drive for automation can also lead to more common security gaps like credential leakage, a problem that Nic Adams, co-founder and CEO of security startup 0rcus, sees growing. He points to AI-backed CI/CD tools that auto-generate infrastructure-as-code and inadvertently create “credential sprawl” by embedding long-lived API keys directly into configuration files. The actionable defense here is to assume AI will make mistakes and build a safety net around it. Teams must integrate real-time secret scanning directly into the pipeline and enforce a strict policy of using ephemeral, short-lived credentials that expire automatically. Beyond specific code vulnerabilities, there is a more strategic gap that AI introduces into the development process itself.

Stop using AI for these 9 work tasks - here's why

Every time you give the AI some information, ask yourself how you would feel if it were posted to the company's public blog or wound up on the front page of your industry's trade journal. This concern also includes information that might be subject to disclosure regulations, such as HIPAA for health information or GDPR for personal data for folks operating in the EU. Regardless of what the AI companies tell you, it's best to simply assume that everything you feed into an AI is now grist for the model-training mill. Anything you feed in could later wind up in a response to somebody's prompt, somewhere else. ... Contracts are designed to be detailed and specific agreements on how two parties will interact. They are considered governing documents, which means that writing a bad contract is like writing bad code. Baaad things will happen. Do not ask AIs for help with contracts. They will make errors and omissions. They will make stuff up. Worse, they will do so while sounding authoritative, so you're more likely to use their advice. ... But when it comes time to ask for real advice that you plan on considering as you make major decisions, just don't. Let's step away from the liability risk issues and focus on common sense. First, if you're using something like ChatGPT for real advice, you have to know what to ask. If you're not trained in these professions, you might not know.

The Evolution of the DBA—More Than Just a Keeper of Databases

Automation has dramatically changed database administration. Routine tasks—such as performance tuning, index management, and backup scheduling—are increasingly handled by AI-driven database tools. Solutions such as Oracle Autonomous Database, Db2 AI for SQL, and Microsoft Azure SQL’s Intelligent Query Processing promise self-optimizing, self-healing databases. While this might sound like a threat to DBAs, it’s actually an opportunity. Instead of focusing on routine maintenance, DBAs can now shift their efforts toward higher-value tasks including data architecture, governance, and security. ... Organizations are no longer tied to a single database platform. With multi-cloud and hybrid cloud strategies becoming the norm, DBAs must manage data across on-premises systems, cloud-native databases, and hybrid architectures. The days of being a single-platform DBA (e.g., only working with one DBMS) are coming to an end. Instead, cross-platform expertise is now a necessity. Knowing how to optimize for multiple platforms and database systems—for example, AWS RDS, Google Cloud Spanner, Azure SQL, and on-prem Db2, Oracle, and PostgreSQL—is more and more a core part of the DBA’s job description. ... With the explosion of data regulations and industry-specific mandates, compliance has become a primary concern for DBAs.

The global challenge of achieving cyber resilience

The barriers to effective cybersecurity include familiar suspects such as budgetary and resource limitations, the increasing complexity of modern systems and challenge of keeping up with rapidly evolving cyber threats. However, topping the list of challenges for many organisations is the ongoing shortage of cybersecurity skills. A recent Cybersecurity Workforce Study from ISC2 found that, although the size of the global cybersecurity workforce increased to 5.5 million workers in 2023 (a rise of 9% over a single year), so did the gap between supply and demand, which rose by 13% over the same period. Unfortunately, it’s more than just a numbers gap. The study also found that the skills gap is an even greater concern, with respondents saying the lack of necessary skills was a bigger factor making their organisations vulnerable. It’s clear the current approach is flawed. The grand plans that governments have for cybersecurity will require significant uplifts to security programs, including major improvements in developer upskilling, skills verification and guardrails for artificial intelligence tools. Organisations also need to modernise their approach by implementing pathways to upskilling that use deep data insights to provide the best possible skills verification. They need to manage and mitigate the inherent risks that developers with low security maturity bring to the table.

Social engineering becomes strategic threat as OT sector faces phishing, deepfakes, and AI deception risks

With the expanding IT/OT footprint, the attack surface is increasingly providing attackers additional opportunities to compromise targets by stealing credentials, impersonating trusted insiders, and moving laterally from one system to another inside the network. AI-driven phishing, voice cloning, and deepfake-enabled pretexting are lowering the barrier to entry, enabling cyber adversaries to deploy powerful tools that have the potential to erode the reliability of human judgment across critical infrastructure installations. Microsoft security researchers warn that a single compromise, say via a contractor’s infected laptop, can breach previously isolated OT systems, turning them into a breach gateway. While phishing and identity theft are now common access tools, the impact in OT environments is much worse. ... AI-driven deception is rapidly reshaping the social engineering landscape. Attackers are using voice cloning and deepfake technology to impersonate executives with unnerving accuracy. Qantas recently fell victim to a similar scheme, where an AI-powered ‘vishing’ attack compromised the personal data of up to six million customers. These incidents highlight how artificial intelligence has lowered the barrier for convincing, high-impact fraud. Across OT environments, such as energy distribution or manufacturing plants, the impact of social engineering goes way beyond stolen funds or data.

When cloud growth outpaces control, waste follows

Access to data does not guarantee accountability. Many organizations have detailed cost reporting but continue to struggle with cloud waste. The issue here shifts from one of visibility towards one of proximity. Our data shows 59% of organizations have a FinOps team that does some or all cloud cost optimization tasks, yet in many cases, these teams still sit at the edge of delivery. So, while they can surface issues, they are often too removed from daily operations to intervene effectively. The most effective models integrate cost ownership into delivery itself. This means that engineering leads, platform teams and product owners have oversight to take action before inefficiencies take hold. As a result, when these roles are supported with relevant reporting and shared financial metrics, cost awareness becomes a natural part of the decision-making process. This makes it easier to adjust workloads, retire underutilized services, and optimize environments in-flight, rather than in hindsight. ... Control is easiest to build before complexity sets in. The longer organizations delay embedding structure into cloud governance, the harder it becomes to retrofit later. Inconsistent tagging, ambiguous ownership and manual reporting all take time to correct once they are entrenched.

The Growing Impact of Technical Solution Architecture in Software Engineering

Technical solution architects serve as the bridge between business objectives and technology implementation. Their role involves understanding organizational needs, designing scalable system architectures, and leading development teams to execute complex solutions efficiently. As companies transition to cloud-native applications and AI-powered automation, technical solution architects must design systems that are adaptable, secure, and optimized for performance. ... “Legacy systems, while functional, often become bottlenecks as organizations grow,” Bodapati, who is also a fellow at the Hackathon Raptors, explains. “By modernizing these systems, we ensure better performance, stronger security, and more streamlined operations—all essential for today’s data-driven enterprises.” ... With experts like Rama Krishna Prasad Bodapati leading the charge in system architecture and software engineering, businesses can ensure scalability, agility, and efficiency in their IT infrastructure. His expertise in full-stack development, cloud engineering, and enterprise software modernization continues to shape the future of digital transformation. “The future of software engineering isn’t just about building applications—it’s about building intelligent, adaptable, and high-performance ecosystems that drive business success,” Bodapati emphasizes.

Daily Tech Digest - August 15, 2025

Quote for the day:

“Become the kind of leader that people would follow voluntarily, even if you had no title or position.” -- Brian Tracy

DevSecOps 2.0: How Security-First DevOps Is Redefining Software Delivery

DevSecOps 2.0 is a true security-first revolution. This paradigm shift transforms software security into a proactive enabler, leveraging AI and policy-as-code to automate safeguards at scale. Security tools now blend seamlessly into developer workflows, and continuous compliance ensures real-time auditing. With ransomware, supply chain attacks, and other attacks on the rise, there is a need for a different approach to delivering resilient software. ... It marks a transformative approach to software development, where security is the foundation of the entire lifecycle. This evolution ensures proactive security that works to identify and neutralize threats early. ... AI-driven security is central to DevSecOps 2.0, which harnesses the power of artificial intelligence to transform security from a reactive process into a proactive defense strategy. By analyzing vast datasets, including security logs, network traffic, and code commit patterns, AI can detect subtle anomalies and predict potential threats before they materialize. This predictive capability enables teams to identify risks early, streamlining threat detection and facilitating automated remediation. For instance, AI can analyze commit patterns to predict code sections likely to contain vulnerabilities, allowing for targeted testing and prevention.

What CIOs can do when AI boosts performance but kills motivation

“One of the clearest signs is copy-paste culture,” Anderson says. “When employees use AI output as-is, without questioning it or tailoring it to their audience, that’s a sign of disengagement. They’ve stopped thinking critically.” To prevent this, CIOs can take a closer look at how teams actually use AI. Honest feedback from employees can help, but there’s often a gap between what people say they use AI for and how they actually use it in practice, so trying to detect patterns of copy-paste usage can help improve workflows. CIOs should also pay attention to how AI affects roles, identities, and team dynamics. When experienced employees feel replaced, or when previously valued skills are bypassed, morale can quietly drop, even if productivity remains high on paper. “In one case, a senior knowledge expert, someone who used to be the go-to for tough questions, felt displaced when leadership started using AI to get direct answers,” Anderson says. “His motivation dropped because he felt his value was being replaced by a tool.” Over time, this expert started to use AI strategically, and saw it could reduce the ad-hoc noise and give him space for more strategic work. “That shift from threatened to empowered is something every leader needs to watch for and support,” he adds.

That ‘cheap’ open-source AI model is actually burning through your compute budget

The inefficiency is particularly pronounced for Large Reasoning Models (LRMs), which use extended “chains of thought” to solve complex problems. These models, designed to think through problems step-by-step, can consume thousands of tokens pondering simple questions that should require minimal computation. For basic knowledge questions like “What is the capital of Australia?” the study found that reasoning models spend “hundreds of tokens pondering simple knowledge questions” that could be answered in a single word. ... The research revealed stark differences between model providers. OpenAI’s models, particularly its o4-mini and newly released open-source gpt-oss variants, demonstrated exceptional token efficiency, especially for mathematical problems. The study found OpenAI models “stand out for extreme token efficiency in math problems,” using up to three times fewer tokens than other commercial models. ... The findings have immediate implications for enterprise AI adoption, where computing costs can scale rapidly with usage. Companies evaluating AI models often focus on accuracy benchmarks and per-token pricing, but may overlook the total computational requirements for real-world tasks.

AI Agents and the data governance wild west

Today, anyone from an HR director to a marketing intern can quickly build and deploy an AI agent simply using Copilot Studio. This tool is designed to be accessible and quick, making it easy for anyone to play around with and launch a sophisticated agent in no time at all. But when these agents are created outside of the IT department, most users aren’t thinking about data classification or access controls, and they become part of a growing shadow IT problem. ... The problem is that most users will not be thinking like a developer with governance in mind when creating their own agents. Therefore, policies must be imposed to ensure that key security steps aren’t skipped in the rush to deploy a solution. A new layer of data governance must be considered with steps that include configuring data boundaries, restricting who can access what data according to job role and sensitivity level, and clearly specifying which data resources the agent can pull from. AI agents should be built for purpose, using principles of least privilege. This will help avoid a marketing intern having access to the entire company’s HR file. Just like any other business-critical application, it needs to be adequately tested and ‘red-teamed’. Perform penetration testing to identify what data the agent can surface, to who, and how accurate the data is.

Monitoring microservices: Best practices for robust systems

Collecting extensive amounts of telemetry data is most beneficial if you can combine, visualize and examine it successfully. A unified observability stack is paramount. By integrating tools like middleware that work together seamlessly, you create a holistic view of your microservices ecosystem. These unified tools ensure that all your telemetry information — logs, traces and metrics — is correlated and accessible from a single pane of glass, dramatically decreasing the mean time to detect (MTTD) and mean time to resolve (MTTR) problems. The energy lies in seeing the whole photograph, no longer just remote points. ... Collecting information is good, but acting on it is better. Define significant service level objectives (SLOs) that replicate the predicted performance and reliability of your offerings. ... Monitoring microservices effectively is an ongoing journey that requires a commitment to standardization of data, using the right tools and a proactive mindset. By utilizing standardized observability practices, adapting a unified observability stack, continuously monitoring key metrics, placing meaningful SLOs and allowing enhanced root cause analysis, you may construct a strong and resilient microservices structure that truly serves your business desires and delights your customers.

How military leadership prepares veterans for cybersecurity success

After dealing with extremely high-pressure environments, in which making the wrong decision can cost lives, veterans and reservists have little trouble dealing with the kinds of risks found in the world of business, such as threats to revenue, brand value and jobs. What’s more, the time-critical mission mindset so essential within the military is highly relevant within cybersecurity, where attacks and breaches must be dealt with confidently, rapidly and calmly. In the armed forces, people often find themselves in situations so intense that Maslow’s hierarchy of needs is flipped on its head. You’re not aiming for self-actualization or more advanced goals, but simply trying to keep the team alive and maintain essential operations. ... Military experience, on the other hand, fosters unparalleled trust, honesty and integrity within teams. Armed forces personnel must communicate really difficult messages. Telling people that many of them may die within hours demands a harsh honesty, but it builds trust. Combine this with an ability to achieve shared goals, and military leaders inspire others to follow them regardless of the obstacles. So veterans bring blunt honesty, communication, and a mission focus to do what is needed to succeed. These are all characteristics that are essential in cybersecurity, where you have to call out critical risks that others might avoid discussing.

Reclaiming the Architect’s Role in the SDLC

Without an active architect guiding the design and implementation, systems can experience architectural drift, a term that describes the gradual divergence from the intended system design, leading to a fragmented and harder-to-manage system. In the absence of architectural oversight, development teams may optimize for individual tasks at the expense of the system’s overall performance, scalability, and maintainability. ... The architect is primarily accountable for the overall design and ensuring the system’s quality, performance, scalability, and adaptability to meet changing demands. However, relying on outdated practices, like manually written and updated design documents, is no longer effective. The modern software landscape, with multiple services, external resources, and off-the-shelf integrations, makes such documents stale almost as soon as they’re written. Consequently, architects must use automated tools to document and monitor live system architectures. These tools can help architects identify potential issues almost in real time, which allows them to proactively address problems and ensure design integrity throughout the development process. These tools are especially useful in the design stage, allowing architects to reclaim the role they once possessed and the responsibilities that come with it.

Is Vibe Coding Ready for Prime Time?

As the vibe coding ecosystem matures, AI coding platforms are rolling out safeguards like dev/prod separation, backups/rollback, single sign-on, and SOC 2 reporting, yet audit logging is still not uniform across tools. But until these enterprise-grade controls become standard, organizations must proactively build their own guardrails to ensure AI-generated code remains secure, scalable and trustworthy. This calls for a risk-based approach, one that adjusts oversight based on the likelihood and impact of potential risks. Not all use cases carry the same weight. Some are low-stakes and well-suited for experimentation, while others may introduce serious security, regulatory or operational risks. By focusing controls where they’re most needed, a risk-based approach helps protect critical systems while still enabling speed and innovation in safer contexts. ... To effectively manage the risks of vibe coding, teams need to ask targeted questions that reflect the unique challenges of AI-generated code. These questions help determine how much oversight is needed and whether vibe coding is appropriate for the task at hand. ... Vibe coding unlocks new ways of thinking for software development. However, it also shifts risk upstream. The speed of code generation doesn’t eliminate the need for review, control and accountability. In fact, it makes those even more important.

7 reasons the SOC is in crisis — and 5 steps to fix it

The problem is that our systems verify accounts, not actual people. Once an attacker assumes a user’s identity through social engineering, they can often operate within normal parameters for extended periods. Most detection systems aren’t sophisticated enough to recognise that John Doe’s account is being used by someone who isn’t actually John Doe. ... In large enterprises with organic system growth, different system owners, legacy environments, and shadow SaaS integrations, misconfigurations are inevitable. No vulnerability scanner will flag identity systems configured inconsistently across domains, cloud services with overly permissive access policies, or network segments that bypass security controls. These misconfigurations often provide attackers with the lateral movement opportunities they need once they’ve gained initial access through compromised credentials. Yet most organizations have no systematic approach to identifying and remediating these architectural weaknesses. ... External SOC providers offer round-the-clock monitoring and specialised expertise, but they lack the organizational context that makes detection effective. They don’t understand your business processes, can’t easily distinguish between legitimate and suspicious activities, and often lack the authority to take decisive action.

One Network: Cloud-Agnostic Service and Policy-Oriented Network Architecture

The goal of One Network is to enable uniform policies across services. To do so, we are looking to overcome the complexities of heterogeneous networking, different language runtimes, and the coexistence of monolith services and microservices. These complexities span multiple environments, including public, private, and multi-cloud setups. The idea behind One Network is to simplify the current state of affairs by asking, "Why do I need so many networks? Can I have one network?" ... One Network enables you to manage such a service by applying governance, orchestrating policy, and managing the small independent services. Each of these microservices is imagined as a service endpoint. This enables orchestrating and grouping these service endpoints without application developers needing to modify service implementation, so everything is done on a network. There are three ways to manage these service endpoints. The first is the classic model: you add a load balancer before a workload, such as a shopping cart service running in multiple regions, and that becomes your service endpoint. ... If you start with a flat network but want to create boundaries, you can segment by exposing only certain services and keeping others hidden.

Daily Tech Digest - August 04, 2025

Quote for the day:

"You don’t have to be great to start, but you have to start to be great." — Zig Ziglar

Why tomorrow’s best devs won’t just code — they’ll curate, coordinate and command AI

It is not just about writing code anymore — it is about understanding systems, structuring problems and working alongside AI like a team member. That is a tall order. That said, I do believe that there is a way forward. It starts by changing the way we learn. If you are just starting out, avoid relying on AI to get things done. It is tempting, sure, but in the long run, it is also harmful. If you skip the manual practice, you are missing out on building a deeper understanding of how software really works. That understanding is critical if you want to grow into the kind of developer who can lead, architect and guide AI instead of being replaced by it. ... AI-augmented developers will replace large teams that used to be necessary to move a project forward. In terms of efficiency, there is a lot to celebrate about this change — reduced communication time, faster results and higher bars for what one person can realistically accomplish. But, of course, this does not mean teams will disappear altogether. It is just that the structure will change. ... Being technically fluent will still remain a crucial requirement — but it won’t be enough to simply know how to code. You will need to understand product thinking, user needs and how to manage AI’s output. It will be more about system design and strategic vision. For some, this may sound intimidating, but for others, it will also open many doors. People with creativity and a knack for problem-solving will have huge opportunities ahead of them.

The Wild West of Shadow IT

From copy to deck generators, code assistants, and data crunchers, most of them were never reviewed or approved. The productivity gains of AI are huge. Productivity has been catapulted forward in every department and across every vertical. So what could go wrong? Oh, just sensitive data leaks, uncontrolled API connections, persistent OAuth tokens, and no monitoring, audit logs, or privacy policies… and that's just to name a few of the very real and dangerous issues. ... Modern SaaS stacks form an interconnected ecosystem. Applications integrate with each other through OAuth tokens, API keys, and third-party plug-ins to automate workflows and enable productivity. But every integration is a potential entry point — and attackers know it. Compromising a lesser-known SaaS tool with broad integration permissions can serve as a stepping stone into more critical systems. Shadow integrations, unvetted AI tools, and abandoned apps connected via OAuth can create a fragmented, risky supply chain. ... Let's be honest - compliance has become a jungle due to IT democratization. From GDPR to SOC 2… your organization's compliance is hard to gauge when your employees use hundreds of SaaS tools and your data is scattered across more AI apps than you even know about. You have two compliance challenges on the table: You need to make sure the apps in your stack are compliant and you also need to assure that your environment is under control should an audit take place.

Edge Computing: Not Just for Tech Giants Anymore

A resilient local edge infrastructure significantly enhances the availability and reliability of enterprise digital shopfloor operations by providing powerful on-premises processing as close to the data source as possible—ensuring uninterrupted operations while avoiding external cloud dependency. For businesses, this translates to improved production floor performance and increased uptime—both critical in sectors such as manufacturing, healthcare, and energy. In today’s hyperconnected market, where customers expect seamless digital interactions around the clock, any delay or downtime can lead to lost revenue and reputational damage. Moreover, as AI, IoT, and real-time analytics continue to grow, on-premises OT edge infrastructure combined with industrial-grade connectivity such as private 4.9/LTE or 5G provides the necessary low-latency platform to support these emerging technologies. Investing in resilient infrastructure is no longer optional, it’s a strategic imperative for organisations seeking to maintain operational continuity, foster innovation, and stay ahead of competitors in an increasingly digital and dynamic global economy. ... Once, infrastructure decisions were dominated by IT and boiled down to a simple choice between public and private infrastructure. Today, with IT/OT convergence, it’s all about fit-for-purpose architecture. On-premises edge computing doesn’t replace the cloud — it complements it in powerful ways.

A Reporting Breakthrough: Advanced Reporting Architecture

Advanced Reporting Architecture is based on a powerful and scalable SaaS architecture, which efficiently addresses user-specific reporting requirements by generating all possible reports upfront. Users simply select and analyze the views that matter most to them. The Advanced Reporting Architecture’s SaaS platform is built for global reach and enterprise reliability, with the following features: Modern User Interface: Delivered via AWS, optimized for mobile and desktop, with seamless language switching (English, French, German, Spanish, and more to come). Encrypted Cloud Storage: Ensuring uploaded files and reports are always secure. Serverless Data Processing: High-precision processing that analyzes user-uploaded data and uses data influenced relevant factors to maximizing analytical efficiencies and lower the cost of processing efforts. Comprehensive Asset Management: Support for editable reports, dashboards, presentations, pivots, and custom outputs. Integrated Payments & Accounting: Powered by PayPal and Odoo. Simple Subscription Model: Pay only for what you use—no expensive licenses, hardware, or ongoing maintenance. Some leading-edge reporting platforms, such as PrestoCharts, are based on Advanced Reporting Architecture and have been successful in enabling business users to develop custom reports on the fly. Thus, Advanced Reporting Architecture puts reporting prowess in the hands of the user.

These jobs face the highest risk of AI takeover, according to Microsoft

According to the report -- which has yet to be peer-reviewed -- the most at-risk jobs are those that are based on the gathering, synthesis, and communication of information, at which modern generative AI systems excel: think translators, sales and customer service reps, writers and journalists, and political scientists. The most secure jobs, on the other hand, are supposedly those that depend more on physical labor and interpersonal skills. No AI is going to replace phlebotomists, embalmers, or massage therapists anytime soon. ... "It is tempting to conclude that occupations that have high overlap with activities AI performs will be automated and thus experience job or wage loss, and that occupations with activities AI assists with will be augmented and raise wages," the Microsoft researchers note in their report. "This would be a mistake, as our data do not include the downstream business impacts of new technology, which are very hard to predict and often counterintuitive." The report also echoes what's become something of a mantra among the biggest tech companies as they ramp up their AI efforts: that even though AI will replace or radically transform many jobs, it will also create new ones. ... It's possible that AI could play a role in helping people practice that skill. About one in three Americans are already using the technology to help them navigate a shift in their career, a recent study found.

AIBOMs are the new SBOMs: The missing link in AI risk management

AIBOMs follow the same formats as traditional SBOMs, but contain AI-specific content and metadata, like model family, acceptable usage, AI-specific licenses, etc. If you are a security leader at a large defense contractor, you’d need the ability to identify model developers and their country of origin. This would ensure you are not utilizing models originating from near-peer adversary countries, such as China. ... The first step is inventorying their AI. Utilize AIBOMs to inventory your AI dependencies, monitor what is approved vs. requested vs. denied, and ensure you have an understanding of what is deployed where. The second is to actively seek out AI, rather than waiting for employees to discover it. Organizations need capabilities to identify AI in code and automatically generate resulting AIBOMs. This should be integrated as part of the MLOps pipeline to generate AIBOMs and automatically surface new AI usage as it occurs. The third is to develop and adopt responsible AI policies. Some of them are fairly common-sense: no contributors from OFAC countries, no copylefted licenses, no usage of models without a three-month track record on HuggingFace, and no usage of models over a year old without updates. Then, enforce those policies in an automated and scalable system. The key is moving from reactive discovery to proactive monitoring.

2026 Budgets: What’s on Top of CIOs’ Lists (and What Should Be)

CIO shops are becoming outcome-based, which makes them accountable for what they’re delivering against the value potential, not how many hours were burned. “The biggest challenge seems to be changing every day, but I think it’s going to be all about balancing long-term vision with near-term execution,” says Sudeep George, CTO at software-delivered AI data company iMerit. “Frankly, nobody has a very good idea of what's going to happen in 2026, so everyone's placing bets,” he continues. “This unpredictability is going to be the nature of the beast, and we have to be ready for that.” ... “Reducing the amount of tech debt will always continue to be a focus for my organization,” says Calleja-Matsko. “We’re constantly looking at re-evaluating contracts, terms, [and] whether we have overlapping business capabilities that are being addressed by multiple tools that we have. It's rationalizing, she adds, and what that does is free up investment. How is this vendor pricing its offering? How do we make sure we include enough in our budget based on that pricing model? “That’s my challenge,” Calleja-Matsko emphasizes. Talent is top of mind for 2026, both in terms of attracting it and retaining it. Ultimately though, AI investments are enabling the company to spend more time with customers.

Digital Twin: Revolutionizing the Future of Technology and Industry

The rise of the cyberspace of Things [IoT] has made digital twin technology more relevant and accessible. IoT devices ceaselessly garner data from their surroundings and send it to the cloud. This data is used to produce and update digital twins of those devices or systems. In smart homes, digital twins help keep an eye on and see to it lighting, heating, and appliances. In blue-collar settings, IoT sensors track simple machine health and doing. Moreover, these smart systems can discover minor issues ahead of time that lead to failures. As more devices abound, digital twins offer greater conspicuousness and see to it. ... Despite its benefits, digital twin technology comes with challenges. One major issue is the high cost of carrying out. Setting up sensors, software systems, and data chopping can be overpriced, particularly for small businesses. There are also concerns about the data security system and privacy. Since digital twins rely on straight data flow, any rift can be risky. Integrating digital twins into existing systems can be involved. Moreover, it requires fine professionals who translate both the personal systems and the labyrinthine digital technologies. A different dispute is ensuring the caliber and truth of the data. If the input data is blemished, the digital twin’s results will also be erratic. Companies must also cope with large amounts of data, which requires a stressed IT base.

Why Banks Must Stop Pretending They’re Not Tech Companies

The most successful "banks" of the future may not even call themselves banks at all. While traditional institutions cling to century-old identities rooted in vaults and branches, their most formidable competitors are building financial ecosystems from the ground up with APIs, cloud infrastructure, and data-driven decision engines. ... The question isn’t whether banks will become technology companies. It’s whether they’ll make that transition fast enough to remain relevant. And to do this, they must rethink their identity by operating as technology platforms that enable fast, connected, and customer-first experiences. ... This isn’t about layering digital tools on top of legacy infrastructure or launching a chatbot and calling it innovation. It’s about adopting a platform mindset — one that treats technology not as a cost center but as the foundation of growth. A true platform bank is modular, API-first, and cloud-native. It uses real-time data to personalize every interaction. It delivers experiences that are intuitive, fast, and seamless — meeting customers wherever they are and embedding financial services into their everyday lives. ... To keep up with the pace of innovation, banks must adopt skills-based models that prioritize adaptability and continuous learning. Upskilling isn’t optional. It’s how institutions stay responsive to market shifts and build lasting capabilities. And it starts at the top.

Colo space crunch could cripple IT expansion projects

For enterprise IT execs who already have a lot on their plates, the lack of available colocation space represents yet another headache to deal with, and one with major implications. Nobody wants to have to explain to the CIO or the board of directors that the company can’t proceed with digitization efforts or AI projects because there’s no space to put the servers. IT execs need to start the planning process now to get ahead of the problem. ... Demand has outstripped supply due to multiple factors, according to Pat Lynch, executive managing director at CBRE Data Center Solutions. “AI is definitely part of the demand scenario that we see in the market, but we also see growing demand from enterprise clients for raw compute power that companies are using in all aspects of their business.” ... It’s not GPU chip shortages that are slowing down new construction of data centers; it’s power. When a hyperscaler, colo operator or enterprise starts looking for a location to build a data center, the first thing they need is a commitment from the utility company for the required megawattage. According to a McKinsey study, data centers are consuming more power due to the proliferation of the power-hungry GPUs required for AI. Ten years ago, a 30 MW data center was considered large. Today, a 200 MW facility is considered normal.

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.

Daily Tech Digest - July 22, 2025

Quote for the day:

“Being responsible sometimes means pissing people off.” -- Colin Powell

It might be time for IT to consider AI models that don’t steal

One option that has many pros and cons is to use genAI models that explicitly avoid training on any information that is legally dicey. There are a handful of university-led initiatives that say they try to limit model training data to information that is legally in the clear, such as open source or public domain material. ... “Is it practical to replace the leading models of today right now? No. But that is not the point. This level of quality was built on just 32 ethical data sources. There are millions more that can be used,” Wiggins wrote in response to a reader’s comment on his post. “This is a baseline that proves that Big AI lied. Efforts are underway to add more data that will bring it up to more competitive levels. It is not there yet.” Still, enterprises are investing in and planning for genAI deployments for the long term, and they may find in time that ethically sourced models deliver both safety and performance. ... Tipping the scales in the other direction is the big model makers’ promises of indemnification. Some genAI vendors have said they will cover the legal costs for customers who are sued over content produced by their models. “If the model provides indemnification, this is what enterprises should shoot for,” Moor’s Andersen said.

The unique, mathematical shortcuts language models use to predict dynamic scenarios

One go-to pattern the team observed, called the “Associative Algorithm,” essentially organizes nearby steps into groups and then calculates a final guess. You can think of this process as being structured like a tree, where the initial numerical arrangement is the “root.” As you move up the tree, adjacent steps are grouped into different branches and multiplied together. At the top of the tree is the final combination of numbers, computed by multiplying each resulting sequence on the branches together. The other way language models guessed the final permutation was through a crafty mechanism called the “Parity-Associative Algorithm,” which essentially whittles down options before grouping them. It determines whether the final arrangement is the result of an even or odd number of rearrangements of individual digits. ... “These behaviors tell us that transformers perform simulation by associative scan. Instead of following state changes step-by-step, the models organize them into hierarchies,” says MIT PhD student and CSAIL affiliate Belinda Li SM ’23, a lead author on the paper. “How do we encourage transformers to learn better state tracking? Instead of imposing that these systems form inferences about data in a human-like, sequential way, perhaps we should cater to the approaches they naturally use when tracking state changes.”

Role of AI in fortifying cryptocurrency security

In the rapidly expanding realm of Decentralised Finance (DeFi), AI will play a critical role in optimising complex lending, borrowing, and trading protocols. AI can intelligently manage liquidity pools, optimise yield farming strategies for better returns and reduced impermanent loss, and even identify subtle arbitrage opportunities across various platforms. Crucially, AI will also be vital in identifying and mitigating novel types of exploits that are unique to the intricate and interconnected world of DeFi. Looking further ahead, AI will be crucial in developing Quantum-Resistant Cryptography. As quantum computing advances, it poses a theoretical threat to the underlying cryptographic methods that secure current blockchain networks. AI can significantly accelerate the research and development of “post-quantum cryptography” (PQC) algorithms, which are designed to withstand the immense computational power of future quantum computers. AI can also be used to simulate quantum attacks, rigorously testing existing and new cryptographic designs for vulnerabilities. Finally, the concept of Autonomous Regulation could redefine oversight in the crypto space. Instead of traditional, reactive regulatory approaches, AI-driven frameworks could provide real-time, proactive oversight without stifling innovation.

From Visibility to Action: Why CTEM Is Essential for Modern Cybersecurity Resilience

CTEM shifts the focus from managing IT vulnerabilities in isolation to managing exposure in collaboration, something that’s far more aligned with the operational priorities of today’s organizations. Where traditional approaches center around known vulnerabilities and technical severity, CTEM introduces a more business-driven lens. It demands ongoing visibility, context-rich prioritization, and a tighter alignment between security efforts and organizational impact. In doing so, it moves the conversation from “What’s vulnerable?” to “What actually matters right now?” – a far more useful question when resilience is on the line. What makes CTEM particularly relevant beyond security teams is its emphasis on continuous alignment between exposure data and operational decision-making. This makes it valuable not just for threat reduction, but for supporting broader resilience efforts, ensuring resources are directed toward the exposures most likely to disrupt critical operations. It also complements, rather than replaces, existing practices like attack surface management (ASM). CTEM builds on these foundations with more structured prioritization, validation, and mobilization, turning visibility into actionable risk reduction.

Driving Platform Adoption: Community Is Your Value

Remember that in a Platform as a Product approach, developers are your customers. If they don’t know what’s available, how to use it or what’s coming next, they’ll find workarounds. These conferences and speaker series are a way to keep developers engaged, improve adoption and ensure the platform stays relevant.There’s a human side to this, too often left out of focusing on “the business value” and outcomes in corporate-land: just having a friendly community of humans who like to spend time with each other and learn. ... Successful platform teams have active platform advocacy. This requires at least one person working full time to essentially build empathy with your users by working with and listening to the people who use your platforms. You may start with just one platform advocate who visits with developer teams, listening for feedback while teaching them how to use the platform and associated methodologies. The advocate acts as both a councilor and delegate for your developers. ... The journey to successful platform adoption is more than just communicating technical prowess. Embracing systematic approaches to platform marketing that include clear messaging and positioning based on customers’ needs and a strong brand ethos is the key to communicating the value of your platform.

9 AI development skills tech companies want

“It’s not enough to know how a transformer model works; what matters is knowing when and why to use AI to drive business outcomes,” says Scott Weller, CTO of AI-powered credit risk analysis platform EnFi. “Developers need to understand the tradeoffs between heuristics, traditional software, and machine learning, as well as how to embed AI in workflows in ways that are practical, measurable, and responsible.” ... “In AI-first systems, data is the product,” Weller says. “Developers must be comfortable acquiring, cleaning, labeling, and analyzing data, because poor data hygiene leads to poor model performance.” ... AI safety and reliability engineering “looks at the zero-tolerance safety environment of factory operations, where AI failures could cause safety incidents or production shutdowns,” Miller says. To ensure the trust of its customers, IFS needs developers who can build comprehensive monitoring systems to detect when AI predictions become unreliable and implement automated rollback mechanisms to traditional control methods when needed, Miller says. ... “With the rapid growth of large language models, developers now require a deep understanding of prompt design, effective management of context windows, and seamless integration with LLM APIs—skills that extend well beyond basic ChatGPT interactions,” Tupe says.

Why AI-Driven Logistics and Supply Chains Need Resilient, Always-On Networks

Something worth noting about increased AI usage in supply chains is that as AI-enabled systems become more complex, they also become more delicate, which increases the potential for outages. Something as simple as a single misconfiguration or unintentional interaction between automated security gates can lead to a network outage, preventing supply chain personnel from accessing critical AI applications. During an outage, AI clusters (interconnected GPU/TPU nodes used for training and inference) can also become unavailable. .. Businesses must increase network resiliency to ensure their supply chain and logistics teams always have access to key AI applications, even during network outages and other disruptions. One approach that companies can take to strengthen network resilience is to implement purpose-built infrastructure like out of band (OOB) management. With OOB management, network administrators can separate and containerize functions of the management plane, allowing it to operate freely from the primary in-band network. This secondary network acts as an always-available, independent, dedicated channel that administrators can use to remotely access, manage, and troubleshoot network infrastructure.

From architecture to AI: Building future-ready data centers

In some cases, the pace of change is so fast that buildings are being retrofitted even as they are being constructed. Once CPUs are installed, O'Rourke has observed data center owners opting to upgrade racks row by row, rather than converting the entire facility to liquid cooling at once – largely because the building wasn’t originally designed to support higher-density racks. To accommodate this reality, Tate carries out in-row upgrades by providing specialized structures to mount manifolds, which distribute coolant from air-cooled chillers throughout the data halls. “Our role is to support the physical distribution of that cooling infrastructure,” explains O'Rourke. “Manifold systems can’t be supported by existing ceilings or hot aisle containment due to weight limits, so we’ve developed floor-mounted frameworks to hold them.” He adds: “GPU racks also can’t replace all CPU racks one-to-one, as the building structure often can’t support the added load. Instead, GPUs must be strategically placed, and we’ve created solutions to support these selective upgrades.” By designing manifold systems with actuators that integrate with the building management system (BMS), along with compatible hot aisle containment and ceiling structures, Tate has developed a seamless, integrated solution for the white space.

Weaving reality or warping it? The personalization trap in AI systems

At first, personalization was a way to improve “stickiness” by keeping users engaged longer, returning more often and interacting more deeply with a site or service. Recommendation engines, tailored ads and curated feeds were all designed to keep our attention just a little longer, perhaps to entertain but often to move us to purchase a product. But over time, the goal has expanded. Personalization is no longer just about what holds us. It is what it knows about each of us, the dynamic graph of our preferences, beliefs and behaviors that becomes more refined with every interaction. Today’s AI systems do not merely predict our preferences. They aim to create a bond through highly personalized interactions and responses, creating a sense that the AI system understands and cares about the user and supports their uniqueness. The tone of a chatbot, the pacing of a reply and the emotional valence of a suggestion are calibrated not only for efficiency but for resonance, pointing toward a more helpful era of technology. It should not be surprising that some people have even fallen in love and married their bots. The machine adapts not just to what we click on, but to who we appear to be. It reflects us back to ourselves in ways that feel intimate, even empathic.

Microsoft Rushes to Stop Hackers from Wreaking Global Havoc

Multiple different hackers are launching attacks through the Microsoft vulnerability, according to representatives of two cybersecurity firms, CrowdStrike Holdings, Inc. and Google's Mandiant Consulting. Hackers have already used the flaw to break into the systems of national governments in Europe and the Middle East, according to a person familiar with the matter. In the US, they've accessed government systems, including ones belonging to the US Department of Education, Florida's Department of Revenue and the Rhode Island General Assembly, said the person, who spoke on condition that they not be identified discussing the sensitive information. ... The breaches have drawn new scrutiny to Microsoft's efforts to shore up its cybersecurity after a series of high-profile failures. The firm has hired executives from places like the US government and holds weekly meetings with senior executives to make its software more resilient. The company's tech has been subject to several widespread and damaging hacks in recent years, and a 2024 US government report described the company's security culture as in need of urgent reforms. ... "There were ways around the patches," which enabled hackers to break into SharePoint servers by tapping into similar vulnerabilities, said Bernard. "That allowed these attacks to happen."

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