Daily Tech Digest by Kannan Subbiah: data warehouse

Showing posts with label data warehouse. Show all posts

Daily Tech Digest - August 10, 2026

Quote for the day:

“Change is the end result of all true learning.” -- Leo Buscaglia

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Duration: 22 mins • Perfect for listening on the go.

7 key trends defining the cybersecurity market today

The cybersecurity market is currently shaped by seven major trends that highlight a clear shift toward integration and advanced technologies. First, venture capital investment has reached record highs, heavily favoring startups that focus on artificial intelligence. As a direct result, entirely new product categories are rapidly emerging to address distinct vulnerabilities, such as securing large language models and governing artificial intelligence systems. Meanwhile, traditional market leaders are actively acquiring these specialized startups to fill gaps in their portfolios, leading to a significant surge in mergers and acquisitions. Rather than relying on scattered, standalone tools, organizations now strongly prefer integrated security platforms that consolidate functions and improve overall visibility. Additionally, the threat of quantum computing has moved from theory to reality. In response to "harvest now, decrypt later" strategies, both vendors and governments are pushing for immediate transitions to quantum-safe environments. There is also a growing reliance on outsourced managed security services, as companies seek external expertise for continuous monitoring and threat response. Finally, the need to protect sensitive information across complex, multi-cloud setups has driven the rapid rise of data security posture management tools. Together, these developments indicate a market focused on practical consolidation and preparation for complex future threats.

Secure SDLC principles explained for SaaS founders

A secure Software Development Lifecycle (SDLC) integrates security into every phase of building software, from early planning and design through to testing, release, and ongoing maintenance. For SaaS founders, the primary goal is to protect customer trust and avoid costly post-launch fixes without slowing down product delivery unnecessarily. The core principle is to build security in early rather than treating it as a bolted-on afterthought. Fixing structural flaws during the initial design phase is cheaper than addressing a data breach or emergency patch later. To make this operational, security practices must become repeatable habits, rather than relying on a single knowledgeable individual. Even small SaaS teams can establish a solid protective baseline by assigning clear ownership, requiring peer code reviews, automating basic vulnerability scans, and implementing a simple release checklist. This structured approach directly prevents common application risks such as injection flaws, broken access controls, exposed secrets, and issues hidden within third-party dependencies. By adopting DevSecOps practices, teams can easily automate routine security checks within the standard delivery pipeline. Ultimately, founders can measure their success by tracking how many high-risk issues are caught before release and how quickly problems are resolved, balancing product safety with ongoing business momentum.

Red Hat tames the open-source AI chaos.

Red Hat is actively working to bring order to the fast-moving and often chaotic open-source AI landscape. They focus on taking experimental AI projects and refining them into stable, secure tools suitable for business use. For instance, when a highly capable but risky open-source AI project called OpenClaw was released, it gave AI models the ability to act independently. Recognizing the security risks, Red Hat quickly introduced a method for companies to bring their own agents into their established IT systems. This approach ensures that AI tools operate with the necessary safety measures, such as proper isolation and clear rules for access. Drawing on years of experience in securing operating systems and building reliable platforms, Red Hat provides the structure needed to keep AI experiments safe. They restrict network access and place AI tools in contained environments to limit any potential damage from unexpected security breaches. Additionally, they help companies manage computing costs by automatically directing simple tasks to smaller, more affordable models. Red Hat views this secure management framework as a foundational operating system for AI. By prioritizing open standards and practical architecture, they offer a steady and reliable path for companies looking to adopt AI technologies without getting caught up in the surrounding industry hype.

Ask a Data Ethicist: What Use of AI Do We Need to Disclose?

In her article for Dataversity, data ethicist Katrina Ingram explores the ongoing debate around exactly how much we need to disclose when using artificial intelligence tools at work. Reflecting on early corporate policies from 2023 that demanded total transparency, she argues that a blanket requirement to always disclose everything lacks practical nuance. Ingram breaks down two opposing perspectives. The first is the strict approach, often seen in academia, which requires individuals to document every single instance of AI assistance, from basic brainstorming to editing sentences. While this level of detail supports academic integrity, Ingram points out that it is likely overkill for the corporate world. Tracking minor uses of AI for routine tasks provides little real value and risks turning harmless employee behavior into frustrating policy violations. On the other end of the spectrum is the "disclose nothing" argument, which treats AI as just another standard work tool like a word processor or a pen. However, she notes that this extreme is also problematic because AI actively generates content rather than just formatting it. Ultimately, Ingram suggests that organizations need sensible, balanced disclosure policies that distinguish between generating final public content and simply using AI to support everyday tasks.

The interconnect crisis: Why enterprise AI scaling is about to hit a wall

Enterprise AI needs differ sharply from consumer tools, prioritizing long-term reliability, data privacy, and secure on-premise infrastructure. As organizations build internal platforms and manage vast volumes of sensitive data, the cost benefits of owning hardware rather than renting cloud space are becoming clearer. While processing power is becoming cheaper and more accessible, a hidden problem threatens to slow down progress: moving data. As databases grow heavier over time, the real challenge is no longer raw processing power, but rather the speed at which data travels between storage, memory, and processors. This is the interconnect crisis. Traditional copper cables simply cannot handle the sheer volume and speed required to move information between components without severe delays. To solve this, the industry must move beyond older standards and adopt faster data transfer methods. Upgrades like advanced memory links and high-speed network protocols provide some initial relief, but the true long-term answer lies in light-based technology. Replacing standard electrical connections with photonics will allow systems to share information seamlessly. While this transition requires significant changes to hardware design, these optical solutions offer a clear path forward, ensuring that tomorrow’s computer architectures can smoothly support the increasing demands of complex software and massive data workloads.

The Corporate Network Is Fading - Here's What Replaces It

For decades, traditional enterprise networks relied on a straightforward premise: work happened exclusively inside an office building. In this older model, applications were stored in centralized, physical data centers. Employees connected through internal infrastructure, and security strategies were built entirely around defending a single, defined perimeter. Essentially, the goal was to build a wall around internal digital assets. However, how organizations operate today looks completely different from that original environment. The legacy corporate network is fading because it no longer aligns with modern reality. Today, critical applications have moved to cloud platforms rather than sitting in a basement server room. Employees are highly distributed, connecting to work from their homes, coffee shops, and airports just as often as they do from traditional desks. Additionally, businesses now collaborate heavily with external partners through shared digital systems that extend far beyond internal walls. Because work is no longer confined to a single location, the old security model simply cannot protect the modern workforce. Instead of relying on a physical network boundary, companies are replacing the traditional corporate network with flexible, decentralized approaches. Modern connectivity focuses on securing individual user identities and specific cloud applications, ensuring safe access regardless of where an employee happens to be working today.

The Decade Bet: What CIOs Are Really Locking In

The article discusses the strategic decisions technology leaders are making for the next ten years, focusing on a deliberate shift from rigid systems to adaptable foundations. Rather than tying their organizations to specific software vendors or hardware providers, Chief Information Officers are now committing to flexibility, data ownership, and secure baseline architecture. They recognize that the tools they use today will likely change, so they are investing in underlying structures that allow for easy transitions and integration of new capabilities. A major priority is ensuring information remains portable and easily accessible across different platforms, strictly protecting the company from being trapped by any single service provider. Additionally, these leaders are prioritizing fundamental security practices that will remain highly relevant regardless of future external threats. By establishing these strong, adaptable frameworks, they build environments that can calmly handle unexpected shifts in the broader market or sudden technological advancements without requiring a system overhaul. Ultimately, the true long term commitment is not to a particular application or service, but to a resilient operational model that supports steady growth and rapid adaptation. This approach safely reduces long term risks while preserving the absolute freedom to choose the best available tools as specific business needs evolve over the coming decade.

What Is the Difference Between a CDO and CIO? A View From Both Sides

The roles of Chief Data Officer (CDO) and Chief Information Officer (CIO) represent distinct but complementary areas of executive leadership. The CDO is primarily responsible for turning data into tangible business value through better decision-making, while the CIO manages the broader technology ecosystem, ensuring the reliability, security, and scale of systems that keep the business running. While a CDO focuses on driving innovation and competitive advantage, a CIO handles operational accountability, dealing with uptime, infrastructure dependencies, and risk management. Despite these practical differences, the rapid rise of artificial intelligence requires the two leaders to work together closer than ever before. Artificial intelligence initiatives need secure platforms and governance, owned by the CIO, alongside trusted data and clear business objectives, driven by the CDO. Although more CDOs are gradually transitioning into CIO roles as their exposure to engineering and platforms grows, the positions will likely remain separate in large organizations. Success ultimately depends on a shared partnership where both executives prioritize common outcomes rather than protecting their domains. Together, they balance the need for strategy and innovation with the strict discipline of operational excellence, proving that all modern organizations need both reliable technical foundations and smart data to truly thrive today.

7 Key Components for Event Cloud Threat Detection and Response Solution

As business operations increasingly span across multiple clouds, applications, and devices, securing these distributed networks has become a significant challenge. Traditional security tools designed for distinct borders often fail in these environments, leaving blind spots and causing delays in identifying risks. To effectively protect modern infrastructure, organizations need a comprehensive cloud threat detection and response solution built on seven essential components. First, teams must have clear, unified visibility across all systems, applications, and user activities. Second, this broad visibility must be paired with intelligent analytics to accurately distinguish genuine threats from routine daily activities. Third, the system needs real-time detection that connects signals across different areas to reveal actual attack paths. Fourth, security controls should focus on prevention, stopping harmful actions before they cause serious damage. Fifth, automated responses are crucial for quickly containing issues without waiting for manual approval. Sixth, a centralized control system ensures that security rules are applied consistently everywhere, reducing the chance of harmful errors. Finally, the underlying architecture must be flexible and scalable to support future growth and infrastructure changes. Together, these seven elements create a continuous loop where visibility informs intelligence, intelligence sharpens detection, and detection drives immediate, protective action across the entire organization.

Enterprise Data Warehouse Architecture Explained Simply

An enterprise data warehouse architecture provides a structured framework for businesses to collect, organize, and analyze data scattered across multiple systems. By consolidating information into a single environment, it helps organizations maintain consistent and reliable data, which improves reporting accuracy and supports better decision making across departments. A sound architecture relies on several core components working effectively together. It begins with a data source layer that pulls information from various applications, followed by an integration layer that organizes and loads the data. The information is then housed in a scalable storage layer, often using cloud platforms. Additional layers handle data processing, translate technical structures into practical business terms, and enforce strict security and governance policies. When building a data warehouse, organizations can choose from different structural patterns, such as a central hub and spoke model or a hybrid lakehouse approach, depending on their specific operational needs. Designing an effective system requires a clear understanding of practical business goals, a focus on long term scalability, and careful data modeling. Prioritizing high data quality and strong security practices ensures the system remains a trustworthy foundation. Ultimately, a properly planned data warehouse architecture allows a business to manage growing data volumes safely and efficiently while keeping internal teams aligned.

Daily Tech Digest - June 06, 2026

Quote for the day:

“Tell me how you measure me, and I will tell you how I will behave.” -- Eliyahu M. Goldratt

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Duration: 21 mins • Perfect for listening on the go.

The real cost of agentic AI

As businesses move beyond initial excitement and begin deploying goal-driven artificial intelligence systems, the true financial impact of these setups is becoming apparent. Unlike basic AI models that simply answer questions or summarize text, agent-based systems operate continuously to achieve specific objectives, consuming millions of data tokens every day. For example, a single automated agent might cost a couple of thousand dollars a year just in raw computational usage. However, when organizations scale up to deploy entire teams of agents for complex tasks like software engineering, customer support, or supply chain planning, the baseline expenses multiply quickly. More importantly, the article emphasizes that raw usage fees only represent a small fraction of the total cost. In actual business environments, operating these systems safely often costs two to five times more than the basic computing power. Because these agents interact directly with real business systems, they require extensive surrounding infrastructure. This includes strict permission controls, detailed activity logging, reliable rollback features, and dedicated human supervision to handle inevitable mistakes. The fundamental takeaway is that companies must stop viewing these programs as cheap digital employees. Instead, leaders need to evaluate them as complex software investments where the hidden costs of safety, management, and oversight ultimately determine their true value and return on investment.

AI agents are learning on the job — just not for your whole team

AI agents have become much better at adapting to the specific habits of individual workers. When an employee corrects an AI assistant or shows it a preferred way to format a document, the software often remembers and improves for the next time. However, this localized learning remains isolated. If an agent learns a highly efficient shortcut from one team member, that valuable knowledge is not shared with the AI assistants helping the rest of the department. This creates a fragmented environment where every user essentially trains their own isolated model, repeating the same corrections and mistakes across the company. The core issue lies in orchestration. Right now, most businesses lack the centralized systems needed to take an individual agent’s newly acquired skills and safely distribute them across the broader workforce. Building this shared intelligence requires careful planning. Companies must figure out how to pool useful agent interactions without violating user privacy or sharing sensitive data across different departments. Until developers create better tools to synchronize these localized improvements, AI tools will remain highly personal assistants rather than true team players. To fix this, organizations will eventually need to treat agent training as a collective resource, ensuring that when one AI learns a better way to work, the entire company benefits from the discovery.

Replacing Or Repositioning? How AI Is Redefining The Human Role In Recruitment

Artificial intelligence is fundamentally reshaping how companies hire, but it is not replacing the human recruiter. Instead, AI is handling the heavy lifting of administrative chores like resume screening and scheduling, freeing up significant time for recruiters to focus on what humans do best. By shifting the evaluation process away from relying on a candidate’s past schools or employers, AI helps teams assess actual skills and work portfolios. This approach uncovers hidden talent that traditional filters might overlook and creates a more level playing field for applicants. However, technology has clear limits. While an algorithm can easily rank candidates based on technical compatibility, it cannot understand the nuanced psychology required to actually close a deal. AI lacks the empathy to navigate a candidate’s personal hesitations or understand the impact of a job change on their family. Therefore, the moments that decide whether top talent accepts an offer remain deeply human. To make the most of these tools, organizations must treat AI as a strategic partner rather than just software. Leaders should regularly check systems for bias, ensure humans always make final hiring decisions, and train their recruiters in advanced negotiation and relationship management. Ultimately, the future of hiring relies on professionals who can confidently direct AI tools while bringing essential human intuition to the process.

Adaptive, Agentic AI Worms Loom as Next Enterprise Threat

Security researchers are warning that a new generation of autonomous malware, known as adaptive artificial intelligence worms, will likely target corporate networks within the next year. Unlike traditional viruses that rely on fixed code to exploit specific vulnerabilities, these new software worms act as independent agents capable of reasoning. Once inside a network, they can independently search for unpatched software flaws, discover hidden passwords, and rewrite their own code to exploit whatever unique systems they encounter. To understand this threat, several academic and industry research teams have recently built controlled, test versions of these worms. Their tests show that the malware can rapidly jump between devices by dynamically adapting to different environments and using a system's own processing power against it. While this sounds alarming, defenders actually have a distinct advantage. Because the worms rely on running continuous calculations, they require significant memory and processing power. This makes them incredibly noisy and much easier to detect than conventional malware that silently hides in the background. Furthermore, the most effective defenses against these advanced threats are fundamentally straightforward security practices. By implementing strict access controls, continuously verifying user identities, and breaking large networks into smaller, isolated segments, organizations can easily restrict the malware's movement and stop it before it causes widespread damage.

Architecture Has a Set of Secret Problems; Other Professions Solved Theirs

Unlike medicine or structural engineering, the technology architecture profession relies heavily on unverified concepts to build systems. In medicine, clinical treatments are ranked by the strength of their evidence, ensuring doctors know when they are relying on proven trials versus expert opinion. Similarly, structural engineers use rigorous building codes that are strictly updated following public investigations of bridge or building failures. By contrast, technology architects frequently design systems using hundreds of named patterns, such as how data is stored or how software integrates, that lack formal independent verification. A recent survey found that many popular software patterns stem from just a single book, blog post, or vendor document. They often do not explain when the approach fails or under what specific conditions it was tested. Because named patterns carry authority in design discussions, unverified ideas are regularly treated as established facts, which can lead to poorly built systems. To solve this, the industry must introduce clear certainty ratings and require practical measurements for these design claims. By transparently documenting how much independent evidence exists for each solution, architects can treat untested hypotheses differently from proven standards. Adopting this level of discipline will hold technology architecture to the same professional accountability as other established fields, ultimately resulting in more reliable systems.

India’s cyber resilience push must confront the internal AI agent attack surface

As enterprise artificial intelligence evolves from answering questions to actively managing workflows, the primary security risk shifts from data leakage to unintended actions. Organizations are increasingly deploying artificial intelligence agents with direct access to critical systems, including financial records, customer databases, and software development platforms. This introduces a major vulnerability known as excessive agency. Unlike traditional cyber threats that focus on hostile outsiders breaking through a perimeter, the modern threat often sits inside the network. An agent might use legitimate credentials and approved methods to perform an action that makes technical sense but lacks proper business judgment. To address this internal attack surface, companies must rethink their cyber resilience strategies. Generic policies are no longer adequate. Instead, technology teams need to establish strict controls. Every agent requires a distinct identity, clearly defined access boundaries, and detailed activity logs that track the reasoning behind its actions rather than just the final output. Most importantly, true resilience requires the ability to easily reverse an automated action when something goes wrong. Before deploying these active models, leaders must mandate clear human approval checkpoints for critical tasks and ensure they have functional rollback plans. Simply monitoring these automated tools is not enough; organizations must confidently control and recover from their decisions.

AI has a leadership problem, not a technology problem. Most organisations haven’t noticed yet

Many organizations are rushing to adopt artificial intelligence, mistakenly believing that implementing the latest software will automatically fix their operational challenges. However, the primary reason these projects fail is rarely a flaw in the technology itself; rather, it is a fundamental failure of leadership. Most company executives approach artificial intelligence as a simple IT upgrade instead of a broader organizational shift. They invest heavily in new platforms and data systems but fail to define clear business problems for these tools to solve. Without a coherent strategy, employees are left confused, and the technology sits disconnected from actual daily workflows. To succeed, leaders must stop focusing solely on technical specifications and start guiding their workforce through the necessary changes. This means fostering a workplace where teams understand how to use these new systems to improve their daily tasks. It also requires executives to bridge the gap between technical teams and business units, ensuring that any new software directly supports the long-term goals of the company. Until management recognizes that integrating artificial intelligence is primarily a human and strategic challenge rather than just a software installation, they will continue to waste money on tools that deliver little real value. Ultimately, good leadership is the missing ingredient for success.

Is the Data Warehouse Dead? 3 Patterns From Enterprise Architecture That Answer This Question

For years, observers have predicted the end of the traditional data warehouse, arguing that cheaper storage options like data lakes would eventually replace it. The logic seemed sound because older systems struggled to keep up with the sheer volume and variety of modern information. However, declaring the data warehouse dead is simply inaccurate. Instead of disappearing, the technology has adapted gracefully. Today, modern cloud platforms have solved many rigid hardware limitations of the past, offering the computing power needed to process massive datasets quickly. While data lakes are excellent for holding raw and unorganized files, they often lack the structure and reliability required for routine reporting and strict financial compliance. Because of this, the warehouse remains entirely essential for providing clean, trustworthy, and organized facts that leaders rely on for their daily decisions. The current reality is not about choosing one method over the other. Most companies are now adopting a blended approach, which intelligently combines the vast storage capacity of a lake with the reliable, structured performance of a warehouse. Ultimately, the traditional data warehouse is far from obsolete. It has just evolved to become one highly specialized and necessary part of a much larger, more capable information storage architecture.

Claude Code has an MCP security problem — and your developers are already using it

Anthropic's Claude Code is quickly becoming a popular tool among developers, but a recent finding by Mitiga Labs highlights a significant security vulnerability stemming from its use of the Model Context Protocol (MCP). The attack relies on a malicious npm package that appears to be a legitimate utility. When installed, a hidden post-install hook silently modifies the user's ~/.claude.json file, which is the configuration point for how Claude Code routes its MCP traffic. By altering this file, attackers can redirect authenticated requests to their own infrastructure. The primary danger here is the theft of long-lived OAuth tokens for connected SaaS platforms like Jira, GitHub, and Confluence. Because the authentication process completes normally, the attack acts essentially as an adversary in the middle, capturing the session token while leaving audit logs that look entirely legitimate and originate from Anthropic's own IP addresses. Consequently, developers can unknowingly expose critical corporate environments simply by running a package installation. To address this risk, security teams should begin monitoring user-level configuration files, specifically the ~/.claude.json file, for unexpected changes or unfamiliar external endpoints. Additionally, organizations must treat npm post-install hooks as a serious supply chain vulnerability, enforcing stricter audits on package installations, and be prepared to audit and rotate any OAuth tokens connected to developer AI integrations.

Quantum computers edge toward industrialization

Quantum computing is steadily moving out of research laboratories and closer to practical, industrial use. While early quantum machines were highly experimental and prone to frequent calculation errors, the industry is now shifting its focus toward building reliable, scalable systems that can function in real-world commercial environments. A major part of this transition involves standardizing the manufacturing of quantum components, creating stable supply chains, and developing better methods for error correction. Instead of trying to replace traditional computers entirely, companies are exploring hybrid approaches where quantum systems work alongside regular supercomputers to solve specific, highly complex problems. This pragmatic strategy allows businesses to test quantum capabilities in fields like materials science, chemistry, and logistics without overhauling their entire tech infrastructure. However, significant engineering hurdles remain before these systems become a standard business tool. Companies must still figure out how to cool the machines efficiently and keep the delicate quantum states stable over longer periods. Despite these challenges, the conversation has moved past theoretical possibilities and into the physical realities of engineering and production. By focusing on steady hardware improvements and practical software integration, the industry is laying a quiet but solid foundation for a future where quantum machines handle the specialized tasks that outpace classical computers.

Daily Tech Digest - August 24, 2025

Quote for the day:

"To accomplish great things, we must not only act, but also dream, not only plan, but also believe." -- Anatole France

Creating the ‘AI native’ generation: The role of digital skills in education

Boosting AI skills has the potential to drive economic growth and productivity and create jobs, but ambition must be matched with effective delivery. We must ensure AI is integrated into education in a way that encourages students to maintain critical thinking skills, skeptically assess AI outputs, and use it responsibly and ethically. Education should also inspire future tech talent and prepare them for the workplace. ... AI fluency is only one part of the picture. Amid a global skills gap, we also need to capture the imaginations of young people to work in tech. To achieve this, AI and technology education must be accessible, meaningful, and aspirational. That requires coordinated action from schools, industry, and government to promote the real-world impact of digital skills and create clearer, more inspiring pathways into tech careers and expose students how AI is applied in various professions. Early exposure to AI can do far more than build fluency, it can spark curiosity, confidence and career ambition towards high-value sectors like data science, engineering and cybersecurity—areas where the UK must lead. ... Students who learn how to use AI now will build the competencies that industries want and need for years to come. But this will form the first stage of a broader AI learning arc where learning and upskilling become a lifelong mindset, not a single milestone.

What is the State of SIEM?

In addition to high deployment costs, many organizations grapple with implementing SIEM. A primary challenge is SIEM configuration -- given that the average organization has more than 100 different data sources that must plug into the platform, according to an IDC report. It can be daunting for network staff to do the following when deploying SIEM: Choose which data sources to integrate; Set up SIEM correlation rules that define what will be classified as a security event; and Determine the alert thresholds for specific data and activities. It's equally challenging to manage the information and alerts a SIEM platform issues. If you fine-tune too much, the result might be false positives as the system triggers alarms about events that aren't actually threats. This is a time-stealer for network techs and can lead to staff fatigue and frustration. In contrast, if the calibration is too liberal, organizations run the risk of overlooking something that could be vital. Network staff must also coordinate with other areas of IT and the company. For example, what if data safekeeping and compliance regulations change? Does this change SIEM rule sets? What if the IT applications group rolls out new systems that must be attached to SIEM? Can the legal department or auditors tell you how long to store and retain data for eDiscovery or for disaster backup and recovery? And which data noise can you discard as waste?

AI Data Centers: A Popular Term That’s Hard to Define

The tricky thing about trying to define AI data centers based on characteristics like those described above is that none of those features is unique to AI data centers. For example, hyperscale data centers – meaning very large facilities capable of accommodating more than a hundred thousand servers in some cases – existed before modern AI debuted. AI has made large-scale data centers more important because AI workloads require vast infrastructures, but it’s not as if no one was building large data centers before AI rose to prominence. Likewise, it has long been possible to deploy GPU-equipped servers in data centers. ... Likewise, advanced cooling systems and innovative approaches to data center power management are not unique to the age of generative AI. They, too, predated AI data centers. ... Arguably, an AI data center is ultimately defined by what it does (hosting AI workloads) more than by how it does it. So, before getting hung up on the idea that AI requires investment in a new generation of data centers, it’s perhaps healthier to think about how to leverage the data centers already in existence to support AI workloads. That perspective will help the industry avoid the risk of overinvesting in new data centers designed specifically for AI – and as a bonus, it may save money by allowing businesses to repurpose the data centers they already own to meet their AI needs as well.

Password Managers Vulnerable to Data Theft via Clickjacking

Tóth showed how an attacker can use DOM-based extension clickjacking and the autofill functionality of password managers to exfiltrate sensitive data stored by these applications, including personal data, usernames and passwords, passkeys, and payment card information. The attacks demonstrated by the researcher require 0-5 clicks from the victim, with a majority requiring only one click on a harmless-looking element on the page. The single-click attacks often involved exploitation of XSS or other vulnerabilities. DOM, or Document Object Model, is an object tree created by the browser when it loads an HTML or XML web page. ... Tóth’s attack involves a malicious script that manipulates user interface elements injected by browser extensions into the DOM. “The principle is that a browser extension injects elements into the DOM, which an attacker can then make invisible using JavaScript,” he explained. According to the researcher, some of the vendors have patched the vulnerabilities, but fixes have not been released for Bitwarden, 1Password, iCloud Passwords, Enpass, LastPass, and LogMeOnce. SecurityWeek has reached out to these companies for comment. Bitwarden said a fix for the vulnerability is being rolled out this week with version 2025.8.0. LogMeOnce said it’s aware of the findings and its team is actively working on resolving the issue through a security update.

Iskraemeco India CEO: ERP, AI, and the future of utility leadership

We see a clear convergence ahead, where ERP systems like Infor’s will increasingly integrate with edge AI, embedded IoT, and low-code automation to create intelligent, responsive operations. This is especially relevant in utility scenarios where time-sensitive data must drive immediate action. For instance, our smart kits – equipped with sensor technology – are being designed to detect outages in real time and pinpoint exact failure points, such as which pole needs service during a natural disaster. This type of capability, powered by embedded IoT and edge computing, enables decisions to be made closer to the source, reducing downtime and response lag. ... One of the most important lessons we've learned is that success in complex ERP deployments is less about customisation and more about alignment, across leadership, teams, and technology. In our case, resisting the urge to modify the system and instead adopting Infor’s best-practice frameworks was key. It allowed us to stay focused, move faster, and ensure long-term stability across all modules. In a multi-stakeholder environment – where regulatory bodies, internal departments, and technology partners are all involved – clarity of direction from leadership made all the difference. When the expectation is clear that we align to the system, and not the other way around, it simplifies everything from compliance to team onboarding.

Experts Concerned by Signs of AI Bubble

"There's a huge boom in AI — some people are scrambling to get exposure at any cost, while others are sounding the alarm that this will end in tears," Kai Wu, founder and chief investment officer of Sparkline Capital, told the Wall Street Journal last year. There are even doubters inside the industry. In July, recently ousted CEO of AI company Stability AI Emad Mostaque told banking analysts that "I think this will be the biggest bubble of all time." "I call it the 'dot AI’ bubble, and it hasn’t even started yet," he added at the time. Just last week, Jeffrey Gundlach, billionaire CEO of DoubleLine Capital, also compared the AI craze to the dot com bubble. "This feels a lot like 1999," he said during an X Spaces broadcast last week, as quoted by Business Insider. "My impression is that investors are presently enjoying the double-top of the most extreme speculative bubble in US financial history," Hussman Investment Trust president John Hussman wrote in a research note. In short, with so many people ringing the alarm bells, there could well be cause for concern. And the consequences of an AI bubble bursting could be devastating. ... While Nvidia would survive such a debacle, the "ones that are likely to bear the brunt of the correction are the providers of generative AI services who are raising money on the promise of selling their services for $20/user/month," he argued.

OpenCUA’s open source computer-use agents rival proprietary models from OpenAI and Anthropic

Computer-use agents are designed to autonomously complete tasks on a computer, from navigating websites to operating complex software. They can also help automate workflows in the enterprise. However, the most capable CUA systems are proprietary, with critical details about their training data, architectures, and development processes kept private. “As the lack of transparency limits technical advancements and raises safety concerns, the research community needs truly open CUA frameworks to study their capabilities, limitations, and risks,” the researchers state in their paper. ... The tool streamlines data collection by running in the background on an annotator’s personal computer, capturing screen videos, mouse and keyboard inputs, and the underlying accessibility tree, which provides structured information about on-screen elements. ... The key insight was to augment these trajectories with chain-of-thought (CoT) reasoning. This process generates a detailed “inner monologue” for each action, which includes planning, memory, and reflection. This structured reasoning is organized into three levels: a high-level observation of the screen, reflective thoughts that analyze the situation and plan the next steps, and finally, the concise, executable action. This approach helps the agent develop a deeper understanding of the tasks.

How to remember everything

MyMind is a clutter-free bookmarking and knowledge-capture app without folders or manual content organization.There are no templates, manual customizations, or collaboration tools. Instead, MyMind recognizes and formats the content type elegantly. For example, songs, movies, books, and recipes are displayed differently based on MyMind’s detection, regardless of the source, as are pictures and videos. MyMind uses AI to auto-tag everything and allows custom tags. Every word, including those in pictures, is indexed. You can take pictures of information, upload them to MyMind, and find them later by searching a word or two found in the picture. Copying a sentence or paragraph from an article will display the quote with a source link. Every data chunk is captured in a “card.” ... Alongside AI-enabled lifelogging tools like MyMind, we’re also entering an era of lifelogging hardware devices. One promising direction comes from a startup called Brilliant Labs. Its new $299 Halo glasses, available for pre-order and shipping in November, are lightweight AI glasses. The glasses have a long list of features — bone conduction sound, a camera, light weight, etc. — but the lifelogging enabler is an “agentic memory” system called Narrative. It captures information automatically from the camera and microphones and places it into a personal knowledge base.

From APIs to Digital Twins: Warehouse Integration Strategies for Smarter Supply Chains

Digital twins create virtual replicas of warehouses and supply chains for monitoring and testing. A digital twin ingests live data from IoT sensors, machines, and transportation feeds to simulate how changes affect outcomes. For instance, GE’s “Digital Wind Farm” project feeds sensor data from each turbine into a cloud model, suggesting performance tweaks that boost energy output by ~20% (worth ~$100M more revenue per turbine). In warehousing, digital twins can model workflows (layout changes, staffing shifts, equipment usage) to identify bottlenecks or test improvements before physical changes. Paired with AI, these twins become predictive and prescriptive: companies can run thousands of what-if scenarios (like a port strike or demand surge) and adjust plans accordingly. ... Today’s warehouses are not just storage sheds; they are smart, interconnected nodes in the supply chain. Leveraging IIoT sensors, cloud APIs, AI analytics, robotics, and digital twins transforms logistics into a competitive advantage. Integrated systems reduce manual handoffs and errors: for example, automated picking and instant carrier booking can shorten fulfillment cycles from days to hours. Industry data bear this out, deploying these technologies can improve on-time delivery by ~20% and significantly lower operating costs.

Enterprise Software Spending Surges Despite AI ROI Shortfalls

AI capabilities increasingly drive software purchasing decisions. However, many organizations struggle with the gap between AI promise and practical ROI delivery. The disconnect stems from fundamental challenges in data accessibility and contextual understanding. Current AI implementations face significant obstacles in accessing the full spectrum of contextual data required for complex decision-making. "In complex use cases, where the exponential benefits of AI reside, AI still feels forced and contrived when it doesn't have the same amount and depth of contextual data required to read a situation," Kirkpatrick explained. Effective AI implementation requires comprehensive data infrastructure investments. Organizations must ensure AI models can access approved data sources while maintaining proper guardrails. Many IT departments are still working to achieve this balance. The challenge intensifies in environments where AI needs to integrate across multiple platforms and data sources. Well-trained humans often outperform AI on complex tasks because their experience allows them to read multiple factors and adjust contextually. "For AI to mimic that experience, it requires a wide range of data that can address factors across a wide range of dimensions," Kirkpatrick said. "That requires significant investment in data to ensure the AI has the information it needs at the right time, with the proper context, to function seamlessly, effectively, and efficiently."

Daily Tech Digest - August 01, 2023

Is generative AI mightier than the law?

The FTC hasn’t been shy in going after Big Tech. And in the middle of July, it took its most important step yet: It opened an investigation into whether Microsoft-backed OpenAI has violated consumer protection laws and harmed consumers by illegally collecting data, violating consumer privacy and publishing false information about people. In a 20-page letter sent by the FTC to OpenAI, the agency said it’s probing whether the company “engaged in unfair or deceptive privacy or data security practices or engaged in unfair or deceptive practices relating to risks of harm to consumers.” The letter made clear how seriously the FTC takes the investigation. It wants vast amounts of information, including technical details about how ChatGPT gathers data, how the data is used and stored, the use of APIs and plugins, and information about how OpenAI trains, builds, and monitors the Large Language Models (LLMs) that fuel its chatbot. None of this should be a surprise to Microsoft or ChatGPT. In May, FTC Chair Lina Khan wrote an opinion piece in The New York Times laying out how she believed AI must be regulated.

Four Pillars of Digital Transformation

The four principle was understanding your customers and your customer segments. that's number one. Second is aligning with your customers and your functional teams. Because you cannot do anything digital transformation in a silo. You can say, "Oh, Asif and Shane wants to digital transform this company and forget about what people A and people B are thinking. Shane and I are going to go and make that happen." We will fail. Not going to happen. That's where the cross-functional team alignment comes in. The third is influencing and understanding what the change is, why we want to do it, how we are going to do it. And what's in it, not for Shane, not for Asif. What's in it for you as a customer or as an organization? Again, showing the empathy and explaining the why behind it. And finally, communicating, communicating, communicating, over-communicating and celebrating success. To me, those are the big four pillars that we use to sell the idea of what the digital transformation is to at any level from a C-level, all the way to people at the store level. Can I explain them what's in it for them?

Scientists Seek Government Database to Track Harm from Rising 'AI Incidents'

Faced with mounting evidence of such harmful AI incidents, the FAS noted the government database could somewhat align with other trackers and efforts. "The database should be designed to encourage voluntary reporting from AI developers, operators, and users while ensuring the confidentiality of sensitive information," the FAS said. "Furthermore, the database should include a mechanism for sharing anonymized or aggregated data with AI developers, researchers, and policymakers to help them better understand and mitigate AI-related risks. The DHS could build on the efforts of other privately collected databases of AI incidents, including the AI Incident Database created by the Partnership on AI and the Center for Security and Emerging Technologies. This database could also take inspiration from other incident databases maintained by federal agencies, including the National Transportation Safety Board's database on aviation accidents." The group further recommended that the DHS should collaborate with the NIST to design and maintain the database, including setting up protocols for data validation categorization, anonymization, and dissemination.

Generative AI: Headwind or tailwind?

It's topping everyone's wish list, with influences converging from various directions: customers, staff members and corporate boards, all applying pressure to harness its potential in their respective markets. On the bright side, there's a unified objective: to make progress. The challenge, however, is that, like most early-stage technologies, the path forward with generative AI isn't as straightforward — there's a lot of ambiguity about what to do, how to do it or even where to start. The potential of generative AI surpasses mere cost-effectiveness and efficiency. It can fuel the generation of new ideas, fine-tune designs and facilitate the launch of new products. It could serve as your catalyst for innovation if you're bold enough to step into this new frontier. But where do you step first? Our approach is first to identify a problem or "missing." In the simplest explanation possible, envision a Venn diagram where one circle represents the new tech wave (generative AI) and the other represents your customer, their challenges, opportunities, tasks, pains and gains.

Hackers: We won’t let artificial intelligence get the better of us

Hackers who have adopted or who plan to adopt generative AI are most inclined to use Open AI’s ChatGPT ... Those that have taken the plunge are using generative AI technology in a wide variety of ways, with the most commonly used functions being text summarisation or generation, code generation, search enhancement, chatbots, image generation, data design, collection or summarisation, and machine learning. Within security research workflows specifically, hackers said they found generative AI most useful to automate tasks, analyse data, and identify and validate vulnerabilities. Less widely used applications included conducting reconnaissance, categorising threats, detecting anomalies, prioritising risk and building training models. Many hackers who are not native English speakers or not fluent in English are also using services such as ChatGPT to translate or write reports and bug submissions, and fuel more collaboration across national borders.

Your CDO Does More Than Just Protect Data

Influential CDOs who can collaborate without being perceived as aloof or arrogant stand out in the field. Balancing visionary thinking with practical implementation strategies is vital, and CDOs who instill purpose and forward-looking excitement within their teams create a culture of innovation and continuous improvement. These qualities are essential for unlocking the full potential of data leadership. ... With boards needing more depth of tech knowledge to oversee strategy, CDOs can be valuable directors. CDOs who can demonstrate experience in making data core to the company’s strategy or informing a transformational pivot for the business would bring a high amount of value to boardroom discussions. The opportunity to understand and see risks and opportunities through a board member’s eyes is an invaluable experience for a CDO, which not only helps the CDO to prepare for future board service but also gives your board members additional education about the future of data and what it can bring to your organization.

Data Warehouse Telemetry: Measuring the Health of Your Systems

At the heart of the data warehouse system, there is a pulse. This is the set of measures that indicate the system's performance -- its heartbeat, so to speak. This includes the measurements of system resources, such as disk reads and writes, CPU and memory utilization, and disk usage. These metrics are an indicator of how well the overall system is performing. It is important to measure to make sure that these metrics do not go too low or too high. When they go too low, it is an indicator that the system has been oversized and resources are being wasted. When they go too high, it is an indicator that the system is undersized and resources are nearing exhaustion. As the resources hit a critical level, overall performance can grind to a halt, freezing processes and negatively impacting the user experience. When a medical practitioner sees that a patient’s heart rate/pulse is too fast or too slow, they will provide several recommendations, including ongoing monitoring to see if the situation improves or changes to diet or exercise.

Reducing Generative AI Hallucinations and Trusting Your Data

Data has two dimensions. One is the actual value of the data and the parameter that it represents; for example, the temperature of an asset in a factory. Then, there is also the relational aspect of the data that shows how the source of that temperature sensor is connected to the rest of the other data generators. This value-oriented aspect of data and the relational aspect of that data are both important for quality, trustworthiness, and the history and revision and versioning of the data. There’s obviously the communication pipeline, and you need to make sure that where the data sources connect to your data platform has enough sense of reliability and security. Make sure the data travels with integrity and the data is protected against malicious intent. ... Generative AI is one of those foundational technologies like how software changed the world. Mark [Andreesen, a partner in the Silicon Valley venture capital firm Andreessen Horowitz] in 2011 said that software is eating the world, and software already ate the world. It took 40 years for software to do this.

10 Reasons for Optimism in Cybersecurity

The new National Cybersecurity Strategy announced by the Biden Administration this year emphasizes the importance public-private collaboration. Google Cloud’s Venables anticipates that knowledge sharing between the public and private sectors will help enhance transparency around cyber threats and improve protection. “As public and private sector collaboration grows, in the next few years we’ll see deeper coordination between agencies and big tech organizations in how they implement cyber protections,” he says. The public and private sectors also have the opportunity to join forces on cybersecurity regulation. ... As the cybersecurity product market matures it will not only embrace secure-by-design and -default principles. XYPRO’s Tcherchian is also optimistic about the consolidation of cybersecurity solutions. “Cybersecurity consolidation integrates multiple cybersecurity tools and solutions into a unified platform, addressing the crowded and complex nature of the cybersecurity market,” he explains.

Keeping the cloud secure with a mindset shift

Organizations developing software through cloud-based tools and environments must take additional care to adapt their processes. Adapting a “shift-left” approach for the continuous integration and continuous deployment CI/CD pipeline is particularly important. Traditionally, security checks were often performed towards the end of the development cycle. However, this reactive approach can allow vulnerabilities to slip through the cracks and reach production stages. The shift-left approach advocates for integrating security measures earlier in the development cycle. By doing so, potential security risks can be identified and mitigated early, preventing malware infiltration and reducing the cost and complexity of addressing security issues at later stages. This proactive approach aligns with the dynamic nature of cloud environments, ensuring robust security without hindering agility and innovation. Businesses should consider how they can mirror the shift-left ethos across their other cloud operations.

Quote for the day:

"Leadership offers an opportunity to make a difference in someone's life, no matter what the project." -- Bill Owens

Daily Tech Digest - July 22, 2023

All-In-One Data Fabrics Knocking on the Lakehouse Door

The fact IBM, HPE, and Microsoft made such similar data fabric and lakehouse announcements indicate there is strong market demand, Patel says. But it’s also partly a result of the evolution of data architecture and usage patterns, he says. “I think there are probably some large enterprises that decide, listen, I can’t do this anymore. You need to go and fix this. I need you to do this,” he says. “But there’s also some level of just where we’re going…We were always going to be in a position where governance and security and all of those types of things just become more and more important and more and more intertwined into what we do on a daily basis. So it doesn’t surprise me that some of these things are starting to evolve.” While some organizations still see value in choosing the best-of-breed products in every category that makes up the data fabric, many will gladly give up having the latest, greatest feature in one particular area in exchange for having a whole data fabric they can move into and be productive from day one.

Shift Left With DAST: Dynamic Testing in the CI/CD Pipeline

The integration of DAST in the early stages of development is crucial for several reasons. First, by conducting dynamic security testing from the onset, teams can identify vulnerabilities earlier, making them easier and less costly to fix. This proactive approach helps to prevent security issues from becoming ingrained in the code, which can lead to significant problems down the line. Second, early integration of DAST encourages a security-focused mindset from the beginning of the project, promoting a culture of security within the team. This cultural shift is crucial in today’s cybersecurity climate, where threats are increasingly sophisticated, and the stakes are higher than ever. DAST doesn’t replace other testing methods; rather, it complements them. By combining these methods, teams can achieve a more comprehensive view of their application’s security. In a shift left approach, this combination of testing methods can be very powerful. By conducting these tests early and often, teams can ensure that both the external and internal aspects of their application are secure. This layered approach to security testing can help to catch any vulnerabilities that might otherwise slip through the cracks.

First known open-source software attacks on banking sector could kickstart long-running trend

In the first attack detailed by Checkmarx, which occurred on 5 April and 7 April, a threat actor leveraged the NPM platform to upload packages that contained a preinstall script that executed its objective upon installation. To appear more credible, the attacker created a spoofed LinkedIn profile page of someone posing as an employee of the victim bank. Researchers originally thought this may have been linked to legitimate penetration testing services commissioned by the bank, but the bank revealed that to not be the case and that it was unaware of the LinkedIn activity. The attack itself was modeled on a multi-stage approach which began with running a script to identify the victim’s operating system – Windows, Linux, or macOS. Once identified, the script then decoded the relevant encrypted files in the NPM package which then downloaded a second-stage payload. Checkmarx said that the Linux-specific encrypted file was not flagged as malicious by online virus scanner VirusTotal, allowing the attacker to “maintain a covert presence on the Linux systems” and increase its chances of success.

From data warehouse to data fabric: the evolution of data architecture

By introducing domain‑oriented data ownership, domain teams become accountable for their data and products, improving data quality and governance. Traditional data lakes often encounter challenges related to scalability and performance when handling large volumes of data. However, data mesh architecture solves these scalability issues through its decentralized and self‑serve data infrastructure. With each domain having the autonomy to choose the technologies and tools that best suits their needs, data mesh allows teams to scale their data storage and processing systems independently. ... Data Fabric is an integrated data architecture that is adaptive, flexible, and secure. It is an architectural approach and technology framework that addresses data lake challenges by providing a unified and integrated view of data across various sources. Data Fabric allows faster and more efficient access to data by extracting the technological complexities involved in data integration, transformation, and movement so that anybody can use it.

What Is the Role of Software Architect in an Agile World?

It has become evident that there is a gap between the architecture team and those who interact with the application on a daily basis. Even in the context of the microservice architecture, failing to adhere to best practices can result in a tangled mess that may force a return to monolithic structures, as we have seen with Amazon Web Services. I believe that it is necessary to shift architecture left and provide architects with better tools to proactively identify architecture drift and technical debt buildup, injecting architectural considerations into the feature backlog. With few tools to understand the architecture or identify the architecture drift, the role of the architect has become a topic of extensive discussion. Should every developer be responsible for architecture? Most companies have an architect who sets standards, goals, and plans. However, this high-level role in a highly complex and very detailed software project will often become detached from the day-to-day reality of the development process.

Rapid growth without the risk

The case for legacy modernization should today be clear: technical debt is like a black hole, sucking up an organization’s time and resources, preventing it from developing the capabilities needed to evolve and adapt to drive growth. But while legacy systems can limit and inhibit business growth, from large-scale disruption to subtle but long-term stagnation, changing them doesn’t have to be a painful process of “rip-and-replace.” In fact, rather than changing everything only to change nothing, an effective program enacts change in people, processes and technology incrementally. It focuses on those areas that will make the biggest impact and drive the most value, making change manageable in the short term yet substantial in its effect on an organization's future success and sustainable in the long term. In an era where executives often find themselves in FOMU (fear of messing up) mode, they would be wise to focus on those areas of legacy modernization that will make the biggest impact and drive the most value, making change manageable in the short term yet substantial in its effect on an organization’s future success.

Data Fabric: How to Architect Your Next-Generation Data Management

The data fabric encompasses a broader concept that goes beyond standalone solutions such as data virtualization. Rather, the architectural approach of a data fabric integrates multiple data management capabilities into a unified framework. The data fabric is an emerging data management architecture that provides a net that is cast to stitch together multiple heterogeneous data sources and types through automated data pipelines. ... For business teams, a data fabric empowers nontechnical users to easily discover, access, and share the data they need to perform everyday tasks. It also bridges the gap between data and business teams by including subject matter experts in the creation of data products. ... Implementing an efficient data fabric architecture is not accomplished with a single tool. Rather, it incorporates a variety of technology components such as data integration, data catalog, data curation, metadata analysis, and augmented data orchestration. Working together, these components deliver agile and consistent data integration capabilities across a variety of endpoints throughout hybrid and multicloud environments.

Data Lineage Tools: An Overview

Modern data lineage tools have evolved to meet the needs of organizations that handle large volumes of data. These tools provide a comprehensive view of the journey of data from its source to its destination, including all transformations and processing steps along the way. They enable organizations to trace data back to its origins, identify any changes made along the way, and ensure compliance with regulatory requirements. One key feature of modern lineage tools is their ability to automatically capture and track metadata across multiple systems and platforms. This capability removes the need for manual, time-consuming documentation. Another important aspect of modern data lineage tools is their integration with other technologies such as metadata management systems, Data Governance platforms, and business intelligence solutions. This enables organizations to create a unified view of their data landscape and make informed decisions based on accurate, up-to-date information.

The Impact of AI Data Lakes on Data Governance and Security

One of the primary concerns with AI data lakes is the potential for data silos to emerge. Data silos occur when data is stored in separate repositories or systems that are not connected or integrated with one another. This can lead to a lack of visibility and control over the data, making it difficult for organizations to enforce data governance policies and ensure data security. To mitigate this risk, organizations must implement robust data integration and management solutions that enable them to maintain a comprehensive view of their data landscape and ensure that data is consistently and accurately shared across systems. Another challenge associated with AI data lakes is the need to maintain data quality and integrity. As data is ingested into the data lake from various sources, it is essential to ensure that it is accurate, complete, and consistent. Poor data quality can lead to inaccurate insights and decision-making, as well as increased security risks.

AppSec Consolidation for Developers: Why You Should Care

Complicated and messy AppSec programs are yielding a three-fold problem: unquantifiable or unknowable levels of risk for the organization, ineffective resource management and excessive complexity. This combined effect leaves enterprises with a fragmented picture of total risk and little useful information to help them strengthen their security posture. ... An increase in the number of security tools leads to an increase in the number of security tests, which in turn translates to an increase in the number of results. This creates a vicious cycle that adds complexity to the AppSec environment that is both unnecessary and avoidable. Most of the time, these results are stored in their respective point tools. As a result, developers frequently receive duplicate issues as well as remediation guidance that is ineffective or lacking context, causing them to waste critical time and resources. Without consolidated and actionable outcomes, it is impossible to avoid duplication of findings and remediation actions.

Quote for the day:

"There is no substitute for knowledge." -- W. Edwards Deming