Daily Tech Digest by Kannan Subbiah: blockchain

Showing posts with label blockchain. Show all posts

Daily Tech Digest - October 23, 2025

Quote for the day:

“The more you loose yourself in something bigger than yourself, the more energy you will have.” -- Norman Vincent Peale

Leadership lessons from NetForm founder Karen Stephenson

is a hot buzzword encouraging individuals to integrate and create with each other, but the simplest way to integrate and create is in the mind of one person — if they’re willing to push forward and do it. Even further, what can an integrated team of diverse minds accomplish when they co-create? ... In the age of AI, humans will need to focus on what humans do well. At the moment, at least, that’s making novel connections, thinking by analogy and creating the new. Our single-field approach to learning, qualifications and career ladders makes it hard for us to compete with machines that are often smarter than we are in any given discipline. For that creative spark and to excel at what messy, forgetful, slow, imperfect humans do best, we need to work, think and live differently. In fact, the founders of five of the largest companies in the world are (or were) — mentally diverse people skilled in multiple disciplines — Bill Gates, Steve Jobs, Warren Buffett, Larry Page and Jeff Bezos. They learn because they’re curious and want to solve problems, not for a career ladder. It’s easier than ever, today, to learn with AI and online materials and to collaborate with tech and humans around the world. All you need to do is open inward to your talents and desires, explore, collect and fuse.

Why cloud and AI projects take longer and how to fix the holdups

In the case of , the problem is that senior management thinks that the cloud is always cheaper, that you can always cut costs by moving to the cloud. This is despite the recent stories on “,” or moving cloud applications back into the data center. In the case of cloud projects, most enterprise IT organizations now understand how to assess a cloud project for cost/benefit, so most of the cases where impossible cost savings are promised are caught in the planning phase. For AI, both senior management and line department management have high expectations with respect to the technology, and in the latter case may also have some experience with AI in the form of as-a-service generative AI models available online. About a quarter of these proposals quickly run afoul of governance policies because of problems with data security, and half of this group dies at this point. For the remaining proposals, there is a whole set of problems that emerge. Most enterprises admit that they really don’t understand what AI can do, which obviously makes it hard to frame a realistic AI project. The biggest gap identified is between an AI business goal and a specific path leading to it. One CIO calls the projects offered by user organizations as “invitations to AI fishing trips” because the goal is usually set in business terms, and these would actually require a project simply to identify how the stated goal could be achieved.

Who pays when a multi-billion-dollar data center goes down?

While the is looking at everything from to drought, there are a handful of risks where it feels the industry isn't adequately preparing. “The big thing that isn't getting on people's radars in a growing way is ," Hayhow says “Looking at this through the lens of the data center owner or developer, it's often very difficult. “It's a bit of an unspoken conversation that the equipment in the white space belongs to the customer. Often you don't have custody over it, you don't have visibility over it, and it’s highly proprietary. But the value of it is growing.” Per square meter of white space, the Lockton partner suggests that the value of the equipment five years from now will be exponentially larger than the value of the equipment five years ago, as more data centers invest in expensive GPUs and other equipment for AI use cases. “Leases have become clearer in terms of placing responsibility for damage to customer equipment more squarely on the shoulders of the owner, developer,” Hayhow says. “We're having that conversation in the US, where the halls are larger, the value of the equipment is greater, and some of the hyperscale customers are being much more prescriptive in terms of wanting to address the topic of damage to our equipment … if you lose 20 megawatts worth of racks of , the lead time to get those replaced, unless you're building elsewhere, is quite significant.”

AI Agents Need Security Training – Just Like Your Employees

“It may not be as candid as what humans would do during those sessions, but AI agents used by your workforce do need to be trained. They need to understand what your company policies are, including what is acceptable behavior, what data they're allowed to access, what actions they're allowed to take,” Maneval explained. ... “Most AI tools are just trained to do the same thing over and over and so it means decisions are based on assumptions from limited information,” she explained to Infosecurity. “Additionally, most AI tools solve real problems but also create real risks and each solve different problems and creates different risks.” While some cybersecurity experts argue that auditing AI tools is no different to auditing any other software or application, Maneval disagrees. ... Maneval’s said her “rule of thumb” is that whether you’re dealing with traditional machine learning algorithms, generative AI applications of AI agents, “treat them like any other employees.” This not only means that AI-powered agents should be trained on security policies but should also be forced to respect security controls that the staff have to respect, such as role-based access controls (RBAC). “You should look at how you treat your humans and apply those same controls to the AI. You probably do a background check before anyone is hired. Do the same thing with your AI agent. ..."

Why must CISOs slay a cyber dragon to earn business respect?

Why should a security leader need to experience a major cyber incident to earn business colleagues’ respect? Jeff Pollard, VP and principal analyst at Forrester, says this enterprise perception problem is “just part of human nature. If we don’t see the bad thing happening, we don’t appreciate all of the things that were done to prevent that bad thing from happening.” Of course, if an attack turns into an incident and defense goes poorly, “it can easily turn from a hero moment to a scapegoat moment,” Pollard says. Oberlaender, who now works as a cybersecurity consultant, is among those who believe hard-earned experience should be rewarded, but that’s not what he’s seeing in the market today. ... CISOs “feel that they need to fight off an attack to show value, but there are many other successes they can do and show,” says Erik Avakian, technical counselor at Info-Tech Research Group. “Building KPIs is a powerful way to show their value.” ... Chris Jackson, a senior cybersecurity specialist with tech education vendor Pluralsight, reinforces the frustration that many enterprise CISOs feel about the lack of appropriate respect from their colleagues and bosses. “CISOs are a lot like pro sports coaches. It doesn’t matter how well they performed during the season or how many games they won. If they don’t win the championship, it’s seen as a failure, and the coach is often the first to go,” Jackson says.

The next cyber crisis may start in someone else’s supply chain

Organizations have improved oversight of their direct partners, but few can see beyond the first layer. This limited view leaves blind spots that attackers can exploit, particularly through third-party software or service providers. “We’re in a new generation of risk, one where cyber, geopolitical, technology, political risk, and other factors are converging and reshaping the landscape. The impact on markets and operations is unfolding faster than many organizations can keep up,” said Jim Wetekamp, CEO of Riskonnect. ... Third-party and nth-party risks continue to expose companies to disruption. Most organizations have business continuity plans for supplier disruptions, but their monitoring often stops at direct partners. Only a small fraction can monitor risks across multiple tiers of their supply chain, and some cannot track their critical technology providers at all. Organizations still underestimate how dependent they are on third parties and continue to rely on paper-based continuity plans that offer a false sense of security. ... More companies now have a chief risk officer, but funding for technology and tools has barely moved. Most risk leaders say their budgets have stayed the same even as they are asked to cover more ground. Many are turning to automation and specialized software to do more with what they already have.

Boardroom to War Room: Translating AI-Driven Cyber Risk into Action

Great CISOs today combine strategic leadership, financial knowledge, technological skills, and empathy to turn cybersecurity from a burden on operations into a strong enabler. This change happens faster with artificial intelligence. AI has a lot of potential, but it also makes things more uncertain. It can do things like forecast threats and automate orchestration. CISOs need to see AI problems as more than just technological problems; they need to see them as business risks that need clear communication, openness, and quick response. ... Not storytelling, but data and graphics win over executives. Suggested metrics include: Predictive accuracy - The percentage of risks that AI flagged before a breach compared to the percentage of threats that AI flagged after it happened; Speed of reaction - The average time it took for AI-enabled confinement to work compared to manual reaction; False positive rate - Tech teams employed AI to improve alerts and cut down on alert fatigue from X to Y; Third-party model risk - The number of outside model calls that were looked at and accepted; Visual callout suggestion - A mock-up of a dashboard that illustrates AI risk KPIs, a trendline of predictive value, and a drop in incidences. ... Change from being an IT responder who reacts to problems to a strategic AI-enabled risk leader. Take ownership of your AI risk story, keep an eye on third-party models, provide your board clear information, and make sure your war room functions quickly.

Govt. faces questions about why US AWS outage disrupted UK tax office and banking firms

“The narrative of bigger is better and biggest is best has been shown for the lie it always has been,” Owen Sayers, an independent security architect and data protection specialist with a long history of working in the public sector, told Computer Weekly. “The proponents of hyperscale cloud will always say they have the best engineers, the most staff and the greatest pool of resources, but bigger is not always better – and certainly not when countries rely on those commodity global services for their own national security, safety and operations. “Nationally important services must be recognised as best delivered under national control, and as a minimum, the government should be knocking on AWS’s door today and asking if they can in fact deliver a service that guarantees UK uptime,” he said. “Because the evidence from this week’s outage suggests that they cannot.” ... “In light of today’s major outage at Amazon Web Services … why has not designated Amazon Web Services or any other major technology firm as a CTP for the purposes of the Critical Third Parties Regime,” asked Hillier, in the letter. “[And] how soon can we expect firms to be brought into this regime?” Hillier also asked HM Treasury for clarification about whether or not it is concerned about the fact that “seemingly key parts of our IT infrastructure are hosted abroad” given the outage originated from a US-based AWS datacentre region but impacted the activities of Lloyds Bank and also HMRC.

Quantum work, federated learning and privacy: Emerging frontiers in blockchain research

It is possible to have a future in which the field of could serve as the foundation for . The future is alluring; quantum algorithms can provide solutions to the issues that classical computers find difficult and the method may be more effective and resistant to brute-force attacks. The danger, however, is significant: when quantum computers are sufficiently robust, existing encryption standards can be compromised. ... Federated learning is another upcoming element of blockchain studies, a machine learning model training technique that avoids data centralisation. Federated learning enables various devices or nodes to feed into a standard model instead of storing sensitive data in a central server inaccessible to third parties. ... The issue of privacy is of specific importance today due to the increased regulatory pressure on exchanges and cryptocurrency companies. A compromise between user privacy and regulatory openness could prove to be the key to success. Studies of privacy-saving instruments provide a competitive advantage to blockchain developers and for exchanges interested in increasing their influence on the global economy. ... The decade of blockchain research to come will not be characterised by fast transactions or cheaper costs. It will redraw the borders of trust, calculation, and privacy in digitally based economies.

Ransomware groups surge as automation cuts attack time to 18 mins

The ransomware group LockBit has recently introduced "LockBit 5.0", reportedly incorporating artificial intelligence for attack randomisation and enhanced targeting options, with a focus on regaining its previous position atop the ransomware ecosystem. Medusa, by contrast, was noted to have fallen behind due in part to lacking widespread automated and customisable features, despite previous activity levels. ReliaQuest's analysis predicts the rise of new groups through the lens of its three-factor model, specifically naming "The Gentlemen" and "DragonForce" as likely to become major threats due to their adoption of advanced technical capabilities. The Gentlemen, for instance, has listed over 30 victims on its data-leak site within its first month of activity, underpinned by automation, prioritised encryption, and endpoint discovery for rapid lateral movement. Conversely, groups such as "Chaos" and "Nova" are likely to remain minor players, lacking the integral features associated with higher victim counts and affiliate recruitment. ... RaaS groups now use automation to reduce breakout times to as little as 18 minutes, making manual intervention too slow. Implement automated containment and response plays to keep pace with attackers. These workflows should automatically isolate hosts, block malicious files, and disable compromised accounts quickly after a critical detection, containing the threat before ransomware can be deployed.

Daily Tech Digest - October 22, 2025

Quote for the day:

"Good content isn't about good storytelling. It's about telling a true story well." -- Ann Handley

When yesterday’s code becomes today’s threat

A striking new supply chain attack is sending shockwaves through the developer community: a worm-style campaign dubbed “” has compromised at least 187 npm packages, including the that has 2 million hits weekly, and spreading to other maintainers' packages. The malicious payload modifies package manifests, injects malicious files, repackages, and republishes — thereby infecting downstream projects. This incident underscores a harsh reality: even code released weeks, months, or even years ago can become dangerous once a dependency in its chain has been compromised. ... Sign your code: All packages/releases should use cryptographic signing. This allows users to verify the origin and integrity of what they are installing. Verify signatures before use: When pulling in dependencies, CI/CD pipelines, and even local dev setups, include a step to check that the signature matches a trusted publisher and that the code wasn’t tampered with. SBOMs are your map of exposure: If you have a for your project(s), you can query it for compromised packages. Find which versions/packages have been modified — even retroactively — so you can patch, remove, or isolate them. Continuous monitoring of risk posture: It's not enough to secure when you ship. You need alerts when any dependency or component’s risk changes: new vulnerabilities, suspicious behavior, misuse of credentials, or signs that a trusted package may have been modified after release.

Cloud Sovereignty: Feature. Bug. Feature. Repeat!

isn’t just a buzzword anymore, argues Kushwaha. “It’s a real concern for businesses across the world. The pattern is clear. The cloud isn’t a one-size-fits-all solution anymore. Companies are starting to realise that sometimes control, cost, and compliance matter more than convenience.” ... Cloud sovereignty is increasingly critical due to the evolving geopolitical scenario, government and industry-specific regulations, and vendor lock-ins with heavy reliance on . The concept has gained momentum and will continue to do so because technology has become pervasive and critical for running a state/country and any misuse by foreign actors can cause major repercussions, the way Bavishi sees it. Prof. Bhatt captures that true digital sovereignty is a distant dream and achieving this requires a robust ecosystem for decades. This isn’t counterintuitive; it’s evolution, as Kushwaha epitomises. “The cloud’s original promise was one of freedom. Today, when it comes to the cloud, freedom means more control. Businesses investing heavily in digital futures can’t afford to ignore the fine print in hyperscaler contracts or the reach of foreign laws. Sovereignty is the foundation for building safely in a fragmented world.” ... Organisations have recognised the risks of digital dependencies and are looking for better options. There is no turning back, Karlitschek underlines.

Securing AI to Benefit from AI

As organizations begin to integrate AI into defensive workflows, identity security becomes the foundation for trust. Every model, script, or autonomous agent operating in a production environment now represents a new identity — one capable of accessing data, issuing commands, and influencing defensive outcomes. If those identities aren't properly governed, the tools meant to strengthen security can quietly become sources of risk. The emergence of make this especially important. These systems don't just analyze; they may act without human intervention. They triage alerts, enrich context, or trigger response playbooks under delegated authority from human operators. ... AI systems are capable of assisting human practitioners like an intern that never sleeps. However, it is critical for security teams to differentiate what to automate from what to augment. Some tasks benefit from full automation, especially those that are repeatable, measurable, and low-risk if an error occurs. ... , , and are prime candidates for automation. These are data-heavy, pattern-driven processes where consistency outperforms creativity. By contrast, , attribution, and response decisions rely on context that AI cannot fully grasp. Here, AI should assist by surfacing indicators, suggesting next steps, or summarizing findings while practitioners retain decision authority. Finding that balance requires maturity in process design.

The Unkillable Threat: How Attackers Turned Blockchain Into Bulletproof Malware Infrastructure

When EtherHiding emerged in September 2023 as part of the CLEARFAKE campaign, it introduced a chilling reality: attackers no longer need vulnerable servers or hackable domains. They’ve found something far better—a global, decentralized infrastructure that literally cannot be shut down. ... When victims visit the infected page, the loader queries a smart contract on or using a read-only function call. ... Forget everything you know about disrupting cybercrime infrastructure. There is no command-and-control server to raid. No hosting provider to subpoena. No DNS to poison. The malicious code exists simultaneously everywhere and nowhere, distributed across thousands of blockchain nodes worldwide. As long as Ethereum or BNB Smart Chain operates—and they’re not going anywhere—the malware persists. Traditional law enforcement tactics, honed over decades of fighting cybercrime, suddenly encounter an immovable object. You cannot arrest a blockchain. You cannot seize a smart contract. You cannot compel a decentralized network to comply. ... The read-only nature of payload retrieval is perhaps the most insidious feature. When the loader queries the smart contract, it uses functions that don’t create transactions or blockchain records.

New 'Markovian Thinking' technique unlocks a path to million-token AI reasoning

Researchers at have proposed a new technique that makes large language models (LLMs) vastly more efficient when performing complex reasoning. Called Markovian Thinking, the approach allows LLMs to engage in lengthy reasoning without incurring the prohibitive computational costs that currently limit such tasks. The team’s implementation, an environment named , structures the reasoning chain into fixed-size chunks, breaking the scaling problem that plagues very long LLM responses. Initial estimates show that for a 1.5B parameter model, this method can cut the costs of training by more than two-thirds compared to standard approaches. ... The researchers compared this to models trained with the standard . Their findings indicate that the model trained with Delethink could reason up to 24,000 tokens, and matched or surpassed a LongCoT model trained with the same 24,000-token budget on math benchmarks. On other tasks like coding and PhD-level questions, Delethink also matched or slightly beat its LongCoT counterpart. “Overall, these results indicate that Delethink uses its thinking tokens as effectively as LongCoT-RL with reduced compute,” the researchers write. The benefits become even more pronounced when scaling beyond the training budget.

The dazzling appeal of the neoclouds

While their purpose-built design gives them an advantage for AI workloads, neoclouds also bring complexities and trade-offs. Enterprises need to understand where these platforms excel and plan how to integrate them most effectively into broader cloud strategies. Let’s explore why this buzzword demands your attention and how to stay ahead in this new era of cloud computing. ... Neoclouds, unburdened by the need to support everything, are outpacing hyperscalers in areas like agility, pricing, and speed of deployment for AI workloads. A shortage of GPUs and data center capacity also benefits neocloud providers, which are smaller and nimbler, allowing them to scale quickly and meet growing demand more effectively. This agility has made them increasingly attractive to AI researchers, startups, and enterprises transitioning to AI-powered technologies. ... Neoclouds are transforming cloud computing by offering purpose-built, cost-effective infrastructure for AI workloads. Their price advantages will challenge traditional cloud providers’ market share, reshape the industry, and change enterprise perceptions, fueled by their expected rapid growth. As enterprises find themselves at the crossroads of innovation and infrastructure, they must carefully assess how neoclouds can fit into their broader architectural strategies.

Wi-Fi 8 is coming — and it’s going to make AI a lot faster

Unlike previous generations of Wi-Fi that competed on peak throughput numbers, Wi-Fi 8 prioritizes consistent performance under challenging conditions. The specification introduces coordinated multi-access point features, dynamic spectrum management, and hardware-accelerated telemetry designed for AI workloads at the network edge. ... A core part of the Wi-Fi 8 architecture is an approach known as (UHR). This architectural philosophy targets the 99th percentile user experience rather than best-case scenarios. The innovation addresses AI application requirements that demand symmetric bandwidth, consistent sub-5-millisecond latency and reliable uplink performance. ... Wi-Fi 8 introduces (ELR) mode specifically for IoT devices. This feature uses lower data rates with more robust coding to extend coverage. The tradeoff accepts reduced throughput for dramatically improved range. ELR operates by increasing symbol duration and using lower-order modulation. This improves the link budget for battery-powered sensors, smart home devices and outdoor IoT deployments. ... Wi-Fi 8 enhances roaming to maintain sub-millisecond handoff latency. The specification includes improved (FILS) and introduces coordinated roaming decisions across the infrastructure. Access points share client context information before handoff.

Life, death, and online identity: What happens to your online accounts after death?

Today, we lack the tools (protocols) and the regulations to enable digital estate management at scale. Law and regulation can force a change in behavior by large providers. However, lacking effective protocols to establish a mechanism to identify the decedent’s chosen individuals who will manage their digital estate, every service will have to design their own path. This creates an exceptional burden on individuals planning their digital estate, and on individuals who manage the digital estates of the deceased. ... When we set out to write this paper, we wanted to influence the large technology and social media platforms, politicians, regulators, estate planners, and others who can help change the status quo. Further, we hoped to influence standards development organizations, such as the and the Internet Engineering Task Force (IETF), and their members. As standards developers in the realm of identity, we have an obligation to the people we serve to consider identity from birth to death and beyond, to ensure every human receives the respect they deserve in life and in death. Additionally, we wrote the planning guide to help individuals plan for their own digital estate. By giving people the tools to help describe, document, and manage their digital estates proactively, we can raise more awareness and provide tools to help protect individuals at one of the most vulnerable moments of their lives.

5 steps to help CIOs land a board seat

Serving on a board isn’t an extension of an operational role. One issue CIOs face is not understanding the difference between executive management and governance, Stadolnik says. “They’re there to advise, not audit or lead the current company’s CIO,” he adds. In the boardroom, the mandate is to provide strategy, governance, and oversight, not execution. That shift, Stadolnik says, can be jarring for tech leaders who’ve spent their careers driving operational results. ... “There were some broad risk areas where having strong technical leadership was valuable, but it was hard for boards to carve out a full seat just for that, which is why having CIO-plus roles was very beneficial,” says Cullivan. The issue of access is another uphill battle for CIOs. As Payne found, the network effect can play a huge role in seeking a board role. But not every IT leader has the right kind of network that can open the door to these opportunities. ... Boards expect directors to bring scope across business disciplines and issues, not just depth in one functional area. Stadolnik encourages CIOs to utilize their strategic orientation, results focus, and collaborative and influence skills to set themselves up for additional responsibilities like procurement, supply chain, shared services, and others. “It’s those executive leadership capabilities that will unlock broader roles,” he says. Experience in those broader roles bolsters a CIO’s board résumé and credibility.

Microservices Without Meltdown: 7 Pragmatic Patterns That Stick

A good sniff test: can we describe the service’s job in one short sentence, and does a single team wake up if it misbehaves? If not, we’ve drawn mural art, not an interface. Start with a small handful of services you can name plainly—orders, payments, catalog—then pressure-test them with real flows. When a request spans three services just to answer a simple question, that’s a hint we’ve sliced too thin or coupled too often. ... Microservices live and die by their contracts. We like contracts that are explicit, versioned, and backwards-friendly. “Backwards-friendly” means old clients keep working for a while when we add fields or new behaviors. For HTTP APIs, plus consistent error formats makes a huge difference. ... We need timeouts and retries that fit our service behavior, or we’ll turn small hiccups into big outages. For east-west traffic, a or smart gateway helps us nudge traffic safely and set per-route policies. We’re fans of explicit settings instead of magical defaults. ... Each service owns its tables; cross-service read needs go through APIs or asynchronous replication. When a write spans multiple services, aim for a sequence of local commits with compensating actions instead of distributed locks. Yes, we’re describing sagas without the capes: do the smallest thing, record it durably, then trigger the next hop.

Daily Tech Digest - October 17, 2025

Quote for the day:

"Listen with curiosity, speak with honesty act with integrity." -- Roy T Bennett

AI Agents Transform Enterprise Application Development

There's now discussion about the and the need to supervise or manage AI agent developers - calling for and infrastructure changes. New products, services and partnerships announced in the past few weeks support this trend. ... Enterprises were cautious about entrusting public models and agents with intellectual property. But the partnership with could make models more trustworthy. "Enterprises are looking for AI they can actually trust with their code, their data and their day-to-day operations," said Mike Krieger, chief product officer at Anthropic. ... Embedding agentic AI within the fabric of enterprise architecture enables organizations to unlock transformative agility, reduce cognitive load and accelerate innovation - without compromising trust, compliance or control - says an report titled "Architecting secure enterprise AI agents with MCP." Developers adopted globally recognized models such as , or , and as paths to improve the software development and maintenance processes. ... Enterprises must be prepared to implement radical process and infrastructure changes to successfully adopt AI agents in software delivery. AI agents must be managed by a central governance framework to enable complete visibility into agents, agent performance monitoring and security.

There’s no such thing as quantum incident response – and that changes everything

CISOs are directing attention to have added to the corporate risk register. It belongs there. But the problem to be solved is not a quick fix, despite what some snake oil salesmen might be pushing. There is no simple configuration checkbox on or or where you “turn on” () and then you’re good to go. ... Without significant engagement from developers, QA teams and product owners, the quantum decryption risk will remain in play. You cannot transfer this risk by adding more policy coverage. The entire cyber insurance industry itself is in a bit of an existential doubt situation regarding whether cybersecurity can reasonably be insured against, given the systemic impacts of that cascade across entire industries. ...The moment when a comes into existence won’t arrive with fanfare or bombast. Hence, the idea of the . But by then, it will be too late for incident response. What you should do Monday morning: Start that . Figure out what needs protecting for the long term versus what has a shorter shelf life. In the world of DNS, we have () that declares how long a resolver can cache a response. Think of a “PQC TTL” for your sensitive data, because not everything needs 30-year protection.

Hackers Use Blockchain to Hide Malware in Plain Sight

At least two hacking groups are using public blockchains to conceal and control malware in ways that make their operations nearly impossible to dismantle, shows research from Google's Threat Intelligence Group. ... The technique, known as , embeds malicious instructions in blockchain smart contracts rather than traditional servers. Since the blockchain is decentralized and immutable, attackers gain what the researchers call a "bulletproof" infrastructure. The development signals an "escalation in the threat landscape," said , consulting leader at , which is part of . Hackers have found a method "resistant to law enforcement takedowns" that and can be "easily modified for new campaigns." ... The group over time expanded its architecture from a single smart contract to a three-tier system mimicking a software "." This allows rapid updates without touching the compromised sites. One contract acts as a router, another fingerprints the victim's system and a third holds encrypted payload data and decryption keys. A single blockchain transaction, costing as little as a dollar in network fees, can change lure URLs or encryption keys across thousands of infected sites. The researchers said the threat actor used social engineering tricks like fake Cloudflare verification or Chrome update prompts to persuade victims to run malicious commands.

Everyone’s adopting AI, few are managing the risk

Across industries, many organizations are caught in what calls the “.” Teams are active, frameworks are updated, and risks are logged, but progress fades after early success. When boards include risk oversight as a standing agenda item and align on shared performance goals, activity becomes consistent and forward-looking. When governance and ownership are unclear, adoption slows and collaboration fades. ... Many enterprises are adopting or updating risk frameworks, but implementation depth varies. The typical organization maps its controls to several frameworks, while leading firms embed thousands of requirements into daily operations. The report warns that “surface compliance” is common. Breadth without depth leaves gaps that only appear during audits or disruptions. Mature programs treat frameworks as living systems that evolve with business and regulatory change. ... The findings show that many organizations are investing heavily in risk management and AI, but maturity depends less on technology and more on integration. Advanced organizations use governance to connect teams and turn data into foresight. AuditBoard’s research suggests that as AI becomes more embedded in enterprise systems, risk leaders will need to move beyond activity and focus on consistency. Those that do will be better positioned to anticipate change and turn risk management into a strategic advantage.

A mini-CrowdStrike moment? Windows 11 update cripples dev environments

The , (), addressed security issues in Windows operating systems (OSes), but also appears to have blocked Windows’ ability to talk within itself. Localhost allows apps and services to communicate internally without using internet or external network access. Developers use the function to develop, test, and debug websites and apps locally on a Windows machine before releasing them to the public. ... When localhost stops working, entire application development environments can be impacted or “even grind to a halt,” causing internal processes and services to fail and stop communicating, he pointed out. This means developers are unable to test or run web applications locally. This issue is really about “denial of service,” where tools and processes dependent on internal loopback services break, he noted. Developers can’t debug locally, and automated testing processes can fail. At the same time, IT departments are left to troubleshoot, field an influx of service tickets, roll back patches, and look for workarounds. “This bug is definitely disruptive enough to cause delays, lost productivity, and frustration across teams,” said Avakian. ... This type of issue underscores the importance of quality control and thorough testing by third-party suppliers and vendors before releasing updates to commercial markets, he said. Not doing so can have significant downstream impacts and “erode trust” in the update process while making teams more cautious about patching.

How Banks of Every Size Can Put AI to Work, and Take Back Control

For smaller banks and credit unions, the AI conversation begins with math. They want the same digital responsiveness as larger competitors but can’t afford the infrastructure or staffing that traditionally make that possible. The promise of AI, especially low-code and automated implementation, changes that equation. What once required teams of engineers months of coding can now be deployed out-of-the-box, configured and pushed live in a day. That shift finally brings digital innovation within reach for smaller institutions that had long been priced out of it. But even when self-service tools are available, many institutions still rely on outside help for routine changes or maintenance. For these players, the first question is whether they’re willing or able to take product dev work inhouse, even with "AI inside"; the next question is whether they can find partners that can meet them on their own terms. ... For mid-sized players, the AI opportunity centers on reclaiming control. These institutions typically have strong internal teams and clear strategic ideas, yet they remain bound by vendor SLAs that slow innovation. The gap between what they can envision and what they can deliver is wide. AI-driven orchestration tools, especially those that let internal teams configure and launch digital products directly, can help close that gap. By removing layers of technical dependency, mid-sized institutions can move from periodic rollouts to something closer to iterative improvement.

Why your AI is failing — and how a smarter data architecture can fix it

Traditional enterprises operate four separate, incompatible technology stacks, each optimized for different computing eras, not for AI reasoning capabilities. ... When you try to deploy AI across these fragmented stacks, chaos follows. The same business data gets replicated across systems with different formats and validation rules. Semantic relationships between business entities get lost during integration. Context critical for intelligent decision-making gets stripped away to optimize for system performance. AI systems receive technically clean datasets that are semantically impoverished and contextually devoid of meaning. ... As organizations begin shaping their enterprise general intelligence (EGI) architecture, critical operational intelligence remains trapped in disconnected silos. Engineering designs live in PLM systems, isolated from the ERP bill of materials. Quality metrics sit locked in MES platforms with no linkage to supplier performance data. Process parameters exist independently of equipment maintenance records. ... Enterprises solving the data architecture challenge gain sustainable competitive advantages. AI deployment timelines are measured in weeks rather than months. Decision accuracy reaches enterprise-grade reliability. Intelligence scales across all business domains. Innovation accelerates as AI creates new capabilities rather than just automating existing processes.

Under the hood of AI agents: A technical guide to the next frontier of gen AI

With agents, authorization works in two directions. First, of course, users require authorization to run the agents they’ve created. But as the agent is acting on the user’s behalf, it will usually require its own authorization to access networked resources. There are a few different ways to approach the problem of authorization. One is with an access delegation algorithm like OAuth, which essentially plumbs the authorization process through the agentic system. ... Agents also need to remember their prior interactions with their clients. If last week I told the restaurant booking agent what type of food I like, I don’t want to have to tell it again this week. The same goes for my price tolerance, the sort of ambiance I’m looking for, and so on. Long-term memory allows the agent to look up what it needs to know about prior conversations with the user. Agents don’t typically create long-term memories themselves, however. Instead, after a session is complete, the whole conversation passes to a separate AI model, which creates new long-term memories or updates existing ones. ... Agents are a new kind of software system, and they require new ways to think about observing, monitoring and auditing their behavior. Some of the questions we ask will look familiar: Whether the agents are running fast enough, how much they’re costing, how many tool calls they’re making and whether users are happy.

Data Is the New Advantage – If You Can Hold On To It

Proprietary data has emerged as one of the most valuable assets for enterprises—and increasingly, the expectation is that data must be stored indefinitely, ready to fuel future models, insights, and innovations as the technology continues to evolve. ... Globally, data architects, managers, and protectors are in uncharted territory. The arrival of generative AI has proven just how unpredictable and fast-moving technological leaps can be – and if there’s one thing the past few years have taught us, it’s that we can’t know what comes next. The only way to prepare is to ensure proprietary data is not just stored but preserved indefinitely. Tomorrow’s breakthroughs – whether in AI, analytics, or some other yet-unimagined technology – will depend on the depth and quality of the data you have today, and how well you can utilize the storage technologies of your choice to serve your data usage and workflow needs. ... The lesson is clear: don’t get left behind, because your competitors are learning these lessons as well. The enterprises that thrive in this next era of digital innovation will be those that recognize the enduring value of their data. That means keeping it all and planning to keep it forever. By embracing hybrid storage strategies that combine the strengths of tape, cloud, and on-premises systems, organizations can rise to the challenge of exponential growth, protect themselves from evolving threats, and ensure they are ready for whatever comes next. In the age of AI, your competitive advantage won’t just come from your technology stack.

Why women are leading the next chapter of data centers

Working her way up through finance and operations into large-scale digital infrastructure, Xiao’s career reflects a steady ascent across disciplines, including senior roles as president of Chindata Group and CFO at Shanghai Wangsu. These roles sharpened her ability to translate high-level strategy into expansion, particularly in the demanding data center sector. ... Today, she shapes ’s commercial playbook, which includes setting capital priorities, driving cost-efficient delivery models, and embedding resilience and sustainability into every development decision. In mission-critical industries like data centers, repeatability is a challenge. Every market has unique variables – land, power, water, regulatory frameworks, contractor ecosystems, and community engagement. ... For the next wave of talent, building credibility in the data center industry requires more than technical expertise. Engaging in forums, networks, and industry resources not only earns recognition and respect but also broadens knowledge and sharpens perspective. ... Peer networks within hyperscaler and operator communities, Xiao notes, are invaluable for exchanging insights and challenging assumptions. “Industry conferences, cross-company working groups, government-industry task forces, and ecosystem media engagements all matter. And for bench strength, I value partnerships with local technology innovators and digital twin or AI firms that help us run safer, greener facilities,” Xiao explains.

Daily Tech Digest - September 05, 2025

Quote for the day:

"Little minds are tamed and subdued by misfortune; but great minds rise above it." -- Washington Irving

Understanding Context Engineering: Principles, Practices, and Its Distinction from Prompt Engineering

Context engineering is the strategic design, management, and delivery of relevant information—or “context”—to AI systems in order to guide, constrain, or enhance their behavior. Unlike prompt engineering, which primarily focuses on crafting effective input prompts to direct model outputs, context engineering involves curating, structuring, and governing the broader pool of information that surrounds and informs the AI’s decision-making process. In practice, context engineering requires an understanding of not only what the AI should know at a given moment but also how information should be prioritized, retrieved, and presented. It encompasses everything from assembling relevant documents and dialogue history to establishing policies for data inclusion and exclusion. ... While there is some overlap between the two domains, context engineering and prompt engineering serve distinct purposes and employ different methodologies. Prompt engineering is concerned with the formulation of the specific text—the “prompt”—that is provided to the model as an immediate input. It is about phrasing questions, instructions, or commands in a way that elicits the desired behavior or output from the AI. Successful prompt engineering involves experimenting with wording, structure, and sometimes even formatting to maximize the performance of the language model on a given task.

How AI and Blockchain Are Transforming Tenant Verification in India

While artificial intelligence provides both intelligence and speed, Blockchain technology provides the essential foundation of trust and security. Blockchain functions as a permanent digital record – meaning that once information is set, it can’t be changed or deleted by third parties. This feature is particularly groundbreaking for ensuring a safe and clear rental history. Picture this: the rental payments and lease contracts of your tenants could all be documented as ‘smart contracts’ using Blockchain technology. ... The combination of AI and Blockchain signifies a groundbreaking transformation, enabling tenants to create ‘self-sovereign identities’ on the Blockchain — digital wallets that hold their verified credentials, which they fully control. When searching for rental properties, tenants can conveniently provide prospective landlords with access to certain details about themselves, such as their history of timely payments and police records. AI leverages secure and authentic Blockchain data to produce an immediate risk score for landlords to assess, ensuring a quick and reliable evaluation. This cohesive approach guarantees that AI outcomes are both rapid and trustworthy, while the decentralized nature of Blockchain safeguards tenant privacy by removing the necessity for central databases that may become susceptible over time.

Adversarial AI is coming for your applications

New research from Cato Networks threat intelligence report, revealed how threat actors can use a large language model jailbreak technique, known as an immersive world attack, to get AI to create infostealer malware for them: a threat intelligence researcher with absolutely no malware coding experience managed to jailbreak multiple large language models and get the AI to create a fully functional, highly dangerous, password infostealer to compromise sensitive information from the Google Chrome web browser. The end result was malicious code that successfully extracted credentials from the Google Chrome password manager. Companies that create LLMs are trying to put up guardrails, but clearly GenAI can make malware creation that much easier. AI-generated malware, including polymorphic malware, essentially makes signature-based detections nearly obsolete. Enterprises must be prepared to protect against hundreds, if not thousands, of malware variants. ... Enterprises can increase their protection by embedding security directly into applications at the build stage: this involves investing in embedded security that is mapped to OWASP controls; such as RASP, advanced Whitebox cryptography, and granular threat intelligence. IDC research shows that organizations protecting mobile apps often lack a solution to test them efficiently and effectively.

Top Pitfalls to Avoid When Responding to Cyber Disaster

Moving too quickly following an attack can also prompt staff to respond to an intrusion without first fully understanding the type of ransomware that was used. Not all ransomware is created equal and knowing if you were a victim of locker ransomware, double extortion, ransomware-as-a-service, or another kind of attack can make all the difference in how to respond because the goal of the attacker is different for each. ... The first couple hours after a ransomware incident is identified are critical. In those immediate hours, work quickly to identify and isolate affected systems and disconnect compromised devices from the network to prevent the ransomware from spreading further. Don’t forget to also preserve forensic evidence as you go, such as screenshots, relevant logs, anything to inform future law enforcement investigations or legal action. Once that has been done, notify the key stakeholders and the cyber insurance provider. ... After the dust settles, analyze how the attack was able to occur and put in place fixes to keep it from happening again. Identify the initial access point and method, and map how the threat actor moved through the network. What barriers were they able to move past, and which held them back? Are there areas where more segmentation is needed to reduce the attack surface? Do any security workflows or policies need to be modified?

How to reclaim control over your online shopping data

“While companies often admit to sharing user data with third parties, it’s nearly impossible to track every recipient. That lack of control creates real vulnerabilities in data privacy management. Very few organizations thoroughly vet their third-party data-sharing practices, which raises accountability concerns and increases the risk of breaches,” said Ian Cohen, CEO of LOKKER. The criminal marketplace for stolen data has exploded in recent years. In 2024, over 6.8 million accounts were listed for sale, and by early 2025, nearly 2.5 million stolen accounts were available at one point. ... Even limited purchase information can prove valuable to criminals. A breach exposing high-value transactions, for example, may suggest a buyer’s financial status or lifestyle. When combined with leaked addresses, that data can help criminals identify and target individuals more precisely, whether for fraud, identity theft, or even physical theft. ... One key mechanism is the right to be forgotten, a legal principle allowing individuals to request the removal of their personal data from online platforms. The European Union’s GDPR is the strongest example of this principle in action. While not as comprehensive as the GDPR, the US has some privacy protections, such as the California Consumer Privacy Act (CCPA), which allow residents to access or delete their personal data.

Mind the Gap: Agentic AI and the Risks of Autonomy

The ink is barely dry on generative AI and AI agents, and now we have a new next big thing: agentic AI. Sounds impressive. By the time this article comes out, there’s a good chance that agentic AI will be in the rear-view mirror and we’ll all be chasing after the next new big thing. Anyone for autonomous generative agentic AI agent bots? ... Some things on the surface seem more irresponsible than others, but for some, agentic AI apparently not so much. Debugging large language models, AI agents, and agentic AI, as well as implementing guardrails are topics for another time, but it’s important to recognize that companies are handing over those car keys. Willingly. Enthusiastically. Would you put that eighth grader in charge of your marketing department? Of autonomously creating collateral that goes out to your customers without checking it first? Of course not. ... We want AI agents and agentic AI to make decisions, but we must be intentional about the decisions they are allowed to make. What are the stakes personally, professionally, or for the organization? What is the potential liability when something goes wrong? And something will go wrong. Something that you never considered going wrong will go wrong. And maybe think about the importance of the training data. Isn’t that what we say when an actual person does something wrong? “They weren’t adequately trained.” Same thing here.

How software engineers and team leaders can excel with artificial intelligence

As long as software development and AI designers continue to fall prey to the substitution myth, we’ll continue to develop systems and tools that, instead of supposedly making humans lives easier/better, will require unexpected new skills and interventions from humans that weren’t factored into the system/tool design ... Software development covers a lot of ground, from understanding requirements, architecting, designing, coding, writing tests, code review, debugging, building new skills and knowledge, and more. AI has now reached a point where it can automate or speed up almost every part of the process. This is an exciting time to be a builder. A lot of the routine, repetitive, and frankly boring parts of the job, the "cognitive grunt work", can now be handled by AI. Developers especially appreciate the help in areas like generating test cases, reviewing code, and writing documentation. When those tasks are off our plate, we can spend more time on the things that really add value: solving complex problems, designing great systems, thinking strategically, and growing our skills. ... The elephant in the room is "whether AI will take over my job one day?". Until this year, I always thought no, but the recent technological advancements and new product offerings in this space are beginning to change my mind. The reality is that we should be prepared for AI to change the software development role as we know it.

6 browser-based attacks all security teams should be ready for in 2025

Phishing tooling and infrastructure has evolved a lot in the past decade, while the changes to business IT means there are both many more vectors for phishing attack delivery, and apps and identities to target. Attackers can deliver links over instant messenger apps, social media, SMS, malicious ads, and using in-app messenger functionality, as well as sending emails directly from SaaS services to bypass email-based checks. Likewise, there are now hundreds of apps per enterprise to target, with varying levels of account security configuration. ... Like modern credential and session phishing, links to malicious pages are distributed over various delivery channels and using a variety of lures, including impersonating CAPTCHA, Cloudflare Turnstile, simulating an error loading a webpage, and many more. The variance in lure, and differences between different versions of the same lure, can make it difficult to fingerprint and detect based on visual elements alone. ... Preventing malicious OAuth grants being authorized requires tight in-app management of user permissions and tenant security settings. This is no mean feat when considering the 100s of apps in use across the modern enterprise, many of which are not centrally managed by IT and security teams

JSON Config File Leaks Azure ActiveDirectory Credentials

"The critical risk lies in the fact that this file was publicly accessible over the Internet," according to the post. "This means anyone — from opportunistic bots to advanced threat actors — could harvest the credentials and immediately leverage them for cloud account compromise, data theft, or further intrusion." ... To exploit the flaw, an attacker can first use the leaked ClientId and ClientSecret to authenticate against Azure AD using the OAuth2 Client Credentials flow to acquire an access token. Once this is acquired, the attacker then can send a GET request to the Microsoft Graph API to enumerate users within the tenant. This allows them to collect usernames and emails; build a list for password spraying or phishing; and/or identify naming conventions and internal accounts, according to the post. The attacker also can query the Microsoft Graph API to enumerate OAuth2 permission grants within the tenant, revealing which applications have been authorized and what scopes, or permissions, they hold. Finally, the acquired token allows an attacker to use group information to identify privilege clusters and business-critical teams, thus exposing organizational structure and identifying key targets for compromise, according to the post. ... "What appears to be a harmless JSON configuration file can in reality act as a master key to an organization’s cloud kingdom," according to the post.

Data centers are key to decarbonizing tech’s AI-fuelled supply chain

Data center owners and operators are uniquely positioned to step up and play a larger, more proactive role in this by pushing back on tech manufacturers in terms of the patchy emissions data they provide, while also facilitating sustainable circular IT product lifecycle management/disposal solutions for their users and customers. ... The hard truth, however, is that any data center striving to meet its own decarbonization goals and obligations cannot do so singlehandedly. It’s largely beholden to the supply chain stakeholders upstream. At the same time, their customers/users tend to accept ever shortening usage periods as the norm. Often, they overlook the benefits of achieving greater product longevity and optimal cost of ownership through the implementation of product maintenance, refurbishment, and reuse programmes. ... As a focal point for the enablement of the digital economy, data centers are ideally placed to take a much more active role: by lobbying manufacturers, educating users and customers about the necessity and benefits of changing conventional linear practices in favour of circular IT lifecycle management and recycling solutions. Such an approach will not only help decarbonize data centers themselves but the entire tech industry supply chain – by reducing emissions.

Daily Tech Digest - August 31, 2025

Quote for the day:

“Our chief want is someone who will inspire us to be what we know we could be.” -- Ralph Waldo Emerson

A Brief History of GPT Through Papers

The first neural network based language translation models operated in three steps (at a high level). An encoder would embed the “source statement” into a vector space, resulting in a “source vector”. Then, the source vector would be mapped to a “target vector” through a neural network and finally a decoder would map the resulting vector to the “target statement”. People quickly realized that the vector that was supposed to encode the source statement had too much responsibility. The source statement could be arbitrarily long. So, instead of a single vector for the entire statement, let’s convert each word into a vector and then have an intermediate element that would pick out the specific words that the decoder should focus more on. ... The mechanism by which the words were converted to vectors was based on recurrent neural networks (RNNs). Details of this can be obtained from the paper itself. These recurrent neural networks relied on hidden states to encode the past information of the sequence. While it’s convenient to have all that information encoded into a single vector, it’s not good for parallelizability since that vector becomes a bottleneck and must be computed before the rest of the sentence can be processed. ... The idea is to give the model demonstrative examples at inference time as opposed to using them to train its parameters. If no such examples are provided in-context, it is called “zero shot”. If one example is provided, “one shot” and if a few are provided, “few shot”.

8 Powerful Lessons from Robert Herjavec at Entrepreneur Level Up That Every Founder Needs to Hear

Entrepreneurs who remain curious — asking questions and seeking insights — often discover pathways others overlook. Instead of dismissing a "no" or a difficult response, Herjavec urged attendees to look for the opportunity behind it. Sometimes, the follow-up question or the willingness to listen more deeply is what transforms rejection into possibility. ... while breakthrough innovations capture headlines, the majority of sustainable businesses are built on incremental improvements, better execution and adapting existing ideas to new markets. For entrepreneurs, this means it's okay if your business doesn't feel revolutionary from day one. What matters is staying committed to evolving, improving and listening to the market. ... setbacks are inevitable in entrepreneurship. The real test isn't whether you'll face challenges, but how you respond to them. Entrepreneurs who can adapt — whether by shifting strategy, reinventing a product or rethinking how they serve customers — are the ones who endure. ... when leaders lose focus, passion or clarity, the organization inevitably follows. A founder's vision and energy cascade down into the culture, decision-making and execution. If leaders drift, so does the company. For entrepreneurs, this is a call to self-reflection. Protect your clarity of purpose. Revisit why you started. And remember that your team looks to you not just for direction, but for inspiration.

The era of cheap AI coding assistants may be over

Developers have taken to social media platforms and GitHub to express their dissatisfaction over the pricing changes, especially across tools like Claude Code, Kiro, and Cursor, but vendors have not adjusted pricing or made any changes that significantly reduce credits consumption. Analysts don’t see any alternative to reducing the pricing of these tools. "There’s really no alternative until someone figures out the following: how to use cheaper but dumber models than Claude Sonnet 4 to achieve the same user experience and innovate on KVCache hit rate to reduce the effective price per dollar,” said Wei Zhou, head of AI utility research at SemiAnalysis. Considering the market conditions, CIOs and their enterprises need to start absorbing the cost and treat vibe coding tools as a productivity expense, according to Futurum’s Hinchcliffe. “CIOs should start allocating more budgets for vibe coding tools, just as they would do for SaaS, cloud storage, collaboration tools or any other line items,” Hinchcliffe said. “The case of ROI on these tools is still strong: faster shipping, fewer errors, and higher developer throughput. Additionally, a good developer costs six figures annually, while vibe coding tools are still priced in the low-to-mid thousands per seat,” Hinchcliffe added. ... “Configuring assistants to intervene only where value is highest and choosing smaller, faster models for common tasks and saving large-model calls for edge cases could bring down expenditure,” Hinchcliffe added.

AI agents need intent-based blockchain infrastructure

By integrating agents with intent-centric systems, however, we can ensure users fully control their data and assets. Intents are a type of building block for decentralized applications that give users complete control over the outcome of their transactions. Powered by a decentralized network of solvers, agentic nodes that compete to solve user transactions, these systems eliminate the complexity of the blockchain experience while maintaining user sovereignty and privacy throughout the process. ... Combining AI agents and intents will redefine the Web3 experience while keeping the space true to its core values. Intents bridge users and agents, ensuring the UX benefits users expect from AI while maintaining decentralization, sovereignty and verifiability. Intent-based systems will play a crucial role in the next phase of Web3’s evolution by ensuring agents act in users’ best interests. As AI adoption grows, so does the risk of replicating the problems of Web2 within Web3. Intent-centric infrastructure is the key to addressing both the challenges and opportunities that AI agents bring and is necessary to unlock their full potential. Intents will be an essential infrastructure component and a fundamental requirement for anyone integrating or considering integrating AI into DeFi. Intents are not merely a type of UX upgrade or optional enhancement.

The future of software development: To what can AI replace human developers?

Rather than replacing developers, AI is transforming them into higher-level orchestrators of technology. The emerging model is one of human-AI collaboration, where machines handle the repetitive scaffolding and humans focus on design, strategy, and oversight. In this new world, developers must learn not just to write code, but to guide, prompt, and supervise AI systems. The skillset is expanding from syntax and logic to include abstraction, ethical reasoning, systems thinking, and interdisciplinary collaboration. In other words, AI is not making developers obsolete. It is making new demands on their expertise. ... This shift has significant implications for how we educate the next generation of software professionals. Beyond coding languages, students will need to understand how to evaluate AI- AI-generated output, how to embed ethical standards into automated systems, and how to lead hybrid teams made up of both humans and machines. It also affects how organisations hire and manage talent. Companies must rethink job descriptions, career paths, and performance metrics to account for the impact of AI-enabled development. Leaders must focus on AI literacy, not just technical competence. Professionals seeking to stay ahead of the curve can explore free programs, such as The Future of Software Engineering Led by Emerging Technologies, which introduces the evolving role of AI in modern software development.

Open Data Fabric: Rethinking Data Architecture for AI at Scale

The first principle, unified data access, ensures that agents have federated real-time access across all enterprise data sources without requiring pipelines, data movement, or duplication. Unlike human users who typically work within specific business domains, agents often need to correlate information across the entire enterprise to generate accurate insights. ... The second principle, unified contextual intelligence, involves providing agents with the business and technical understanding to interpret data correctly. This goes far beyond traditional metadata management to include business definitions, domain knowledge, usage patterns, and quality indicators from across the enterprise ecosystem. Effective contextual intelligence aggregates information from metadata, data catalogs, business glossaries, business intelligence tools, and tribal knowledge into a unified layer that agents can access in real-time. ... Perhaps the most significant principle involves establishing collaborative self-service. This is a significant shift as it means moving from static dashboards and reports to dynamic, collaborative data products and insights that agents can generate and share with each other. The results are trusted “data answers,” or conversational, on-demand data products for the age of AI that include not just query results but also the business context, methodology, lineage, and reasoning that went into generating them.

A Simple Shift in Light Control Could Revolutionize Quantum Computing

A research collaboration led by Vikas Remesh of the Photonics Group at the Department of Experimental Physics, University of Innsbruck, together with partners from the University of Cambridge, Johannes Kepler University Linz, and other institutions, has now demonstrated a way to bypass these challenges. Their method relies on a fully optical process known as stimulated two-photon excitation. This technique allows quantum dots to emit streams of photons in distinct polarization states without the need for electronic switching hardware. In tests, the researchers successfully produced high-quality two-photon states while maintaining excellent single-photon characteristics. ... “The method works by first exciting the quantum dot with precisely timed laser pulses to create a biexciton state, followed by polarization-controlled stimulation pulses that deterministically trigger photon emission in the desired polarization,” explain Yusuf Karli and Iker Avila Arenas, the study’s first authors. ... “What makes this approach particularly elegant is that we have moved the complexity from expensive, loss-inducing electronic components after the single photon emission to the optical excitation stage, and it is a significant step forward in making quantum dot sources more practical for real-world applications,” notes Vikas Remesh, the study’s lead researcher.

AI and the New Rules of Observability

The gap between "monitoring" and true observability is both cultural and technological. Enterprises haven't matured beyond monitoring because old tools weren't built for modern systems, and organizational cultures have been slow to evolve toward proactive, shared ownership of reliability. ... One blind spot is model drift, which occurs when data shifts, rendering its assumptions invalid. In 2016, Microsoft's Tay chatbot was a notable failure due to its exposure to shifting user data distributions. Infrastructure monitoring showed uptime was fine; only semantic observability of outputs would have flagged the model's drift into toxic behavior. Hidden technical debt or unseen complexity in code can undermine observability. In machine learning, or ML, systems, pipelines often fail silently, while retraining processes, feature pipelines and feedback loops create fragile dependencies that traditional monitoring tools may overlook. Another issue is "opacity of predictions." ... AI models often learn from human-curated priorities. If ops teams historically emphasized CPU or network metrics, the AI may overweigh those signals while downplaying emerging, equally critical patterns - for example, memory leaks or service-to-service latency. This can occur as bias amplification, where the model becomes biased toward "legacy priorities" and blind to novel failure modes. Bias often mirrors reality.

Dynamic Integration for AI Agents – Part 1

An integration of components within AI differs from an integration between AI agents. The former relates to integration with known entities that form a deterministic model of information flow. The same relates to inter-application, inter-system and inter-service transactions required by a business process at large. It is based on mapping of business functionality and information (an architecture of the business in organisations) onto available IT systems, applications, and services. The latter shifts the integration paradigm since the very AI Agents decide that they need to integrate with something at runtime based on the overlapping of the statistical LLM and available information, which contains linguistic ties unknown even in the LLM training. That is, an AI Agent does not know what a counterpart — an application, another AI Agent or data source — it would need to cooperate with to solve the overall task given to it by its consumer/user. The AI Agent does not know even if the needed counterpart exists. ... Any AI Agent may have its individual owner and provider. These owners and providers may be unaware of each others and act independently when creating their AI Agents. No AI Agent can be self-sufficient due to its fundamental design — it depends on the prompts and real-world data at runtime. It seems that the approaches to integration and the integration solutions differ for the humanitarian and natural science spheres.

Counteracting Cyber Complacency: 6 Security Blind Spots for Credit Unions

Organizations that conduct only basic vendor vetting lack visibility into the cybersecurity practices of their vendors’ subcontractors. This creates gaps in oversight that attackers can exploit to gain access to an institution’s data. Third-party providers often have direct access to critical systems, making them an attractive target. When they’re compromised, the consequences quickly extend to the credit unions they serve. ... Cybercriminals continue to exploit employee behavior as a primary entry point into financial institutions. Social engineering tactics — such as phishing, vishing, and impersonation — bypass technical safeguards by manipulating people. These attacks rely on trust, familiarity, or urgency to provoke an action that grants the attacker access to credentials, systems, or internal data. ... Many credit unions deliver cybersecurity training on an annual schedule or only during onboarding. These programs often lack depth, fail to differentiate between job functions, and lose effectiveness over time. When training is overly broad or infrequent, staff and leadership alike may be unprepared to recognize or respond to threats. The risk is heightened when the threats are evolving faster than the curriculum. TruStage advises tailoring cyber education to the institution’s structure and risk profile. Frontline staff who manage member accounts face different risks than board members or vendors.