Daily Tech Digest by Kannan Subbiah: vCISO

Showing posts with label vCISO. Show all posts

Daily Tech Digest - February 20, 2026

Quote for the day:

"Hold yourself responsible for a higher standard than anybody expects of you. Never excuse yourself." -- Henry Ward Beecher

From in-house CISO to consultant. What you need to know before making the leap

A growing number of CISOs are either moving into consulting roles or seriously considering it. The appeal is easy to see: more flexibility and quicker learning, alongside steady demand for experienced security leaders. Some of these professionals work as virtual CISOs (vCISOs), advising companies from a distance. Others operate as fractional CISOs, embedding into the organization one or two days a week. ... CISOs line up their first clients while they’re still employed. Otherwise, he says, it can take a long time to build momentum. And the pressure to make it work can quickly turn into panic. In that moment, security professionals may start “underpricing themselves because they need money immediately,” he says. Once rates are set out of desperation, they’re often hard to reset without straining the relationship. Other CISOs-turned-consultants also emphasize preparation. ... Many of the skills CISOs honed inside large organizations translate directly to the new consulting job, while others suddenly matter more than they ever did before. In addition to technical skills, it is often the practical ones that prove most valuable. The ability to prioritize — sharpened over years in a CISO role — becomes especially important in consulting. ... Crisis management is another essential skill. Paired with hands-on knowledge of cybersecurity processes and best practices, it gives former CISOs a real advantage as they move into consulting.

New phishing campaign tricks employees into bypassing Microsoft 365 MFA

The message purports to be about a corporate electronic funds payment, a document about salary bonuses, a voicemail, or contains some other lure. It also includes a code for ‘Secure Authorization’ that the user is asked to enter when they click on the link, which takes them to a real Microsoft Office 365 login page. Victims think the message is legitimate, because the login page is legitimate, so enter the code. But unknown to the victim, it’s actually the code for a device controlled by the threat actor. What the victim has done is issued an granting the hacker’s device access to their Microsoft account. From there, the hacker has access to everything the account allows the employee to use. Note that this isn’t about credential theft, although if the attacker wants credentials, they can be stolen. It’s about stealing the victim’s OAuth access and refresh tokens for persistent access to their Microsoft account, including to applications such as Outlook, Teams, and OneDrive. ... The main defense against the latest version of this attack is to restrict the applications users are allowed to connect to their account, he said. Microsoft provides enterprise administrators with the ability to allowlist specific applications that the user may authorize via OAuth. ... The easiest defense is to turn off the ability to add extra login devices to Office 365, unless it’s needed, he said. In addition, employees should also be continuously educated about the risks of unusual login requests, even if they come from a familiar system.

The 200ms latency: A developer’s guide to real-time personalization

The first hurdle every developer faces is the “.” How do you personalize for a user with no history or an anonymous session? Traditional collaborative filtering fails here because it relies on a sparse matrix of past interactions. If a user just landed on your site for the first time, that matrix is empty. To solve this within a 200ms budget, you cannot afford to query a massive data warehouse to look for demographic clusters. You need a strategy based on session vectors. We treat the user’s current session as a real-time stream. ... Another architectural flaw I frequently encounter is the dogmatic attempt to run everything in real-time. This is a recipe for cloud bill bankruptcy and latency spikes. You need a strict decision matrix to decide exactly what happens when the user hits “load.” We divide our strategy based on the “Head” and “Tail” of the distribution. ... Speed means nothing if the system breaks. In a distributed system, a 200ms timeout is a contract you make with the frontend. If your sophisticated AI model hangs and takes 2 seconds to return, the frontend spins and the user leaves. We implement strict circuit breakers and degraded modes. ... We are moving away from static, rule-based systems toward agentic architectures. In this new model, the system does not just recommend a static list of items. It actively constructs a user interface based on intent. This shift makes the 200ms limit even harder to hit. It requires a fundamental rethink of our data infrastructure.

Spec-Driven Development – Adoption at Enterprise Scale

Spec-Driven Development emerged as AI models began demonstrating sustained focus on complex tasks for extended periods of time. Operating in a continuous back-and-forth pattern, instructional interactions between humans and AI is not the best use of this capability. At the same time, allowing AI to operate independently for long periods risks significant deviation from intended outcomes. We need effective context engineering to ensure intent alignment in this scenario. SDD addresses this need by establishing a shared understanding with AI, with specs facilitating dialogue between humans and AI, rather than serving as instruction manuals. ... When senior engineers collaborate, communication is conversational, rather than one-way instructions. We achieve shared understanding through dialogue. That shared understanding defines what we build. SDD facilitates this same pattern between humans and AI agents, where agents help us think through solutions, challenge assumptions, and refine intent before diving into execution. ... Given this significant cultural dimension, treating SDD as a technical rollout leaves substantial value on the table. SDD adoption is an organizational capability to develop, not just a technical practice to install. Those who have lived through enterprise agile adoption will recognize the pattern. Tools and ceremonies are easy to install, but without the cultural shifts we risk "SpecFall" (the equivalent of "Scrumerfall").

Tech layoffs in 2026: Why skills matter more than experience in tech

The impact of is becoming visible as companies prioritise data science and machine learning skills over conventional IT roles. During decades, layoffs were typically associated with the economic recession or lack of revenue in companies. The difference between the present wave is the involvement of automation and strategic restructuring. Although automation has had beneficial impacts on increasing productivity, it implies that jobs that aim at routine and repetitive duties continue to be at risk. ... The traditional career trajectories based on experience or seniority are replaced by market needs of niche skills in machine learning, data engineering, cloud architecture, and product leadership. Employees whose skills have not increased are more exposed to displacement in the event of reorganisation of the companies. These developments explain why tech professionals must reskill to remain employable in an AI-driven industry. The tech labor force in India, which is also one of the largest in the world, is especially vulnerable to the change. ... The future of tech jobs in India 2026 will favour professionals who combine technical expertise with analytical and problem-solving skills. The layoffs in early 2026 explain why the technology industry is vulnerable to job losses because corporate interests can change rapidly. To individuals, it entails being future-ready through the development of skills that would be relevant in the industry direction, including AI integration, cybersecurity, cloud computing, and advanced analytics.

Secrets Management Failures in CI/CD Pipelines

are still the most entrenched security issue. API keys, access tokens and private certificates continue to live in the configuration files of the pipeline, shell scripts or application manifests. While the repository is private, security exposure is the result of only one misconfiguration or breached account. Once committed, secrets linger for months or even years, far outlasting the necessary rotation period. Another common failure is secret sprawl. accumulate credentials over time with no clear ownership. Old tokens remain active because nobody remembers which service depends on them. Thus, as the pipeline develops, secrets management becomes reactive rather than intentional, compromising the likelihood of exposing credentials. Over-permissioned credentials make things worse. ... Technology is not the reason for most secrets management failures; it’s people. Developers tend to copy and paste credentials when they’re trying to get to the bottom of some problem or other. They might even just bypass the security safeguards because things are tight against the wire. It’s pretty easy for nobody to keep absolutely on top of their security posture as your CI/CD pipelines evolve. It’s just exactly for this reason that a DevSecOps culture is important. It has got to be more than just the tools; it has got to be how we all work together to get the job done. Security teams must recognize that what is needed is to consider the CI/CD pipeline as production infrastructure, not some internal tool that can be altered ‘on the fly’.

Agentic AI systems don’t fail suddenly — they drift over time

As organizations move from experimentation to real operational deployment of agentic AI, a new category of risk is emerging — one that traditional AI evaluation, testing and governance practices often struggle to detect. ... Most enterprise AI governance practices evolved around a familiar mental model: a stateless model receives an input and produces an output. Risk is assessed by measuring accuracy, bias or robustness at the level of individual predictions. Agentic systems strain that model. The operational unit of risk is no longer a single prediction, but a behavioral pattern that emerges over time. An agent is not a single inference. It is a process that reasons across multiple steps, invokes tools and external services, retries or branches when needed, accumulates context over time and operates inside a changing environment. Because of that, the unit of failure is no longer a single output, but the sequence of decisions that leads to it. ... In real environments, degradation rarely begins with obviously incorrect outputs. It shows up in subtler ways, such as verification steps running less consistently, tools being used differently under ambiguity, retry behavior shifting or execution depth changing over time. ... Without operational evidence, governance tends to rely more on intent and design assumptions than on observed reality. That’s not a failure of governance so much as a missing layer. Policy defines what should happen, diagnostics help establish what is actually happening and controls depend on that evidence.

Prompt Control is the New Front Door of Application Security

Application security has always been built around a simple assumption: There is a front door. Traffic enters through known interfaces, authentication establishes identity, authorization constrains behavior, and controls downstream enforcement of policy. That model still exists, but our most recent research shows it no longer captures where risk actually concentrates in AI-driven systems. ... are where intent enters the system. They define not only what a user is asking, but how the model should reason, what context it should retain, and which safeguards it should attempt to bypass. That is why prompt layers now outrank traditional integration points as the most impactful area for both application security and delivery. ... Output moderation still matters, and our research shows it remains a meaningful concern. But its lower ranking is telling. Output controls catch problems after the system has already behaved badly. They are essential guardrails, not primary defenses. It’s always more efficient to stop the thief on the way in rather than try to catch him after the fact, and in the case of inference, it’s less costly because stopping on the ingress means no token processing costs incurred. ... Our second set of findings reinforces this point. Authentication and observability lead the methods organizations use to secure and deliver AI inference services, cited by 55% and 54% of respondents, respectively. This holds true across roles, with the exception of developers, who more often prioritize protection against sensitive data leaks.

The 'last-mile' data problem is stalling enterprise agentic AI — 'golden pipelines' aim to fix it

Traditional ETL tools like dbt or Fivetran prepare data for reporting: structured analytics and dashboards with stable schemas. AI applications need something different: preparing messy, evolving operational data for model inference in real-time. calls this distinction "inference integrity" versus "reporting integrity." Instead of treating data preparation as a separate discipline, golden pipelines integrate normalization directly into the AI application workflow, collapsing what typically requires 14 days of manual engineering into under an hour, the company says. Empromptu's "golden pipeline" approach is a way to accelerate data preparation and make sure that data is accurate. ... "Enterprise AI doesn't break at the model layer, it breaks when messy data meets real users," Shanea Leven, CEO and co-founder of Empromptu told VentureBeat in an exclusive interview. "Golden pipelines bring data ingestion, preparation and governance directly into the AI application workflow so teams can build systems that actually work in production." ... Golden pipelines target a specific deployment pattern: organizations building integrated AI applications where data preparation is currently a manual bottleneck between prototype and production. The approach makes less sense for teams that already have mature data engineering organizations with established ETL processes optimized for their specific domains, or for organizations building standalone AI models rather than integrated applications.

From installation to predictive maintenance: The new service backbone of AI data centers

bring together several shifts at once: much higher rack densities, more dynamic load profiles, new forms of cooling, and tighter integration between electrical and digital systems. A single misconfiguration in the power chain can have much wider consequences than would have been the case in a traditional facility. This is happening at a time when many operators struggle to recruit and retain experienced operations and maintenance staff. The personnel on site often have to cope with hybrid environments that combine legacy air-cooled rooms with liquid-ready zones, energy storage, and multiple software layers for control and monitoring. In such an environment, services are not a ‘nice to have’. ... As architectures become more intricate, human error remains one of the main residual risks. AI-ready infrastructures combine complex electrical designs, liquid cooling circuits, high-density rack layouts, and multiple software layers such as . Operating and maintaining such systems safely requires clear procedures and a high level of discipline. ... In an AI-driven era, service strategy is as important as the choice of UPS topology, cooling technology or energy storage. Commissioning, monitoring, maintenance, and training are not isolated activities. Together, they form a continuous backbone that supports the entire lifecycle of the data center. Well-designed service models help operators improve availability, optimise energy performance and make better use of the assets they already have.

Daily Tech Digest - May 17, 2025

Quote for the day:

“Only those who dare to fail greatly can ever achieve greatly.” -- Robert F.

Top 10 Best Practices for Effective Data Protection

Your first instinct may be to try to keep up with all your data, but this may be a fool's errand. The key to success is to have classification capabilities everywhere data moves, and rely on your DLP policy to jump in when risk arises. Automation in data classification is becoming a lifesaver thanks to the power of AI. AI-powered classification can be faster and more accurate than traditional ways of classifying data with DLP. Ensure any solution you are evaluating can use AI to instantly uncover and discover data without human input. ... Data loss prevention (DLP) technology is the core of any data protection program. That said, keep in mind that DLP is only a subset of a larger data protection solution. DLP enables the classification of data (along with AI) to ensure you can accurately find sensitive data. Ensure your DLP engine can consistently alert correctly on the same piece of data across devices, networks, and clouds. The best way to ensure this is to embrace a centralized DLP engine that can cover all channels at once. Avoid point products that bring their own DLP engine, as this can lead to multiple alerts on one piece of moving data, slowing down incident management and response. Look to embrace Gartner's security service edge approach, which delivers DLP from a centralized cloud service.

4 Keys To Successful Change Management From The Bain Playbook

From the start, Bain was crystal clear about its case for change, according to Razdan. The company prioritized change management, which meant IT partnering with finance; it also meant cultivating a mindset conducive to change. “We owned the change; we identified a group of high performers within our finance and our IT teams. This community of super-users could readily identify and deal with any of the problems that typically arise in an implementation of this size and scale,” Mackey said. “This was less just changing their technology; it’s changing employee behaviors and setting us up for how we want to grow and change processes going forward.” ... “We actually set up a program to be always measuring the value,” Razdan said. “You have internal stakeholders, you have external stakeholders, you have partnerships; we kind of built an ecosystem of governance and partnership that enabled us to keep everybody on the same page because transparency and communication is critical to success.” Gauging progress via transparent key performance indicators was all the more impressive, given that most of this happened during the worldwide, pandemic-driven move to remote work. “We could assess the implementation, as we went through it, to keep us on track [and] course correct,” Mackey said.

Emerging AI security risks exposed in Pangea's global study

A significant finding was the non-deterministic nature of large language model (LLM) security. Prompt injection attacks, a method where attackers manipulate input to provoke undesired responses from AI systems, were found to succeed unpredictably. An attack that fails 99 times could succeed on the 100th attempt with identical input, due to the underlying randomness in LLM processing. The study also revealed substantial risks of data leakage and adversarial reconnaissance. Attackers using prompt injection can manipulate AI models to disclose sensitive information or contextual details about the environment in which the system operates, such as server types and network access configurations. 'This challenge has given us unprecedented visibility into real-world tactics attackers are using against AI applications today,' said Oliver Friedrichs, Co-Founder and Chief Executive Officer of Pangea. 'The scale and sophistication of attacks we observed reveal the vast and rapidly evolving nature of AI security threats. Defending against these threats must be a core consideration for security teams, not a checkbox or afterthought.' Findings indicated that basic defences, such as native LLM guardrails, left organisations particularly exposed.

Dynamic DNS Emerges as Go-to Cyberattack Facilitator

Dynamic DNS (DDNS) services automatically update a domain name's DNS records in real-time when the Internet service provider changes the IP address. Real-time updating for DNS records wasn't needed in the early days of the Internet when static IP addresses were the norm. ... It sounds simple enough, yet bad actors have abused the services for years. More recently, though, cybersecurity vendors have observed an increase in such activity, especially this year. The notorious cybercriminal collective Scattered Spider, for instance, has turned to DDNS to obfuscate its malicious activity and impersonate well-known brands in social engineering attacks. This trend has some experts concerned about a rise in abuse and a surge in "rentable" subdomains. ... In an example of an observed attack, Scattered Spider actors established a new subdomain, klv1.it[.]com, designed to impersonate a similar domain, klv1.io, for Klaviyo, a Boston-based marketing automation company. Silent Push's report noted that the malicious domain had just five detections on VirusTotal at the time of publication. The company also said the use of publicly rentable subdomains presents challenges for security researchers. "This has been something that a lot of threat actors do — they use these services because they won't have domain registration fingerprints, and it makes it harder to track them," says Zach Edwards, senior threat researcher at Silent Push.

The Growing and Changing Threat of Deepfake Attacks

To ensure their deepfake attacks are convincing, malicious actors are increasingly focusing on more believable delivery, enhanced methods, such as phone number spoofing, SIM swapping, malicious recruitment accounts and information-stealing malware. These methods allow actors to convincingly deliver deepfakes and significantly increase a ploy’s overall credibility. ... High-value deepfake targets, such as C-suite executives, key data custodians, or other significant employees, often have moderate to high volumes of data available publicly. In particular, employees appearing on podcasts, giving interviews, attending conferences, or uploading videos expose significant volumes of moderate- to high-quality data for use in deepfakes. This dictates that understanding individual data exposure becomes a key part of accurately assessing the overall enterprise risk of deepfakes. Furthermore, ACI research indicates industries such as consulting, financial services, technology, insurance and government often have sufficient publicly available data to enable medium-to high-quality deepfakes. Ransomware groups are also continuously leaking a high volume of enterprise data. This information can help fuel deepfake content to “talk” about genuine internal documents, employee relationships and other internal details.

Binary Size Matters: The Challenges of Fitting Complex Applications in Storage-Constrained Devices

Although we are here focusing on software, it is important to say that software does not run in a vacuum. Having an understanding of the hardware our programs run on and even how hardware is developed can offer important insights into how to tackle programming challenges. In the software world, we have a more iterative process, new features and fixes can usually be incorporated later in the form of over-the-air updates, for example. That is not the case with hardware. Design errors and faults in hardware can at the very best be mitigated with considerable performance penalties. These errors can introduce the meltdown and spectre vulnerabilities, or render the whole device unusable. Therefore the hardware design phase has a much longer and rigorous process before release than the software design phase. This rigorous process also impacts design decisions in terms of optimizations and computational power. Once you define a layout and bill of materials for your device, the expectation is to keep this constant for production as long as possible in order to reduce costs. Embedded hardware platforms are designed to be very cost-effective. Designing a product whose specifications such as memory or I/O count are wasted also means a cost increase in an industry where every cent in the bill of materials matters.

Cyber Insurance Applications: How vCISOs Bridge the Gap for SMBs

Proactive risk evaluation is a game-changer for SMBs seeking to maintain robust insurance coverage. vCISOs conduct regular risk assessments to quantify an organization’s security posture and benchmark it against industry standards. This not only identifies areas for improvement but also helps maintain compliance with evolving insurer expectations. Routine audits—led by vCISOs—keep security controls effective and relevant. Third-party risk evaluations are particularly valuable, given the rise in supply chain attacks. By ensuring vendors meet security standards, SMBs reduce their overall risk profile and strengthen their position during insurance applications and renewals. Employee training programs also play a critical role. By educating staff on phishing, social engineering, and other common threats, vCISOs help prevent incidents before they occur. ... For SMBs, navigating the cyber insurance landscape is no longer just a box-checking exercise. Insurers demand detailed evidence of security measures, continuous improvement, and alignment with industry best practices. vCISOs bring the technical expertise and strategic perspective necessary to meet these demands while empowering SMBs to strengthen their overall security posture.

How to establish an effective AI GRC framework

Because AI introduces risks that traditional GRC frameworks may not fully address, such as algorithmic bias and lack of transparency and accountability for AI-driven decisions, an AI GRC framework helps organizations proactively identify, assess, and mitigate these risks, says Heather Clauson Haughian, co-founding partner at CM Law, who focuses on AI technology, data privacy, and cybersecurity. “Other types of risks that an AI GRC framework can help mitigate include things such as security vulnerabilities where AI systems can be manipulated or exposed to data breaches, as well as operational failures when AI errors lead to costly business disruptions or reputational harm,” Haughian says. ... Model governance and lifecycle management are also key components of an effective AI GRC strategy, Haughian says. “This would cover the entire AI model lifecycle, from data acquisition and model development to deployment, monitoring, and retirement,” she says. This practice will help ensure AI models are reliable, accurate, and consistently perform as expected, mitigating risks associated with model drift or errors, Haughian says. ... Good policies balance out the risks and opportunities that AI and other emerging technologies, including those requiring massive data, can provide, Podnar says. “Most organizations don’t document their deliberate boundaries via policy,” Podnar says.

How to Keep a Consultant from Stealing Your Idea

The best defense is a good offense, Thirmal says. Before sharing any sensitive information, get the consultant to sign a non-disclosure agreement (NDA) and, if needed, a non-compete agreement. "These legal documents set clear boundaries on what can and can't do with your ideas." He also recommends retaining records -- meeting notes, emails, and timestamps -- to provide documented proof of when and where the idea in question was discussed. ... If a consultant takes an idea and commercializes it, or shares it with a competitor, it's time to consult legal counsel, Paskalev says. The legal case's strength will hinge on the exact wording within contracts and documentation. "Sometimes, a well-crafted cease-and-desist letter is enough; other times, litigation is required." ... The best way to protect ideas isn't through contracts -- it's by being proactive, Thirmal advises. "Train your team to be careful about what they share, work with consultants who have strong reputations, and document everything," he states. "Protecting innovation isn’t just a legal issue -- it's a strategic one." Innovation is an IT leader's greatest asset, but it's also highly vulnerable, Paskalev says. "By proactively structuring consultant agreements, meticulously documenting every stage of idea development, and being ready to enforce protection, organizations can ensure their competitive edge."

Even the Strongest Leaders Burn Out — Here's the Best Way to Shake the Fatigue

One of the most overlooked challenges in leadership is the inability to step back from the work and see the full picture. We become so immersed in the daily fires, the high-stakes meetings, the make-or-break moments, that we lose the ability to assess the battlefield objectively. The ocean, or any intense, immersive activity, provides that critical reset. But stepping away isn't just about swimming in the ocean. It's about breaking patterns. Leaders are often stuck in cycles — endless meetings, fire drills, back-to-back calls. The constant urgency can trick you into believing that everything is critical. That's why you need moments that pull you out of the daily grind, forcing you to reset before stepping back in. This is where intentional recovery becomes a strategic advantage. Top-performing leaders across industries — from venture capitalists to startup founders — intentionally carve out time for activities that challenge them in different ways. ... The most effective leaders understand that managing their energy is just as important as managing their time. When energy levels dip, cognitive function suffers, and decision-making becomes less strategic. That's why companies known for their progressive workplace cultures integrate mindfulness practices, outdoor retreats and wellness programs — not as perks, but as necessary investments in long-term performance.

Daily Tech Digest - May 12, 2025

Quote for the day:

"Our greatest fear should not be of failure but of succeeding at things in life that don't really matter." -- Francis Chan

The rise of vCISO as a viable cybersecurity career path

Companies that don’t have the means to hire a full-time CISO still face the same harsh realities their peers do — heightened compliance demands, escalating cyber incidents, and growing tech-related risks. A part-time security leader can help them assess their state of security and build out a program from scratch, or assist a full-time director-level security leader with a project. ... In some of these ongoing relationships this could be to fill the proverbial chair of the CISO, doing all the traditional work of the role on a part-time basis. This is the kind of arrangement most likely to be referred to as a fractional role. Other retainer arrangements may just be for an advisory position where the client is buying regular mindshare of the vCISO to supplement their tech team’s knowledge pool. They could be a strategic sounding board to the CIO or even a subject-matter expert to the director of security or newly installed CISO. But vCISOs can work on a project-by-project or hourly basis as well. “It’s really what works best for my potential client,” says Demoranville. “I don’t want to force them into a box. So, if a subscription model works or a retainer, cool. If they only want me here for a short engagement, maybe we’re trying to put in a compliance regimen for ISO 27001 or you need me to review NIST, that’s great too.”

Why Indian Banks Need a Sovereign Cloud Strategy

Enterprises need to not only implement better compliance strategies but also rethink the entire IT operating model. Managed sovereign cloud services can help enterprises address this need. ... The need for true sovereignty becomes crucial in a world where many global cloud providers, even when operating within Indian data centers, are subject to foreign laws such as the U.S. Clarifying Lawful Overseas Use of Data Act or the Foreign Intelligence Surveillance Act. These regulations can compel disclosure of Indian banking data to overseas governments, undermining trust and violating the spirit of data localization mandates. "When an Indian bank chooses a global cloud provider with U.S. exposure, they're essentially opening a backdoor for foreign jurisdictions to access sensitive Indian financial data," Rajgopal said. "Sovereignty is a strategic necessity." Managed sovereign clouds not only align with India's compliance frameworks but also reduce complexity by integrating regulatory controls directly into the cloud stack. Instead of treating compliance as an afterthought, it is incorporated in the architecture. ... "Banks today are not just managing money; they are managing trust, security and compliance at unprecedented levels. Sovereign cloud is no longer optional. It's the future of financial resilience," said Pai.

Study Suggests Quantum Entanglement May Rewrite the Rules of Gravity

Entanglement entropy measures the degree of quantum correlation between different regions of space and plays a key role in quantum information theory and quantum computing. Because entanglement captures how information is shared across spatial boundaries, it provides a natural bridge between quantum theory and the geometric fabric of spacetime. In conventional general relativity, the curvature of spacetime is determined by the energy and momentum of matter and radiation. The new framework adds another driver: the quantum information shared between fields. This extra term modifies Einstein’s equations and offers an explanation for some of gravity’s more elusive behaviors, including potential corrections to Newton’s gravitational constant. ... One of the more striking implications involves black hole thermodynamics. Traditional equations for black hole entropy and temperature rely on Newton’s constant being fixed. If gravity “runs” with energy scale — as the study proposes — then these thermodynamic quantities also shift. ... Ultimately, the study does not claim to resolve quantum gravity, but it does reframe the problem. By showing how entanglement entropy can be mathematically folded into Einstein’s equations, it opens a promising path that links spacetime to information — a concept familiar to quantum computer scientists and physicists alike.

Maximising business impact: Developing mission-critical skills for organisational success

Often, L&D is perceived merely as an HR-led function tasked with building workforce capabilities. However, this narrow framing extensively limits its potential impact. As Cathlea shared, “It’s time to educate leaders that L&D is not just a support role—it’s a business-critical responsibility that must be shared across the organisation. By understanding what success looks like through the eyes of different functions, L&D teams can design programmes that support those ambitions — and crucially, communicate value in language that business leaders understand. The panel referenced a case from a tech retailer with over 150,000 employees, where the central L&D team worked to identify cross-cutting capability needs, such as communication, project management, and leadership, while empowering local departments to shape their training solutions. This balance of central coordination and local autonomy enabled the organisation to scale learning in a way that was both relevant and impactful. ... The shift towards skill-based development is also transforming how learning experiences are designed and delivered. What matters most is whether these learning moments are recognised, supported, and meaningfully connected to broader organisational goals.

What software developers need to know about cybersecurity

Training developers to write secure code shouldn’t be looked at as a one-time assignment. It requires a cultural shift. Start by making secure coding techniques are the standard practice across your team. Two of the most critical (yet frequently overlooked) practices are input validation and input sanitization. Input validation ensures incoming data is appropriate and safe for its intended use, reducing the risk of logic errors and downstream failures. Input sanitization removes or neutralizes potentially malicious content—like script injections—to prevent exploits like cross-site scripting (XSS). ... Authentication and authorization aren’t just security check boxes—they define who can access what and how. This includes access to code bases, development tools, libraries, APIs, and other assets. ... APIs may be less visible, but they form the connective tissue of modern applications. APIs are now a primary attack vector, with API attacks growing 1,025% in 2024 alone. The top security risks? Broken authentication, broken authorization, and lax access controls. Make sure security is baked into API design from the start, not bolted on later. ... Application logging and monitoring are essential for detecting threats, ensuring compliance, and responding promptly to security incidents and policy violations. Logging is more than a check-the-box activity—for developers, logging can be a critical line of defense.

Why security teams cannot rely solely on AI guardrails

The core issue is that most guardrails are implemented as standalone NLP classifiers—often lightweight models fine-tuned on curated datasets—while the LLMs they are meant to protect are trained on far broader, more diverse corpora. This leads to misalignment between what the guardrail flags and how the LLM interprets inputs. Our findings show that prompts obfuscated with Unicode, emojis, or adversarial perturbations can bypass the classifier, yet still be parsed and executed as intended by the LLM. This is particularly problematic when guardrails fail silently, allowing semantically intact adversarial inputs through. Even emerging LLM-based judges, while promising, are subject to similar limitations. Unless explicitly trained to detect adversarial manipulations and evaluated across a representative threat landscape, they can inherit the same blind spots. To address this, security teams should move beyond static classification and implement dynamic, feedback-based defenses. Guardrails should be tested in-system with the actual LLM and application interface in place. Runtime monitoring of both inputs and outputs is critical to detect behavioral deviations and emergent attack patterns. Additionally, incorporating adversarial training and continual red teaming into the development cycle helps expose and patch weaknesses before deployment.

Finding the Right Architecture for AI-Powered ESG Analysis

Rather than choosing between competing approaches, we developed a hybrid architecture that leverages the strengths of both deterministic workflows and agentic AI: For report analysis: We implemented a structured workflow that removes the Intent Agent and Supervisor from the process, instead providing our own intention through a report workflow. This orchestrates the process using the uploaded sustainability file, synchronously chaining prompts and agents to obtain the company name and relevant materiality topics, then asynchronously producing a comprehensive analysis of environmental, social, and governance aspects. For interactive exploration: We maintained the conversational, agentic architecture as a core component of the solution. After reviewing the initial structured report, analysts can ask follow-up questions like, “How does this company’s emissions reduction claims compare to their industry peers?” ... By marrying these approaches, enterprise architects can build systems that maintain human oversight while leveraging AI to handle data-intensive tasks – keeping human analysts firmly in the driver’s seat with AI serving as powerful analytical tools rather than autonomous decision-makers. As we navigate the rapidly evolving landscape of AI implementation, this balanced approach offers a valuable pathway forward.

The Rise of xLMs: Why One-Size-Fits-All AI Models Are Fading

To reach its next evolution, the LLM market will follow all other widely implemented technologies and fragment into an “xLM” market of more specialized models, where the x stands for various models. Language models are being implemented in more places with application- and use case-specific demands, such as lower power or higher security and safety measures. Size is another factor, but we’ll also see varying functionality and models that are portable, remote, hybrid, and domain and region-specific. With this progression, greater versatility and diversity of use cases will emerge, with more options for pricing, security, and latency. ... We must rethink how AI models are trained to fully prepare for and embrace the xLM market. The future of more innovative AI models and the pursuit of artificial general intelligence hinge on advanced reasoning capabilities, but this necessitates restructuring data management practices. ... Preparing real-time data pipelines for the xLM age inherently increases pressure on data engineering resources, especially for organizations currently relying on static batch data uploads and fine-tuning. Historically, real-time accuracy has demanded specialized teams to complete regular batch uploads while maintaining data accuracy, which presents cost and resource barriers.

Ernst & Young exec details the good, bad and future of genAI deployments

“There is a huge skills gap in data science in terms of the number of people that can do that well, and that is not changing. Everywhere else we can talk about what jobs are changing and where the future is. But AI scientists, data scientists, continue to be the top two in terms of what we’re looking for. I do think organizations are moving to partner more in terms of trying to leverage those skills gap….” The more specific the case for the use of AI, the more easily you can calculate the ROI. “Healthcare is going to be ripe for it. I’ve talked to a number of doctors who are leveraging the power of AI and just doing their documentation requirements, using it in patient booking systems, workflow management tools, supply chain analysis. There, there are clear productivity gains, and they will be different per sector. “Are we also far enough along to see productivity gains in R&D and pharmaceuticals? Yes, we are. Is it the Holy Grail? Not yet, but we are seeing gains and that’s where I think it gets more interesting. “Are we far enough along to have systems completely automated and we just work with AI and ask the little fancy box in front of us to print out the balance sheet and everything’s good? No, we’re a hell of a long way away from that.

How Human-Machine Partnerships Are Evolving in 2025

“Soon, there will be no function that does not have AI as a fundamental ingredient. While it’s true that AI will replace some jobs, it will also create new ones and reduce the barrier of entry into many markets that have traditionally been closed to just a technical or specialized group,” says Bukhari. “AI becoming a part of day-to-day life will also force us to embrace our humanity more than ever before, as the soft skills AI can’t replace will become even more critical for success in the workplace and beyond.” ... CIOs and other executives must be data and AI literate, so they are better equipped to navigate complex regulations, lead teams through AI-driven transformations and ensure that AI implementations are aligned with business goals and values. Cross-functional collaboration is also critical. ... AI innovation is already outpacing organizational readiness, so continuous learning, proactive strategy alignment and iterative implementation approaches are important. CIOs must balance infrastructure investments, like GPU resource allocation, with flexibility in computing strategies to stay competitive without compromising financial stability. “As the enterprise landscape increasingly incorporates AI-driven processes, the C-suite must cultivate specific skills that will cascade effectively through their management structures and their entire human workforce,” says Miskawi.

Daily Tech Digest - March 07, 2025

Quote for the day:

"The actions of a responsible executive are contagious." -- Joe D. Batton

Operational excellence with AI: How companies are boosting success with process intelligence everyone can access

The right tooling can make a company’s processes visible and accessible to more than just its process experts. With strategic stakeholders and lines of business users involved, the very people who best know the business can contribute to innovation, design new processes and cut out endless wasted hours briefing process experts. AI, essentially, lowers the barrier to entry so everyone can come into the conversation, from process experts to line-of-business users. This speeds up time-to-value in transformation. ... Rather than simply ‘survive,’ companies can use AI to build true resilience — or antifragility — in which they learn from system failures or cybersecurity breaches and operationalize that knowledge. By putting AI into the loop on process breaks and testing potential scenarios via a digital twin of the organization, non-process experts and stakeholders are empowered to mitigate risk before escalations. ... Non-process experts must be able to make data-driven decisions faster with AI powered insights that recommend best practices and design principles for dashboards. Any queries that arise should be answered by means of automatically generated visualizations which can be integrated directly into apps — saving time and effort.

Why Security Leaders Are Opting for Consulting Gigs

CISOs are asked to balance business objectives alongside product and infrastructure security, ransomware defense, supply chain security, AI governance, and compliance with increasingly complex regulations like the SEC's cyber-incident disclosure rules. Increased pressure for transparency puts CISOs in a tough situation when they must choose between disclosing an incident that could have adverse effects on the business or not disclosing it and risking personal financial ruin. ... The vCISO model emerged as a practical solution, particularly for midsize companies that need executive-level security expertise but can't justify a full-time CISO's compensation package. ... The surge in vCISOs should serve as a warning to boards and executives. If you're struggling to retain security leadership or considering a virtual CISO, you need to examine why. Is it about flexibility and cost, or have you created an environment where security leaders can't succeed? The pendulum will inevitably swing back as organizations realize that effective security leadership requires consistent, dedicated attention. ... Your CISO is working hard to protect your organization. So who will protect your CISO? Now is a great time to check in on them. Make sure they feel like they're fighting a winnable fight.

How to Build a Reliable AI Governance Platform

An effective AI governance platform includes four fundamental components: data governance, technical controls, ethical guidelines and reporting mechanisms, says Beena Ammanath, executive director of the Global Deloitte AI Institute. "Data governance is necessary for ensuring that data within an organization is accurate, consistent, secure and used responsibly," she explains in an online interview. Technical controls are essential for tasks such as testing and validating GenAI models to ensure their performance and reliability, Ammanath says. "Ethical and responsible AI use guidelines are critical, covering aspects such as bias, fairness, and accountability to promote trust across the organization and with key stakeholders." ... "AI governance requires a multi-disciplinary or interdisciplinary approach and may involve non-traditional partners such as data science and AI teams, technology teams for the infrastructure, business teams who will use the system or data, governance and risk and compliance teams -- even researchers and customers," Baljevic says. Clark advises working across stakeholder groups. "Technology and business leaders, as well as practitioners -- from ML engineers to IT to functional leads -- should be included in the overall plan, especially for high-risk use case deployments," she says.

Reality Check: Is AI’s Promise to Deliver Competitive Advantage a Dangerous Mirage?

What happens when AI makes our bank’s products completely commoditized and undifferentiated? It’s not a defeatist question for the industry. Instead, it suggests a shortcoming in bank and credit union strategic planning about AI, Henrichs says. "Everyone’s asking about efficiency gains, risk management, and competitive advantages from AI," he suggests. "The uncomfortable truth is that if every bank has access to the same AI capabilities [and increasingly do through vendors like nCino, Q2, and FIS], we’re racing toward commoditization at an unprecedented speed." ... How can boards lead the institution to use AI to amplify existing competitive advantages? It’s not just about the technology. It’s "the combination of technology stack," say Jim Marous, Co-Publisher of The Financial Brand, with "people, leadership and willingness to take risks that will result in the quality of AI looking far different from bank A to bank Z. AI [is about] rethinking what we do. Further, fast follower doesn’t cut it because trying to copy… ignores the fundamental strategic changes [happening] behind the scenes." Creativity is not exactly a top priority in an industry accountable day-in and day-out to regulators, yet it’s required as technology applies commoditization pressure.

A strategic playbook for entrepreneurs: 4 paths to success

To make educated choices as an entrepreneur, Scott and Stern recommend a sequential learning process known as test two, choose one for the four strategies within the compass. This is a systematic process where entrepreneurs consider multiple strategic alternatives and identify at least two that are commercially viable before choosing just one. As the authors write in their book, “The intellectual property and architectural strategies are worth testing for entrepreneurs who prefer to put in the work developing and maintaining proprietary technology; meanwhile, value chain and disruption may work better for leaders looking to execute quickly.” Scott referred to Vera Wang as a classic example of sequential learning. As a Ralph Lauren employee and bride-to-be at 35, Wang told her team that she felt there was an untapped market for older women shopping for wedding dresses. The company disagreed, so Wang opened her own shop — but she didn’t launch her line of dresses immediately. Instead, Scott said, Wang filled her shop with traditional dresses and offered only one new dress of her own. The goal was to see which types of customers were interested, as well as which aesthetics ultimately sold, before she started designing her new line. “[Wang] was able to take what she learned about design, customer, messaging, and price point and build it into her venture,” Scott said.

Increasing Engineering Productivity, Develop Software Fast and in a Sustainable Way

The real problem comes when speed means cutting corners - skipping tests, ignoring telemetry, rushing through code reviews. That might seem fine in the moment, but over time, it leads to tech debt and makes development slower, not faster. It’s kind of like skipping sleep to get more done. One late night? No problem. But if you do it every night, your productivity tanks. Same with software - if you never take time to clean up, everything gets harder to change. ... Software engineering productivity and sustainability are influenced by many factors and can mean different things to different people. For me, the two primary drivers that stand out are code quality and efficient processes. High-quality code is modular, readable, and well-documented, which simplifies maintenance, debugging, and scaling, while reducing the burden of technical debt. ... if the developers are not complaining enough, it’s probably because they’ve become complacent with, or resigned to, the status quo. In those cases, we can adopt the "we’re all one team" mindset and actually help them deliver features for a while – on the very clear understanding that we will be taking notes about everything that causes friction and then going and fixing that. That’s an excellent way to get the ground truth about how development is really going: listening, and hands-on learning.

Rethinking System Architecture: The Rise of Distributed Intelligence with eBPF

In an IT world driven by centralized decision-making, gathering insights and applying intelligence often follows a well-established — yet limiting — pattern. At the heart of this model, large volumes of telemetry, observability, and application data are collected by “dumb” data collectors. For analysis, these collectors gather information and ship it to centralized systems, such as databases, security information, event management (SIEM) platforms, or data warehouses. ... By processing data at its origin, we significantly reduce the amount of unnecessary or irrelevant data sent over the network, resulting in lower information transfer overhead. This minimizes the load on the infrastructure itself and cuts down on data storage and processing requirements. The scalability of our systems no longer needs to hinge on the ability to expand storage and analytics power, which is both expensive and inefficient. With eBPF, distributed systems can now analyze data locally, allowing the system to scale out more efficiently as each node can handle its own data processing needs without overwhelming a centralized point of control — and failure. Instead of transferring and storing every piece of data, eBPF can selectively extract the most relevant information, reducing noise and improving the overall signal quality.

How Explainable AI Is Building Trust in Everyday Products

Explainable AI has already picked up tremendous momentum in almost every industry. E-commerce platforms are now starting to avail detailed insight to the user on why a certain product is recommended to them. This reduces decision fatigue and improves the overall shopping experience. Even streaming services such as Netflix and Spotify make suggestions like “Because you watched…” or “Inspired by your playlist.” These insights make users much more connected with what they consume. In healthcare and fitness, the stakes are higher. Users literally rely on apps for critical insight into their health and well-being. Take a dietary suggestion or an exercise recommendation: If explainable AI provides insight into the whys, then users are more likely to feel knowledgeable and confident in those decisions. Even virtual assistants like Alexa and Google Assistant have added explainability features that provide much-needed context for their suggestions and enhance the user experience. ... Explainable AI has quite a number of challenges that stand in the way of its implementation. The need for simplifying such a very complex AI decision to some explainable form consumable by users is not a trivial task. The balance lies in clear explanations without oversimplification or misrepresentation of the logic.

IT execs need to embrace a new role: myth-buster

It’s more imperative than ever that IT leaders from the CIO on down educate their colleagues. It’s far too easy for eager early adopters to get into tech trouble, and it’s better to head off problems before your corporate data winds up, say, being used to train a genAI model. This teaching role is critical for high-ranking execs (C-level execs, board members) in addition to those on the enterprise front lines. CFOs tend to fall in love with promised efficiencies and would-be workforce reductions without understanding all of the implications. CEOs often want to support what their direct reports want — when possible — and board members rarely have any in-depth knowledge of technology issues. It’s especially critical for IT Directors, working with the CIO, to become indispensable sources of tech truth for any company. Not so long ago, business units almost always had to route their technology needs through IT. No more. It’s not a battle that can be won by edicts or directives. IT directives are often ignored by department heads, and memo mayhem won’t help. You have to position your advice as cautionary, educational — helpful even — all in a bid to spare the business unit various disasters. You are their friend. Only then does it have a chance of working.

Increased Investment in Industrial Cybersecurity Essential for 2025

“The software used in machine controls and other components should be continuously updated by manufacturers to close newly discovered security gaps,” said the CEO of ONEKEY. He cites typical examples such as manufacturing robots, CNC machines, conveyors, packaging machines, production equipment, building automation systems, and heating and cooling systems, which, in some cases, rely on outdated software, making them targets for hackers. ... Firmware, the software embedded in digital control systems, connected devices, machines, and equipment, should be systematically tested for cyber resilience, advises Jan Wendenburg, CEO of ONEKEY. However, according to a report, less than a third (31 percent) of companies regularly conduct security checks on the software integrated into connected devices to identify and close vulnerabilities, thereby reducing potential entry points for hackers. ... Current practices fall far behind the required standards, as shown by the “OT + IoT Cybersecurity Report” by ONEKEY. ... “Manufacturers should align their software development with the upcoming regulatory requirements,” advised Jan Wendenburg. He added, “It is also recommended that the industry requires its suppliers to guarantee and prove the cyber resilience of their products.”

Daily Tech Digest - January 12, 2025

Data Architecture Trends in 2025

While unstructured data makes up the lion’s share of data in most companies (typically about 80%), structured data does its part to bulk up business’ storage needs. Sixty-four percent of organizations manage at least one petabyte of data, and 41% of organizations have at least 500 petabytes of data, according to the AI & Information Management Report. By 2028, global data creation is projected to grow to more than 394 zettabytes – and clearly enterprises will have more than their fair share of that. Time to open the door to the data lakehouse, which combines the capabilities of data lakes and data warehouses, simplifying data architecture and analytics with unified storage and processing of structured, unstructured, and semi-structured data. “Businesses are increasingly investing in data lakehouses to stay competitive,” according to MarketResearch, which sees the market growing at a 22.9% CAGR to more than $66 billion by 2033. ... “Through 2026, two-thirds of enterprises will invest in initiatives to improve trust in data through automated data observability tools addressing the detection, resolution, and prevention of data reliability issues,” according to Matt Aslett.

How Does a vCISO Leverage AI?

CISOs design and inform policy that shapes security at a company. They inform the priorities of their organizations’ cyberdefense deployment and design, develop, or otherwise acquire the tools needed to achieve the goals they set up. They implement tools and protections, monitor effectiveness, make adjustments, and generally ensure that security functions as desired. However, all that responsibility comes at immense costs, and CISOs are in high demand. It can be challenging to recruit and retain top-level talent for the role, and many smaller or growing organizations—and even some larger older ones—do not employ a traditional, full-time CISO. Instead, they often turn to vCISOs. This is far from a compromise, as vCISOs offer all of the same functionality as their traditional counterparts through an entire team of dedicated service providers rather than a single employee. Since vCISOs are available on a fractional basis, organizations only pay for specific services they need. ... As with all technological breakthroughs, AI is not without its risks and drawbacks. Thankfully, working with a vCISO allows organizations to take advantage of all the benefits of AI while also minimizing its potential downsides. A capable vCISO team doesn’t use AI or any other tool just for the sake of novelty or appearances; their choices are always strategic and risk-informed.

The Transformative Benefits of Enterprise Architecture

Enterprise Architecture review or development is essential for managing complexity, particularly when changes involve multiple systems with intricate interdependencies. ... Enterprise Architecture provides a structured approach to handle these complexities effectively. Often, key stakeholders, such as department heads, project managers, or IT leaders, identify areas of change required to meet new business goals. For example, an IT leader may highlight the need for system upgrades to support a new product launch or a department head might identify process inefficiencies impacting customer satisfaction. These stakeholders are integral to the change process, and the role of the architect is to: Identify and refine the requirements of the stakeholders; Develop architectural views that address concerns and requirements; Highlight trade-offs needed to reconcile conflicting concerns among stakeholders. Without Enterprise Architecture, it is highly unlikely that all stakeholder concerns and requirements will be comprehensively addressed. This can lead to missed opportunities, unanticipated risks, and inefficiencies, such as misaligned systems, redundant processes, or overlooked security vulnerabilities, all of which can undermine business goals and stakeholder trust.

Listen to your technology users — they have led to the most disruptive innovations in history

First, create a culture of open innovation that values insights from outside the organization. While the technical geniuses in your R&D department are experts in how to build something new, they aren’t the only authorities on what it is you should build. Our research suggests that it’s especially important to seek out user-generated disruption at times when customer needs are changing rapidly. Talk to your customers and create channels for dialogue and engagement. Most companies regularly survey users and conduct focus groups. But to identify truly disruptive ideas, you need to go beyond reactions to existing products and plumb unmet needs and pain points. Customer complaints also offer insight into how existing solutions fall short. AI tools make it easier to monitor user communities online and analyze customer feedback, reviews, and complaints. Keep your pulse on social media and online user communities where people share innovative ways to adapt existing products and wish lists for new functionalities. ... Lastly, explore co-creation initiatives that foster direct collaboration with user innovators. For instance, run a contest where customers submit ideas for new products or features, some of which could turn out to be truly disruptive. Or sponsor hackathons that bring together users with needs and technical experts to design solutions.

Guide to Data Observability

Data observability is critical for modern data operations because it ensures systems are running efficiently, detecting anomalies, finding root causes, and actively addressing data issues before they can impact business outcomes. Unlike traditional monitoring, which focuses only on system health or performance metrics, observability provides insights into why something is wrong and allows teams to understand their systems in a more efficient way. In the digital age, where companies rely heavily on data-driven decisions, data observability isn’t only an operational concern but a critical business function. ... When we talk about data observability, we’re focusing on monitoring the data that flows through systems. This includes ensuring data integrity, reliability, and freshness across the lifecycle of the data. It’s distinct from database observability, which focuses more on the health and performance of the databases themselves. ... On the other hand, database observability is specifically concerned with monitoring the performance, health, and operations of a database system—for example, an SQL or MongoDB server. This includes monitoring query performance, connection pools, memory usage, disk I/O, and other technical aspects, ensuring the database is running optimally and serving requests efficiently.

Data maturity and the squeezed middle – the challenge of going from good to great

Breaking through this stagnation does not require a complete overhaul. Instead, businesses can take small but decisive steps. First, they must shift their mindset from seeing data collection as an end in itself, to viewing it as a tool for creating meaningful customer interactions. This means moving beyond static metrics and broad segmentations to dynamic, real-time personalisation. The use of artificial intelligence (AI) can be transformative in this regard. Modern AI tools can analyse customer behaviour in real time, enabling businesses to respond with tailored content, promotions, and experiences. For instance, rather than relying on broad-brush email campaigns, companies can use AI-driven insights to craft (truly) hyper-personalised messages based on individual customer journeys. Such efforts not only improve conversion rates, but also build deeper customer loyalty. ... It’s important to never lose sight of the fact that data maturity is about people and culture as much as tech. Organisations need to foster a culture that values experimentation, learning, and continuous improvement. Behaviourally, this can be uncomfortable for slow-moving or cautious businesses and requires breaking down silos and encouraging cross-functional collaboration.

Finding a Delicate Balance with AI Regulation and Innovation

The first focus needs to be on protecting individuals and diverse groups from the misuse of AI. We need to ensure transparency when AI is used, which in turn will limit the amount of mistakes and biased outcomes, and when errors are still made, transparency will help rectify the situation. It is also essential that regulation tries to prevent AI from being used for illegal activity, including fraud, discrimination and faking documents and creating deepfake images and videos. It should be a requirement for companies of a certain size to have an AI policy in place that is publicly available for anyone to consult. The second focus should be protecting the environment. Due to the amount of energy needed to train the AI, store the data and deploy the technology ones it’s ready for market, AI innovation comes at a great cost for the environment. It shouldn’t be a zero-sum game and legislation should nudge companies to create AI that is respectful to the our planet. The third and final key focus is data protection. Thankfully there is strong regulation around data privacy and management: the Data Protection Act in the UK and GDPR in the EU are good examples. AI regulation should work alongside existing data regulation and protect the huge steps that have already been taken.

Quantum Machine Learning for Large-Scale Data-Intensive Applications

Quantum machine learning (QML) represents a novel interdisciplinary field that merges principles of quantum computing with machine learning techniques. The foundation of quantum computing lies in the principles of quantum mechanics, which govern the behavior of subatomic particles and introduce phenomena such as superposition and entanglement. These quantum properties enable quantum computers to perform computations probabilistically, offering potential advantages over classical systems in specific computational tasks ... Integrating quantum machine learning (QML) with traditional machine learning (ML) models is an area of active research, aiming to leverage the advantages of both quantum and classical systems. One of the primary challenges in this integration is the necessity for seamless interaction between quantum algorithms and existing classical infrastructure, which currently dominates the ML landscape. Despite the resource-intensive nature of classical machine learning, which necessitates high-speed computer hardware to train state-of-the-art models, researchers are increasingly exploring the potential benefits of quantum computing to optimize and expedite these processes.

Generative Architecture Twins (GAT): The Next Frontier of LLM-Driven Enterprise Architecture

A Generative Architecture Twin (GAT) is a virtual, LLM-coordinated environment that mirrors — and continuously evolves with — your actual production architecture. ... Despite the challenges, Generative Architecture Twins represent an ambitious leap forward. They propose a world where:Architectural decisions are no longer static but evolve with real-time feedback loops. Compliance, security, and performance are integrated from day one rather than tacked on later. EA documentation isn’t a dusty PDF but a living blueprint that changes as the system scales. Enterprises can experiment with high-risk changes in a safe, cost-controlled manner, guided by autonomous AI that learns from every iteration. As we refine these concepts, expect to see the first prototypes of GAT in innovative startups or advanced R&D divisions of large tech enterprises. A decade from now, GAT may well be as ubiquitous as DevOps pipelines are today. Generative Architecture Twins (GAT) go beyond today’s piecemeal LLM usage and envision a closed-loop, AI-driven approach to continuous architectural design and validation. By combining digital twins, neuro-symbolic reasoning, and ephemeral simulation environments, GAT addresses long-standing EA challenges like stale documentation, repetitive compliance overhead, and costly rework.

Is 2025 the year of (less cloud) on-premises IT?

For an external view here outside of OWC, Vadim Tkachenko, technology fellow and co-founder at Percona thinks that whether or not we’ll see a massive wave of data repatriation take place in 2025 is still hard to say. “However, I am confident that it will almost certainly mark a turning point for the trend. Yes, people have been talking about repatriation off and on and in various contexts for quite some time. I firmly believe that we are facing a real inflection point for repatriation where the right combination of factors will come together to nudge organisations towards bringing their data back in-house to either on-premises or private cloud environments which they control, rather than public cloud or as-a-Service options,” he said. Tkachenko further states that companies across the private sector (and tech in particular) are tightening their purse strings considerably. “We’re also seeing more work on enhanced usability, ease of deployment, and of course, automation. The easier it becomes to deploy and manage databases on your own, the more organizations will have the confidence and capabilities needed to reclaim their data and a sizeable chunk of their budgets,” said the Percona man. It turns out then, cloud is still here and on-premises is still here and… actually, a hybrid world is typically the most prudent route to go down.

Quote for the day:

"The greatest leaders mobilize others by coalescing people around a shared vision." -- Ken Blanchard