Daily Tech Digest by Kannan Subbiah

Daily Tech Digest - February 23, 2026

Quote for the day:

"Prepare, work smarter, Learn from your Mistakes. These are the secret to success!" -- Elizabeth McCormick

What’s wrong (and right) with AI coding agents

“At the scale AI is generating pull requests today, humans simply can’t keep up. You don’t check the accuracy of Excel with an abacus… and in 2026 we shouldn’t expect maintainers to manually inspect machine-speed code without machine-speed assistance,” said Fox. “AI reviews can go deeper than humans in many cases. They don’t get tired, they can reason across large codebases… and they can spot patterns at a scale no individual reviewer can hold in their head. If AI is generating more code, the only viable answer is to use AI to help review and validate it. You have to fight fire with fire.” ... He reminds us that quantity does not always equal quality – especially in the AI-driven world we now live in. He notes that, at least for now, the reality is that AI development tools and ‘vibe coding’ can generate a lot of code very quickly, but code that’s often slower and more memory‑hungry than what a skilled developer would write. ... Although this entire discussion is focused on the now-increasingly-automated command line, it feels like the real focus should be higher and architecture has been mentioned already. “We’re entering a world where, with AI, software changes are propagating faster than governance models can track them. That means AI tools are, plain and simple, accelerating systemic complexity. When an AI agent can generate and deploy changes across interconnected enterprise systems, there’s real danger in the invisible dependencies and downstream effects most orgs can’t fully see,” said Ido Gaver

Identity verification systems are struggling with synthetic fraud

The researchers tied the growth of synthetic identity fraud to the increasing use of AI tools, which can generate convincing fake documents that pass casual inspection. “The biggest risk I see in the next 12 to 18 months is the growing and advancing use of AI. AI is creating fake people, fake voices, and fake documents. Bad actors are using these capabilities to open accounts, take over existing accounts, and impersonate real people in places like bank branches,” Lewis said. ... Financial institutions remain a major target for identity fraud due to access to credit, account funding, and cash movement. A successful fraudster can monetize a single fake or synthetic identity for tens of thousands of dollars before detection, making the sector a frequent target. Online-only retail banks recorded the highest rate of failed identity verification among the financial institution categories in Intellicheck’s dataset. The report also found elevated failure rates across businesses serving underbanked consumers, including check cashing, payday lending, subprime lending, and lease-to-own services. ... AI tools are being used to produce synthetic IDs that are difficult for humans to spot. Lewis said attackers are already using AI and large language models to generate documents that can bypass basic checks. “AI and LLM can create fake ID’s that can easily pass the templating test, old methods don’t work and ID verification service providers can’t rest on their laurels,” Lewis said.

Neoclouds: Meeting demand for AI acceleration

This surge in demand for AI acceleration has seen a surprising benefactor. According to Tiger Research, cryptocurrency mining firms, seeking to reduce their exposure to bitcoin’s volatile pricing, are redirecting their graphics processing unit (GPU) farms toward AI acceleration applications. ... Before the emergence of neoclouds a few years ago, if an organisation wanted to work with AI, it had no choice but to go to a hyperscaler like Amazon Web Services (AWS) or Google. While the hyperscalers offer AI infrastructure as part of their vast public cloud services portfolio, Roy Illsley, chief analyst at Omdia, says the hyperscalers tend to be expensive and, as he recalls, a few years ago, there was very little choice other than Google’s AI offerings. ... AI infrastructure strategies are becoming inherently hybrid and multicloud by design – not as a by-product of supplier sprawl, but as a deliberate response to workload reality. The cloud market is fragmenting along functional lines, and neoclouds occupy a clear and growing role within that landscape. “Neoclouds started as GPU as a service. If you needed GPUs, these companies bought or leased GPUs from Nvidia, and then they would slice them and sell them off to people in smaller groups and bundles,” says Omdia’s Illsley. However, over time, neocloud providers have added software stacks and developed other services to meet the demand of IT buyers who need GPU power and the software stack required for AI training or AI inferencing.

Sam Altman just said what everyone is thinking about AI layoffs

This isn’t the first time industry stakeholders questioned the veracity of AI-related layoffs. A study by Oxford Economics in January this year claimed most layoffs are due to “more traditional drivers” such as overhiring or poor financial performance. ... "While a rising number of firms are pinning job losses on AI, other more traditional drivers of job layoffs are far more commonly cited,” the report said. “What's more, we suspect some firms are trying to dress up layoffs as a good news story rather than bad news, such as past over-hiring." ... “There’s some real displacement by AI of different kinds of jobs,” he said. “We’ll find new kinds of jobs as we do with every tech revolution. I would expect that the real impact of AI doing jobs in the next few years will begin to be palpable.” Altman’s prediction here aligns with research from Gartner and Forrester on the potential impact of AI on the global jobs market. In January, Forrester predicted 10 million jobs could be lost worldwide as enterprise adoption ramps up. ... Despite a string of studies pointing to the contrary, some tech industry figures still believe that AI will eventually render some workers obsolete. In a recent interview with the Financial Times, for example, Microsoft AI CEO Mustafa Suleyman insisted AI will begin replacing “white collar” workers within 18 months. “I think we’re going to have a human-level performance on most if not all professional tasks,” Suleyman told

Jailbreaking the matrix: How researchers are bypassing AI guardrails to make them safer

As AI assistants move from novelty to infrastructure, helping write code, summarizing medical notes and answering customer questions, the biggest question isn't just what these systems can do, but what happens when they are pushed to do what they shouldn't. "By showing exactly how these defenses break, we give AI developers the information they need to build defenses that actually hold up," Jha said. "The public release of powerful AI is only sustainable if the safety measures can withstand real scrutiny, and right now, our work shows that there's still a gap. We want to help close it." ... Focusing on the internal workings of the LLM allows more accurate measurements of failures while encouraging the development of more robust defenses against the failure of safety measures. According to the researchers, HMNS can help reveal whether specific internal pathways, if exploited, could cause a breakdown. That information can guide stronger training, monitoring and defense strategies. ... Understanding the security shortcomings of LLMs is critical as they become more widespread. Companies like Meta, Alibaba and others have released powerful AI models that are available to anyone. While each platform incorporates safety layers meant to keep it from being misused, the UF team has found that those safety layers can be systematically bypassed.

Plan vs. planning: Why continuous planning must traverse time

The problem is not the plan’s quality. The problem is that a plan freezes a moment in time while the organization continues to move through time. Planning, by contrast, must be a continuous discipline, remaining active as assumptions decay, signals emerge and constraints shift. ... Planning exists to test those assumptions continuously, a distinction long recognized in leadership and management literature that separates planning as an ongoing discipline from planning as a static artifact. Plans are optimized for agreement and commitment. Planning is optimized for learning, decision-making and managing consequences in the face of uncertainty. In practice, this means consequences must be visible at the moment of decision, not discovered months later through execution. ... Many enterprises optimize for compliance, predictability and approval at the expense of feedback and adaptation. Learning is pushed downstream, arriving only after outcomes are locked in and costs incurred. Systems theorist Russell Ackoff described this dynamic clearly: “Most organizations are not short of information. They are short of the ability to learn from it.” Continuous planning restores learning by design, not as postmortem analysis, but as pre-decision feedback. Feedback that arrives before commitment changes behavior. Feedback that arrives after execution becomes an explanation. In volatile environments, that timing difference is decisive, which is why scenario planning and structured foresight have re-emerged as critical executive tools.

The rise of AI factories: Powering an era of pervasive intelligence

In India alone, Google is building a gigawatt-scale AI hub in Visakhapatnam. Microsoft is expanding its cloud and AI footprint in Pune and Chennai and creating a new “India South Central” region in Hyderabad. In partnership with NVIDIA, Reliance Jio is developing a major AI data center in Jamnagar for nationwide GPU-as-a-service offerings. TCS is planning a 1-gigawatt AI data center, likely in Gujarat or Maharashtra, to support startups, hyperscalers, and government institutions. And as part of its Stargate project, OpenAI is actively scouting locations in India for what could become one of the largest AI data centers in all of Asia. ... The growth of AI represents a fundamental transformation in how the world builds and operates computing infrastructure. While traditional data centers are designed for general-purpose workloads, AI superclusters are purpose-built facilities that function as industrial-scale intelligence production systems. And their output is defined by new metrics — most notably tokens per watt and tokens per dollar — that quantify the efficiency and productivity of intelligence at scale. ... To deliver the performance at scale that AI requires, silicon designers are increasingly turning to multi-die designs, including 3D integrated circuits (3DIC) and chiplet-based architectures. While these chip designs offer gains that traditional monolithic SoCs cannot achieve cost-effectively, they also introduce significant complexity to the design process.

Cognizant CAIO Babak Hodjat explains how Agentic AI will transform enterprises

One of the things that agentic systems do is they allow for a diversity of data sources because you can actually have an agent responsible for a data source talking to other agents responsible for other data sources. Your interface into this system could be a consolidation of information and decisions that come from these disparate sources. It is the first time that we can actually have a mapping between intent and disparate sources of data and applications. I think that will work well. That kind of design can work well in a country like India with such diversity of data. ... Population-based approaches like genetic algorithms are very good at non-linear optimisation, especially if you are looking at multiple outcomes at the same time. Pretty much every problem that we look at is multi-objective. Every problem that we look at has improved revenue but reduced costs. You look at curing disease but reduce impact on the economy. It is always more than one outcome that we are looking at. In problems like optimisation of power grids or managing urban traffic systems, these are very well-suited algorithms. ... There are two opposing forces when it comes to AI. Scaling laws mean that building bigger is more powerful, and building bigger typically means using more energy. Many companies are looking at green sources for that additional consumption. On the other hand, companies are optimising models to be smaller and less energy-hungry. For multi-agent systems, smaller models can be more cost-effective and greener.

Inference Becomes the Next AI Chip Battleground

Inference has fundamentally different economics and performance requirements than training, said Karl Freund, founder and principal analyst at Cambrian AI Research. Training AI models is a cost center, while inference is a “profit center” that directly generates revenue. Freund and Kimball noted that while GPUs deliver excellent performance, they often carry architectural features optimized for training that don’t always translate to lower latency or higher efficiency in pure inference use cases. Purpose-built inference chips – ASICs and other accelerators – can deliver faster responses, improved energy efficiency, and lower total cost of ownership. ... "As inference workloads exceed the total amount of training workloads in terms of token output, there will be a greater need for diversity because alternative XPU architectures can achieve better efficiency on some specific inferencing tasks,” said Brendan Burke, research director of semiconductors, supply chain, and emerging tech at Futurum Group. ... Inference opportunities span data centers and the edge, and requirements vary widely by workload and deployment. “The inference you do in your autonomous vehicle is far different than the inferencing you do when you’re an online customer service bot,” Kimball said. ... Analysts expect Nvidia to maintain dominance in both training and inference, but diverse requirements create space for specialized solutions to capture share.

Why the CFO's Playbook Belongs on Every CIO's Desk

Recent research from Gartner on how CFOs are allocating budgets gives CIOs insight into what priorities look like across departments, and where technology and AI can help move the needle. The research firm's CFO Report: Q1 2026 finds that while budgets are shifting and AI ambitions are high, enterprise-wide AI success remains an aspiration rather than a reality. ... AI is also changing the conversation on ROI for both finance and technology leaders. "There's a lot more to evaluating the success of some of this investment in technology than simply just ROI, and AI is definitely helping change that," Abbasi said. "AI isn't your traditional asset." Unlike standard hardware expenditures, AI investments don't have predictable depreciation curves, and the ways in which returns on AI investment may show up across the business can vary. They may manifest in time to market, customer satisfaction or competitive positioning, not just in cost savings, Abbasi said. CIOs should be sure to articulate how AI will generate strategic returns rather than focus on pitching it as a capital project. "It changes the way you measure the effectiveness of AI, as well as how you measure your business more holistically," he said. "It's not like a traditional asset because you don't necessarily know what the outcomes are going to be for some of these AI projects."

Daily Tech Digest - February 22, 2026

Quote for the day:

"If you care enough for a result, you will most certainly attain it." -- William James

The data center gold rush is warping reality

The real impact isn’t people—it’s power, land, transmission capacity, and water. When you drop 10 massive facilities into a small grid, demand spikes don’t just happen inside the fence line. They ripple outward. Utilities must upgrade substations, reinforce transmission lines, procure new-generation equipment, and finance these investments. ... Here’s the part we don’t say out loud often enough: High-tech companies are spending massive amounts of money on data centers because the market rewards them for doing so. Capital expenditures have become a kind of corporate signaling mechanism. On earnings calls, “We’re investing aggressively” has become synonymous with “We’re winning,” even when the investment is built on forecasts that are, at best, optimistic and, at worst, indistinguishable from wishful thinking. ... The bet is straightforward: When demand spikes, prices and utilization rise, and those who built first make bank. Build the capacity, fill the capacity, charge a premium for the scarce resource, and ride the next decade of digital expansion. It’s the same playbook we’ve seen before in other infrastructure booms, except this time the infrastructure is made of silicon and electrons, and the pitch is wrapped in the language of transformation. ... Then there’s the cost reality. AI systems, especially those that deliver meaningful, production-grade outcomes, often cost five to ten times as much as traditional systems once you account for compute, data movement, storage, tools, and the people required to run them responsibly.

Chip-processing method could assist cryptography schemes to keep data secure

Just like each person has unique fingerprints, every CMOS chip has a distinctive “fingerprint” caused by tiny, random manufacturing variations. Engineers can leverage this unforgeable ID for authentication, to safeguard a device from attackers trying to steal private data. But these cryptographic schemes typically require secret information about a chip’s fingerprint to be stored on a third-party server. This creates security vulnerabilities and requires additional memory and computation. ... “The biggest advantage of this security method is that we don’t need to store any information. All the secrets will always remain safe inside the silicon. This can give a higher level of security. As long as you have this digital key, you can always unlock the door,” says Eunseok Lee, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this security method. ... A chip’s PUF can be used to provide security just like the human fingerprint identification system on a laptop or door panel. For authentication, a server sends a request to the device, which responds with a secret key based on its unique physical structure. If the key matches an expected value, the server authenticates the device. But the PUF authentication data must be registered and stored in a server for access later, creating a potential security vulnerability.

What MCP Can and Cannot Do for Project Managers Today

The most mature MCPs for PM are official connectors from the platforms themselves. Atlassian’s Rovo MCP Server connects Jira and Confluence, generally available since late 2025. Wrike has its own MCP server for real-time work management. Dart exposes task creation, updates, and querying through MCP. ClickUp does not have an official MCP server, but multiple community implementations wrap its API for task management, comments, docs, and time tracking. ... Most PM work is human and stays human. No LLM replaces the conversation where you talk a frustrated team member through a scope change, or the negotiation where you push back on an unrealistic deadline from the sponsor. No LLM runs a planning workshop or navigates the politics of resource allocation. But woven through all of that is documentation. Every conversation, every decision, every planning session produces written output. The charter that captures what was agreed. ... Beyond documentation, scheduling is where I expected MCP to add the most computational value. This is where the investigation got interesting. Every PM builds schedules. The standard method is CPM: define tasks, set dependencies, estimate durations, calculate the critical path. MS Project does this. Primavera does this. A spreadsheet with formulas does this. CPM is well understood and universally used. CPM does exactly what it says: it calculates the critical path given dependencies and durations.

How to Write a Good Spec for AI Agents

Instead of overengineering upfront, begin with a clear goal statement and a few core requirements. Treat this as a “product brief” and let the agent generate a more elaborate spec from it. This leverages the AI’s strength in elaboration while you maintain control of the direction. This works well unless you already feel you have very specific technical requirements that must be met from the start. ... Many developers using a strong model do exactly this. The spec file persists between sessions, anchoring the AI whenever work resumes on the project. This mitigates the forgetfulness that can happen when the conversation history gets too long or when you have to restart an agent. It’s akin to how one would use a product requirements document (PRD) in a team: a reference that everyone (human or AI) can consult to stay on track. ... Treat specs as “executable artifacts” tied to version control and CI/CD. The GitHub Spec Kit uses a four-phase gated workflow that makes your specification the center of your engineering process. Instead of writing a spec and setting it aside, the spec drives the implementation, checklists, and task breakdowns. Your primary role is to steer; the coding agent does the bulk of the writing. ... Experienced AI engineers have learned that trying to stuff the entire project into a single prompt or agent message is a recipe for confusion. Not only do you risk hitting token limits; you also risk the model losing focus due to the “curse of instructions”—too many directives causing it to follow none of them well.

NIST’s Quantum Breakthrough: Single Photons Produced on a Chip

The arrival of quantum computing is future, but the threat is current. Commercial and federal organizations need to protect against quantum computing decryption now. Various new mathematical approaches have been developed for PQC, but while they may be theoretically secure, they are not provably secure. Ultimately, the only provably secure key distribution must be based on physics rather than math. ... While this basic approach is secure, it is neither efficient nor cheap. “Quantum key distribution is an expensive solution for people that have really sensitive information,” continues Bruggeman. “So, think military primarily, and some government agencies where nuclear weapons and national security are involved.” Current implementations tend to use available dark fiber that still has leasing costs. ... “The big advance from NIST is they are able to provide single photons at a time, as opposed to sending multiple photons,” continues Bruggeman. Single photons aren’t new, but in the past, they’ve usually been photons in a stream of photons. “So, they encode the key information on those strings, and that leads to replication. And in cryptography, you don’t want to have replication of data.” There is currently a comfort level in this redundancy, since if one photon in the stream fails, the next one might succeed. But NIST has separately developed Superconducting Nanowire Single-Photon Detectors (SNSPDs) which would allow single photons to be reliably sent and received over longer distances – up to 600 miles.

Quantum security is turning into a supply chain problem

The core issue is timing. Sensitive supplier and contract data has a long shelf life, and adversaries have already started collecting encrypted traffic for future decryption. This is the “harvest now, decrypt later” model, where encrypted records are stolen and stored until quantum computing becomes capable of breaking current public-key encryption. That creates a practical security problem for cybersecurity teams supporting procurement, third-party risk, and supply chain operations. ... There’s growing pressure to adopt post-quantum cryptography (PQC), including partner expectations, insurance scrutiny, and regulatory direction. It argues that PQC adoption is increasingly being driven through procurement requirements, especially from large enterprises and public-sector organizations. Vendors without a PQC roadmap may face longer audits or disqualification during sourcing decisions. ... Beyond cryptographic threats, the researchers argue that quantum computing may eventually improve supply chain risk management by addressing complex optimization problems that overwhelm classical systems. It describes supply chain risk as a “wicked problem,” where variables shift continuously and disruptions propagate in unpredictable ways. ... Quantum readiness spans both cybersecurity and supply chain management. For cybersecurity professionals, the near-term work focuses on long-term encryption durability across vendor ecosystems, along with cryptographic migration planning and third-party dependencies.

CEOs aren't seeing any AI productivity gains, yet some tech industry leaders are still convinced AI will destroy white collar work within two years

Most companies are yet to record any AI productivity gains despite widespread adoption of the technology. That's according to a massive survey by the US National Bureau of Economic Research (NBER), which asked 6,000 executives from a range of firms across the US, UK, Germany, and Australia how they use AI. The study found 70% of companies actively use AI, but the picture is different among execs themselves. Among top executives – including CFOs and CEOs – a quarter don't use the technology at all, while two-thirds say they use it for 1.5 hours a week at most. ... "The most commonly cited uses are ‘text generation using large language models’ followed by ‘visual content creation’ and ‘data processing using machine learning’," the survey added. When it comes to employment savings, 90% of execs said they'd seen no impact from AI over the last three years, with 89% saying they saw no productivity boost, either. The report noted that previous studies have found large productivity gains in specific settings – in particular customer support and writing tasks. ... Despite the lack of impact to date, business leaders still predict AI will start to boost productivity and reduce the number of employees needed in the coming years. Respondents predict a 1.4% productivity boost and 0.8% increase in output thanks to the technology over the next three years, for example. Yet the NBER survey also reveals a "sizable gap in expectations", with senior execs saying AI would cut employment by 0.7% over the next three years — which the report said would mean 1.75 million fewer jobs.

Observability Without Cost Telemetry Is Broken Engineering

Cost isn't an operational afterthought. It's a signal as essential as CPU saturation or memory pressure, yet we've architected it out of the feedback loop engineers actually use. ... Engineers started evaluating architectural choices through a cost lens without needing MBA training. “Should we cache this aggressively?” became answerable with data: cache infrastructure costs $X/month, API calls saved cost $Y/month, net impact is measurable, not theoretical. ... The anti-pattern I see most often is siloed visibility. Finance gets billing dashboards. SREs get operational dashboards. Developers get APM traces. Nobody sees the intersection where cost and performance influence each other. You debug a performance issue — say, slow database queries. The fix is to add an index. Query time drops from 800 ms to 40 ms. Victory. Except the database is now using 30% more storage for that index, and your storage tier bills by the gigabyte-month. If you're on a flat-rate hosting plan, maybe that cost is absorbed. If you're on Aurora or Cosmos DB with per-IOPS pricing, you've just traded latency for dollars. Without cost telemetry, you won't notice until the bill arrives. ... Alerting without cost dimensions misses failure modes. Your error rate is fine. Latency is stable. But egress costs just doubled because a misconfigured service is downloading the same 200 GB dataset on every request instead of caching it.

A New Way To Read the “Unreadable” Qubit Could Transform Quantum Technology

“Our work is pioneering because we demonstrate that we can access the information stored in Majorana qubits using a new technique called quantum capacitance,” continues the scientist, who explains that this technique “acts as a global probe sensitive to the overall state of the system.” ... To better understand this achievement, Aguado explains that topological qubits are “like safe boxes for quantum information,” only that, instead of storing data in a specific location, “they distribute it non-locally across a pair of special states, known as Majorana zero modes.” That unusual structure is what makes them attractive for quantum computing. “They are inherently robust against local noise that produces decoherence, since to corrupt the information, a failure would have to affect the system globally.” In other words, small disturbances are unlikely to disrupt the stored information. Yet this strength has also created a major experimental challenge. As Aguado notes, “this same virtue had become their experimental Achilles’ heel: how do you “read” or “detect” a property that doesn’t reside at any specific point?.” ... The project brings together an advanced experimental platform developed primarily at Delft University of Technology and theoretical work carried out by ICMM-CSIC. According to the authors, this theoretical input was “crucial for understanding this highly sophisticated experiment,” highlighting the importance of close collaboration between theory and experiment in pushing quantum technology forward.

When Excellent Technology Architecture Fails to Deliver Business Results

Industry research consistently shows that most large-scale transformations fail to achieve their expected business outcomes, even when the underlying technology decisions are considered sound. This suggests that the issue is not technical quality. It is structural. ... The real divergence begins later, in day-to-day decision-making. Under delivery pressure, teams make choices driven by deadlines, budget constraints, and individual accountability. Temporary workarounds are accepted. Deviations are justified as exceptions. Risks are taken implicitly rather than explicitly assessed. Architecture is often aware of these decisions, but it is not structurally embedded in the moment where choices are made. As a result, architecture remains correct, but unused. ... When architecture cannot explain the economic and operational consequences of a decision, it loses relevance. Statements such as “this violates architectural principles” carry little weight if they are not translated into impact on cost of change, delivery speed, or operational risk. ... What is critical is that these compromises are rarely tracked, assessed cumulatively, or reintroduced into management discussions. Architecture may be aware of them, but without a mechanism to record and govern them, their impact remains invisible until flexibility is lost and change becomes expensive. Architecture debt, in this sense, is not a technical failure. It is a governance outcome. When decision trade-offs remain unmanaged, architecture is blamed for consequences it was never empowered to influence.

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 - February 19, 2026

Quote for the day:

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

The new paradigm for raising up secure software engineers

were already struggling to help developers keep up with secure code principles at the speed of DevOps. Now, with reshaping how code gets written and shipped, the challenge is rapidly intensifying. ... What is needed to get thrown out are traditional training methods. Consensus among security leaders is that dev training needs to be bite-sized, hands-on, and mostly embedded in developer tool chains. ... Rather than focus on preparing developers for line-by-line code review, the emphasis moves toward evaluating whether their features and functions behave securely in context of deployment conditions, says ... Developers need to recognize when introduces unsafe assumptions, insecure defaults, or integrations that can scale vulnerabilities across systems. And with more security enforcement built into automated engineering pipelines, developers should ideally also be trained to understand what automated gates catch, and what still requires human judgment. “Security awareness in engineering has shifted to a system-level approach rather than focusing on individual vulnerabilities,” Pinna says. ... The data from guardrails and controls being triggered can be used by the AppSec team to drive creation and delivery of more in-depth, but targeted education. When the same vulnerability or integration pattern pops up again and again, that’s a signal for focused training on a subject.

New agent framework matches human-engineered AI systems — and adds zero inference cost to deploy

In experiments on complex coding and software engineering tasks, substantially outperformed existing self-improving frameworks. Perhaps most notably for enterprise decision-makers, the system autonomously evolved agents that matched or exceeded the performance of frameworks painstakingly designed by human experts. ... Unlike traditional systems where an agent only learns from its direct parent, GEA creates a shared pool of collective experience. This pool contains the evolutionary traces from all members of the parent group, including code modifications, successful solutions to tasks, and tool invocation histories. Every agent in the group gains access to this collective history, allowing them to learn from the breakthroughs and mistakes of their peers. ... The results demonstrated a massive leap in capability without increasing the number of agents used. This collaborative approach also makes the system more robust against failure. In their experiments, the researchers intentionally broke agents by manually injecting bugs into their implementations. GEA was able to repair these critical bugs in an average of 1.4 iterations, while the baseline took 5 iterations. The system effectively leverages the "healthy" members of the group to diagnose and patch the compromised ones. ... The success of GEA stems largely from its ability to consolidate improvements. The researchers tracked specific innovations invented by the agents during the evolutionary process.

GitHub readies agents to automate repository maintenance

In order to help developers and enterprises manage the operational drag of maintaining repositories, GitHub is previewing , a new feature that uses AI to automate most routine tasks associated with repository hygiene. It won’t solve maintenance problems all by itself, though. Developers will still have to describe the automation workflows in natural language that agents can follow, storing the instructions as Markdown files in the repo created either from the terminal via the GitHub CLI or inside an editor such as Visual Studio Code. ... “Mid-sized engineering teams gain immediate productivity benefits because they struggle most with repetitive maintenance work like triage and documentation drift,” said Dion Hinchcliffe ... Patel also warned that beyond precision and signal-to-noise concerns, there is a more prosaic risk teams may underestimate at first: As agentic workflows scale across repositories and run more frequently, the underlying compute and model-inference costs can quietly compound, turning what looks like a productivity boost into a growing operational line item if left unchecked. This can become a boardroom issue for engineering heads and CIOs because they must justify return on investment, especially at a time when they are grappling with what it really means to let software agents operate inside production workflows, Patel added.

One stolen credential is all it takes to compromise everything

dominated incident response activity in 2025. Identity weaknesses played a material role in almost 90% of investigations. Initial access was driven by identity-based techniques in 65% of cases, including phishing, stolen credentials, brute force attempts, and insider activity. ... Rubin said the growing dominance of identity attacks reflects how enterprise environments have changed over the past few years, creating more opportunities for adversaries to quietly slip in through legitimate access pathways. “The increasing role of identity as the main attack vector is a result of a fundamental change in the enterprise environment,” Rubin said. “This dynamic is driven by two key factors.” He said the first driver is the rapid expansion of SaaS adoption, cloud infrastructure, and machine identities, which in many organizations now outnumber human accounts. That shift has created what he described as a “massive, unmanaged shadow estate,” where each integration represents “a new, potentially unmonitored, path into the network.” ... The time window for defenders is shrinking. The fastest 25% of intrusions reached in 72 minutes in 2025. The same metric was 285 minutes in 2024. A separate simulation described an AI-assisted attack that reached exfiltration in 25 minutes. Threat actors also began automating extortion operations. Unit 42 negotiators observed consistent tone and cadence in ransom communications, suggesting partial automation or AI-assisted negotiation messaging.

The emerging enterprise AI stack is missing a trust layer

This is not simply a technology problem. It is an architectural one. Today’s enterprise AI stack is built around compute, data and models, but it is missing its most critical component: a dedicated trust layer. As AI systems move from suggesting answers to taking actions, this gap is becoming the single biggest barrier to scale. ... Our ability to generate AI outputs is scaling exponentially, while our ability to understand, govern and trust those outputs remains manual, retrospective and fragmented across point solutions. ... This layer isn’t a single tool; it’s a . I often think of it as the avionics system in a modern aircraft. It doesn’t make the plane fly faster, but it continuously measures conditions and makes adjustments to keep the flight within safe parameters. Without it, you’re flying blind — especially at scale. ... Agentic systems collapse the distance between recommendation and action. When decisions are automated, there is far less tolerance for opacity or after-the-fact explanations. If an AI-driven action cannot be reconstructed, justified and owned, the risk is no longer theoretical — it is operational. This is why trust is becoming a prerequisite for autonomy. Governance models built for dashboards and quarterly reviews are not sufficient when systems act in real time. CIOs need architectures that assume scrutiny, not exception handling and that treat accountability as a design constraint rather than a policy requirement.

India Is Not a Back Office — It’s a Core Engine of Our Global Innovation

We have a very clear data and AI strategy. We are running multiple proof-of-concept initiatives across the organisation to ensure AI becomes more than just a buzzword. The key question is: how does AI create real value for ? It helps us become more agile and faster, whether in product development, improving internal process efficiency, or enhancing decision-making quality. India plays a crucial role here. We have a large team working on data analytics, intelligent automation, and AI, supporting these initiatives and shaping our agenda. ... It’s not just access to talent, it’s also the mindset. Indian society is highly adaptable. You often face unforeseen situations and must find solutions quickly. That agility and ability to always have a “Plan B” drive innovation, creativity, and speed. ... Data protection is a global priority. Many regions have introduced regulations, India’s , GDPR in the European Union, and similar laws in China. For global organisations, managing how data is transferred and processed across borders is a significant challenge. For example, certain data, like Chinese customer data, may need to remain within that country. Beyond regulatory compliance, cybersecurity threats are constant. Like most organisations, we experience attempted attacks on our networks. We have a robust cybersecurity team working continuously to secure both data and infrastructure.

AI likely to put a major strain on global networks—are enterprises ready?

Retrieval-heavy architecture types such as —an AI framework that boosts large language models by first retrieving relevant, current information from external sources—create significant network traffic because data is moving across regions, object stores, and vector indexes, Kale says. “Agent-like, multi-step workflows further amplify this by triggering an additional set of retrievals and evaluations at each step,” Kale says. “All of these patterns create fast and unpredictable bursts of network traffic that today’s networks were never designed to handle. These trends will not abate, as enterprises transition from piloting AI services to running them continually.” ... In 2026, “we will see significant disruption from accelerated appetite for all things AI,” research firm Forrester noted in a late-year predictions post. “Business demands of AI systems, network connectivity, AI for IT operations, the conversational AI-powered service desk, and more are driving substantial changes that tech leaders must enable within their organizations.” ... “Inference workloads in particular create continuous, high-intensity, globally distributed traffic patterns,” Barrow says. “A single AI feature can trigger millions of additional requests per hour, and those requests are heavier—higher bandwidth, higher concurrency, and GPU-accelerated compute on the other side of the network.”

Quantum Scientists Publish Manifesto Opposing Military Use of Quantum Research

The scientists’ primary goals include: to express a unified rejection of military uses of ; to open debate within the quantum community about ethical implications; to create a forum for researchers concerned about militarization; and to advocate for a public database listing all research projects at public universities funded by military or defense agencies. Quantum technologies rely on the behavior of matter and light at the smallest scales, enabling ultra-secure communication, highly sensitive sensors and powerful computing systems. According to the manifesto, these capabilities are increasingly being folded into defense strategies worldwide. ... The manifesto places these developments in the context of rising defense budgets, particularly in Europe following Russia’s invasion of Ukraine. The scientists write in the manifesto that the research and development sector is not exempt from the broader rearmament trend and that dual-use technologies — those that can serve both civilian and military ends — are increasingly prioritized in policy documents. The scientists acknowledge that are not inherently military tools. However, according to the manifesto, once such systems are developed, their applications may be difficult to control. The scientists argue that closer institutional ties between universities and defense agencies risk undermining academic independence. .

From pilot purgatory to productive failure: Fixing AI's broken learning loop

"Model performance can drift with data changes, user behavior, and policy updates, so a 'set it and forget it' KPI can reward the wrong thing, too late," Manos said. The penalty for CIOs, however, comes from the time lag between the misread KPI signal and the CIO's moves to correct it. Timing is everything, and "by the time a quarterly metric flags a problem, the root cause has already compounded across workflows," Manos said. ... Waiting until the end of a POC to figure out why a concept doesn't scale is clearly too late, but neither is it prudent to abandon a "trial, observation, and refine" cycle entirely, Alex Tyrrell, head of advanced technologies at Wolters Kluwer and CTO at Wolters Kluwer Health, said. Instead, Tyrrell argues for refining the interaction process itself to detect issues earlier in a safe setting, particularly in regulated, high-trust environments like healthcare. He recommends pairing each iteration with both predictive and diagnostic signals, so IT teams can intervene before the error ripples down to the customer level. ... AI pilots fail for the same non-technical reasons that have always plagued technology performance, such as a governance vacuum, organizational unreadiness, low usage rates, or "measurement theater," which is when tech performance can't be tied to a specific business value, explained Baker.

How AI agents and humans can play together in the same sandbox

Unlike traditional automation, which is rigid and rules-based, AI agents are goal-driven. They can plan, adapt, and respond to changing conditions. That makes them especially powerful for modern business processes that are dynamic by nature - processes that span systems, teams, and time zones. Another defining characteristic is endurance. AI agents don't get tired, sick, or distracted. They can operate continuously, scaling up or down as needed, and executing tasks with consistent precision. This doesn't make humans obsolete. ... Trust plays a central role here. Agents must demonstrate that they are reliable and predictable. At the same time, humans must define boundaries - what agents can do autonomously, where approvals are required, and what guardrails must always be respected. There is a fine balance to strike. Constrain agents too tightly, and you eliminate the benefits of autonomy. ... A logical approach enables AI agents to access views of data directly from source systems, in real time, without first having to replicate or move that data. For Agentic AI, this is critical: agents need live data, delivered in the shortest possible time, in order to plan, act, and adapt effectively. By abstracting physical data complexity and unifying access across sources, a logical data layer provides AI agents with fast, trusted, and governed data - exactly what autonomous systems require to operate at scale. A shared data plane provides all consumers - human or machine - with the same source of truth. It also provides context, consistency, and traceability.

Daily Tech Digest - February 18, 2026

Quote for the day:

"Engagement is a leadership responsibility—never the employee’s, and not HR’s." -- Gordon Tredgold

Why cloud outages are becoming normal

As the headlines become more frequent and the incidents themselves start to blur together, we have to ask: Why are these outages becoming a monthly, sometimes even weekly, story? What’s changed in the world of cloud computing to usher in this new era of instability? In my view, several trends are converging to make these outages not only more common but also more disruptive and more challenging to prevent. ... The predictable outcome is that when experienced engineers and architects leave, they are often replaced by less-skilled staff who lack deep institutional knowledge. They lack adequate experience in platform operations, troubleshooting, and crisis response. While capable, these “B Team” employees may not have the skills or knowledge to anticipate how minor changes affect massive, interconnected systems like . ... Another trend amplifying the impact of these outages is the relative complacency about resilience. For years, organizations have been content to “” workloads to the cloud, reaping the benefits of agility and scalability without necessarily investing in the levels of redundancy and disaster recovery that such migrations require. There is growing cultural acceptance among enterprises that cloud outages are unavoidable and that mitigating their effects should be left to providers. This is both an unrealistic expectation and a dangerous abdication of responsibility.

AI agents are changing entire roles, not just task augmentation

Task augmentation was about improving individual tasks within an existing process. Think of a source-to-pay process in which specific steps are automated. That is relatively easy to visualize and implement in a classic process landscape. Role transformation, however, requires a completely different approach. You have to turn your entire end-to-end business process architecture into a role-based architecture, explains Mueller. ... Think of an agent that links past incidents to existing problems. Or an agent that automatically checks licenses and certifications for all running systems. “I wonder why everyone isn’t already doing this,” says Mueller. In the event of an incident with a known problem, the agent can intervene immediately without human intervention. That’s an autonomous circle. For more complex tasks, you can start in supervised mode and later transition to autonomous mode. ... The real challenge is that companies are so far behind in their capabilities to handle the latest technology. Many cannot even visualize what AI means. The executive has a simple recommendation: “If you had to build it from scratch on greenfield, would you do it the same way you do now?” That question gets to the heart of the matter. “Everyone looks at the auto industry and sees that it is being disrupted by Chinese companies. This is because Chinese companies can do things much faster than old economies,” Mueller notes.

Why are AI leaders fleeing?

Normally, when big-name talent leaves Silicon Valley giants, the PR language is vanilla: they’re headed for a “new chapter” or “grateful for the journey” — or maybe there’s some vague hints about a stealth startup. In the world of AI, though, recent exits read more like a whistleblower warnings. ... Each individual story is different, but I see a thread here. The AI people who were concerned about “what should we build and how to do it safely?” are leaving. They’ll be replaced by people whose first, if not only, priority is “how fast can we turn this into a profitable business?” Oh, and not just profitable; not even a unicorn with a valuation of $1 billion is enough for these people. If the business isn’t a “,” a privately held startup company valued at more than $10 billion, they don’t want to hear about it. I think it’s very telling that Peter Steinberger, the creator of the insanely — in every sense of the word — hot , has already been hired by OpenAI. Altman calls him a “genius” and says his ideas “will quickly become core to our product offerings.” Actually, OpenClaw is a security disaster waiting to happen. Someday soon, some foolhardy people or companies will lose their shirts because they trusted valuable information with it. And, its inventor is who Altman wants at the heart of OpenAI!? Gartner needs to redo its hype cycle. With AI, we’re past the “Peak of Inflated Expectations” and charging toward the “Pinnacle of Hysterical Financial Fantasies.”

Poland Energy Survives Attack on Wind, Solar Infrastructure

The attack on Poland's energy sector late last year might have failed, but it's also the first large-scale attack against decentralized energy resources (DERs) like wind turbines and solar farms. ... The attacks were destructive by nature and "occurred during a period when Poland was struggling with low temperatures and snowstorms just before the New Year." ... Dragos said that over the past year, Electrum has worked alongside another threat actor, tracked as Kamicite, to conduct destructive attacks against Ukrainian ISPs and persistent scanning of industrial devices in the US. Kamicite gained initial access and persistence against organizations, and Electrum executed follow-on activity. Dragos has tracked Kamicite activities against the European ICS/OT supply chain since late 2024. "Electrum remains one of the most aggressive and capable OT/ICS-adjacent threat actors in the world," Dragos said. "Even when targeting IT infrastructure, Electrum's destructive malware often affects organizations that provide critical operational services, telecommunications, logistics, and infrastructure support, blurring the traditional boundary between IT and OT. Kamacite's continuous reconnaissance and access development directly enable Electrum's destructive operations. These activities are neither theoretical nor preparatory, they are part of active campaigns culminating in real-world outages, data destruction, and coordinated destabilization campaigns."

Why SaaS cost optimization is an operating model problem, not a budget exercise

When CIOs ask why SaaS costs spiral, the answer is rarely “poor discipline.” It’s usually structural. ... In the engagement I described, had accumulated over years for understandable reasons: Business units bought tools to move faster; IT teams enabled experimentation during growth phases; Mergers brought duplicate platforms; and Pandemic-era urgency favored speed over standardization. No one made a single bad decision. Hundreds of reasonable decisions added up to an unreasonable outcome. ... During a review session, I asked a simple question about one of the highest-cost platforms: “Who owns this product?” The room went quiet. IT assumed the business owned it. The business assumed IT managed it. Procurement negotiated the contract. Security reviewed access annually. No one was accountable for adoption, value realization or lifecycle decisions. This lack of accountability wasn’t unique to that tool — it was systemic. Best-practice guidance on SaaS governance consistently emphasizes the importance of assigning a clearly named owner for every application, accountable for cost, security, compliance and ongoing value. Without that ownership, redundancy and unmanaged spend tend to persist across portfolios. ... CIOs focus on licenses and contracts, but the real issue is the absence of a product mindset. SaaS platforms behave like products, but many organizations manage them like utilities.

Finding a common language around risk

The CISO warns about ransomware threats. Operations worries about supply chain breakdowns. The board obsesses over market disruption. They’re all talking about risk, but they might as well be on different planets. When the crisis hits (and it always does), everyone scrambles in their own direction while the place burns down. ... The Organizational Risk Culture Standard (ORCS) offers something most frameworks miss: it treats culture as the foundation, not the afterthought. You can’t bolt culture onto existing processes and call it done. Culture is how people actually think about risk when no one is watching. It’s the shared beliefs that guide decisions under pressure. Think of it as a dynamic system in which people, processes and technology must dance together. People are the operators who judge and act on risks. Processes provide standards, so they don’t have to improvise in a crisis. Technology provides tools to detect patterns, monitor threats and respond faster than human reflexes. But here’s the catch: these three elements have to align across all three risk domains. Your cybersecurity team needs to understand how their decisions affect operations. Your operations team needs to grasp strategic implications. ... The ORCS standard provides a maturity model with five levels. Most organizations start at Level 1, where risk management is reactive and fragmented. People improvise. Policies exist on paper, but nobody follows them. Crises catch everyone off guard.

Harnessing curated threat intelligence to strengthen cybersecurity

Improving one’s cybersecurity posture with up-to-date threat intelligence is a foundational element of any modern security stack. This enables automated blocking of known threats and reduces the workload on security teams while keeping the network protected. Curated threat intelligence also plays a broader role across cybersecurity strategies, like blocking malicious IP addresses from accessing the network to support intrusion prevention and defend against distributed denial-of-service (DDoS) attacks. ... Organizations overwhelmed by massive amounts of cybersecurity data can gain clarity and control with curated threat intelligence. By validating, enriching and verifying the data, curated intelligence dramatically reduces false positives and noise, enabling security teams to focus on the most relevant and credible threats. Improved accuracy and certainty accelerates time-to-knowledge, sharpens prioritization based on threat severity and potential impact, and ensures resources are applied and deployed where they matter most. With higher confidence and certainty, teams can respond to incidents faster and more decisively, while also shifting from reactive to proactive and ultimately preventative – using known adversary indicators and patterns to investigate threats, strengthen controls, and stop attacks before they cause damage. Curated threat Intelligence transforms one’s cybersecurity from reactive to resilient.

Password managers’ promise that they can’t see your vaults isn’t always true

All eight of the top password managers have adopted the term “” to describe the complex encryption system they use to protect the data vaults that users store on their servers. The definitions vary slightly from vendor to vendor, but they generally boil down to one bold assurance: that there is no way for malicious insiders or hackers who manage to compromise the cloud infrastructure to steal vaults or data stored in them. ... New research shows that these claims aren’t true in all cases, particularly when account recovery is in place or password managers are set to share vaults or organize users into groups. The researchers reverse-engineered or closely analyzed Bitwarden, Dashlane, and LastPass and identified ways that someone with control over the server—either administrative or the result of a compromise—can, in fact, steal data and, in some cases, entire vaults. The researchers also devised other attacks that can weaken the encryption to the point that ciphertext can be converted to plaintext. ... Three of the attacks—one against Bitwarden and two against LastPass—target what the researchers call “item-level encryption” or “.” Instead of encrypting a vault in a single, monolithic blob, password managers often encrypt individual items, and sometimes individual fields within an item. These items and fields are all encrypted with the same key.

Poor documentation risks an AI nightmare for developers

Poor documentation not only slows down development and makes bug fixing difficult, but its effects can multiply. Misunderstandings can propagate through codebases, creating issues that can take a long time to fix. The use of AI accelerates this problem. AI coding assistants rely on documentation to understand how software should be used. Without AI, there is the option of institutional knowledge, or even simply asking the developer behind the code. AI doesn’t have this choice and will confidently fill in the gaps where no documentation exists. We’re familiar with – and developers will be checking for these kinds of errors – but a lack of documentation will likely cause an AI to simply take a stab in the dark. ... Developers need to write documentation around complete workflows: the full path from local development to production deployment, including failures and edge cases. It can be tricky to spot errors in your own work, so AI can be used to help here, following the documentation end-to-end and observing where confusion and errors appear. AI can also be used to draft documentation and generally does a pretty good job of putting together documentation when presented with code. ... Document development should be an ongoing process – just as software is patched and updated, so should the documentation. Questions that come in from support tickets and community forums – especially repeat problems – can be used to highlight issues in documentation, particularly those caused by assumed knowledge.

Branding Beyond the Breach: How Cybersecurity Companies Can Lead with Trust, Not Fear

The almost constant stream of cyberattack headlines in the news only highlights the importance for cybersecurity companies to ensure their messaging is creating trust and confidence for B2B businesses. ... It is easy to take issues such as AI- powered attacks and and create fear-based messaging in hopes of capturing attention. However, when cybersecurity companies endlessly recycle breach risks as reasons to do business, it can overload prospective clients with the dangers and cause them to disengage. It also minimises cybersecurity services down to being solely reactive, rather than proactive and preventative. By following fear-based messaging, cybersecurity companies are blending in, not standing out. ... To navigate the complexities of cybersecurity, B2B businesses need a partner to guide them, not just sell to them. By including thought-leadership, education initiatives, consultation services, partnerships and customised strategies into a cybersecurity company’s messaging and offering, it highlights their authenticity, credibility and reliability. ... The cybersecurity landscape is wide and complex, and the market will only continue to diversify as threats evolve. Cybersecurity organisations need messaging that shows they can support businesses to expand in new sectors, communicate complex offerings clearly and become the optimal solution for risk-conscious enterprises.