Daily Tech Digest by Kannan Subbiah: budget

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Daily Tech Digest - March 10, 2026

Quote for the day:

"A leader has the vision and conviction that a dream can be achieved. He inspires the power and energy to get it done." -- Ralph Nader

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Job disruption by AI remains limited — and traditional metrics may be missing the real impact

This article on computerworld explores the current state of artificial intelligence in the workforce. Despite widespread alarm, data from Challenger, Gray & Christmas indicates that AI accounted for roughly 8 to 10 percent of job cuts in early 2026. Researchers from Anthropic argue that traditional metrics fail to capture the nuances of AI integration, introducing an "observed exposure" methodology. This technique combines theoretical large language model capabilities with actual usage data, revealing that while certain roles—such as computer programmers and customer service representatives—have high exposure to automation, actual deployment lags significantly behind technical potential. Currently, AI functions primarily as a tool for task-based augmentation rather than full-scale replacement, which enhances worker productivity but complicates entry-level hiring. The report suggests that while immediate mass unemployment hasn't materialized, the long-term impact will require a fundamental re-engineering of workflows. This shift may disproportionately affect younger workers as companies struggle to balance AI efficiency with the necessity of maintaining a pipeline of human talent. Ultimately, the transition necessitates a strategic realignment of human roles to ensure sustainable growth in an intelligence-native era.

Why Password Audits Miss the Accounts Attackers Actually Want

This article on BleepingComputer highlights a critical disconnect between standard compliance-driven password audits and the actual tactics used by cybercriminals. While traditional audits prioritize technical requirements like complexity and rotation, they often overlook the context that makes an account vulnerable. For instance, a password can be statistically "strong" yet already compromised in a previous breach; research indicates that 83% of leaked passwords still meet regulatory standards. Furthermore, audits frequently neglect "orphaned" accounts belonging to former employees or contractors, which provide silent entry points for attackers. Service accounts—often over-privileged and exempt from expiry policies—represent another major blind spot. The piece argues that point-in-time snapshots are insufficient against continuous threats like credential stuffing. To be truly effective, security teams must shift toward continuous monitoring, incorporating breached-password screening and risk-based prioritization. By expanding the scope to include dormant, external, and service accounts, organizations can move beyond mere compliance to address the high-value targets that attackers prioritize. Ultimately, securing a digital environment requires recognizing that a compliant password is not necessarily a safe one in the face of modern, targeted exploitation.

AI is supercharging cloud cyberattacks - and third-party software is the most vulnerable

The latest Google Cloud Threat Report, as analyzed by ZDNET, highlights a significant escalation in cybersecurity risks where artificial intelligence is increasingly being used to "supercharge" cloud-based attacks. The report reveals a dramatic collapse in the window between the disclosure of a vulnerability and its mass exploitation, shrinking from weeks to mere days. Rather than targeting the highly secured core infrastructure of major cloud providers, threat actors are now focusing their efforts on unpatched third-party software and code libraries. This shift emphasizes that the modern supply chain remains a critical weak point for many organizations. Furthermore, the report notes a transition away from traditional brute force attacks toward more sophisticated identity-based compromises, including vishing, phishing, and the misuse of stolen human and non-human identities. Data exfiltration is also evolving, with "malicious insiders" increasingly using consumer-grade cloud storage services to move confidential information outside the corporate perimeter. To combat these AI-powered threats, Google’s experts recommend that businesses adopt automated, AI-augmented defenses, prioritize immediate patching of third-party tools, and strengthen identity management protocols. Ultimately, the report serves as a stark warning that in the current threat landscape, speed and automation are no longer optional but essential components of a robust cybersecurity strategy.

Change as Metrics: Measuring System Reliability Through Change Delivery Signals

This article highlights that system changes account for the vast majority of production incidents, necessitating their treatment as primary reliability indicators. To manage this risk, the author proposes a framework centered on three core business metrics: Change Lead Time, Change Success Rate, and Incident Leakage Rate. While aligned with DORA principles, this model specifically focuses on delivery quality by distinguishing between immediate deployment failures and latent defects that manifest as post-release incidents. To operationalize these goals, technical control metrics such as Change Approval Rate, Progressive Rollout Rate, and Change Monitoring Windows are introduced to provide actionable insights into pipeline friction and risk. The piece further advocates for a platform-agnostic, event-centric data architecture to collect these signals across diverse, distributed environments. This centralized approach avoids the brittleness of platform-specific logging and provides a unified view of system health. Ultimately, the framework empowers organizations to transform change management from a reactive necessity into a proactive, measurable engineering capability. By integrating these metrics, development teams can effectively balance the need for high-speed delivery with the imperative of system stability, ensuring that rapid innovation does not come at the expense of user experience or operational reliability.

The future of generative AI in software testing

In this article on Techzine, experts Hélder Ferreira and Bruno Mazzotta discuss the transformative shift of AI from a simple task accelerator to a fundamental structural layer within delivery pipelines. As global IT investment in AI is projected to surge toward $6.15 trillion by 2026, the software testing landscape is evolving beyond early challenges like hallucinations and "vibe coding" toward a sophisticated "quality intelligence layer." The authors outline four critical areas where AI adds strategic value: generating complex scenario-based datasets, suggesting high-risk exploratory prompts, automating defect triage to identify regression patterns, and enabling context-aware execution that prioritizes testing based on actual risk rather than volume. Crucially, the piece argues that while AI can significantly enhance velocity, sustainable success depends on maintaining "humans-in-the-loop" to ensure traceability and accountability. In this new era, the primary differentiator for enterprises will not be the sheer amount of AI deployed, but the effectiveness of their governance frameworks. By linking intent with execution and using AI as connective tissue across the lifecycle, organizations can achieve a balance where rapid delivery is supported by explainable automation and human-verified confidence in software quality.

CIOs cut IT corners to manufacture budget for AI

In this CIO.com article, author Esther Shein examines the aggressive strategies IT leaders are employing to fund artificial intelligence initiatives amidst stagnant overall budgets. Faced with intense pressure from boards and executive leadership to prioritize AI, many CIOs are being forced to make difficult trade-offs that jeopardize long-term stability. Common tactics include delaying non-critical infrastructure refreshes, such as server expansions and network improvements, which are often pushed out by twelve to eighteen months. Additionally, organizations are aggressively consolidating vendors, renegotiating contracts, and cutting legacy software subscriptions to free up capital. Some leaders have even implemented strict "self-funding" mandates where every new AI project must be offset by equivalent cuts elsewhere. Beyond technical sacrifices, the human element is also affected, with many departments reducing reliance on contractors or trimming internal staff to reallocate funds toward high-impact AI use cases. While these measures enable rapid deployment, they frequently lead to the accumulation of technical debt and a narrower scope for implementations. Ultimately, the piece warns that while these "corners" are being cut to fuel innovation, the resulting lack of focus on foundational maintenance could present significant operational risks in the future.

Beyond Prompt Injection: The Hidden AI Security Threats in Machine Learning Platforms

In the article "Beyond Prompt Injection: The Hidden AI Security Threats in Machine Learning Platforms," the focus of AI security shifts from headline-grabbing prompt injections to the critical vulnerabilities within MLOps infrastructure. While many security teams prioritize protecting chatbots from manipulation, the underlying platforms used to train and deploy models often present a far more dangerous attack surface. Through a red team engagement, researchers demonstrated how a simple self-registered trial account could be used to achieve remote code execution on a provider’s cloud infrastructure. By deploying a seemingly legitimate but malicious machine learning model, attackers can exploit the fact that these platforms must execute arbitrary code to function. The study highlights a significant risk: once RCE is achieved, weak network segmentation can allow adversaries to bypass trust boundaries and access sensitive internal databases or services. This effectively turns a managed ML environment into a gateway for lateral movement within a corporate network. To mitigate these threats, the article stresses that organizations must move beyond model-centric security and adopt robust infrastructure protections, including strict network isolation, continuous behavior monitoring, and a "zero-trust" approach to user-deployed artifacts, ensuring that the convenience of rapid AI development does not come at the cost of total system compromise.

Enterprise agentic AI requires a process layer most companies haven’t built

The VentureBeat article emphasizes that while 85% of enterprises aspire to implement agentic AI within the next three years, a staggering 76% acknowledge that their current operations are fundamentally unequipped for this transition. The core issue lies in the absence of a "process layer"—a critical foundation of optimized workflows and operational intelligence that provides AI agents with the necessary context to function effectively. Without this layer, agents are essentially "guessing," leading to a lack of reliability that causes 82% of decision-makers to fear a failure in return on investment. The piece argues that the primary hurdle is not merely technological but rather rooted in organizational structure and change management. Most companies suffer from siloed data and fragmented processes that hinder the seamless integration of autonomous systems. To overcome these barriers, businesses must prioritize process optimization and operational visibility, ensuring that AI-driven initiatives are linked to strategic executive outcomes. Simply layering advanced AI over inefficient, legacy frameworks will likely result in costly friction. Ultimately, for agentic AI to move beyond experimental pilots and deliver scalable value, organizations must first build a robust architectural bridge that connects sophisticated models with the complex, real-world logic of their daily business operations and high-stakes organizational decision cycles.

Building resilient foundations for India’s expanding Data Centre ecosystem

In "Building resilient foundations for India's expanding Data Centre ecosystem," Saurabh Verma explores the rapid evolution of India’s data infrastructure and the urgent necessity of prioritizing long-term resilience over mere capacity. As cloud adoption and 5G accelerate growth across hubs like Mumbai, Chennai, and Hyderabad, the sector faces escalating challenges that demand a sophisticated understanding of risk management. The article argues that modern data centres are no longer just IT assets but critical infrastructure whose failure directly impacts the digital economy. Beyond physical damage, business interruptions often result in massive financial losses, contractual penalties, and significant reputational harm. Climate change has emerged as a significant operational reality, with heatwaves and flooding stressing cooling systems and electrical grids. Furthermore, the convergence of cyber and physical risks means that digital disruptions can quickly translate into tangible infrastructure damage. Construction complexities and logistical interdependencies further amplify potential losses, making early risk engineering essential for success. Ultimately, the piece emphasizes that resilience must be a core design pillar rather than an afterthought. By integrating disciplined risk management from site selection through operations, Indian providers can gain a commercial advantage, securing better investment and insurance terms while building a sustainable, trustworthy backbone for the nation’s digital future.

CVE program funding secured, easing fears of repeat crisis

The Common Vulnerabilities and Exposures (CVE) program has successfully secured stable funding, alleviating industry-wide fears of a repeat of the 2025 crisis that nearly crippled global vulnerability tracking. As detailed in the CSO Online report, the Cybersecurity and Infrastructure Security Agency (CISA) and the MITRE Corporation have renegotiated their contract, transitioning the 26-year-old program from a discretionary expenditure to a protected line item within CISA's budget. This structural change effectively eliminates the "funding cliff" that previously required a last-minute emergency extension. While CISA leadership emphasizes that the program is now fully funded and evolving, some experts note that the specifics of the "mystery contract" remain opaque. The resolution comes at a critical time, as the cybersecurity community had already begun developing contingencies, such as the independent CVE Foundation, to reduce reliance on a single government source. Despite the financial stability, challenges regarding transparency, modernization, and international governance persist. The article underscores that while the immediate threat of a service lapse has faded, the incident served as a stark reminder of the global security ecosystem's fragility. Moving forward, the focus shifts toward ensuring this essential public resource remains resilient against future political or administrative shifts within the United States government.

Daily Tech Digest - February 25, 2026

Quote for the day:

"To strongly disagree with someone, and yet engage with them with respect, grace, humility and honesty, is a superpower" -- Vala Afshar

Is ‘sovereign cloud’ finally becoming something teams can deploy – not just discuss?

Historically, sovereign cloud discussions in Europe have been driven primarily by risk mitigation. , legal jurisdiction, and protection from international legislation have dominated the narrative. These concerns are valid, but they have framed sovereign cloud largely as a defensive measure – a way to reduce exposure – rather than as an enabler of innovation or value creation. Without a clear value proposition beyond compliance, sovereign cloud has struggled to compete with hyperscale public cloud platforms that offer scale, maturity, and rich developer ecosystems. The absence of enforceable regulation has further compounded this. ... Policymakers and enterprises are also beginning to ask a more practical question: where does sovereign cloud actually create the most value? The answer increasingly points to innovation ecosystems, critical national capabilities, and trust. First, there is a growing recognition that sovereign cloud can underpin domestic innovation, particularly in areas such as AI, advanced research, and data-intensive start-ups. Organisations working with sensitive datasets, intellectual property, or public funding often require cloud environments that are both scalable and secure. ... Second, the sovereign cloud is increasingly being aligned with critical digital infrastructure. Sectors like healthcare, energy, transportation, and defence depend on continuity, accountability, and control.

India’s DPDP rules 2025: Why access controls are priority one for CIOs

The security stack has traditionally broken down at the point of data rendering or exfiltration. Firewalls and encryption protect the data in transit and at rest, but once the data is rendered on a screen, the risk of data breaches from smartphone cameras, screenshots, or unauthorized sharing occurs outside of the security stack’s ability to protect it. ... Poor enterprise access practices amplify this risk. Over-provisioned user accounts, inconsistent multi-factor authentication, poor logging, and the absence of contextual checks make it easy for insider threats, credential compromise, and supply chain breaches to succeed. Under DPDP, accountability also extends to processors, so third-party CRM or cloud access must meet the same security standards. ... Shift from trust by implication to trust by verification. Implement to ensure users view only required apps and data. Add device posture with device binding, location, time, watermarking and behavior analysis to deny suspicious access. ... Implement identity infrastructure for just-in-time access and automated de-Provisioning based on role changes. Record fine-grained, immutable logs (user, device, resource, date/time) for breach analysis and annual retention. ... Enable dynamic, user-level watermarks (injecting username, IP address, timestamp) for forensic analysis. Prohibit unauthorized screen capture, sharing, or download activity during sensitive sessions, while permitting approved business processes.

What really caused that AWS outage in December?

The back-story was broken by the Financial Times, which reported the 13-hour outage was caused by a that decided to improve operations by deleting and then recreating a key environment. AWS on Friday shot back to flag what it dubbed “inaccuracies” in the FT story. “The brief service interruption they reported on was the result of user error — specifically — not AI as the story claims,” AWS said. ... “The issue stemmed from a misconfigured role — the same issue that could occur with any developer tool (AI powered or not) or manual action.” That’s an impressively narrow interpretation of what happened. AWS then promised it won’t do it again. ... The key detail missing — which AWS would not clarify — is just what was asked and how the engineer replied. Had the engineer been asked by Kiro “I would like to delete and then recreate this environment. May I proceed?” and the engineer replied, “By all means. Please do so,” that would have been user error. But that seems highly unlikely. The more likely scenario is that the system asked something along the lines of “Do you want me to clean up and make this environment more efficient and faster?” Did the engineer say “Sure” or did the engineer respond, “Please list every single change you are proposing along with the likely result and the worst-case scenario result. Once I review that list, I will be able to make a decision.”

Model Inversion Attacks: Growing AI Business Risk

A is a form of privacy attack against machine learning systems in which an adversary uses the outputs of a model to infer sensitive information about the data used to train it. Rather than breaching a database or stealing credentials, attackers observe how a model responds to input queries and leverage those outputs, often including confidence scores or probability values, to reconstruct aspects of the training data that should remain private. ... This type of attack differs fundamentally from other ML attacks, such as membership inference, which aims to determine whether a specific data point was part of the training set, and model extraction, which seeks to copy the model itself. ... Successful model inversion attacks can inflict significant damage across multiple areas of a business. When attackers extract sensitive training data from machine learning models, organizations face not only immediate financial losses but also lasting reputational harm and operational setbacks that continue well beyond the initial incident. ... Attackers target inference-time privacy by moving through multiple stages, submitting carefully crafted queries, studying the model’s responses, and gradually reconstructing sensitive attributes from the outputs. Because these activities can resemble normal usage patterns, such attacks frequently remain undetected when monitoring systems are not specifically tuned to identify machine learning–related security threats.

It’s time to rethink CISO reporting lines

The age-old problem with reporting into CIOs is that it could present — or at least appear to present — a conflict of interest. Cybersecurity consultant Brian Levine, a former federal prosecutor who serves as executive director of FormerGov, says that concern is even more warranted today. “It’s the legacy model: Treat security as a technical function instead of an enterprise‑wide risk discipline,” he says. ... Enterprise CISOs should be reporting a notch higher, Levine argues. “Ideally, the CISO would report to the CEO or the general counsel, high-level roles explicitly accountable for enterprise risk. Security is fundamentally a risk and governance function, not a cost‑center function,” Levine points out. “When the CISO has independence and a direct line to the top, organizations make clearer decisions about risk, not just cheaper ones." ... Painter is “less dogmatic about where the CISO reports and more focused on whether they actually have a seat at the table,” he says. “Org charts matter far less than influence,” he adds. “Whether the CISO reports to the CIO, the CEO, or someone else, the real question is this: Are they brought in early, listened to, and empowered to shape how the business operates? When that’s true, the structure works. When it’s not, no reporting line will save it.” ... “When the CISO reports to the CIO, risk can be filtered, prioritized out of sight, or reshaped to fit a delivery narrative. It’s not about bad actors. It’s about role tension. And when that tension exists within the same reporting line, risk loses.”

AI drives cyber budgets yet remains first on the chop list

Cybersecurity budgets are rising sharply across large organisations, but a new multinational survey points to a widening gap between spending on artificial intelligence and the ability to justify that spending in business terms. ... "Security leaders are getting mandates to invest in AI, but nobody's given them a way to prove it's working. You can't measure AI transformation with pre-AI metrics," Wilson said. He added that security teams struggle to translate operational data into board-level evidence of reduced risk. "The problem isn't that security teams lack data. They're drowning in it. The issue is they're tracking the wrong things and speaking a language the board doesn't understand. Those are the budgets that get cut first. The window to fix this is closing fast," Wilson said. ... "We need new ways to measure security effectiveness that actually show business impact, because boards don't fund faster ticket closure, they fund measurable risk reduction and business resilience. We have to show that we're not just responding quickly but eliminating and improving the conditions that allow incidents to happen in the first place," he said. ... Security leaders reported pressure to invest in AI, while also struggling to link those investments to outcomes executives recognise as resilience and risk reduction. The report argues this tension may become harder to sustain if economic conditions tighten and boards begin looking for costs to cut.

A cloud-smart strategy for modernizing mission-critical workloads

As enterprises mature in their cloud journeys, many CIOs and senior technology leaders are discovering that modernization is not about where workloads run — it’s about how deliberately they are designed. This realization is driving a shift from cloud-first to cloud-smart, particularly for systems the business cannot afford to lose. A cloud-smart strategy, as highlighted by the Federal Cloud Computing Strategy, encourages agencies to weigh the long-term, total costs of ownership and security risks rather than focusing only on immediate migration. ... Sticking indefinitely with legacy systems can lead to rising maintenance costs, inability to support new business initiatives, security vulnerabilities and even outages as old hardware fails. Many organizations reach a tipping point where they must modernize to stay competitive. The key is to do it wisely — balancing speed and risk and having a solid strategy in place to navigate the complexity. ... A cloud-smart strategy aligns workload placement with business risk, performance needs and regulatory expectations rather than ideology. Instead of asking whether a system can move to the cloud, cloud-smart organizations ask where it performs best. ... Rather than lifting and shifting entire platforms, teams separate core transaction engines from decisioning, orchestration and experience layers. APIs and event-driven integration enable new capabilities around stable cores, allowing systems to evolve incrementally without jeopardizing operational continuity.

Enterprises still can't get a handle on software security debt – and it’s only going to get worse

Four-in-five organizations are drowning in software security debt, new research shows, and the backlog is only getting worse. ... "The speed of software development has skyrocketed, meaning the pace of flaw creation is outstripping the current capacity for remediation,” said Chris Wysopal, chief security evangelist at Veracode. “Despite marginal gains in fix rates, security debt is becoming a much larger issue for many organizations." Organizations are discovering more vulnerabilities as their testing programs mature and expand. Meanwhile, the accelerating pace of software releases creates a continuous stream of new code before existing vulnerabilities can be addressed. ... "Now that AI has taken software development velocity to an unprecedented level, enterprises must ensure they’re making deliberate, intelligent choices to stem the tide of flaws and minimize their risk," said Wysopal. The rise in flaws classed as both “severe” and “highly exploitable” means organizations need to shift from generic severity scoring to prioritization based on real-world attack potential, advised Veracode. As such, researchers called for a shift from simple detection toward a more strategic framework of Prioritize, Protect, and Prove. ... “We are at an inflection point where running faster on the treadmill of vulnerability management is no longer a viable strategy. Success requires a deliberate shift,” said Wysopal.

Protecting your users from the 2026 wave of AI phishing kits

To protect your users today, you have to move past the idea of reactive filtering and embrace . This means your software needs to be smart enough to validate that a user is who they say they are, regardless of the credentials they provide. We’re seeing a massive shift toward behavioral analytics. Instead of just checking a password, your platform should be looking at communication patterns and login behaviors. If a user who typically logs in from Chicago suddenly tries to authorize a high-value financial transfer from a new device in a different country, your system should do more than just send a push notification. ... Beyond the tech, you need to think about the “human” friction you’re creating. We often prioritize convenience over security, but in the current climate, that’s a losing bet. Implementing “probabilistic approval workflows” can help. For example, if your system’s AI is 95% sure a login is legitimate, let it through. If that confidence drops, trigger a more rigorous verification step. ... The phishing scams of 2026 are successful because they leverage the same tools we use for productivity. To counter them, we have to be just as innovative. By building identity validation and phishing-resistant protocols into the core of your product, you’re doing more than just securing data. You’re securing the trust that your business is built on.

GitOps Implementation at Enterprise Scale — Moving Beyond Traditional CI/CD

Most engineering organizations running traditional CI/CD pipelines eventually hit the ceiling. Deployments work until they don’t, and when they break, the fixes are manual, inconsistent and hard to trace. ... We kept Jenkins and GitHub Actions in the stack for build and test stages where they already worked well. Harness remained an option for teams requiring more sophisticated approval workflows and governance controls. We ruled out purely script-based push deployment approaches because they offered poor drift control and scaled badly. ... Organizational resistance proved more challenging to address than the technical work. Teams feared the new approach would introduce additional bureaucracy. Engineers accustomed to quick kubectl fixes worried about losing agility. We ran hands-on workshops demonstrating that GitOps actually produced faster deployments, easier rollbacks and better visibility into what was running where. We created golden templates for common deployment patterns, so teams did not have to start from scratch. ... Unexpected benefits emerged after full adoption. Onboarding improved as deployment knowledge now lived in Git history and manifests rather than in senior engineers’ heads. Incident response accelerated because traceability let teams pinpoint exactly what changed and when, and rollback became a consistent, reliable operation. The shift from push-based to pull-based operations improved security posture by limiting direct cluster access.

Daily Tech Digest - December 21, 2025

Quote for the day:

"Don't worry about being successful but work toward being significant and the success will naturally follow." -- Oprah Winfrey

Is it Possible to Fight AI and Win?

What’s the most important thing security teams need to figure out? Organizations must stop talking about AI like it’s a death star of sorts. AI is not a single, all-powerful, monolithic entity. It’s a stack of threats, behaviors, and operational surfaces and each one has its own kill chain, controls, and business consequences. We need to break AI down into its parts and conduct a real campaign to defend ourselves. ... If AI is going to be operationalized inside your business, it should be treated like a business function. Not a feature or experiment, but a real operating capability. When you look at it that way, the approach becomes clearer because businesses already know how to do this. There is always an equivalent of HR, finance, engineering, marketing, and operations. AI has the same needs. ... Quick fixes aren’t enough in the AI era. The bad actors are innovating at machine speed, so humans must respond at machine speed with appropriate human direction and ethical clarity. AI is a tool. And the side that uses it better will win. If that isn’t enough, AI will force another reality that organizations need to prepare for. Security and compliance will become an on-demand model. Customers will not wait for annual reports or scheduled reviews. They will click into a dashboard and see your posture in real time. Your controls, your gaps, and your response discipline will be visible when it matters, not when it is convenient.

Cybersecurity Budgets are Going Up, Pointing to a Boom

Nearly all of the security leaders (99%) in the 2025 KPMG Cybersecurity Survey plan on upping their cybersecurity budgets in the two-to-three years to come, in preparation for what may be the upcoming boom in cybersecurity. More than half (54%) say budget increases will fall between 6%-10%. “The data doesn’t just point to steady growth; it signals a potential boom. We’re seeing a major market pivot where cybersecurity is now a fundamental driver of business strategy,” Michael Isensee, Cybersecurity & Tech Risk Leader, KPMG LLP, said in a release. “Leaders are moving beyond reactive defense and are actively investing to build a security posture that can withstand future shocks, especially from AI and other emerging technologies. This isn’t just about spending more; it’s about strategic investment in resilience.” ... The security leaders recognize AI is amassing steam as a dual catalyst—38% are challenged by AI-powered attacks in the coming three years, with 70% of organizations currently committing 10% of their budgets to combating such attacks. But they also say AI is their best weapon to proactively identify and stop threats when it comes to fraud prevention (57%), predictive analytics (56%) and enhanced detection (53%). But they need the talent to pull it off. And as the boom takes off, 53% just don’t have enough qualified candidates. As a result, 49% are increasing compensation and the same number are bolstering internal training, while 25% are increasingly turning to third parties like MSSPs to fill the skills gap.

How Neuro-Symbolic AI Breaks the Limits of LLMs

While AI transforms subjective work like content creation and data summarization, executives rightfully hesitate to use it when facing objective, high-stakes determinations that have clear right and wrong answers, such as contract interpretation, regulatory compliance, or logical workflow validation. But what if AI could demonstrate its reasoning and provide mathematical proof of its conclusions? That’s where neuro-symbolic AI offers a way forward. The “neuro” refers to neural networks, the technology behind today’s LLMs, which learn patterns from massive datasets. A practical example could be a compliance system, where a neural model trained on thousands of past cases might infer that a certain policy doesn’t apply in a scenario. On the other hand, symbolic AI represents knowledge through rules, constraints, and structure, and it applies logic to make deductions. ... Neuro-symbolic AI introduces a structural advance in LLM training by embedding automated reasoning directly into the training loop. This uses formal logic and mathematical proof to mechanically verify whether a statement, program, or output used in the training data is correct. A tool such as Lean,4 is precise, deterministic, and gives provable assurance. The key advantage of automated reasoning is that it verifies each step of the reasoning process, and not just the final answer.

Three things they’re not telling you about mobile app security

With the realities of “wilderness survival” in mind, effective mobile app security must be designed for specific environmental exposures. You may need to wear some kind of jacket at your office job (web app), but you’ll need a very different kind of purpose-built jacket as well as other clothing layers, tools, and safety checks to climb Mount Everest (mobile app). Similarly, mobile app development teams need to rigorously test their code for potential security issues and also incorporate multi-layered protections designed for some harsh realities. ... A proactive and comprehensive approach is one that applies mobile application security at each stage of the software development lifecycle (SDLC). It includes the aforementioned testing in the stages of planning, design, and development as well as those multi-layered protections to ensure application integrity post-release. ... Whether stemming from overconfidence or just kicking the can down the road, inadequate mobile app security presents an existential risk. A recent survey of developers and security professionals found that organizations experienced an average of nine mobile app security incidents over the previous year. The total calculated cost of each incident isn’t just about downtime and raw dollars, but also “little things” like user experience, customer retention, and your reputation.

Cybersecurity in 2026: Fewer dashboards, sharper decisions, real accountability

The way organisations perceive risk is one of the most important changes predicted in 2026. Security teams spent years concentrating on inventory, which included tracking vulnerabilities, chasing scores and counting assets. The model is beginning to disintegrate. Attack-path modelling, on the other hand, is becoming far more useful and practical. These models are evolving from static diagrams to real-world settings where teams may simulate real attacks. Consider it a cyberwar simulation where defenders may test “what if” scenarios in real time, comprehend how a threat might propagate via systems and determine whether vulnerabilities truly cause harm to organisations. This evolution is accompanied by a growing disenchantment with abstract frameworks that failed to provide concrete outcomes. The emphasis is shifting to risk-prioritized operations, where teams start tackling the few problems that actually provide attackers access instead than responding to clutter. Success in 2026 will be determined more by impact than by activities. ... Many companies continue to handle security issues behind closed doors as PR disasters. However, an alternative strategy is gaining momentum. Communicate as soon as something goes wrong. Update frequently, share your knowledge and acknowledge your shortcomings. Post signs of compromise. Allow partners and clients to defend themselves. Particularly in the middle of disorder, this seems dangerous.

AI and Latency: Why Milliseconds Decide Winners and Losers in the Data Center Race

Many traditional workloads can tolerate latency. Batch processing doesn’t care if it takes an extra second to move data. AI training, especially at hyperscale, can also be forgiving. You can load up terabytes of data in a data center in Idaho and process it for days without caring if it’s a few milliseconds slower. Inference is a different beast. Inference is where AI turns trained models into real-time answers. It’s what happens when ChatGPT finishes your sentence, your banking AI flags a fraudulent transaction, or a predictive maintenance system decides whether to shut down a turbine. ... If you think latency is just a technical metric, you’re missing the bigger picture. In AI-powered industries, shaving milliseconds off inference times directly impacts conversion rates, customer retention, and operational safety. A stock trading platform with 10 ms faster AI-driven trade execution has a measurable financial advantage. A translation service that responds instantly feels more natural and wins user loyalty. A factory that catches a machine fault 200 ms earlier can prevent costly downtime. Latency isn’t a checkbox, it’s a competitive differentiator. And customers are willing to pay for it. That’s why AWS and others have “latency-optimized” SKUs. That’s why every major hyperscaler is pushing inference nodes closer to urban centers.

Why developers need to sharpen their focus on documentation

“One of the bigger benefits of architectural documentation is how it functions as an onboarding resource for developers,” Kalinowski told ITPro. “It’s much easier for new joiners to grasp the system’s architecture and design principles, which means the burden’s not entirely on senior team members’ shoulders to do the training," he added. “It also acts as a repository of institutional knowledge that preserves decision rationale, which might otherwise get lost when team members move to other projects or leave the company." ... “Every day, developers lose time because of inefficiencies in their organization – they get bogged down in repetitive tasks and waste time navigating between different tools,” he said. “They also end up losing time trying to locate pertinent information – like that one piece of documentation that explains an architectural decision from a previous team member,” Peters added. “If software development were an F1 race, these inefficiencies are the pit stops that eat into lap time. Every unnecessary context switch or repetitive task equals more time lost when trying to reach the finish line.” ... “Documentation and deployments appear to either be not routine enough to warrant AI assistance or otherwise removed from existing workflows so that not much time is spent on it,” the company said. ... For developers of all experience levels, Stack Overflow highlighted a concerning divide in terms of documentation activities.

AI Pilots Are Easy. Business Use Cases Are Hard

Moving from pilot to purpose is where most AI journeys lose momentum. The gap often lies not in the model itself, but in the ecosystem around it. Fragmented data, unclear ROI frameworks and organizational silos slow down scaling. To avoid this breakdown, an AI pilot must be anchored to clear business outcomes - whether that's cost optimization, data-led infrastructure or customer experience. Once the outcomes are defined, the organization can test the system with the specific data and processes that will support it. This focus sets the stage for the next 10 to 14 months of refinement needed to ready the tool for deeper integration. When implementation begins, workflows become self-optimizing, decisions accelerate and frontline teams gain real-time intelligence. As AI moves beyond pilots, systems begin spotting patterns before people do. Teams shift from retrospective analysis to live decision-making. Processes improve themselves through constant feedback loops. These capabilities unlock efficiency and insight across businesses, but highly regulated industries such as banking, insurance, and healthcare face additional hurdles. Compliance, data privacy and explainability add layers of complexity, making it essential for AI integration to include process redesign, staff retraining and organizationwide AI literacy, not just within technical teams.

Why your next cloud bill could be a trap

“AI-ready” often means “AI–deeply embedded” into your data, tools, and runtime environment. Your logs are now processed through their AI analytics. Your application telemetry routes through their AI-based observability. Your customer data is indexed for their vector search. This is convenient in the short term. In the long term, it shifts power. The more AI-native services you consume from a single hyperscaler, the more they shape your architecture and your economics. You become less likely to adopt open source models, alternative GPU clouds, or sovereign and private clouds that might be a better fit for specific workloads. You are more likely to accept rate changes, technical limits, and road maps that may not align with your interests, simply because unwinding that dependency is too painful. ... For companies not prepared to fully commit to AI-native services from a single hyperscaler or in search of a backup option, these alternatives matter. They can host models under your control, support open ecosystems, or serve as a landing zone for workloads you might eventually relocate from a hyperscaler. However, maintaining this flexibility requires avoiding the strong influence of deeply integrated, proprietary AI stacks from the start. ... The bottom line is simple: AI-native cloud is coming, and in many ways, it’s already here. The question is not whether you will use AI in the cloud, but how much control you will retain over its cost, architecture, and strategic direction.

IT and Security: Aligning to Unlock Greater Value

While many organisations have made strides in aligning IT and security, communication breakdowns can remain a challenge. Historically, friction between these two departments was driven by a lack of communication and competing priorities. For the CISO or head of the security team, reducing the company’s attack surface, limiting access privileges, or banning apps that might open their organisation up to unnecessary, additional risks are likely to be core focus areas. ... The good news is, there are more opportunities now than ever before for IT and security operations to naturally converge – in endpoint management, patch deployment, identity and access management, you name it. It can help to clearly document IT and security’s roles and responsibilities and practice scenarios with tabletop exercises to get everyone on the same page and identify coverage gaps. ... In addition to building versatile teams, organisations should focus on consolidating IT and security toolkits by prioritising solutions that expedite time to value and boost visibility. We’ve said this in security for a long time: you can’t protect (or defend against) what you can’t see. With shared visibility through integrated platforms and consolidated toolkits, both IT and security teams can gain real-time insights into infrastructure, threats, vulnerabilities, and risks before they can impact business. Solutions that help IT and security teams rapidly exchange critical information, accelerate response to incidents, and document the triaging process will make it easier to address similar instances in the future.

Daily Tech Digest - December 14, 2025

Quote for the day:

“It is never too late to be what you might have been.” -- George Eliot

Six questions to ask when crafting an AI enablement plan

As we near the end of 2025, there are two inconvenient truths about AI that every CISO needs to take into their heart. Truth #1: Every employee who can is using generative AI tools for their job. Even when your company doesn’t provide an account for them, even when your policy forbids it, even when the employee has to pay out of pocket. Truth #2: Every employee who uses generative AI will (or likely has already) provided this AI with internal and confidential company information. ... In the case of AI, this refers to the difference between the approved business apps that are trusted to access company data and the growing number of untrusted and unmanaged apps that have access to that data without the knowledge of IT or security teams. Essentially, employees are using unmonitored devices, which can hold any number of unknown AI apps, and each of those apps can introduce a whole lot of risk to sensitive corporate data. ... Simply put, organizations cannot afford to wait any longer to get a handle on AI governance. ... So now, the job is to craft an AI enablement plan that promotes productive use and throttles reckless behaviors. ... Think back to the mid‑2000s, when SaaS crept into the enterprise through expense reports and project trackers. IT tried to blacklist unvetted domains, finance balked at credit‑card sprawl, and legal wondered whether customer data belonged on “someone else’s computer.” Eventually, we accepted that the workplace had evolved, and SaaS became essential to modern business.

Why most enterprise AI coding pilots underperform (Hint: It's not the model)

When organizations introduce agentic tools without addressing workflow and environment, productivity can decline. A randomized control study this year showed that developers who used AI assistance in unchanged workflows completed tasks more slowly, largely due to verification, rework and confusion around intent. The lesson is straightforward: Autonomy without orchestration rarely yields efficiency. ... Security and governance, too, demand a shift in mindset. AI-generated code introduces new forms of risk: Unvetted dependencies, subtle license violations and undocumented modules that escape peer review. Mature teams are beginning to integrate agentic activity directly into their CI/CD pipelines, treating agents as autonomous contributors whose work must pass the same static analysis, audit logging and approval gates as any human developer. GitHub’s own documentation highlights this trajectory, positioning Copilot Agents not as replacements for engineers but as orchestrated participants in secure, reviewable workflows. ... Under the hood, agentic coding is less a tooling problem than a data problem. Every context snapshot, test iteration and code revision becomes a form of structured data that must be stored, indexed and reused. As these agents proliferate, enterprises will find themselves managing an entirely new data layer: One that captures not just what was built, but how it was reasoned about.

Enabling small language models to solve complex reasoning tasks

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) developed a collaborative approach where an LLM does the planning, then divvies up the legwork of that strategy among smaller ones. Their method helps small LMs provide more accurate responses than leading LLMs like OpenAI’s GPT-4o, and approach the precision of top reasoning systems such as o1, while being more efficient than both. Their framework, called “Distributional Constraints by Inference Programming with Language Models” (or “DisCIPL”), has a large model steer smaller “follower” models toward precise responses when writing things like text blurbs, grocery lists with budgets, and travel itineraries. ... You may think that larger-scale LMs are “better” at complex prompts than smaller ones when it comes to accuracy and efficiency. DisCIPL suggests a surprising counterpoint for these tasks: If you can combine the strengths of smaller models instead, you may just see an efficiency bump with similar results. The researchers note that, in theory, you can plug in dozens of LMs to work together in the DisCIPL framework, regardless of size. In writing and reasoning experiments, they went with GPT-4o as their “planner LM,” which is one of the models that helps ChatGPT generate responses.

Key trends accelerating Industrial Secure Remote Access (ISRA) Adoption

As essential maintenance and diagnostic activities continue to shift toward remote and digital execution, they become exposed to cyber risks that were not present when plants, fleets, and factories operated as isolated, closed systems. Compounding the challenge, many industrial organizations still lack the expertise and skill sets to select and operate the proper technologies that establish secure remote connections efficiently and securely. This, unfortunately, results in operational delays and slower response in critical or emergency situations. Industrial Cyber emphasizes that controlled, identity-bound, and fully auditable access to critical tasks is key to ensuring secure remote access functions as an operational and business enabler—without introducing new pathways for malicious actors. ... Compounding the risk, OT environments frequently rely on legacy hardware that lacks modern encryption capabilities, leaving these connections especially vulnerable. By centralizing access governance, securely managing vendor credentials, streamlining access-request workflows, and maintaining consistent audit trails, industrial organizations can regain control over third-party access. ... Industrial Cyber recognizes two solutions from SSH. 1) PrivX OT is purpose-built for industrial environments. The solution provides passwordless, keyless, and just-in-time industrial secure remote access using short-lived certificates and micro-segmentation to reduce risk. 2) NQX delivers quantum-safe, high-speed network encryption for site-to-site connectivity.

Navigating AI Liability: What Businesses That Utilize AI Need to Know

Cybercriminals can now use generative AI to create extremely convincing deepfakes. These deepfakes can then be used for corporate espionage, identity theft and phishing scams. AI software may end up automatically aggregating and analyzing huge amounts of data from multiple sources. This can increase privacy invasion risks when comprehensive profiles of people are compiled without their awareness or consent. AI systems which experience glitches or malfunctions, let others have unauthorized access to them, or lack robust security could lead to sensitive data being exposed. ... It is risky for your business to publish AI-generated content because AI models are trained on vast amounts of copyrighted material. The models thus end up not always creating original material, and sometimes create material which is identical to or extremely similar to copyrighted content. “It was the AI’s fault” will not be a valid argument in court if this happens to your business. Ignorance is not a defense in a copyright infringement claim. ... Content that is fully generated by AI has no copyright protection. AI-generated content that is significantly edited by humans may receive copyright protection, but the situation is murky. Original content that is created by humans and is then slightly edited or optimized by AI will usually receive full copyright protection. A lot of businesses now document the process of content creation to prove that humans created the content and preserve copyright protection.

When the Cloud Comes Home: What DBAs Need to Know About Cloud Repatriation

One of the main drivers for cloud repatriation is cost. Early cloud migrations were often justified by projected savings because there would be no more hardware to maintain. Furthermore, the cloud promised flexible scaling and pay-as-you-go pricing. Nevertheless, for many enterprises, those savings have proven elusive. Data-intensive workloads, in particular, can rack up significant cloud bills. Every I/O operation, network transfer, and storage request adds up. When workloads are steady and predictable, the cloud’s on-demand elasticity can actually become more expensive than on-prem capacity. DBAs, who often have a front-row seat to performance and utilization metrics, can play a crucial role in identifying when cloud costs are out of alignment with business value. ... In highly regulated industries, compliance concerns are another driver. Regulations such as HIPAA, PCI-DSS, GDPR and more, require your applications and the data they access to be secure and controlled. Organizations may find that managing sensitive data in the cloud introduces risk, especially when data residency, auditability, or encryption requirements evolve. Repatriating workloads can restore a sense of control and predictability—key traits valued by DBAs. ... Today’s computing needs demand an IT architecture that embraces the cloud, but also on premises workloads, including the mainframe. Remember, data gravity attracts applications to where the data resides.

SaaS price hikes put CIOs’ budgets in a bind

Subscription prices from major SaaS vendors have risen sharply in recent months, putting many CIOs in a bind as they struggle to stay within their IT budgets. ... While inflation may have driven some cost increases in past months, rates have since stabilized, meaning there are other factors at play, Tucciarone says. Vendors are justifying subscription price hikes with frequent product repackaging schemes, consumption-based subscription models, regional pricing adjustments, and evolving generative AI offerings, he adds. “Vendors are rationalizing this as the cost of innovation and gen AI development,” he says. ... SaaS data platforms fall into a similar category as other mission-critical applications, Aymé adds, because the cost of moving an organization’s data can be prohibitively expensive, in addition to the price of a new SaaS tool. Kunal Agarwal, CEO and cofounder of data observability platform Unravel Data, also pointed to price increases for data-related SaaS tools. Data infrastructure costs, including cloud data warehouses, lakehouses, and analytics platforms, have risen 30% to 50% in the past year, he says. Several factors are driving cost increases, including the proliferation of computing-intensive gen AI workloads and a lack of visibility into organizational consumption, he adds. “Unlike traditional SaaS, where you’re paying for seats, these platforms bill based on consumption, making costs highly variable and difficult to predict,” Agarwal says.

How to simplify enterprise cybersecurity through effective identity management

“It is challenging for a lot of organizations to get a complete picture of what their assets are and what controls apply to those assets,” Persaud says. He explains that Deloitte’s identity solution assisted the customer in connecting users with the assets they utilized. As they discovered these assets, they were able to fine-tune the security controls that were applied to each in a more refined fashion. “If the system is going to [process] financial data and other private information, we need to put the right controls in place on the identity side,” he says. “We’ve been able to bring those two pieces together by correlating discovery of assets with discovery of identity and lining that up with controls from the IT asset management system.” ... “If you think from a broader risk management perspective, this has been fundamental to our security model,” he says. The ability to simply track the locations of employees and assign risk accordingly is a significant advancement in risk monitoring for a company growing its international presence. The company looks out for instances of impossible travel, such as if an employee has entered the system in one location and then in another at a distant location that they could not have possibly reached during a specified period, an alert is raised. Security analysts also use the software to scan for risky sign-ins. If a user logs in from an IP that has been blacklisted, an alert is raised. They have increasingly relied on conditional access policies that rely on monitoring user behavior.

When an AI Agent Says ‘I Agree,’ Who’s Consenting?

The most autonomous agents can execute a chain of actions related to a transaction—such as comparing, booking, paying, forwarding the invoice. The broader the autonomy, the tighter the frame: precise contractual rules, allow-lists, budgets, a kill-switch, clear user notices, and, where required, electronic signatures. At this point the question stops being technical and becomes legal: under what framework does each agent-made click have effect, on whose authority, and with what safeguards? European law and national laws already offer solid anchors—agency and online contracting, signatures and secure payments, fair disclosure—now joined by the newer eIDAS 2 and the AI Act. ... Under European law, an AI agent has no will of its own. It is a means of expressing—or failing to express—someone’s will. Legally, someone always consents: the user (consumer) or a representative in the civil law sense. If an agent “accepts” an offer, we are back to agency: the act binds the principal only within the authority granted; beyond that, it is unenforceable. The agent is not a new subject of law. ... Who is on the hook if consent is tainted? First, the business that designs the onboarding. Europe’s Digital Services Act (DSA) bans deceptive interfaces (“dark patterns”) that materially impair a user’s ability to make a free, informed choice. A pushy interface can support a finding of civil fraud and a regulatory breach. Second, the principal is bound only within the mandate.

AI cybercrime agents will strike in 2026: Are defenders ready?

The prediction itself isn’t novel. What’s sobering is the math behind it—and the widening gap between how fast organisations can defend versus how quickly they’re being attacked. “The groups that convert intelligence into monetisation the fastest will set the tempo,” Rashish Pandey, VP of marketing & communications for APAC at Fortinet, told journalists at a media briefing earlier this week. “Throughput defines impact.” This isn’t about whether AI will be weaponised—that’s already happening. The urgent question is whether defenders can close what Fortinet calls the “tempo differential” before autonomous AI agents fundamentally alter the economics of cybercrime. ... The evolution extends beyond speed. Fortinet’s predictions highlight how attackers are weaponising generative AI for rapid data analysis—sifting through stolen information to identify the most valuable targets and optimal extortion strategies before defenders even detect the breach. This aligns with broader attack trends: ransomware operations increasingly blend system disruption with data theft and multi-stage extortion. Critical infrastructure sectors—healthcare, manufacturing, utilities—face heightened risk as operational technology systems become targets. ... “The ‘skills gap’ is less about scarcity and more about alignment—matching expertise to the reality of machine-speed, data-driven operations,” Pandey noted during the briefing.

Daily Tech Digest - November 29, 2025

Quote for the day:

"Whenever you see a successful person you only see the public glories, never the private sacrifices to reach them." -- Vaibhav Shah

6 coding myths that refuse to die

A typical day as a developer can feel like you’re juggling an array (no pun intended) of tasks. You’re reading vague requirements, asking questions, reviewing designs, planning architecture, investigating bugs, reading someone else's code, writing documentation, attending standups, and occasionally, you actually get to write code. Why? Because software development is about problem-solving, not just code-producing. Real-world problems are messy. Users don’t always know what they want. Clients change their minds. Systems behave in mysterious ways. Before you even think about writing code, you often need to untangle the people-side and the process-side. ... The truth is that coding rewards persistence, curiosity, and willingness to improve far more than raw talent. Most developers I’ve worked with weren’t prodigies. They were people who kept showing up, kept asking questions, and kept refining their skills. ... Every working developer, no matter how experienced, looks up syntax constantly. We search the docs, we skim examples, we peek at old code, we search for things we’ve forgotten. Nobody expects you to memorize every keyword, operator, or built-in function. What matters in programming is the ability to break down a problem, think through the logic, and design a solution. Syntax is simply the tool you use to express that solution. It’s the grammar, not the message. So don't make this programming mistake and myth waste your time.

The Cost of Doing Nothing: Why Unstructured Data Is Draining IT Budgets

Think of it this way: the fundamental problem contemporary enterprises have with unstructured data isn’t actually the volume they own but the lack of visibility into what exists, where it resides, who owns it, and whether it still holds value. In this context, the only alternative they have is to store everything indefinitely, including redundant, obsolete, or trivial data that serves no business purpose. The key question here, of course, is how to manage data through its lifecycle? Ideally, an effective and strategic data management process should begin by establishing a single, enterprise-wide view of unstructured data to uncover inefficiencies and risks. ... Lifecycle management plays a central role in this, with files that have not been accessed for an extended period of time can be moved to lower-cost storage, while data that has been inactive for many years can be archived or deleted altogether. Many organizations discover that more than 60% of their stored information falls into these categories, illustrating just how much wasted capacity can be reclaimed with a policy-driven approach. ... It’s an approach that also benefits from the integration of vendor-neutral data management platforms capable of integrating data across diverse storage environments and clouds, eliminating lock-in while maintaining scalability. The outcome is greater cost control, improved compliance posture, and stronger decision-making foundations across the enterprise.

Agentic AI is supercharging the deepfake crisis: How companies can take action

As agentic AI propels fraud to a whole new level, the best way to keep your company secure is by fighting fire with fire, or in this case, AI with AI. To do so, companies need to implement multi-layered AI defense strategies that make it exponentially harder for bad actors to succeed. Enterprises can’t rely on traditional verification methods that add more layers of friction or collect more personal data as that would deter customers. Instead, businesses need to rethink digital identity protection to reduce fraud and fraud-related losses, but to also preserve customer trust and digital engagement. To achieve this, organizations’ defense systems should contextualize individual actions, granularly isolate scopes of impact, and rely on ongoing reassessments of authorization. In other words, a highly secure system doesn’t just check a user’s identity once but continuously evaluates what the user is doing, where they are doing it, and why they are doing it. ... Using layered risk signals throughout the lifecycle of users—not just during onboarding— can provide companies with detailed information on potential risks, especially from internal sources like employees who can be fouled or whose access can be hijacked to compromise a company’s key assets. Companies can continuously check the reputation of users’ email addresses, phone numbers, and IP addresses to see if any of those channels have previously been used for fraudulent activity, identifying fraud rings that are deploying AI agents at scale.

Cyber resilience, AI & energy shape IT strategies for 2026

The historical approach - that of considering cyber resilience as a stand-alone issue, where one vendor can protect an entire company - will be put to bed. Organisations will move away from using point solutions and embrace the wider ecosystem of options as understanding grows that they can't go it alone. An interconnected framework can help prevent a ripple effect when an attack happens - users should be able to identify and halt an attack in progress. The rate and scale of attacks will continue and having a properly integrated framework is vital to mitigate risk and speed up recovery. ... As AI inference workloads are becoming part of the production workflow, organisations are going to have to ensure their infrastructure supports not just fast access but high availability, security and non-disruptive operations. Not doing this will be costly both from a results perspective and an operational perspective in terms of resource (GPUs) utilisation. ... By 2026, organisations will face a new problem: accounts and credentials that belong to people no longer with the company, but which still look and act like insiders. As HR and IT systems become more automated, old identities are easily missed. Accounts from former employees, departed contractors, and dormant service bots will linger in cloud environments and company software. Attackers will exploit these 'digital ghosts' because they appear legitimate, bypass automated offboarding, and blend in with normal system activity.

Enterprises are neglecting backup plans, and experts warn it could come back to haunt them

Crucially, only 45% consistently follow the ‘3-2-1’ backup rule - three copies of data, stored on two different media types, with one copy kept off-site. The same number are failing to keep tamper-proof copies by using immutability across all their organizational backup data to ensure resilience against cyber attacks. ... "Most organizations now recognize the need to identify phishing scams or social engineering tactics; however, we can’t lose sight of what to do when disaster does strike. While complete prevention is near impossible, assurance of rapid recovery is fully within organizational control," he said. "Our research shows that UK organizations still aren’t taking adequate precautions when it comes to data backups. By storing data on immutable platforms, they can ensure business-critical information remains beyond the reach of adversaries and that operations stay up and running, even when systems are compromised." ... Backup strategies are now front of mind for many IT professions, alternative research shows. A survey from Kaseya earlier this year found 30% are losing sleep over lackluster backup and recovery strategies, with some pushing for a stronger focus on this area. Complacency was also identified as a recurring problem for many enterprises, according to Kaseya. Nearly two-thirds (60%) of respondents said they believed they could fully recover from a data loss incident in the space of a day.

Ransomware Moves: Supply Chain Hits, Credential Harvesting

Attack volume remains high. The quantity of victims listed across ransomware groups' data leak sites increased by one-third from September to October, says a report from cybersecurity firm Cyble. Groups listing the most victims included high-fliers Qilin and Akira, newcomer Sinobi - which only appeared in July - and stalwarts INC Ransom and Play. ... After a run of attacks targeting zero-day flaws in managed file transfer software, the group used the same strategy against Oracle E-Business Suite versions 12.2.3 through 12.2.14 to steal data. Clop appears to have targeted two zero-day vulnerabilities, "both of which allow unauthenticated access to core EBS components," giving the group "a fast and reliable entry point, which explains the scale of the campaign," said cybersecurity firm SOCRadar. Oracle issued updates fixing both of those flaws. Data theft tied to that campaign appeared to begin by August, although it didn't come to light until Clop revealed it ... One of the big reasons for ransomware's success has been cryptocurrency, which makes it easier for groups to monetize and cash out their attacks. Another has been the rise of the ransomware-as-a-service business model. This allows for specialization: operators can develop malware and shake down victims, while affiliated business partners focus on hacking, rather than malware development, with both reaping the rewards. Every time a victim pays a ransom, the industry standard is for an affiliate to keep 70% to 80%.

Essential 2026 skills that DevOp leaders need to prioritize

It may sound radical, but you should prepare for a future where DevOps professionals will no longer need to learn programming languages. The DevOps role will shift up more than most people expect, enabling your team members to become supervisory architects rather than hands-on coders. ... DevOps professionals will no longer need to rely on programming languages. Instead, they will use natural language to supervise and orchestrate processes across requirements, planning, development, testing, and deployment. This leads to the elimination of hand-offs between teams and a significant blurring of traditional roles. ... However, for this shift-up to be truly successful and safe in practice, that foundational knowledge of software engineering principles remains vital. Without understanding the why behind what you are asking AI to do, your team cannot evaluate the quality of the output. This lack of evaluation can easily lead to significant risks, such as vulnerabilities that result in security breaches. In the age of AI, human judgment remains as important as ever, but only if it’s informed by a deep understanding of what the AI is being asked to produce. ... As a leader, your challenge is to guide your organization through this transformative period. The future of software development isn’t about AI replacing humans; it’s about AI empowering humans to perform at a higher, more strategic level.

Building the Future: AI’s Role in Enterprise Evolution

The biggest obstacle we see for AI adoption isn't the technology itself, but the lack of clarity on the purpose for using it. The most critical part of any AI initiative is to understand why you want to use AI and how it can enhance your organisation’s unique attributes. There is no one-size-fits-all approach, since what works for one organisation may not work for others. A healthcare business needs data privacy for patient records, while a small startup’s goal is agility to release new product and sign new deals. These use cases will require different infrastructure investments and most workloads are not suited to the public cloud. ... Consider AI with a broader view, beyond just the technology itself. Dell approaches AI with three distinct perspectives in mind: the business side, the technical side and the people side. GenAI will provide a 20-30 per cent increase in productivity, eliminating mundane tasks and freeing people to focus on higher value work. Your employees are now available to use that extra time to reimagine processes and outcomes, creating value and efficiencies for the company.
From a people standpoint, the demand for curious, smart, adaptable employees will skyrocket. ... Many of our customers are in the early stages of their AI journey, experimenting with basic applications. Small and basic can have a big impact, so keep pushing forward. It's worth starting with pilot projects as they give you room to test and experiment with an application.

We Need to Teach the ‘Inuit’ Mindset to Young Computing Engineers

Becoming accustomed to over-provisioned resources has brought further concerns. The decreasing cost of hardware encourages a certain complacency: if a code is inefficient in memory or CPU usage, one tends to trust that a more powerful machine or extra memory will solve the problem. ... This mindset contrasts with the traditional discipline of programming education, in which every instruction and every byte mattered, and optimization was an essential part of the computer science student’s training. The point here is that even while leveraging the benefits offered by AI in programming, an excessive dependence on AI-generated solutions and the over-provisioning of resources can undermine the proper development of computational, logical, and algorithmic thinking in future programmers or computing scientists. ... It is important to clarify that this is not about rejecting the use of AI and reverting to a former era of computing. Instead, we should integrate the best of both worlds. We must harness the tremendous potential of AI while instilling in students the ability to evaluate and improve solutions using their own sound judgement. As a direct consequence, a well-trained programmer will think twice before accepting an AI-generated solution if it uses resources disproportionately or does not guarantee adequate resilience when execution scenarios change drastically.

Your Platform is Not an Island: Embracing Evolution in Your Ecosystem

The challenges facing smaller organizations versus larger organizations are really quite different, and the very requirement for a platform is typically indicative of you having multiple teams, so you probably don't really need a platform in a startup, particularly if you've got one 10-star full-stack developer wearing all of those hats. ... On-premises dependencies for your app will increase the number of interfaces and contributes to what we lovingly call application sprawl, and overly distributed architectures. The more teams that you have, the more people that you're probably going to need to speak to, and unfortunately, that means an increased number of working practices, and probably it's going to be far harder to reach any kind of consensus. If you work in a large organization, I'm sure that will resonate with you. ... The more features that you try to predict ahead of time, the more you risk building something that your customers actually don't want. The more minimal your MVP, the more likely your customers will see it as a motel, not a hotel. ... Developers still needed infrastructure knowledge, when we'd kind of sold that vision that they wouldn't need any, they would need little baseline understanding of Kubernetes. Integration with other legacy services across the organization, because they weren't designed by us and didn't always have APIs, was a little bit clunky.