Daily Tech Digest by Kannan Subbiah: FinOps

Showing posts with label FinOps. Show all posts

Daily Tech Digest - April 16, 2026

Quote for the day:

“You may be disappointed if you fail, but you are doomed if you don’t try.” -- Beverly Sills

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

How technical debt turns your IT infrastructure into a game you can’t win

Technical debt is compared to a high-stakes game of Jenga where every shortcut or deferred refactoring pulls a vital block from an organization’s structural foundation. Initially, quick fixes seem harmless, driven by aggressive deadlines and resource constraints; however, they eventually create a "velocity trap" where development speed plummets because engineers spend more time navigating fragile code than building new features. Beyond slow shipping, this debt manifests as a silent budget killer through architectural mismatches—such as using stateless frameworks for real-time systems—resulting in exorbitant cloud costs and significant cybersecurity vulnerabilities, evidenced by massive data breaches at firms like . While agile startups leverage modern, scalable architectures to outpace incumbents, many established organizations suffer because their internal culture discourages developers from addressing these structural issues, viewing refactoring as a distraction from value creation. To break this cycle, businesses must move beyond pretending the trade-off doesn’t exist. Successful companies explicitly measure their "technical debt ratio," tracking the percentage of engineering time spent on maintenance versus innovation. By acknowledging that high-quality code is a strategic asset rather than an optional luxury, organizations can stop pulling the "safe blocks" of their infrastructure and instead build the resilient, high-velocity systems required to survive in an increasingly competitive global market.

The Compliance Blueprint: Handling Minors’ Data in the Post-DPDP Era

The blog post titled "The Compliance Blueprint: Handling Minors’ Data in the Post-DPDP Era" explores the stringent regulatory landscape established by India’s regarding users under eighteen. Under Section 9, organizations face significant mandates, including securing , prohibiting behavioral tracking, and banning targeted advertising to children. Failure to comply can result in catastrophic penalties of up to ₹200 Crore, making data protection a critical operational priority rather than a mere policy update. The author outlines various verification methods, such as utilizing government-backed tokens or linked family accounts, while highlighting the "implementation paradox" where verifying age often requires collecting even more sensitive data. Operationally, businesses must redesign user interfaces to "fork" into protective modes for minors, provide itemized notices in multiple languages, and maintain detailed audit logs. Despite the heavy compliance burden and challenges like the "death of personalization" for EdTech and gaming firms, the Act serves as a vital safeguard for India’s 450 million children. Ultimately, the article advises companies to adopt a "Safety First" mindset, viewing children’s data as a potential liability that necessitates a fundamental shift in product design and data governance to ensure long-term viability in the Indian digital ecosystem.

The need for a board-level definition of cyber resilience

The article emphasizes that the lack of a standardized definition for cyber resilience creates significant systemic risks for organizational boards and executive teams. Currently, conceptual fragmentation across various regulatory frameworks makes it difficult for leadership to determine what to oversee or how to measure success. To address this, the focus must shift from technical metrics and security controls toward broader business outcomes, such as maintaining operational continuity, preserving stakeholder confidence, and ensuring financial stability during disruptions. Cyber resilience is increasingly framed as a core leadership responsibility, with many jurisdictions now legally requiring boards to oversee these outcomes. However, a major point of contention remains regarding the scope of resilience—specifically whether it includes proactive preparedness or is limited strictly to response and recovery phases. Furthermore, resilience is no longer just about defending against cybercrime; it encompasses all forms of digital disruption, including unintentional outages. As global economies become more interdependent, an individual organization’s ability to recover quickly is essential not only for its own survival but also for overall economic stability. Ultimately, establishing a clear, board-level definition is a critical governance requirement that provides the foundation for navigating the complexities of modern digital economies and ensuring long-term institutional health.

2026 global semiconductor industry outlook: Delloite

forecasts a transformative year, with annual sales projected to reach a historic peak of $975 billion. Driven primarily by an intensifying artificial intelligence infrastructure boom, the sector expects a remarkable 26% growth rate following a robust 2025. This surge is reflected in the staggering $9.5 trillion market capitalization of the top ten global chip companies, though wealth remains highly concentrated among the top three leaders. While generate half of total revenue, they represent less than 0.2% of total unit volume, creating a stark structural divergence. Personal computing and smartphone markets may face declines as specialized AI demand causes consumer memory prices to spike. Technological advancements will likely focus on integrating high-bandwidth memory via 3D stacking and adopting co-packaged optics to reduce power consumption by up to 50%. However, the outlook warns of a "high-stakes paradox." While the immediate future appears solid due to backlogged orders, 2027 and 2028 may face significant headwinds from power grid constraints—requiring 92 gigawatts of additional energy—and potential return-on-investment concerns. Ultimately, long-term success hinges on balancing aggressive AI investments with proactive risk mitigation against infrastructure limits and geopolitical shifts, including India’s emergence as a vital back-end assembly hub.

New Executive Leadership Challenges Emerging—And What’s Driving Them

In the article "New Executive Leadership Challenges Emerging—And What's Driving Them," members of the Forbes Coaches Council highlight a significant shift in the corporate landscape driven by , AI integration, and rapid systemic change. Today’s executives face a "leadership vortex," where they must navigate role compression and overwhelming demands while maintaining strategic clarity. A primary challenge is rebuilding connection in hybrid environments, where communication gaps are more visible and psychological safety is harder to cultivate. Leaders are moving beyond traditional performance metrics to focus on their "being"—cultivating a leadership identity that prioritizes generative dialogue and mutual accountability over mere individual contribution. The rise of AI has introduced systemic ambiguity, requiring a pivot from "expert" to "explorer" to manage fears of obsolescence. Furthermore, the modern era demands a heightened appetite for change and a renewed focus on team cohesion, as previous playbooks rewarding certainty and control become less effective. Ultimately, successful leadership now hinges on expanding personal capacity and translating technical uncertainty into a shared, meaningful vision. This evolution reflects a broader trend where emotional intelligence and adaptive identity are as critical as technical expertise in steering organizations through unprecedented volatility and complexity.

New US Air Force Office Will Focus on OT Cybersecurity

The U.S. Air Force has pioneered a critical shift in military defense by establishing the Cyber Resiliency Office for Control Systems (CROCS), the first dedicated office within the American military services focused specifically on . Launched to address vulnerabilities in essential infrastructure like power grids, water supplies, and HVAC systems, CROCS serves as a central "front door" for managing the security of non-traditional IT assets that are vital for mission readiness. While the office reached initial operating capability in 2024, its creation followed years of bureaucratic effort to recognize OT systems as primary targets for foreign adversaries seeking asymmetric advantages. A significant milestone for the office was successfully integrating OT security costs into the Department of Defense’s long-term budgeting process, ensuring that assessments, training, and mitigations are formally funded rather than treated as secondary mandates. Directed by Daryl Haegley, CROCS does not execute all security tasks directly but instead coordinates contracts, personnel, and prioritized strategies to bridge reporting gaps between engineering teams and the CIO. By modeling itself after the Air Force’s existing weapon systems resiliency office, CROCS aims to build a robust defense pipeline, ultimately securing the foundational utilities that allow the military to function globally.

Rethinking Business Processes for the Age of AI

The article "Rethinking Business Processes for the Age of AI" by Vasily Yamaletdinov explores the fundamental evolution of business architecture as organizations transition from human-centric automation to . Traditionally, business processes have relied on BPMN 2.0, a notation designed for deterministic, repeatable, and rigid sequences. However, these classical methods struggle with the non-deterministic nature of AI, which requires dynamic planning and context-driven decision-making. The author argues that modern AI-native processes must shift from "rigid conveyor belts" to flexible systems that prioritize goals, guardrails, and autonomy over strict algorithmic steps. To address the limitations of traditional BPMN—such as poor exception handling and an inability to model uncertainty—the article advocates for Goal-Oriented BPMN (GO-BPMN). This approach decomposes processes into a tree of objectives and modular plans, allowing AI agents to dynamically select the best path based on real-time context. By integrating a "Human-in-the-loop" framework and supporting the "Reason-Act-Observe" cycle, GO-BPMN enables a hybrid environment where deterministic operations and intelligent agents coexist. Ultimately, while traditional modeling remains valuable for highly regulated tasks, GO-BPMN provides the necessary framework for building resilient, adaptive, and truly intelligent enterprise operations in the burgeoning age of AI.

Runtime FinOps: Making Cloud Cost Observable

The article "Runtime FinOps: Making Cloud Cost Observable" argues for transforming cloud spend from a delayed financial report into a real-time system metric. Author David Iyanu Jonathan identifies a "structural information deficit" in modern engineering, where the lag between code deployment and billing visibility prevents timely remediation of expensive inefficiencies. Runtime FinOps addresses this by integrating cost data directly into observability tools like Grafana, enabling "dollars-per-minute" tracking alongside traditional metrics like latency and CPU usage. While static infrastructure estimation tools like Infracost provide initial value, they often fail to capture variable operational costs such as data transfer and API calls that scale with traffic patterns. To bridge this gap, the piece advocates for adopting SRE-inspired practices, including cost-based error budgets, robust tagging governance, and routing anomaly alerts directly to on-call engineering teams rather than isolated finance departments. This shift fosters a culture of accountability where costs are treated as visceral signals during blameless postmortems and architectural reviews. Ultimately, the article concludes that the primary barriers to effective FinOps are cultural rather than technical; success requires clear service-level ownership and a fundamental commitment to treating cloud expenditure as a critical performance indicator that is functionally inseparable from the code itself.

Shadow AI and the new visibility gap in software development

The rise of "shadow AI" in software development has introduced a significant visibility gap, posing new challenges for organizations and managed service providers. As developers increasingly turn to unapproved AI tools and agents to boost productivity, they inadvertently create a "lethal trifecta" of risks involving sensitive private data, external communications, and vulnerability to malicious prompt injections. This unauthorized usage bypasses traditional security monitoring like SaaS discovery platforms because AI agents often operate within local engineering environments or through personal API keys. To address this, the article suggests shifting from futile attempts to block AI toward a governance-first infrastructure. By routing AI access through centrally managed platforms and implementing process-level controls at runtime, organizations can secure data flows and restrict agents to approved services without stifling innovation. This approach allows developers to maintain their preferred workflows while providing the oversight necessary to prevent code leaks and compliance breaches. Ultimately, closing the visibility gap requires building governance around fundamental development processes rather than individual tools, enabling partners to guide businesses through a secure evolution of AI integration that scales from initial modernization to advanced agentic automation.

Audit: Big Tech Often Ignores CA Privacy Law Opt-Out Requests

A recent independent audit conducted by privacy organization WebXray reveals that major technology companies, specifically Google, Meta, and Microsoft, frequently fail to honor legally mandated data collection opt-out requests in California. Despite the California Consumer Privacy Act (CCPA) requiring businesses to respect the Global Privacy Control (GPC) signal—a browser-based mechanism allowing users to decline personal data sharing—the audit found widespread non-compliance. Google emerged as the worst offender with an 86% failure rate, followed by Meta at 69% and Microsoft at 50%. Researchers observed that Google’s servers often respond to opt-out signals by explicitly commanding the creation of advertising cookies, such as the “IDE” cookie, effectively ignoring the user's preference in "plain sight." In response, Meta dismissed the findings as a “marketing ploy,” while Microsoft claimed that some cookies remain necessary for operational functions rather than unauthorized tracking. This systemic disregard for privacy signals underscores the ongoing tension between Big Tech and state regulations. To address these gaps, the report recommends that security professionals treat privacy telemetry with the same rigor as security data, conducting frequent audits of third-party data flows and aligning runtime behavior with privacy controls to ensure legitimate regulatory compliance.

Daily Tech Digest - April 13, 2026

Quote for the day:

“Winners are not afraid of losing. But losers are. Failure is part of the process of success. People who avoid failure also avoid success.” -- Robert T. Kiyosaki

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The Vibe Coding Trap Ambitious Founders Fall Into (And What To Do About It)

In her Forbes article, Jodie Cook examines the "," a modern hazard for ambitious founders who leverage AI to build software at speeds that outpace their engineering teams. This newfound superpower allows non-technical leaders to generate products through natural language, yet it frequently results in a dangerous illusion of progress. The trap occurs when founders become so enamored with rapid execution that they neglect vital strategic priorities, such as sales and market positioning, while inadvertently creating and organizational friction. By diving into production themselves, founders risk undermining their specialists’ expertise and eroding trust within technical departments. To navigate this challenge, Cook advises founders to treat vibe coding as a tool for high-level communication and rapid prototyping rather than a replacement for professional development. Instead of getting bogged down in the minutiae of output, leaders must transition into "decision architects," focusing on judgment, vision, and accountability. By establishing disciplined boundaries between initial exploration and final execution, founders can harness AI's efficiency without compromising product scalability or team morale. Ultimately, the solution lies in slowing down to think clearly, ensuring that technical acceleration aligns with the company's long-term strategic objectives and cultural health.

Your developers are already running AI locally: Why on-device inference is the CISO’s new blind spot

In "Your developers are already running AI locally," VentureBeat explores the emergence of "," a trend where developers bypass cloud-based AI in favor of local, on-device inference. Driven by powerful consumer hardware and sophisticated quantization techniques, this "Bring Your Own Model" (BYOM) movement allows engineers to run complex directly on laptops. While this offers privacy and speed, it creates a significant "blind spot" for Chief Information Security Officers (CISOs). Traditional Data Loss Prevention (DLP) tools, which typically monitor cloud-bound traffic, are unable to detect these offline interactions. This shift relocates the primary enterprise risk from data exfiltration to issues of integrity, provenance, and compliance. Specifically, unvetted models can introduce security vulnerabilities through "contaminated" code or malicious payloads hidden within older model file formats like Pickle-based PyTorch files. To mitigate these risks, the article suggests that organizations must treat model weights as critical software artifacts rather than mere data. This involves establishing governed internal model hubs, implementing robust endpoint monitoring, and ensuring that corporate security frameworks adapt to a landscape where the perimeter has effectively shifted back to the device, requiring a comprehensive Software Bill of Materials (SBOM) to manage all local AI models effectively.

FinOps for Engineers: Turning Cloud Bills Into Runtime Signals

The article explores the critical integration of financial management into engineering workflows, treating cloud costs not as a back-office accounting task but as a real-time telemetry signal comparable to latency or uptime. Traditionally, a broken feedback loop exists where engineers prioritize performance while finance monitors quarterly bills, often leading to expensive surprises like scaling anomalies caused by inefficient code. By adopting FinOps, developers embrace "cost as a runtime signal," enabling them to observe the immediate financial impact of their architectural decisions. This approach centers on unit economics—such as the marginal cost per API call or database query—transforming abstract billing data into visceral, actionable insights. The author emphasizes that cloud infrastructure often obscures its own economics, making it easy to overspend without immediate awareness. Ultimately, shifting cost-consciousness "left" into the development lifecycle allows teams to build more efficient systems, ensuring that auto-scaling and resource allocation are driven by value rather than waste. This cultural transformation empowers engineers to treat financial efficiency as a core engineering discipline, bridging the gap between technical execution and business value to optimize the overall health and sustainability of cloud-native environments.

The Tool That Predates Every Privacy Law — and May Just Outlive Them All

Devika Subbaiah’s article explores the enduring legacy of the , a foundational technology created by in 1994 to solve the web’s "state" problem. Initially designed to help websites remember users, cookies have evolved from a simple functional tool into a controversial mechanism for mass surveillance and targeted advertising. This shift triggered a global wave of regulation, resulting in the pervasive cookie banners mandated by the and CCPA. However, as the digital landscape shifts toward a privacy-first era, major players like Google are phasing out third-party cookies in favor of new tracking frameworks like the Privacy Sandbox. Despite these systemic changes and the legal scrutiny surrounding data harvesting, the article argues that the cookie’s fundamental utility ensures its survival. While third-party tracking faces an uncertain future, first-party cookies remain the essential backbone of the modern internet, enabling everything from persistent logins to shopping carts. Ultimately, the cookie predates our current legal frameworks and will likely outlive them because the internet as we know it cannot function without the basic ability to remember user interactions across sessions. It remains a resilient piece of digital infrastructure that continues to define our online experience even as privacy norms undergo radical transformation.

The AI information gap and the CIO’s mandate for transparency

In the 2026 B2B landscape, the initial excitement surrounding artificial intelligence has shifted toward a healthy skepticism, creating a significant "information gap" that vendors must bridge to maintain client trust. According to Bryan Wise, modern CIOs are now tasked with a critical mandate for transparency, as buyers increasingly prioritize data integrity and governance over mere performance hype. Recent industry reports indicate that over half of B2B buyers engage sales teams earlier than in previous years due to implementation uncertainties, frequently raising sharp questions about training datasets, privacy protocols, and security guardrails. To overcome these trust-based obstacles, CIOs must serve as the central hub for cross-functional transparency initiatives. This proactive strategy involves creating comprehensive "AI dossiers" that document model functionality and training sources, while simultaneously arming sales and support teams with detailed technical documentation. By aligning marketing messaging with legal compliance and providing tangible evidence of ethical AI usage, organizations can transform transparency into a distinct competitive advantage. Ultimately, the modern CIO's role has expanded beyond technical oversight to include being the custodian of organizational truth, ensuring that AI narratives across all customer-facing channels remain consistent, verifiable, and grounded in accountability to prevent complex deals from stalling during the due diligence phase.

Why Codefinger represents a new stage in the evolution of ransomware

The Codefinger ransomware attack marks a significant evolution in cyber threats by shifting the focus from malicious code to credential exploitation. Discovered in early 2025, this breach specifically targeted Amazon S3 storage keys that were poorly managed by developers and stored in insecure locations. Unlike traditional ransomware that relies on planting malware to encrypt files, Codefinger hijackers simply utilized stolen access credentials to encrypt cloud-based data. This transition highlights critical vulnerabilities in the cloud’s , where users are responsible for securing their own access keys rather than the provider. Furthermore, the attack exposes the limitations of conventional backup strategies; if encrypted data is automatically backed up, the recovery points become useless. To combat such sophisticated threats, organizations must move beyond basic defenses and implement robust secrets management, including systematic identification, periodic cycling, and granular access controls. Codefinger serves as a stark reminder that as ransomware tactics evolve, businesses must proactively map their attack vectors and prioritize secure configuration of cloud resources. Relying solely on off-site backups is no longer sufficient in an era where attackers directly manipulate administrative permissions to hold vital corporate data hostage.

Software Engineering 3.0: The Age of the Intent-Driven Developer

Software Engineering 3.0 marks a paradigm shift where the fundamental unit of programming transitions from technical syntax to human intent. While the first era focused on craftsmanship and manual machine translation, and the second on abstraction through frameworks, the third era utilizes artificial intelligence to absorb the heavy lifting of code generation. In this new landscape, developers act less like manual laborers and more like architects or curators who orchestrate complex systems. The article emphasizes that intent-driven development requires a unique set of skills: the ability to write precise specifications, critically evaluate AI-generated outputs for subtle errors, and use testing as a primary method for documenting intent. Rather than replacing the engineer, these tools elevate the profession, allowing practitioners to solve higher-level problems while automating boilerplate tasks. Success in SE 3.0 depends on clear thinking and rigorous judgment rather than just typing speed or syntax memorization. Ultimately, this "antigravity" moment in software development narrows the gap between imagination and implementation, transforming the developer into a high-level conductor who manages probabilistic components and complex orchestration to create resilient systems. This evolution reflects a broader historical trend where each layer of abstraction empowers engineers to build more ambitious technology.

Can We Trust AI? No – But Eventually We Must

Artificial intelligence, specifically Large Language Models, currently operates on a foundation of mathematical probability rather than objective truth, making it fundamentally untrustworthy in its present state. As explored in Kevin Townsend’s analysis, AI is plagued by persistent issues including , inherent biases, and a tendency toward sycophancy, where models mirror user expectations rather than providing factual accuracy. Furthermore, the phenomenon of model collapse suggests an inevitable systemic decay—akin to the second law of thermodynamics—whereby AI-generated data pollutes future training sets, compounding errors over generations. Despite these significant risks and the lack of a verifiable ground truth, the rapid pace of modern business and the demand for immediate return on investment are driving enterprises to deploy these technologies prematurely. We find ourselves in a paradoxical situation where, although we cannot safely trust AI today, the competitive necessity and overwhelming promise of the technology mean that society must eventually find a way to do so. Achieving this transition requires a deep understanding of AI’s limitations, a focus on securing systems against adversarial abuse, and a shift from viewing AI as a fact-based database to recognizing its probabilistic, token-based nature. Ultimately, while current systems are built on sand, the trajectory of innovation makes reliance inevitable.

The business mobility trends driving workforce performance in 2026

The article outlines the pivotal business mobility trends set to redefine workforce performance and productivity by 2026, emphasizing the shift toward integrated, secure, and efficient digital ecosystems. A primary driver is zero-touch device enrollment, which streamlines the large-scale deployment of pre-configured hardware, effectively eliminating traditional IT bottlenecks. Complementing this is the transition to Zero Trust security architectures, which replace implicit trust with continuous verification to protect distributed workforces from escalating cyber threats. Furthermore, the integration of unified cloud and connectivity services through single-vendor partnerships is highlighted as a critical method for reducing operational complexity and enhancing business resilience. This holistic approach extends to comprehensive end-to-end device lifecycle management, which leverages standardisation and refurbishment to achieve long-term cost-efficiency and support environmental sustainability goals. Ultimately, the article argues that navigating the complexities of hybrid work and rapid innovation requires a coherent mobility strategy managed by a single experienced partner. By consolidating these technological pillars, ranging from initial provisioning to secure retirement, organizations can ensure consistent security postures and allow internal teams to focus on high-value initiatives rather than day-to-day operational tasks. This strategic alignment is essential for maintaining a competitive edge in an increasingly mobile-first global landscape.

Fixing vulnerability data quality requires fixing the architecture first

Art Manion, Deputy Director at Tharros, argues that resolving the persistent issues within vulnerability data quality necessitates a fundamental overhaul of underlying architectures rather than just refining the data itself. In this interview, Manion explains that current repositories often suffer from inconsistency and a lack of trust because they were not designed with effective collection and management in mind. A central concept discussed is Minimum Viable Vulnerability Enumeration (MVVE), which represents the necessary assertions to deduplicate vulnerabilities across different systems. Interestingly, research suggests that no static "minimum" exists; instead, assertions must remain variable and evolve alongside our understanding of threats. Manion proposes that vulnerability records should be viewed as collections of independently verifiable, machine-usable assertions that prioritize provenance and transparency. He further critiques the security community's over-reliance on metrics like CVSS scores, which often distort perceptions and distract from the critical task of assessing actual risk within a specific context. Ultimately, the proposal suggests that before the industry develops new tools or specifications, it must establish a solid foundation of shared terms and principles. By addressing architectural flaws and accepting that information will naturally be incomplete, organizations can build more resilient, trustworthy systems for managing global vulnerability information.

Daily Tech Digest - March 03, 2026

Quote for the day:

“Appreciate the people who give you expensive things like time, loyalty and honesty.” -- Vala Afshar

Making sense of 6G: what will the ‘agentic telco’ look like?

6G will be the fundamental network for physical AI, promises Nvidia. Think of self-driving cars, robots in warehouses, or even AI-driven surgery. It’s all very futuristic; to actually deliver on these promises, a wide range of industry players will be needed, each developing the functionality of 6G. ... The ultimate goal for network operators is full automation, or “Level 5” automation. However, this seems too ambitious for now in the pre-6G era. Google refers to the twilight zone between Levels 4 and 5, with 4 assuming fully autonomous operation in certain circumstances. Currently, the obvious example of this type of automation is a partially self-driving car. As a user, you must always be ready to intervene, but ideally, the vehicle will travel without corrections. A Waymo car, which regularly drives around without a driver, is officially Level 4. ... Strikingly, most users hardly need this ongoing telco innovation. Only exceptionally extensive use of 4K streams, multiple simultaneous downloads, and/or location tracking can exceed the maximum bandwidth of most forms of 5G. Switch to 4G and in most use cases of mobile network traffic, you won’t notice the difference. You will notice a malfunction, regardless of the generation of network technology. However, the idea behind the latest 5G and future 6G networks is that these interruptions will decrease. Predictions for 6G assume a hundredfold increase in speed compared to 5G, with a similar improvement in bandwidth.

FinOps for agents: Loop limits, tool-call caps and the new unit economics of agentic SaaS

FinOps practitioners are increasingly treating AI as its own cost domain. The FinOps Foundation highlights token-based pricing, cost-per-token and cost-per-API-call tracking and anomaly detection as core practices for managing AI spend. Seat count still matters, yet I have watched two customers with the same licenses generate a 10X difference in inference and tool costs because one had standardized workflows and the other lived in exceptions. If you ship agents without a cost model, your cloud invoice quickly becomes the lesson plan ... In early pilots, teams obsess over token counts. However, for a scaled agentic SaaS running in production, we need one number that maps directly to value: Cost-per-Accepted-Outcome (CAPO). CAPO is the fully loaded cost to deliver one accepted outcome for a specific workflow. ... We calculate CAPO per workflow and per segment, then watch the distribution, not just the average. Median tells us where the product feels efficient. P95 and P99 tell us where loops, retries and tool storms are hiding. Note, failed runs belong in CAPO automatically since we treat the numerator as total fully loaded spend for that workflow (accepted + failed + abandoned + retried) and the denominator as accepted outcomes only, so every failure is “paid for” by the successes. Tagging each run with an outcome state and attributing its cost to a failure bucket allows us to track Failure Cost Share alongside CAPO and see whether the problem is acceptance rate, expensive failures or retry storms.

AI went from assistant to autonomous actor and security never caught up

The first is the agent challenge. AI systems have moved past assistants that respond to queries and into autonomous agents that execute multi-step tasks, call external tools, and make decisions without per-action human approval. This creates failure conditions that exist without any external attacker. An agent with overprivileged access and poor containment boundaries can cause damage through ordinary operation. ... The second category is the visibility challenge. Sixty-three percent of employees who used AI tools in 2025 pasted sensitive company data, including source code and customer records, into personal chatbot accounts. The average enterprise has an estimated 1,200 unofficial AI applications in use, with 86% of organizations reporting no visibility into their AI data flows. ... The third is the trust challenge. Prompt injection moved from academic research into recurring production incidents in 2025. OWASP’s 2025 LLM Top 10 list ranked prompt injection at the top. The vulnerability exists because LLMs cannot reliably separate instructions from data input. ... Wang recommended tiering agents by risk level. Agents with access to sensitive data or production systems warrant continuous adversarial testing and stronger review gates. Lower-risk agents can rely on standardized controls and periodic sampling. “The goal is to make continuous validation part of the engineering lifecycle,” she said.

A scorecard for cyber and risk culture

Cybersecurity and risk culture isn’t a vibe. It’s a set of actions, behaviors and attitudes you can point to without raising your voice. ... You can’t train people into that. You have to build an environment where that behavior makes sense, an environment based on trust and performance not one or the other ... Ownership is a design outcome. Treat it like product design. Remove friction. Clarify choices. Make it hard to do the wrong thing by accident and easy to make the best possible decision. ... If you can’t measure the behavior, you can’t claim the culture. You can claim a feeling. Feelings don’t survive audits, incidents or Board scrutiny. We’ve seen teams measure what’s easy and then call the numbers “maturity.” Training completion. Controls “done.” Zero incidents. Nice charts. Clean dashboards. Meanwhile, the real culture runs beneath the surface, making exceptions, working around friction and staying quiet when speaking up feels risky. ... One of the most dangerous culture metrics is silence dressed up as success. “Zero incidents reported” can mean you’re safe. It can also mean people don’t trust the system enough to speak up. The difference matters. The wrong interpretation is how organizations walk into breaches with a smile. Measure culture as you would safety in a factory. ... Metrics without governance create cynical employees. They see numbers. They never see action. Then they stop caring. Be careful not to make compliance ‘the culture’ as it’s what people do when no one is looking that counts.

Why encrypted backups may fail in an AI-driven ransomware era

For 20 years, I've talked up the benefits of the tech industry's best-practice 3-2-1 backup strategy. This strategy is just how it's done, and it works. Or does it? What if I told you that everything you know and everything you do to ensure quality backups is no longer viable? In fact, what if I told you that in an era of generative AI, when it comes to backups, we're all pretty much screwed? ... The easy-peasy assumption is that your data is good before it's backed up. Therefore, if something happens and you need to restore, the data you're bringing back from the backup is also good. Even without malware, AI, and bad actors, that's not always the way things turn out. Backups can get corrupted, and they might not have been written right in the first place, yada, yada, yada. But for this article, let's assume that your backup and restore process is solid, reliable, and functional. ... Even if the thieves are willing to return the data, their AI-generated vibe-coded software might be so crappy that they're unable to keep up their end of the bargain. Do you seriously think that threat actors who use vibe coding test their threat engines? ... Some truly nasty attacks specifically target immutable storage by seeking out misconfigurations. Here, they attack the management infrastructure, screwing with network data before it ever reaches the backup system. The net result is that before encryption of off-site backups begins, and before the backups even take place, the malware has suitably corrupted and infected the data.

How Deepfakes and Injection Attacks Are Breaking Identity Verification

Unlike social media deception, these attacks can enable persistent access inside trusted environments. The downstream impact is durable: account persistence, privilege-escalation pathways, and lateral movement opportunities that start with a single false verification decision. ... One practical problem for deepfake defense is generalization: detectors that test well in controlled settings often degrade in “in-the-wild” conditions. Researchers at Purdue University evaluated deepfake detection systems using their real-world benchmark based on the Political Deepfakes Incident Database (PDID). PDID contains real incident media distributed on platforms such as X, YouTube, TikTok, and Instagram, meaning the inputs are compressed, re-encoded, and post-processed in the same ways defenders often see in production. ... It’s important to be precise: PDID measures robustness of media detection on real incident content. It does not model injection, device compromise, or full-session attacks. In real identity workflows, attackers do not choose one technique at a time; they stack them. A high-quality deepfake can be replayed. A replay can be injected. An injected stream can be automated at scale. The best media detectors still can be bypassed if the capture path is untrusted. That’s why Deepsight goes even deeper than asking “Is this video a deepfake?”

Virtual twins and AI companions target enterprise war rooms

Organisations invest millions digitising processes and implementing enterprise systems. Yet when business leaders ask questions spanning multiple domains, those systems don’t communicate effectively. Teams assemble to manually cross-reference data, spending days producing approximations rather than definitive answers. Manufacturing experts at the conference framed this as decades of incomplete digitisation. ... Addressing this requires fundamentally changing how enterprise data is structured and accessed. Rather than systems operating independently with occasional data exchanges, the approach involves projecting information from multiple sources onto unified representations that preserve relationships and context. Zimmerman used a map analogy to explain the concept. “If you take an Excel spreadsheet with location of restaurants and another Excel spreadsheet with location of flower shops, and you try to find a restaurant nearby a flower shop, that’s difficult,” he said. “If it’s on the map, it is simple because the data are correlated by nature.” ... Having unified data representations solves part of the problem. Accessing them requires interfaces that don’t force users to understand complex data structures or navigate multiple applications. The conversational AI approach – increasingly common across enterprise software – aims to let users ask questions naturally rather than construct database queries or click through application menus.

The rise of the outcome-orchestrating CIO

Delivering technology isn’t enough. Boards and business leaders want results — revenue, measurable efficiency, competitive advantage — and they’re increasingly impatient with IT organizations that can’t connect their work to those outcomes. ... Funding models change, too. Traditional IT budgets fund teams to deliver features. When the business pivots, that becomes a change request — creating friction even when it’s not an adversarial situation. “Instead, fund a value stream,” Sample says. “Then, whatever the business needs, you absorb the change and work toward shared goals. It doesn’t matter what’s on the bill because you’re all working toward the same outcome.” It’s a fundamental reframing of IT’s role. “Stop talking about shared services,” says Ijam of the Federal Reserve. “Talk about being a co-owner of value realization.” That means evolving from service provider to strategic partner — not waiting for requirements but actively shaping how technology creates business results. ... When outcome orchestration is working, the boardroom conversation changes. “CIOs are presenting business results enabled by technology — not just technology updates — and discussing where to invest next for maximum impact,” says Cox Automotive’s Johnson. “The CFO begins to see technology as an investment that generates returns, not just a cost to be managed.” ... When outcome orchestration takes hold, the impact shows up across multiple dimensions — not just in business metrics, but in how IT is perceived and how its people experience their work.

The future of banking: When AI becomes the interface

Experiences must now adapt to people—not the other way around. As generative capabilities mature, customers will increasingly expect banking interactions to be intuitive, conversational, and personalized by default, setting a much higher bar for digital experience design. ... Leadership teams must now ask harder questions. What proprietary data, intelligence, or trust signals can only our bank provide? How do we shape AI-driven payment decisions rather than merely fulfill them? And how do we ensure that when an AI decides how money moves, our institution is not just compliant, but preferred? ... AI disruption presents both significant risk and transformative opportunity for banks. To remain relevant, institutions must decide where AI should directly handle customer interactions, how seamlessly their services integrate into AI-driven ecosystems, and how their products and content are surfaced and selected by AI-led discovery and search. This requires reimagining the bank’s digital assistant across seven critical dimensions: being front and centre at the point of intent, contextual in understanding customer needs, multi-modal across voice, text, and interfaces, agentic in taking action on the customer’s behalf, revenue-generating through intelligent recommendations, open and connected to broader ecosystems, and capable of providing targeted, proactive support.

The End of the ‘Observability Tax’: Why Enterprises are Pivoting to OpenTelemetry

For enterprises to reclaim their budget, they must first address inefficiency—the “hidden tax” of observability facing many DevOps teams. Every organization is essentially rebuilding the same pipeline from scratch, and when configurations aren’t standardized, engineers aren’t learning from each other; they’re actually repeating the same trial-and-error processes thousands of times over. This duplicated effort leads to a waste of time and resources. It often takes weeks to manually configure collectors, processors, and exporters, plus countless hours of debugging connection issues. ... If data engineers are stuck in a cycle of trial-and-error to manage their massive telemetry, then organizations are stuck drinking from a firehose instead of proactively managing their data in a targeted manner. In a world where AI demands immediate access to enormous volumes of data, this lack of flexibility becomes a fatal competitive disadvantage. If enterprises want to succeed in an AI-driven world, their data infrastructure must be able to handle the rapid velocity of data in motion without sacrificing cost-efficiency. Identifying and mitigating these hidden challenges and costs is imperative if enterprises want to turn their data into an asset rather than a liability. ... When organizations reclaim complete control of their data pipelines, they can gain a competitive edge.

Daily Tech Digest - December 07, 2025

Quote for the day:

"Definiteness of purpose is the starting point of all achievement." -- W. Clement Stone

Balancing AI innovation and cost: The new FinOps mandate

Yet as AI moves from pilot to production, an uncomfortable truth is emerging: AI is expensive. Not because of reckless spending, but because the economics of AI are unlike anything technology leaders have managed before. Most CIOs and CTOs underestimate the financial complexity of scaling AI. Models that double in size can consume ten times the compute. Exponential should be your watchword. Inference workloads run continuously, consuming GPU cycles long after training ends, which creates a higher ongoing cost compared to traditional IT projects. ... The irony is that even as AI drives operational efficiency, its own operating costs are becoming one of the biggest drags on IT budgets. IDC’s research shows that, without tighter alignment between line of business, finance, and platform engineering, enterprises risk turning AI from an innovation catalyst into a financial liability. ... AI workloads cut across infrastructure, application development, data governance, and business operations. Many AI workloads will run in a hybrid environment, meaning cost impacts for on-premises as well as cloud and SaaS are expected. Managing this multicloud and hybrid landscape demands a unified operating model that connects technical telemetry with financial insight. The new FinOps leader will need fluency in both IT engineering and economics — a rare but rapidly growing skill set that will define next-generation IT leadership.

Local clouds shape Europe’s AI future

The new “sovereign” offerings from US-based cloud providers like Microsoft, AWS, and Google represent a significant step forward. They are building cloud regions within the EU, promising that customer data will remain local, be overseen by European citizens, and comply with EU laws. They’ve hired local staff, established European governance, and crafted agreements to meet strict EU regulations. The goal is to reassure customers and satisfy regulators. For European organizations facing tough questions, these steps often feel inadequate. Regardless of how localized the infrastructure is, most global cloud giants still have their headquarters in the United States, subject to US law and potential political pressure. There is always a lingering, albeit theoretical, risk that the US government might assert legal or administrative rights over data stored in Europe. ... As more European organizations pursue digital transformation and AI-driven growth, the evidence is mounting: The new sovereign cloud solutions launched by the global tech giants aren’t winning over the market’s most sensitive or risk-averse customers. Those who require freedom from foreign jurisdiction and total assurance that their data is shielded from all external interference are voting with their budgets for the homegrown players. ... In the months and years ahead, I predict that Europe’s own clouds—backed by strong local partnerships and deep familiarity with regulatory nuance—will serve as the true engine for the region’s AI ambitions.

When Innovation and Risks Collide: Hexnode and Asia’s Cybersecurity Paradox

“If you look at the way most cyberattacks happen today—take ransomware, for example—they often begin with one compromised account. From there, attackers try to move laterally across the network, hunting for high-value data or systems. By segmenting the network and requiring re-authentication at each step, ZT essentially blocks that free movement. It’s a “verify first, then grant access” philosophy, and it dramatically reduces the attacker’s options,” Pavithran explained. Unfortunately, way too many organisations still view Zero Trust as a tool rather than a strategic framework. Others believe it requires ripping out existing infrastructure. In reality, however, Zero Trust can be implemented incrementally and is both adaptable and scalable. It integrates technologies such as multifactor authentication, microsegmentation, and identity and access management into a cohesive architecture. Crucially, Zero Trust is not a one-off project. It is a continuous process of monitoring, verification, and fine-tuning. As threats evolve, so too must policies and controls. “Zero Trust isn’t a box you check and move on from,” Pavithran emphasised. “It’s a continuous, evolving process. Threats evolve, technologies evolve, and so do business needs. That means policies and controls need to be constantly reviewed and fine-tuned. It’s about continuous monitoring and ongoing vigilance—making sure that every access request, every single time, is both appropriate and secure.”

CIOs take note: talent will walk without real training and leadership

“Attracting and retaining talent is a problem, so things are outsourced,” says the CIO of a small healthcare company with an IT team of three. “You offload the responsibility and free up internal resources at the risk of losing know-how in the company. But at the moment, we have no other choice. We can’t offer the salaries of a large private group, and IT talent changes jobs every two years, so keeping people motivated is difficult. We hire a candidate, go through the training, and see them grow only to see them leave. But our sector is highly specialized and the necessary skills are rare.” ... CIOs also recognize the importance of following people closely, empowering them, and giving them a precise and relevant role that enhances motivation. It’s also essential to collaborate with the HR function to develop tools for welfare and well-being. According to the Gi Group study, the factors that IT candidates in Italy consider a priority when choosing an employer are, in descending order, salary, a hybrid job offer, work-life balance, the possibility of covering roles that don’t involve high stress levels, and opportunities for career advancement and professional growth. But there’s another aspect that helps solve the age-old issue of talent management. CIOs need to recognize more of the role of their leadership. At the moment, Italian IT directors place it at the bottom of their key qualities.

Rethinking the CIO-CISO Dynamic in the Age of AI

Today's CIOs are perpetual jugglers, balancing budgets and helping spur technology innovation at speed while making sure IT goals are aligned with business priorities, especially when it comes to navigating mandates from boards and senior leaders to streamline and drive efficiency through the latest AI solutions. ... "The most common concern with having the CISO report into legal is that legal is not technically inclined," she said. "This is actually a positive as cybersecurity has become more of a business-enabling function over a technological one. It also requires the CISO to translate tech-speak into language that is understandable by non-tech leaders in the organization and incorporate business and strategic drivers." As organizations undergo digital transformation and incorporate AI into their tech stacks, more are creating alternate C-suite roles such as "Chief Digital Officer" and "Chief AI Officer." ... When it comes to AI systems, the CISO's organization may be better positioned to lead enterprise-wide transformation, Sacolick said. AI systems are nondeterministic - they can produce different outputs and follow different computational paths even when given the exact same input - and this type of technology may be better suited for CISOs. CIOs have operated in the world of deterministic IT systems, where code, infrastructure systems, testing frameworks and automation provide predictable and consistent outputs, while CISOs are immersed in a world of ever-changing, unpredictable threats.

The AI reckoning: How boards can evolve

AI-savvy boards will be able to help their companies navigate these risks and opportunities. According to a 2025 MIT study, organizations with digitally and AI-savvy boards outperform their peers by 10.9 percentage points in return on equity, while those without are 3.8 percent below their industry average.5 What boards should do, however, is the bigger question—and the focus of this article. The intensity of the board’s role will depend on the extent to which AI is likely to affect the business and its competitive dynamics and the resulting risks and opportunities. Those competitive dynamics should shape the company’s AI posture and the board’s governance stance. ... What matters is that the board aligns on the business’s aspirational strategy using a clear view of the opportunities and risks so that it can tailor the governance approach. As the business gains greater experience with AI, the board can modify its posture. ... Directors should focus on determining whether management has the entrepreneurial experience, technological know-how, and transformational leadership experience to run an AI-driven business. The board’s role is particularly important in scrutinizing the sustainability of these ventures—including required skills, implications on the traditional business, and energy consumption—while having a clear view of the range of risks to address, such as data privacy, cybersecurity, the global regulatory environment, and intellectual property (IP).

Do Tariffs Solicit Cyber Attention? Escalating Risk in a Fractured Supply Chain

Offensive cyber operations are a fourth possibility largely serving to achieve the tactical and strategic objectives of decisionmakers, or in the case of tariff imposition, retaliation. Depending on its goals, a government may use the cyber domain to steal sensitive information such as amount and duration of a potential tariff or try to ascertain the short- and long-term intent of the tariff-imposing government. A second option may be a more aggressive response, executing disruptive operations to signal its dissatisfaction over tariff rates. ... It’s tempting to think of tariffs as purely a policy lever, and a way to increase revenue or ratchet up pressure on foreign governments. But in today’s interconnected world, trade policy and cybersecurity policy are deeply intertwined. When they aren’t aligned, companies risk becoming collateral damage in the larger geopolitical space, where hostile actors jockey to not only steal data for profit, but also look to steal secrets, compromise infrastructure, and undermine trust. This offers adversaries new ways to facilitate cyber intrusion to accomplish all of these objectives, requiring organizations to up their efforts in countering these threats via a variety of established practices. These include rigorous third-party vetting; continuous monitoring of third-party access through updates, remote connections, and network interfaces; implementing zero trust architecture; and designing incident response playbooks specifically around supply-chain breaches, counterfeit-hardware incidents, and firmware-level intrusions.

Resilience: How Leaders Build Organizations That Bend, Not Break

Resilient leaders don’t aim to restore what was; they reinvent what’s next. Leadership today is less about stability and more about elasticity—the ability to stretch, adapt, and rebound without breaking. ... Resilient cultures don’t eliminate risk—they absorb it. Leaders who privilege learning over blame and transparency over perfection create teams that can think clearly under pressure. In my companies, we’ve operationalized this with short, ritualized cadences—weekly priorities, daily huddles, and tight AARs that focus on behavior, not ego. The goal is never to defend a plan; it’s to upgrade it. ... “Resilience is mostly about adaptation rather than risk mitigation.” The distinction matters. Risk mitigation reduces downside. Adaptation converts disruption into forward motion. The organizations that redefine their categories after shocks aren’t the ones that avoid volatility; they’re the ones that metabolize it. ... In uncertainty, people don’t expect perfection—they expect presence. Transparent leadership doesn’t eliminate volatility, but it changes how teams experience it. Silence erodes trust faster than any market correction; people fill gaps with assumptions that are worse than reality. ... Treat resilience as design, not reaction. Build cultures that absorb shock, operating systems that learn fast, and communication habits that anchor trust. In an era where strategy half-life keeps shrinking, these are the leaders—and organizations—that won’t just survive volatility.

AI-Powered Quality Engineering: How Generative Models Are Rewriting Test Strategies

Despite significant investments in automation, many organizations still struggle with the same bottlenecks. Test suites often collapse due to minor UI changes. Maintenance cycles grow longer each quarter. Even mature teams rarely achieve effective coverage that truly exceeds 70-80%. Regression cycles stretch for days or weeks, slowing down release velocity and diluting confidence across engineering teams. It isn’t just productivity that suffers; it’s trust. These problems reduce teams’ confidence in releasing immediately and diminish automation ROI in addition to slowing down delivery. Traditional test automation has reached its limits because it automates execution, not understanding. And this is exactly where Generative AI changes the conversation. ... Synthetic data that mirrors production variability can be produced without waiting for dependent systems. Scripts no longer break every time a button shifts. As AI self-heal selectors and locators without human assistance, tests start to regenerate themselves. While predictive signals identify defects early through examining past data and patterns, natural-language inputs streamline test descriptions. ... GenAI isn’t magic, though. When generative models are fed ambiguous input, they can produce brittle or incorrect test cases. Ingesting production logs without adequate anonymization introduces privacy and compliance risks. Risks to data privacy and compliance must be considered while using production traces.

The Great Cloud Exodus: Why European Companies Are Massively Returning to Their Own Infrastructure

Many European managers and policymakers live under the assumption that when they choose "Region Western Europe" (often physically located in datacenters around Amsterdam or Eemshaven), their data is safely shielded from American interference. "The data is in our country, isn't it?" is the oft-heard defense. This is, legally speaking, a dangerous illusion. American legislation doesn't look at the ground on which the server stands, but at who holds the keys to the front door. ... The legal criterion is not the location of the server, but the control ("possession, custody, or control") that the American parent company has over the data. Since Microsoft Corporation in Redmond, Washington, has full control over subsidiary Microsoft Netherlands BV, data in the datacenter in the Wieringermeer legally falls under the direct scope of an American subpoena. ... Additionally, Microsoft applies "consistent global pricing," meaning European customers often see additional increases to align Euro prices with the strong US dollar. This makes budgeting a nightmare of foreign exchange risks. AWS shows a similar pattern. The complexity of the AWS bill is now notorious; an entire industry of "FinOps" consultants has emerged to help companies understand their invoice. ... or organizations seeking ultimate control and data sovereignty, purchasing own hardware and placing it in a Dutch datacenter is the best option. This approach combines the advantages of on-premise with the infrastructure of a professional datacenter.

Daily Tech Digest - September 29, 2025

Quote for the day:

"Remember that stress doesn't come from what is going on in your life. It comes from your thoughts on what is going on in your life." -- Andrew Bernstein

Agentic AI in IT security: Where expectations meet reality

The first decision regarding AI agents is whether to layer them onto existing platforms or to implement standalone frameworks. The add-on model treats agents as extensions to security information and event management (SIEM), security orchestration, automation and response (SOAR), or other security tools, providing quick wins with minimal disruption. Standalone frameworks, by contrast, act as independent orchestration layers, offering more flexibility but also requiring heavier governance, integration, and change management. ... Agentic AI adoption rarely happens overnight. As Checkpoint’s Weigman puts it, “Most security teams aren’t swapping out their whole SOC for some shiny new AI system, and one can understand that: It’s expensive, and it demands time and human effort, which at the end of the day could appear be too disruptive and costly.” Instead, leaders look for ways to incrementally layer new capabilities without jeopardizing ongoing operations, which makes pilots a common first step. ... “An agent designed to carry out a sequence of actions in response to a threat could inadvertently create new risks if misused or deployed inappropriately,” says Goje. “For instance, there’s potential for unregulated scripts or newly discovered vulnerabilities.” ... “Pricing remains a friction point,” says Fifthelement.ai’s Garini. “Vendors are playing with usage-based models, but organizations are finding value when they tie spend to analyst hours saved rather than raw compute or API calls.”

Anthropic, surveillance and the next frontier of AI privacy

Democratic legal systems are built on due process: Law enforcement must have grounds to investigate. Surveillance is meant to be targeted, not generalized. Allowing AI to conduct mass, speculative profiling would invert that principle, treating everyone as a potential suspect and granting AI the power to decide who deserves scrutiny. By saying “no” to this use case, Anthropic has drawn a red line. It is asserting that there are domains where the risk of harm to civil liberties outweighs the potential utility. ... How much should technology companies be able to control how their products are used, particularly once they are sold into government? Better yet, do they have a responsibility to ensure their products are used as intended? There is no easy answer. Enforcement of “terms of service” in highly sensitive contexts is notoriously difficult. A government agency may purchase access to an AI model and then apply it in ways that the provider cannot see or audit. ... The real challenge ahead is to establish publicly accountable frameworks that balance security needs with fundamental rights. Surveillance powered by AI will be more powerful, more scalable and more invisible than anything that came before. It has enormous potential when it comes to national security use cases. Yet without clear limits, it threatens to normalize perpetual, automated suspicion.

How attackers poison AI tools and defenses

AI systems that act with a high degree of autonomy carry another risk: impersonating users or trusting impostors. One tactic is known as a “Confused Deputy” attack. Here, an AI agent with high privileges performs a task on behalf of a low-privileged attacker. Another involves spoofed API access, where attackers trick integrations with services like Microsoft 365 or Gmail into leaking information or sending fraudulent emails. ... One crucial step is to make filters aware of how LLMs generate content, so they can flag anomalies in tone, behavior or intent that might slip past older systems. Another is to validate what AI systems remember over time. Without that check, poisoned data can linger in memory and influence future decisions. Isolation also matters. AI assistants should run in contained environments where unverified actions are blocked before they can cause damage. Identity management needs to follow the principle of least privilege, giving AI integrations only the access they require. Finally, treat every instruction with skepticism. Even routine requests must be verified before execution if zero-trust principles are to hold. ... The next wave of threats will involve agentic AI-powered systems that reason, plan and act on their own. While these tools can deliver tremendous productivity gains to users, their autonomy makes them attractive targets. If attackers succeed in steering an agent, the system could make decisions, launch actions or move data undetected.

‘AI and ML the main focus in tech right now’

AI and machine learning are undoubtedly the main focuses in technology right now, with mentions everywhere. A great way to upskill in this area is by attending talks and seminars, which are frequently held and provide valuable insights into how these technologies are being applied in the industry. These events also help you stay up to date on the latest developments. If you have a strong interest in the field, taking an online course, even a free one, can be a great way to grasp the fundamentals, learn the terminology, and understand how to effectively apply these technologies in your current role. Cloud technology is another area that’s here to stay. It’s widely adopted and incredibly versatile. Cloud certifications are highly accessible, with plenty of resources available to help you prepare for the exams and follow the learning paths they offer. ... Being a people person is incredibly beneficial in this field. A significant part of the job involves communication – whether it’s sharing ideas or networking with coworkers in your area. Building these connections can greatly enhance your ability to perform and succeed in your role. Problem-solving is another key aspect of software engineering, and it’s something I’ve always enjoyed. While it can be particularly challenging at times, the sense of accomplishment and reward when your efforts pay off is unmatched.

Better Data Beats Better Models: The Case for Data Quality in ML

Data quality is a broad and abstract concept, but it becomes more measurable when we break it down into different dimensions. Accuracy is the most important and obvious one: If the input data is wrong (e.g., mislabeled transactions in fraud detection models), the model will simply learn incorrect patterns. Completeness is equally important. Without a high degree of coverage for important features, the model will lack context and produce weaker predictions. For example, a recommender system missing key user attributes will fail to provide personalized recommendations. Freshness plays a subtle but powerful role in data quality. Outdated data appears correct, but does not reflect real-world conditions. ... Detecting data quality issues is not just about a single check but rather about continuous monitoring. Statistical distribution checks are the first line of defense, helping detect anomalies or sudden shifts that can indicate broken data pipelines. ... Ignoring data quality can often turn out to be very expensive. Teams spend large amounts of compute to retrain models on flawed data, to observe little to no business impact. Launch timelines get pushed back since teams spend weeks debugging data issues, a time that could have been spent otherwise on feature development. In industries that are regulated, like finance and healthcare, poor data quality can cause compliance violations and increased legal expenses.

DORA 2025: Faster, But Are We Any Better?

The newest DORA report — the “State of AI-Assisted Software Development” — lands at a time when AI is eating everything from code generation to documentation to operations. And just like those early DORA reports reframed speed versus stability, this one is reframing what AI is actually doing to our software delivery pipelines. Spoiler alert: It’s not as simple as “AI makes everything better.” ... Now here’s the counterintuitive part. For the first time, DORA shows AI adoption is linked to higher throughput. That’s right — teams using AI are moving work through the system faster than those who aren’t. But before you pop the champagne, look at the other half of the finding: Instability is still higher in AI-heavy teams. Faster, yes. Safer? Not so much. If you’ve been around the block, this won’t shock you. We saw the same thing in the early days of automation — speed without discipline just meant you hit the wall quicker. ... Another gem buried in the report is the role of value stream management. AI tends to deliver “local optimizations” — an engineer codes faster, a test suite runs quicker — but without VSM, those wins don’t always roll up into business outcomes. With VSM in place, AI-driven productivity gains translate into measurable improvements at the team and product level. That, to me, is vintage DORA. Remember when they proved that culture — psychological safety, autonomy, collaboration — wasn’t just a warm fuzzy HR concept but directly correlated with elite performance? Same here. VSM turns AI from a toy into a force multiplier.

The 5 Technology Trends For 2026 Everyone Must Prepare For Now

In recent years, we've seen industry, governments, education and everyday folk scrambling to adapt to the disruptive impact of AI. But by 2026, we're starting to get answers to some of the big questions around its effect on jobs, business and day-to-day life. Now, the focus shifts from simply reacting to reinventing and reshaping in order to find our place in this brave, different and sometimes frightening new world. ... In tech, agents were undoubtedly the hot buzzword of 2025, representing a meaningful evolution over previous AI applications like chatbots and generative AI. Rather than simply answering questions and generating content, agents take action on our behalf, and in 2026, this will become an increasingly frequent and normal occurrence in everyday life. From automating business decision-making to managing and coordinating hectic family schedules, AI agents will handle the “busy work” involved in planning and problem-solving, freeing us up to focus on the big picture or simply slowing down and enjoying life. ... Quantum computing harnesses the strange and seemingly counterintuitive behavior of particles at the sub-atomic level to accomplish many complex computing tasks millions of times faster than "classic" computers. For the last decade, there's been excitement and hype over their performance in labs and research environments, but in 2026, we are likely to see further adoption in the real world.

GreenOps and FinOps: Strategic Convergence in the Cloud Transformation Journey

FinOps, short for “Financial Operations,” is a cultural practice designed to bring financial accountability to the cloud. It blends engineering, finance, and business teams to manage cloud costs collaboratively and transparently. The goal is clear: maximize business value from the cloud by making spending decisions grounded in data and aligned with business objectives. ... GreenOps, on the other hand, is all about sustainability in cloud operations. It’s a discipline that encourages organizations to monitor, manage, and minimize the environmental footprint of their cloud usage. GreenOps revolves around using renewable energy-powered cloud resources, recycling or reusing digital assets, optimizing workloads, and selecting eco-friendly services, all with the aim of reducing carbon emissions and supporting broader sustainability goals. ... In practical terms, GreenOps activities such as deleting unused storage volumes, rightsizing virtual machines, and consolidating workloads not only shrink the carbon footprint but also slash monthly cloud bills. Thus, sustainability efforts act as “passive” cost optimizers—delivering FinOps benefits without explicit financial tracking. ... FinOps and GreenOps aren’t one-off projects but ongoing practices. Regular reviews, “cost and sustainability audits,” and optimization sprints keep teams focused.

Rethinking AI’s Role in Mental Health with GPT-5

GPT-5 has surfaced critical questions in the AI mental health community: What happens when people treat a general purpose chatbot as a source of care? How should companies be held accountable for the emotional effects of design decisions? What responsibilities do we bear, as a health care ecosystem, in ensuring these tools are developed with clinical guardrails in place? ... OpenAI has since taken steps to restore user confidence by making its personality “warmer and friendlier,” and encouraging breaks during extended sessions. However, it doesn’t change the fact that ChatGPT was built for engagement, not clinical safety. The interface may feel approachable, especially appealing to those looking to process feelings around high-stigma topics – from intrusive thoughts to identity struggles – but without thoughtful design, that comfort can quickly become a trap. ... Designing for engagement alone won’t get us there, and we must design for outcomes rooted in long-term wellbeing. At the same time, we should broaden our scope to include AI systems that shape the care experience, such as reducing the administrative burden on clinicians by streamlining billing, reimbursement, and other time-intensive tasks that contribute to burnout. Achieving this requires a more collaborative infrastructure to help shape what that looks like, and co-create technology with shared expertise from all corners of the industry including AI ethicists, clinicians, engineers, researchers, policymakers and users themselves.

Cybersecurity skills shortage: can upskilling close the talent gap?

According to reports, the global cybersecurity workforce gap exceeded 4 million professionals in 2023, with India alone requiring more than 500,000 skilled experts to meet current demand. This shortage is not merely a hiring challenge; it is a business risk. ... The traditional answer to talent shortages has been to hire more people. But in cybersecurity, where demand far outstrips supply, hiring alone cannot solve the problem. Upskilling training existing employees to meet evolving requirements offers a sustainable solution. Upskilling is not about starting from scratch. It leverages existing talent pools, such as IT administrators, network engineers, or even software developers, and equips them with cybersecurity expertise. ... While technology plays a central role in cybersecurity, the human factor remains the ultimate line of defense. Many high-profile breaches stem not from technical weaknesses but from human errors such as phishing clicks or misconfigured systems. Upskilling programs must therefore go beyond technical mastery to also emphasise behavioral awareness, ethical responsibility, and decision-making under pressure. ... The cybersecurity talent gap is unlikely to vanish overnight. However, the organisations that will thrive are those that view the challenge not as a bottleneck but as an opportunity to reimagine workforce development. Upskilling is the most pragmatic path forward, enabling companies to build resilience, retain talent, and remain competitive in an era of escalating cyber risks.