Daily Tech Digest by Kannan Subbiah: edge

Showing posts with label edge. Show all posts

Daily Tech Digest - January 18, 2026

Quote for the day:

"Surround yourself with great people; delegate authority; get out of the way" -- Ronald Reagan

Data sovereignty: an existential issue for nations and enterprises

Law-making bodies have in recent years sought to regulate data flows to strengthen their citizens’ rights – for example, the EU bolstering individual citizens’ privacy through the General Data Protection Regulation (GDPR). This kind of legislation has redefined companies’ scope for storing and processing personal data. By raising the compliance bar, such measures are already reshaping C-level investment decisions around cloud strategy, AI adoption and third-party access to their corporate data. ... Faced with dynamic data sovereignty risks, enterprises have three main approaches ahead of them: First, they can take an intentional risk assessment approach. They can define a data strategy addressing urgent priorities, determining what data should go where and how it should be managed - based on key metrics such as data sensitivity, the nature of personal data, downstream impacts, and the potential for identification. Such a forward-looking approach will, however, require a clear vision and detailed planning. Alternatively, the enterprise could be more reactive and detach entirely from its non-domestic public cloud service providers. This is riskier, given the likely loss of access to innovation and, worse, the financial fallout that could undermine their pursuit of key business objectives. Lastly, leaders may choose to do nothing and hope that none of these risks directly affects them. This is the highest-risk option, leaving no protection from potentially devastating financial and reputational consequences of an ineffective data sovereignty strategy.

Verification Debt: When Generative AI Speeds Change Faster Than Proof

Software delivery has always lived with an imbalance. It is easier to change a system than to demonstrate that the change is safe under real workloads, real dependencies, and real failure modes. ... The risk is not that teams become careless. The risk is that what looks correct on the surface becomes abundant while evidence remains scarce. ... A useful name for what accumulates in the mismatch is verification debt. It is the gap between what you released and what you have demonstrated, with evidence gathered under conditions that resemble production, to be safe and resilient. Technical debt is a bet about future cost of change. Verification debt is unknown risk you are running right now. Here, verification does not mean theorem proving. It means evidence from tests, staged rollouts, security checks, and live production signals that is strong enough to block a release or trigger a rollback. It is uncertainty about runtime behavior under realistic conditions, not code cleanliness, not maintainability, and not simply missing unit tests. If you want to spot verification debt without inventing new dashboards, look at proxies you may already track. ... AI can help with parts of verification. It can suggest tests, propose edge cases, and summarize logs. It can raise verification capacity. But it cannot conjure missing intent, and it cannot replace the need to exercise the system and treat the resulting evidence as strong enough to change the release decision. Review is helpful. Review is evidence of readability and intent.

Executive-level CISO titles surge amid rising scope strain

Executive-level CISOs were more likely to report outside IT than peers with VP or director titles, according to the findings. The report frames this as part of a broader shift in how organisations place accountability for cyber risk and oversight. The findings arrive as boards and senior executives assess cyber exposure alongside other enterprise risks. The report links these expectations to the need for security leaders to engage across legal, risk, operations and other functions. ... Smaller organisations and industries with leaner security teams showed the highest levels of strain, the report says. It adds that CISOs warn these imbalances can delay strategic initiatives and push teams towards reactive security operations. The report positions this issue as a management challenge as well as a governance question. It links scope creep with wider accountability and higher expectations on security leaders, even where budgets and staffing remain constrained. ... Recruiters and employers have watched turnover trends closely as demand for senior security leadership has remained high across many sectors. The report suggests that title, scope and reporting structure form part of how CISOs evaluate roles. ... "The demand for experienced CISOs remains strong as the role continues to become more complex and more 'executive'," said Martano. "Understanding how organizations define scope, reporting structure, and leadership access and visibility is critical for CISOs planning their next move and for companies looking to hire or retain security leaders."

What’s in, and what’s out: Data management in 2026 has a new attitude

Data governance is no longer a bolt-on exercise. Platforms like Unity Catalog, Snowflake Horizon and AWS Glue Catalog are building governance into the foundation itself. This shift is driven by the realization that external governance layers add friction and rarely deliver reliable end-to-end coverage. The new pattern is native automation. Data quality checks, anomaly alerts and usage monitoring run continuously in the background. ... Companies want pipelines that maintain themselves. They want fewer moving parts and fewer late-night failures caused by an overlooked script. Some organizations are even bypassing pipes altogether. Zero ETL patterns replicate data from operational systems to analytical environments instantly, eliminating the fragility that comes with nightly batch jobs. ... Traditional enterprise warehouses cannot handle unstructured data at scale and cannot deliver the real-time capabilities needed for AI. Yet the opposite extreme has failed too. The highly fragmented Modern Data Stack scattered responsibilities across too many small tools. It created governance chaos and slowed down AI readiness. Even the rigid interpretation of Data Mesh has faded. ... The idea of humans reviewing data manually is no longer realistic. Reactive cleanup costs too much and delivers too little. Passive catalogs that serve as wikis are declining. Active metadata systems that monitor data continuously are now essential.

How Algorithmic Systems Automate Inequality

The deployment of predictive analytics in public administration is usually justified by the twin pillars of austerity and accuracy. Governments and private entities argue that automated decision-making systems reduce administrative bloat while eliminating the subjectivity of human caseworkers. ... This dynamic is clearest in the digitization of the welfare state. When agencies turn to machine learning to detect fraud, they rarely begin with a blank slate, training their models on historical enforcement data. Because low-income and minority populations have historically been subject to higher rates of surveillance and policing, these datasets are saturated with selection bias. The algorithm, lacking sociopolitical context, interprets this over-representation as an objective indicator of risk, identifying correlation and deploying it as causality. ... Algorithmic discrimination, however, is diffuse and difficult to contest. A rejected job applicant or a flagged welfare recipient rarely has access to the proprietary score that disqualified them, let alone the training data or the weighting variable—they face a black box that offers a decision without a rationale. This opacity makes it nearly impossible for an individual to challenge the outcome, effectively insulating the deploying organisation from accountability. ... Algorithmic systems do not observe the world directly; they inherit their view of reality from datasets shaped by prior policy choices and enforcement practices. To assess such systems responsibly requires scrutiny of the provenance of the data on which decisions are built and the assumptions encoded in the variables selected.

DevSecOps for MLOps: Securing the Full Machine Learning Lifecycle

The term "MLSecOps" sounds like consultant-speak. I was skeptical too. But after auditing ML pipelines at eleven companies over the past eighteen months, I've concluded we need the term because we need the concept — extending DevSecOps practices across the full machine learning lifecycle in ways that account for ML-specific threats. The Cloud Security Alliance's framework is useful here. Securing ML systems means protecting "the confidentiality, integrity, availability, and traceability of data, software, and models." That last word — traceability — is where most teams fail catastrophically. In traditional software, you can trace a deployed binary back to source code, commit hash, build pipeline, and even the engineer who approved the merge. ... Securing ML data pipelines requires adopting practices that feel tedious until the day they save you. I'm talking about data validation frameworks, dataset versioning, anomaly detection at ingestion, and schema enforcement like your business depends on it — because it does. Last September, I worked with an e-commerce company deploying a recommendation model. Their data pipeline pulled from fifteen different sources — user behavior logs, inventory databases, third-party demographic data. Zero validation beyond basic type checking. We implemented Great Expectations — an open-source data validation framework — as a mandatory CI check.

Autonomous Supply Chains: Catalyst for Building Cyber-Resilience

Autonomous supply chains are becoming essential for building resilience amid rising global disruptions. Enabled by a strong digital core, agentic architecture, AI and advanced data-driven intelligence, together with IoT and robotics, they facilitate operations that continuously learn, adapt and optimize across the value chain. ... Conventional thinking suggests that greater autonomy widens the attack surface and diminishes human oversight turning it into a security liability. However, if designed with cyber resilience at its core, autonomous supply chain can act like a “digital immune system,” becoming one of the most powerful enablers of security. ... As AI operations and autonomous supply chains scale, traditional perimeter simply won’t work. Organizations must adopt a Zero Trust security model to eliminate implicit trust at every access point. A Zero Trust model, centered on AI-driven identity and access management, ensures continuous authentication, network micro-segmentation and controlled access across users, devices and partners. By enforcing “never trust, always verify,” organizations can minimize breach impact and contain attackers from freely moving across systems, maintaining control even in highly automated environments. ... Autonomy in the supply chain thrives on data sharing and connectivity across suppliers, carriers, manufacturers, warehouses and retailers, making end-to-end visibility and governance vital for both efficiency and security.

When enterprise edge cases become core architecture

What matters most is not the presence of any single technology, but the requirements that come with it. Data that once lived in separate systems now must be consistent and trusted. Mobile devices are no longer occasional access points but everyday gateways. Hiring workflows introduce identity and access considerations sooner than many teams planned for. As those realities stack up, decisions that once arrived late in projects are moving closer to the start. Architecture and governance stop being cleanup work and start becoming prerequisites. ... AI is no longer layered onto finished systems. Mobile is no longer treated as an edge. Hiring is no longer insulated from broader governance and security models. Each of these shifts forces organizations to think earlier about data, access, ownership and interoperability than they are used to doing. What has changed is not just ambition, but feasibility. AI can now work across dozens of disparate systems in ways that were previously unrealistic. Long-standing integration challenges are no longer theoretical problems. They are increasingly actionable -- and increasingly unavoidable. ... As a result, integration, identity and governance can no longer sit quietly in the background. These decisions shape whether AI initiatives move beyond experimentation, whether access paths remain defensible and whether risk stays contained or spreads. Organizations that already have a clear view of their data, workflows and access models will find it easier to adapt.

Why New Enterprise Architecture Must Be Built From Steel, Not Straw

Architecture must reflect future ambition. Ideally, architects build systems with a clear view of where the product and business are heading. When a system architecture is built for the present situation, it’s likely lacking in flexibility and scalability. That said, sound strategic decisions should be informed by well-attested or well-reasoned trends, not just present needs and aspirations. ... Tech leaders should avoid overcommitting to unproven ideas—i.e., not get "caught up" in the hype. Safe experimentation frameworks (from hypothesis to conclusion) reduce risk by carefully applying best practices to testing out approaches. In a business context with something as important as the technology foundation the organization runs in, do not let anyone mischaracterize this as timidity. Critical failure is a career-limiting move, and potentially an organizational catastrophe. ... The art lies in designing systems that can absorb future shifts without constant rework. That comes from aligning technical decisions not only with what the company is today, but also what it intends to become. Future-ready architecture isn’t the comparatively steady and predictable discipline it was before AI-enabled software features. As a consequence, there’s wisdom in staying directional, rather than architecting for the next five years. Align technical decisions with long-term vision but built with optionality wherever possible.

Why Engineering Culture Is Everything: Building Teams That Actually Work

The culture is something that is a fact and it's also something intrinsic with human beings. We're people, we have a background. We were raised in one part of the world versus another. We have the way that we talk and things that we care about. All those things influence your team indirectly and directly. It's really important, you as a leader, to be aware that as an engineer, I use a lot of metaphors from monitoring and observability. We always talk about known knowns, known unknowns, and unknown unknowns. Those are really important to understand on a systems level, period, because your social technical system is also a system. The people that you work with, the way you work, your organization, it's a system. And if you're not aware of what are the metrics you need to track, what are the things that are threats to it, the good old strengths, weaknesses, opportunities, and threats. ... What we can learn from other industries is their lessons. Again, we are now on yet another industrial revolution. This time it's more of a knowledge revolution. We can learn from civil engineering like, okay, when the brick was invented, that was a revolution. When the brick was invented, what did people do in order to make sure that bricks matter? That's a fascinating and very curious story about the Freemasons. People forget the Freemasons were a culture about making sure that these constructions techniques, even more than the technologies, the techniques, were up to standards.

Daily Tech Digest - December 08, 2025

Quote for the day:

"You don't build business, you build people, and then people build the business." -- Zig Ziglar

CIOs shift from ‘cloud-first’ to ‘cloud-smart’

The is being influenced by better on-prem technology, longer hardware cycles, ultra-high margins with , and the typical hype cycles of the industry, according to . All favor hybrid infrastructure approaches. However, “AI has added another major wrinkle with siloed data and compute,” he adds. “Many organizations aren’t interested in or able to build high-performance , and need to use the cloud. But if they’ve been conservative or cost-averse, their data may be in the on-prem component of their hybrid infrastructure.” These variables have led to complexity or unanticipated costs, either through migration or data egress charges, McElroy says. ... IT has parsed out what should be in a private cloud and what goes into a public cloud. “Training and fine-tuning large models requires strong control over customer and telemetry data,” explains. “So we increasingly favor hybrid architectures where inference and data processing happen within secure, private environments, while orchestration and non-sensitive services stay in the public cloud.” starts with data classification and workload profiling. Anything with customer-identifiable information, diagnostic traces, and model feedback loops are processed within regionally compliant private clouds, he says. ... “Many organizations are wrestling with cloud costs they know instinctively are too high, but there are few incentives to take on the risky work of repatriation when a CFO doesn’t know what savings they’re missing out on,” he says.

Harmonizing EU's Expanding Cybersecurity Regulations

Aligning , and is difficult, since each framework approaches risks differently, which creates overlapping obligations for reporting, controls and vendor oversight, leading to areas that require careful interpretation. Given these overlapping requirements, organizations should establish an integrated governance model that consolidates risk management to report workflows and third-party oversight across all relevant EU frameworks. Strengthening internal coordination - especially between legal, compliance, cybersecurity and executive teams - helps ensure consistent interpretation of obligations and reduces fragmentation in implementation. ... Developers must build safeguards into AI systems, including adversarial testing, robust access controls and monitoring for unexpected behavior. Transparent development practices and collaboration with cybersecurity teams help prevent AI models from being exploited for malicious purposes. ... A trust-based ecosystem depends on transparency, consistent governance and strong cybersecurity practices across all stakeholders. Key elements still missing include harmonized standards, comprehensive regulatory guidance, and mechanisms to verify compliance and foster confidence among users and businesses. ... Ethical frameworks guide responsible decision-making by balancing societal impact, individual right and technological innovation. Organizations can apply them through policies, AI oversight and risk assessments that incorporate principles from , , and into everyday operations and strategic planning.

Invisible IT is becoming the next workplace priority

defines as support that runs in the background and prevents problems before employees notice them. The report highlights two areas that bring this approach to life. The first is . Eighty three percent of leaders say this approach is essential, but only 21 percent have achieved it. With that monitor telemetry data across devices, support teams can detect early signs of failure and trigger automated fixes. If a fix requires human involvement, the repair can happen before the user experiences downtime. This reduces disruptions and shifts support teams away from repetitive tasks that slow down operations. The second area is hyper personalization. Many organizations personalize support by role or seniority, but the study argues this does not reflect how people work. AI systems can now create personas based on individual usage patterns. This lets support teams tailor responses and rollouts to real conditions rather than assumptions. ... Although interest in invisible IT is high, most companies are still using manual processes. Sixty five percent detect issues only when users contact support. Fifty five percent resolve them through manual interventions. Hyper personalization is also limited, with 51 percent of organizations offering standard support for all employees. Barriers are widespread. Fifty one percent cite fragmented systems as their top challenge. Another 47 percent point to cost concerns or uncertain return on investment. Limited AI capabilities and skills gaps also slow progress, along with slow upgrade cycles and a lack of time for planning.

Why AI coding agents aren’t production-ready: Brittle context windows, broken refactors, missing operational awareness

AI agents have demonstrated a critical lack of awareness regarding OS machine, command-line and environment installations. This deficiency can lead to frustrating experiences, such as the agent attempting to execute Linux commands on PowerShell, which can consistently result in ‘unrecognized command’ errors. Furthermore, agents frequently exhibit inconsistent ‘wait tolerance’ on reading command outputs, prematurely declaring an inability to read results before a command has even finished, especially on slower machines. ... Working with AI coding agents often presents a longstanding challenge of , or incorrect or incomplete pieces of information (such as small code snippets) within a larger set of changesexpected to be fixed by a developer with trivial-to-low effort. However, what becomes particularly problematic is when incorrect behavior is repeated within a single thread, forcing users to either start a new thread and re-provide all context, or intervene manually to “unblock” the agent. ... Agents may not consistently leverage the latest , instead generating more verbose and harder-to-maintain implementations. ... Despite the allure of autonomous coding, the reality of AI agents in enterprise development often demands constant human vigilance. Instances like an agent attempting to execute Linux commands on PowerShell, false-positive safety flags or introduce inaccuracies due to domain-specific reasons highlight critical gaps; developers simply cannot step away.

Offensive security takes center stage in the AI era

Now a growing percentage of see as a must-have and, as such, are building up offensive capabilities and integrating them into their security processes to ensure the information revealed during offensive exercises leads to improvements in their overall security posture. ... sees several buckets of activities involved in offensive security, starting with at the bottom end of the maturity scale, and then moving up to and , to threat hunting and , such as . “Then there’s the concept of where the organization looks at an attack scenario and what were the defenses that should have alerted but didn’t and how to rectify those,” he says. ... Many CISOs also have had team members with specific offensive security skills for many years. In fact, the Offensive Security Certified Professional (OSCP), the Offensive Security Experienced Penetration Tester (OSEP), and the Offensive Security Certified Expert (OSCE) certifications from are all credentials that have been in demand for years. ... Another factor that keeps CISOs from incorporating more offensive security into their strategies is concern about exposing vulnerabilities they don’t have the ability to address, Mellen adds. “They can’t unknow that they have those vulnerabilities if they’re not able to do something about them, although the hackers are going to find them whether or not you identify them,” he says.

Securing AI for Cyber Resilience: Building Trustworthy and Secure AI Systems

Attackers increasingly target the - poisoning training data, manipulating models, or exploiting vulnerabilities during deployment and operations. When an AI system or model is compromised, it can quietly skew decisions. This poses significant risks for autonomous systems or analytics engines. Thus, it is important that we embed security and resilience into our AI systems, ensuring robust protection from design to deployment and operations. ... Visibility is key. You can’t protect what you can’t see. Without visibility into data flows, model behavior and system interactions, threats can remain undetected until it is too late. Continuous validation and monitoring help surface anomalies and early, enabling timely interventions. Explainability is just as pivotal. Detecting an anomaly is one thing, but understanding why it happened drives true resilience. Explainability clarifies the reasoning behind AI systems and their decisions, helps verify threats, traces manipulations, makes AI systems auditable, and strengthens trust. Assurance must be continuous. ... Attackers are exploiting AI-specific security weaknesses, such as , , and adversarial manipulations. As AI adoption accelerates, its threats will follow in equal sophistication and scale. The rapid proliferation of AI systems across industries not only drives innovation but also expands the attack surface, drawing the attention from both state-sponsored and criminal actors.

From silos to strategy: What the era of cloud 'coopetition' means for CIOs

This week, historic competitors announced the launch of a cross-cloud interconnect service, effectively tearing down the digital iron curtain that once separated their ecosystems. With expected to join this framework in 2026, the cloud industry is pivoting toward "coopetition"-- a strategic truce driven by the modern enterprise's embrace of multi-cloud. ... One of the primary drivers accelerating AWS and Google's cross-cloud interconnect service is AI. The potential of enterprise AI has been hampered by data silos, with fragmented pockets of information trapped in different systems, which then prevents the training of comprehensive models. found that integration challenges are a leading cause of stalled AI initiatives, with nearly 95% of 1,050 IT leaders surveyed citing connectivity issues as a major hurdle. A cross-cloud partnership is a critical tool for dismantling these barriers -- one that could even eliminate the challenge of data silos, according to . ... However, coopetition is not a silver bullet. It also introduces new friction points where the complexity of managing multiple environments can outweigh the benefits if not addressed properly. Peterson warned that there may not be sufficient value when workloads are "highly dependent and intertwined, requiring low-latency communication across different providers".

Simplicity, speed & scalability are the key pillars of our AI strategy: Siddharth Sureka, Motilal Oswal Financial Services

AI is here to stay, and will transform all industries. Naturally, the sector tends to be on the leading edge of this journey, following closely behind pure technology companies. However, rather than viewing this purely through a technology lens, we approached it from an end-to-end organisational transformation lens. ... The first pillar is simplicity. To reach tier two, three, and four cities, we must make the financial experience intuitive. Simplicity is driven by personalisation, which means how we curate the information delivered to clients and ensure their digital journey is frictionless. The second pillar is speed. We are in the business of providing the right insights at the speed of the market. As an event occurs, we must be able to serve our clients with immediate insights. A prime example of this is our ‘’ product. As news arrives, the system measures the sentiment and analyses how it may impact the market, and then serves that insight directly to the client instantly. The third vertical is scalability. Once we have achieved simplicity and speed, our focus is to scale this architecture to reach the deeper pockets of the country. This scalability is essential for the financial inclusion journey we are embarked upon, ensuring that investors in tier three and four cities can take full advantage of the markets. ... In software engineering, you are delivering a . However, when you move into the domain of AI, the outcomes become or probabilistic in nature. As leaders, we must understand the use cases we are working on and, crucially, the ‘cost of getting it wrong’.

Observability at the Edge: A Quiet Shift in Reliability Thinking

Most organizations still don’t really know what’s happening inside their own digital systems. A survey found that 84% of companies struggle with , the basic ability to understand if their systems are working as they should. The reasons are familiar: monitoring tools are expensive, their architectures clumsy, and when scaled across thousands of locations, the complexity often overwhelms the promise. The cost of that opacity is not abstract. Every minute of downtime is lost revenue. Every unnoticed glitch is a frustrated customer. And every delay in diagnosis erodes trust. In this sense, observability is not just a matter for engineers; it’s central to how modern businesses function. ... When systems fail, the speed of diagnosis becomes critical. In fact, organizations can lose an average of $1 million per hour during unplanned downtime, a striking testament to the high cost of delays. The standard approach, engineers combing through logs, traces, and deployment histories, often slows response when time is most precious. ... What stands out is not only the design of these solutions but their uptake elsewhere. The edge observability model first proven in retail has been mirrored in other industries, including banking. The approach has been picked up by financial services firms seeking to sharpen digital performance. And the reflects a broader shift toward embedding AI into reliability practices. Industry peers have described the work as “innovative, cost-effective, and cloud-native.”

2026 DevOps Predictions - Part 1

In 2026, software teams will begin challenging the rising complexity of their own development environments, shifting from simply executing work to questioning why that work exists in the first place. After years of accumulating tools, rituals, and dependencies, developers will increasingly pause to ask whether a feature, deadline, or workflow actually warrants the effort. ... Death of agile as we used to know it: Agile methodologies have dominated software development for the past 20+ years. However, most organizations still "do agile" rather than be agile: they have adopted the agile practices and rituals that foster team collaboration and have become somewhat faster in both executing and reacting to changes. Meanwhile, AI agents have entered the stage. The speed of getting things done is multiplying and a single developer can sometimes replace a whole team. This means on one hand that the traditional human-centered agile practices become less relevant and on the other hand, that agile may become easier to scale. The death of Agile as we used to know it is a positive thing: now we become agile rather than keep doing agile. ... The momentum is shifting from "" to what's becoming known as "": instead of placing specialized responsibilities on developers, organizations are building development platforms that present opinionated paths and implement best practices by default. That change in momentum is bound to accelerate in 2026.