Daily Tech Digest by Kannan Subbiah: AI risk

Showing posts with label AI risk. Show all posts

Daily Tech Digest - April 02, 2026

Quote for the day:

"Emotional intelligence may be called a soft skill. But it delivers hard results in leadership." -- Gordon Tredgold

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Duration: 19 mins • Perfect for listening on the go.

No joke: data centers are warming the planet

The article discusses a provocative study revealing that AI data centers significantly impact local climates through what researchers call the "data heat island effect." According to the findings, the land surface temperature (LST) around these facilities increases by an average of 2°C after operations commence, with thermal changes detectable up to ten kilometers away. As the AI boom accelerates, data centers are becoming some of the most power-hungry infrastructures globally, potentially exceeding the energy consumption of the entire manufacturing sector within years. This environmental footprint raises concerns about "thermal saturation," where the concentration of facilities in a single region degrades the operating environment, making cooling less efficient and resource competition more intense. While industry analysts warn that strategic planning must now account for these regional system dynamics, some skeptics argue that the temperature rise is merely a standard urban heat island effect caused by land transformation and construction rather than specific compute activities. Regardless of the exact cause, the study highlights a critical challenge for : the physical infrastructure required for digital growth is tangibly altering the surrounding environment. This necessitates a shift in location strategy, prioritizing long-term environmental sustainability over simple site-level optimization to mitigate second-order risks in a warming world.

The Importance of Data Due Diligence

Data due diligence is a critical multi-step assessment process designed to evaluate the health, reliability, and usability of an organization's data assets before making significant investment or business decisions. It encompasses vital components such as data quality assessment, security evaluation, compliance checks, and compatibility analysis. In the modern landscape where data is a cornerstone across sectors like finance and healthcare, performing this diligence ensures that investors and businesses identify hidden risks that could compromise return on investment or operational stability. This process is particularly essential during mergers and acquisitions, where understanding data transferability and integration can prevent costly technical hurdles. Neglecting these checks can lead to catastrophic consequences, including severe financial losses, expensive legal penalties for regulatory non-compliance, and lasting damage to a brand's reputation among consumers and partners. Furthermore, poor data handling practices can disrupt daily operations and impede future growth. By prioritizing , organizations protect themselves from inaccurate insights and security breaches, ultimately fostering a culture of transparency and informed decision-making. This comprehensive approach transforms data from a potential liability into a strategic asset, securing the genuine value of a business undertaking in an increasingly data-driven global economy.

Top global and US AI regulations to look out for

As artificial intelligence evolves at a breakneck pace, global regulatory landscapes are shifting rapidly to address emerging risks, often outstripping traditional legislative speeds. China pioneered generative AI oversight in 2023, while the provides a comprehensive, risk-based framework that currently influences global standards. Conversely, the United States relies on a patchwork of state-level mandates from California, Colorado, and others, as federal legislation remains stalled. The article highlights a pivot toward regulating ""—interconnected systems that perform complex tasks—which presents unique challenges for accountability and monitoring. Experts suggest that instead of chasing specific, unstable laws, organizations should adopt established best practices like the or ISO 42001 to build resilient governance. Enterprises are advised to focus on AI literacy and real-time monitoring rather than periodic audits, given that AI behavior can fluctuate daily. While the current regulatory environment is fragmented and complex, companies with strong existing cybersecurity and privacy foundations are well-positioned to adapt. Ultimately, staying ahead of these legal shifts requires a proactive, framework-oriented approach that balances innovation with safety as global authorities continue to refine their oversight strategies through 2027 and beyond.

Agentic AI Software Engineers: Programming with Trust

The article "Agentic AI Software Engineers: Programming with Trust" explores the transformative shift from simple AI-assisted coding to autonomous agentic systems that mimic human software engineering workflows. Unlike traditional models that merely suggest code snippets, agentic AI operates with significant autonomy, utilizing standard developer tools like shells, editors, and test suites to perform complex tasks. The authors argue that the successful deployment of these "AI engineers" hinges on establishing a level of trust that meets or even exceeds that of human counterparts. This trust is bifurcated into technical and human dimensions. Technical trust is built through rigorous quality assurance, including automated testing, static analysis, and formal verification, ensuring code is correct, secure, and maintainable. Conversely, human trust is fostered through explainability and transparency, where agents clarify their reasoning and align with existing team cultures and ethical standards. As software engineering transitions toward "programming in the large," the role of the developer evolves from a primary code writer to a strategic assembler and reviewer. By integrating intent extraction and program analysis, agentic systems can provide the essential justifications necessary for developers to confidently adopt AI-generated solutions. Ultimately, the paper presents a roadmap for a collaborative future where AI agents serve as reliable, trustworthy teammates.

Security awareness is not a control: Rethinking human risk in enterprise security

In the article "Security awareness is not a control: Rethinking human risk in enterprise security," Oludolamu Onimole argues that organizations must stop treating security awareness training as a primary defense mechanism. While awareness fosters a security-conscious culture, it is fundamentally an educational tool rather than a structural control. Unlike technical safeguards like network segmentation or conditional access, awareness relies on consistent human performance, which is inherently variable due to cognitive load and decision fatigue. Onimole points out that attackers increasingly exploit these predictable human vulnerabilities through sophisticated social engineering and business email compromise, where even well-trained employees can fall victim under pressure. Consequently, viewing awareness as a "layer of defense" unfairly shifts the blame for breaches onto individuals rather than systemic design flaws. The article advocates for a shift toward "human-centric" engineering, where systems are designed to be resilient to inevitable human errors. This includes implementing phishing-resistant authentication, enforced out-of-band verification for high-risk transactions, and robust identity telemetry. Ultimately, while awareness remains a valuable cultural component, true requires moving beyond the "blame game" to build architectural safeguards that absorb mistakes rather than allowing a single human lapse to cause material disaster.

The Availability Imperative

In "The Availability Imperative," Dmitry Sevostiyanov argues that the fundamental differences between Information Technology (IT) and Operational Technology (OT) necessitate a paradigm shift in cybersecurity. Unlike IT’s "best-effort" Ethernet standards, OT environments like power grids and factories demand determinism—predictable, fixed timing for critical control systems. Standard Ethernet lacks guaranteed delivery and latency, leading to dropped frames and jitter that can trigger catastrophic failures in high-stakes industrial loops. To address these limitations, specialized protocols like EtherCAT and PROFINET were engineered for strict timing. However, the introduction of conventional security measures, particularly Deep Packet Inspection (DPI) via firewalls, often introduces significant latency and performance degradation. Sevostiyanov asserts that in OT, the traditional must be reordered to prioritize Availability above all else. Effective cybersecurity in these settings requires protocol-aware, ruggedized Next-Generation Firewalls that minimize the latency penalty while providing granular protection. Ultimately, security professionals must validate performance against industrial safety requirements to ensure that protective measures do not inadvertently silence the machines they aim to defend. By bridging the gap between IT transport rules and the physics of industrial processes, organizations can maintain system stability while securing critical infrastructure against evolving digital threats.

Microservices Without Tears: Shipping Fast, Sleeping Better

The article "Microservices Without Tears: Shipping Fast, Sleeping Better" explores the common pitfalls of transitioning to a and provides a roadmap for successful implementation. While microservices promise scalability and independent deployments, they often result in complex "distributed monoliths" that increase operational stress. To avoid this, the author emphasizes the importance of and establishing clear bounded contexts to ensure services are truly decoupled. Central to this approach is an "API-first" mindset, which allows teams to work independently while maintaining stable contracts. Furthermore, the post highlights that robust —encompassing metrics, logs, and distributed tracing—is non-negotiable for diagnosing issues in a distributed system. Automation through CI/CD pipelines is equally critical to manage the overhead of numerous services. Ultimately, the transition is as much about culture as it is about technology; adopting a "you build it, you run it" mentality empowers teams and improves system reliability. By focusing on developer experience and incremental changes, organizations can harness the speed of microservices without sacrificing peace of mind or stability. This holistic strategy transforms the architectural shift from a source of frustration into a powerful engine for rapid, reliable software delivery and long-term maintainability.

Trust, friction, and ROI: A CISO’s take on making security work for the business

In this Help Net Security interview, PPG’s CISO John O’Rourke discusses how modern cybersecurity functions as a strategic business driver rather than a mere cost center. He argues that mature security programs act as revenue enablers by reducing friction during critical growth phases, such as mergers and acquisitions or complex sales cycles. By implementing standardized frameworks like NIST or ISO, organizations can accelerate due diligence and build essential digital trust with increasingly sophisticated buyers. O’Rourke highlights how PPG utilizes automated identity management and audit readiness to ensure business initiatives move forward without unnecessary delays. He contrasts this approach with less-regulated industries that often defer security investments, resulting in prohibitively expensive technical debt and fragile architectures. Looking ahead, companies that prioritize foundational security controls will be significantly better positioned to integrate emerging technologies like artificial intelligence while maintaining business continuity. Conversely, those viewing security as an optional expense face heightened risks of prolonged incident recovery, regulatory exposure, and lost customer confidence. Ultimately, O'Rourke emphasizes that while security may not generate revenue directly, its operational maturity is indispensable for protecting a brand's reputation and ensuring long-term, uninterrupted financial growth in an increasingly competitive global landscape.

In the wake of Claude Code's source code leak, 5 actions enterprise security leaders should take now

On March 31, 2026, inadvertently exposed the internal mechanics of its flagship AI coding agent, Claude Code, by shipping a 59.8 MB source map file in an npm update. This leak revealed 512,000 lines of TypeScript, uncovering the "agentic harness" that orchestrates model tools and memory, alongside 44 unreleased features like the "KAIROS" autonomous daemon. Beyond strategic exposure, the incident highlights critical security vulnerabilities, including three primary attack paths: context poisoning through the compaction pipeline, sandbox bypasses via shell parsing differentials, and supply chain risks from unprotected Model Context Protocol (MCP) server interfaces. Security leaders are warned that AI-assisted commits now leak credentials at double the typical rate, reaching 3.2%. Consequently, experts recommend five urgent actions: auditing project configuration files like CLAUDE.md as executable code, treating MCP servers as untrusted dependencies, restricting broad bash permissions, requiring robust vendor SLAs, and implementing commit provenance verification. Furthermore, since the codebase is reportedly 90% AI-generated, the leak underscores unresolved legal questions regarding intellectual property protections for automated software. As competitors now possess a blueprint for high-agency agents, the incident serves as a systemic signal for enterprises to prioritize operational maturity and architect provider-independent boundaries to mitigate the expanding risks of the AI agent supply chain.

AI gives attackers superpowers, so defenders must use it too

This article explores how artificial intelligence is fundamentally transforming the cybersecurity landscape, shifting the balance of power toward attackers. Sergej Epp, CISO of Sysdig, explains that the window between vulnerability disclosure and active exploitation has dramatically collapsed from eighteen months in 2020 to just a few hours today, with the potential to shrink to minutes. This acceleration is driven by AI’s ability to automate attacks and verify exploits with binary efficiency. While attackers benefit from immediate feedback on their efforts, defenders struggle with complex verification processes and high rates of false positives. To combat these AI-powered "superpowers," organizations must abandon traditional, human-dependent response cycles and monthly patching in favor of full automation and "human-out-of-the-loop" security models. Epp emphasizes the importance of context graphs, noting that while attackers think in interconnected networks, defenders often remain stuck in list-based mentalities. Furthermore, established principles like and blast radius containment remain essential, but they require 100% implementation because AI is remarkably adept at identifying and exploiting the slightest 1% gap in coverage. Ultimately, the survival of modern digital infrastructure depends on matching the machine-scale speed of adversaries through integrated, autonomous defensive strategies.

Daily Tech Digest - March 28, 2026

Quote for the day:

"We are moving from a world where we have to understand computers to a world where they will understand us." -- Jensen Huang

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Duration: 16 mins • Perfect for listening on the go.

When clean UI becomes cold UI

The article "When Clean UI Becomes Cold UI" explores the pitfalls of over-minimalism in modern digital interface design, arguing that a "clean" aesthetic can easily shift from elegant to emotionally distant. This "cold UI" occurs when essential guidance—such as text labels, instructions, and reassuring feedback—is stripped away in favor of a sleek, portfolio-worthy appearance. While such designs may impress other designers, they often fail real-world users by forcing them to rely on assumptions, which increases cognitive friction and erodes the human connection. The central premise is that designers must shift their focus from "clean" design to "clear" design. Every element removed for the sake of aesthetics involves a trade-off that often sacrifices functional clarity for visual simplicity. To avoid creating a "ghost town" interface, the author encourages prioritizing meaning over layout, ensuring icons are paired with labels and that the design supports users during moments of uncertainty. Ultimately, a truly successful interface is not one that is simply empty, but one that knows when to provide direction and when to step back, balancing aesthetic minimalism with the transparency required for a user to feel genuinely supported and understood.

5 Practical Techniques to Detect and Mitigate LLM Hallucinations Beyond Prompt Engineering

The article "5 Practical Techniques to Detect and Mitigate LLM Hallucinations Beyond Prompt Engineering" from Machine Learning Mastery explores advanced system-level strategies to ensure AI reliability. While basic prompting can improve performance, it often fails in production settings where strict accuracy is critical. The first technique, Retrieval-Augmented Generation (RAG), anchors model responses in real-time, external verified data, moving away from reliance on static, often outdated training memory. Second, the article advocates for Output Verification Layers, where a secondary model or automated cross-referencing system validates initial drafts before they reach the user. Third, Constrained Generation utilizes structured formats like JSON or XML to limit speculative or tangential output, ensuring machine-readable consistency. Fourth, Confidence Scoring and Uncertainty Handling encourage models to quantify their own reliability or admit ignorance through "I don’t know" responses rather than guessing. Finally, Human-in-the-Loop Systems integrate human oversight to refine results, provide feedback, and build essential user trust. Collectively, these methods transition LLM applications from experimental prototypes to robust, factual tools. By implementing these architectural patterns, developers can move beyond trial-and-error prompting to create production-ready systems capable of handling high-stakes tasks where the cost of a hallucination is significantly high.

Agentic GRC: Teams Get the Tech. The Mindset Shift Is What's Missing

In "Agentic GRC: Teams Get the Tech, the Mindset Shift Is What's Missing," Yair Kuznitsov explores the transformative impact of AI agents on Governance, Risk, and Compliance. Traditionally, GRC professionals derived value from operational competence, specifically manual evidence collection and audit management. However, agentic AI now automates these workflows, creating an identity crisis for those whose roles were defined by execution. The author argues that while technology is ready, many teams remain reluctant because they struggle to redefine their professional purpose beyond operational tasks. Crucially, GRC was intended as a strategic risk management function, but it became consumed by scaling inefficiencies. Agentic GRC offers a return to these roots, transitioning practitioners toward "GRC Engineering" where controls are managed as code via Git and . This essential shift requires moving from a "checkbox" mentality to strategic risk leadership. Humans must provide critical judgment, define risk appetite, and translate business context into compliance logic—capabilities AI cannot replicate. Ultimately, successful organizations will empower their GRC teams to stop merely managing operational machines and start leading proactive, risk-based initiatives. This evolution represents an opportunity for professionals to finally perform the high-level work they were originally trained to do.

The Missing Layer in Agentic AI

The article "The Missing Layer in Agentic AI" argues that while current AI development focuses heavily on large language models and reasoning capabilities, a critical "middleware" layer is currently absent. This missing component, referred to as an agentic orchestration layer, is essential for transforming static models into truly autonomous systems capable of executing complex, multi-step tasks in dynamic environments. The author explains that for AI agents to be effective, they require more than just raw intelligence; they need robust frameworks for memory management, tool integration, and state persistence. This layer acts as the glue that connects high-level planning with low-level execution, ensuring that agents can maintain context and recover from errors during long-running processes. Furthermore, the piece highlights that without this specialized infrastructure, developers are forced to build bespoke, brittle solutions that do not scale. By establishing a standardized orchestration layer, the industry can move toward more reliable, observable, and interoperable agentic workflows. Ultimately, the article suggests that the next frontier of AI progress lies not just in better models, but in the sophisticated software engineering required to manage how those models interact with the world and each other.

Edge clouds and local data centers reshape IT

For over a decade, enterprise cloud strategy prioritized centralization on hyperscale platforms to achieve economies of scale and reduce infrastructure sprawl. However, the rise of and local data centers is fundamentally reshaping this paradigm toward a selectively distributed architecture. Modern digital systems increasingly require real-time responsiveness, adherence to regional data sovereignty regulations, and efficient handling of massive data volumes from sensors and video feeds. To meet these demands, enterprises are adopting a dual architecture that combines the strengths of centralized cloud platforms—well-suited for model training and storage—with localized infrastructure positioned closer to the source of interaction. This shift is visible in sectors like retail and manufacturing, where proximity reduces latency and operational costs. Despite its benefits, the transition to edge computing introduces significant complexities, including fragmented life-cycle management, security hardening, and the need for robust observability across hundreds of distributed sites. Rather than replacing the cloud, the edge serves as a coordinated layer within an integrated hybrid model. By placing workloads where they are most operationally and economically effective, organizations can navigate bandwidth limitations and physical-world complexities, ensuring their digital infrastructure remains agile and resilient in a changing technological landscape.

AI frenzy feeds credential chaos, secrets leak through code, tools, and infrastructure

highlights an alarming surge in cybersecurity risks, revealing that 28.65 million new were detected in public GitHub commits during 2025. This multi-year upward trend demonstrates that credentials, including access keys, tokens, and passwords, are increasingly leaking through code, development tools, and infrastructure. Beyond public repositories, the report underscores a significant shift toward internal environments, which often carry a higher density of sensitive production credentials. The explosion of AI development has exacerbated the problem; AI-assisted coding and the proliferation of new model providers and agent frameworks have introduced vast numbers of fresh credentials that are frequently mismanaged. Furthermore, collaboration platforms like Slack and Jira, along with self-hosted Docker registries, serve as additional points of exposure. A particularly concerning finding is the longevity of these leaks, as many credentials remain active and usable for years due to the operational complexities of remediation across fragmented systems. Ultimately, the report illustrates a widening gap between the rapid pace of software innovation and the governance required to secure the expanding surface area of modern, interconnected development workflows, leaving critical infrastructure vulnerable to exploitation.

Architecting Autonomy at Scale: Raising Teams Without Creating Dependencies

In “Architecting Autonomy at Scale,” Shweta Aggarwal and Ron Klein argue that traditional, centralized architectural governance becomes a significant bottleneck as organizations grow, necessitating a fundamental shift toward decentralized decision-making. Utilizing a “parental metaphor,” the article describes the evolution of architecture from “infancy,” where strong central guidance is required to prevent chaos, to “adulthood,” where teams operate autonomously within established systems. The authors propose a structured framework built on clear decision boundaries, shared principles, and robust guardrails rather than restrictive approval gates. Key technical practices include documenting decisions via Architecture Decision Records (ADRs) to preserve context, utilizing “fitness functions” for automated governance within CI/CD pipelines, and leveraging AI for detecting architectural drift. By aligning architectural authority with the C4 model levels, organizations can clarify ownership and reduce delivery friction. Ultimately, the role of the architect evolves from a top-down gatekeeper to a coach and platform enabler, focusing on creating “paved roads” that allow teams to experiment safely. This transition is framed as a socio-technical transformation that requires cultural shifts, leadership support, and a trust-based governance model to successfully balance local agility with enterprise-wide coherence and long-term technical sustainability.

Regulators crack down on EU age assurance — self-declaration no longer enough

The European Commission is intensifying its enforcement of the Digital Services Act (DSA) by moving away from "self-declaration" as a valid method for online age assurance. Following a series of investigations, regulators have determined that simple "click-to-confirm" mechanisms on major adult content platforms, including Pornhub, Stripchat, XNXX, and XVideos, are insufficient to protect minors from harmful material. These platforms are now being urged to implement more robust, privacy-preserving age verification measures to ensure compliance with EU standards. Simultaneously, the Commission has opened a formal investigation into Snapchat over concerns that its reliance on self-declaration fails to prevent underage children from accessing the app or to provide age-appropriate experiences for teenagers. Beyond the European Commission's actions, the UK Information Commissioner's Office (ICO) is also pressuring social media giants to strengthen their age-gate systems. Potential solutions being discussed include the use of the , facial age estimation technology, and identity document scans. This coordinated regulatory crackdown signals a major shift in the digital landscape, where platforms must now prioritize societal risks to minors over business-centric concerns. Failure to adopt these more stringent verification methods could lead to significant financial penalties across the European Union.

5 reasons why the tech industry is failing women

The CIO.com article, “Women in Tech Statistics: The Hard Truths of an Uphill Battle,” highlights the persistent gender gap and systemic challenges women face in the technology sector. Despite representing 42% of the global workforce, women hold only 26-28% of tech roles and just 12% of C-suite positions. A significant “leaky pipeline” begins in academia, where women earn only 21% of computer science degrees, and continues into the workplace. Troublingly, 50% of women leave the industry by age 35—a rate 45% higher than men—driven by toxic cultures, microaggressions, and a lack of flexible work-life balance. Economic instability further compounds these issues, with women being 1.6 times more likely to face layoffs; during 2022’s mass tech layoffs, they accounted for 69% of job losses. Financial disparities remain stark, as women earn approximately $15,000 less annually than their male counterparts. Furthermore, the rise of artificial intelligence presents new risks, with women’s roles 34% more likely to be disrupted by automation compared to 25% for men. Collectively, these statistics underscore that achieving gender parity requires more than corporate pledges; it necessitates fundamental shifts in recruitment, retention, and structural support systems.

15+ Global Banks Exploring Quantum Technologies

The article titled "15+ global banks probing the wonderful world of quantum technologies," published by The Quantum Insider on March 27, 2026, highlights the accelerating integration of within the global financial sector. Central to this movement is the "," a benchmarking tool developed in collaboration with HorizonX Consulting, which identifies top performers like JPMorgan Chase, HSBC, and Goldman Sachs. These institutions are leading a group of over fifteen major banks that have transitioned from theoretical research to practical experimentation. The report details how these banks are leveraging quantum advantages for high-dimensional computational tasks, including portfolio optimization, complex risk modeling through Monte Carlo simulations, and real-time fraud detection. Furthermore, the article emphasizes a proactive shift toward "quantum readiness" to combat cryptographic threats, with banks like HSBC trialing quantum-secure trading for digital assets. With nearly 80% of the world’s fifty largest banks now exploring these frontier technologies, the narrative has shifted from whether quantum will disrupt finance to when its full-scale implementation will occur. This trend is bolstered by significant investments, such as JPMorgan’s backing of Quantinuum, underscoring a strategic imperative to maintain competitiveness and ensure systemic stability in a post-quantum world.

Daily Tech Digest - February 28, 2026

Quote for the day:

"Stories are the single most powerful weapon in a leader's arsenal." -- Howard Gardner

AI ambitions collide with legacy integration problems

Many enterprises have moved beyond experimentation and are preparing for formal deployment. The survey found that 85% have begun adopting AI or expect to do so within the next 12 months. Respondents also reported efforts to formalise AI governance, reflecting greater attention to risk, accountability and oversight. ... Integration sits at the centre of that tension. AI initiatives often depend on clean data, consistent definitions and reliable access across multiple applications, requirements that legacy estates can complicate. The survey links these constraints to compliance risks, including data retention, access controls and auditability across connected systems. ... Security and privacy concerns featured prominently. Data privacy across systems was cited as a top risk by 49% of respondents, while 48% said they were concerned about third parties handling sensitive data. The results highlight the difficulty of managing information flows when AI systems interact with multiple internal applications and external providers. Governance approaches varied. Fewer than half (47%) said board-level reporting forms part of risk management for AI and related technology work, suggesting uneven executive oversight as AI moves into operational settings where incidents can carry regulatory and reputational consequences. ... Despite pressure to move quickly on AI initiatives, respondents said engineering quality remains a priority.

Striking the Right Balance Between Automation and Manual Processes in IT

Rather than thinking of applying AI wherever possible and over-automating, leaders should think about the most beneficial uses of the technology and begin implementation of the technology in those areas first before expanding further. Automation is a powerful tool, but humans are the most powerful tool in the IT stack. Let’s discuss how today’s IT leaders can strike the right balance between automation and manual processes. ... Even with the many benefits of automation, human-led processes still reign supreme in certain areas. For example, optimal IT operations happen at the intersection of tools and teamwork. IT teams must still foster a collaborative culture, working with other departments to ensure cross team visibility and alignment on business goals. While the latest AI technology can help in these efforts, ultimately, humans must do this collaborative work. Team dynamics can also be complex at times. Conflict resolution and major team decisions are not things that automation can solve. Moreover, if there is a critical system issue, DBAs must be able to work with IT leaders to resolve this issue and forge a path forward. Finally, manual processes are often necessitated by convoluted workflows. Many DBA teams have workflows in which every step is a set of if-then-else decisions, with each possible outcome also encumbered with many if-then decisions cascading through multiple levels of decisions.

Translating data science capabilities into business ROI

The fundamental challenge in demonstrating data science ROI is that most analytics infrastructure feels optional until it becomes essential. During normal operations, executives tolerate delays in reporting and gaps in visibility. During a crisis, those same gaps become existential threats. ... The turning point came when I realized we weren’t facing a data problem or a technology problem. We were facing a decision-making problem. Our leadership needed to maintain operational stability for a multi-trillion-dollar asset manager during unprecedented disruption. Every day without visibility meant delayed decisions, missed opportunities, and compounding uncertainty. ... Speed-to-value often trumps technical sophistication. The COVID dashboard taught me this lesson definitively. We could have spent months building a comprehensive data warehouse with sophisticated ETL pipelines and machine learning-powered forecasting. Instead, we focused ruthlessly on the minimum viable solution that executives needed immediately. ... Strategic positioning creates a disproportionate impact. I served as strategic architect for a major product repositioning — a multi-million-dollar initiative essential for our competitive positioning. My data-backed strategies produced immediate, quantifiable market share gains and resulted in substantially larger deal sizes and accelerated acquisition rates that fundamentally altered our market position.

The reliability cost of default timeouts

Many widely used libraries and systems default to infinite or extremely large timeouts. In Java, common HTTP clients treat a timeout of zero as “wait indefinitely” unless explicitly configured. In Python, requests will wait indefinitely unless a timeout is set explicitly. The Fetch API does not define a built-in timeout at all. These defaults aren’t careless. They’re intentionally generic. Libraries optimize for the correctness of a single request because they can’t know what “too slow” means for your system. Survivability under partial failure is left to the application. ... Long timeouts can also mask deeper design problems. If a request regularly times out because it returns thousands of items, the issue isn’t the timeout itself. It’s missing pagination or poor request shaping. By optimizing for individual request success, teams unintentionally trade away system-level resilience. ... A timeout defines where a failure is allowed to stop. Without timeouts, a single slow dependency can quietly consume threads, connections and memory across the system. With well-chosen timeouts, slowness stays contained instead of spreading into a system-wide failure. ... A timeout is a decision about value. Past a certain point, waiting longer does not improve user experience. It increases the amount of wasted work a system performs after the user has already left. A timeout is also a decision about containment. Without bounded waits, partial failures turn into system-wide failures through resource exhaustion: blocked threads, saturated pools, growing queues and cascading latency.

From dashboards to decisions: How streaming data transforms vertical software

For years, the standard for vertical software has been the nightly sync. You collect data all day, run a massive batch job at 2:00 AM, and provide your customers with a clean report the next morning. In a world of 2026, that delay is becoming a liability rather than a best practice. ... Data streaming isn’t just about moving bits faster; it’s about changing the fundamental value proposition of your application. Instead of being a system of record that tells a user what happened, your software becomes a system of agency that tells them what is happening right now. This shift requires a mental move away from static databases toward event-driven architectures. You’re no longer just storing a “state” (like current inventory); you’re capturing every “event” (every scan, every sale, every sensor ping) that leads to that state. ... One of the biggest mistakes I see software leaders make is treating real-time data as a “table stakes” feature that they give away for free. Streaming infrastructure is expensive to run and even more expensive to maintain. If you bake these costs into your standard subscription without a clear monetization strategy, you’ll watch your gross margins shrink as your customers’ data volumes grow. ... When you process data at the edge, you’re also solving the “data gravity” problem. Sending thousands of high-frequency sensor pings from a factory floor to the cloud just to filter out the noise is a waste of bandwidth and money.

MCP leaves much to be desired when it comes to data privacy and security

From a data privacy standpoint, one of the major issues is data leakage, while from a security perspective, there are several things that may cause issues, including prompt injections, difficulty in distinguishing between verified and unverified servers, and the fact that MCP servers sit below typical security controls. ... Fulkerson went on to say that runtime execution is another issue, and legacy tools for enforcing policies and privacy are static and don’t get enforced at runtime. When you’re dealing with non-deterministic systems, there needs to be a way to verifiably enforce policies at runtime execution because the blast radius of runtime data access has outgrown the protection mechanisms organizations have. He believes that confidential AI is the solution to these problems. Confidential AI builds on the properties of confidential computing, which involves using hardware that has an encrypted cache, allowing data and inference to be run inside an encrypted environment. While this helps prove that data is encrypted and nobody can see it, it doesn’t help with the governance challenge, which is where Fulkerson says confidential AI comes in. Confidential AI treats everything as a resource with its own set of policies that are cryptographically encoded. For example, you could limit an agent to only be able to talk to a specific agent, or only allow it to communicate with resources on a particular subnet.

3 Ways OT-IT Integration Helps Energy and Utilities Providers Modernize Grid Operations

Increasingly, energy providers are turning to digital twins to model and simulate critical infrastructure across generation, transmission and distribution environments. By feeding live telemetry from supervisory control and data acquisition systems, intelligent electronic devices and other OT assets into IT-based simulation platforms, utilities can create real-time digital replicas of substations, turbines, transformers and even entire grid segments. This enables teams to test load-balancing strategies, maintenance schedules or DER integrations without disrupting service. ... Private 5G networks offer a compelling alternative. Designed for high reliability and low latency, private 5G can operate effectively in interference-heavy environments such as substations or generation facilities. When paired with TSN, utilities can achieve deterministic, sub-millisecond communication between protection systems, controllers and analytics platforms. ... Federated machine learning allows utilities to train AI models locally at the edge — analyzing equipment performance, detecting anomalies and refining predictive maintenance strategies — without centralizing raw operational data. For industries such as energy and oil, remote sites can run local anomaly detection models tailored to site-specific conditions, while still sharing insights that strengthen enterprisewide safety and operational protocols.

Even if AI demand fades, India need not worry - about data centres

AI pushes rack densities from ~5–10kW to 50–100kW+, making liquid cooling, greater power capacity, and purpose‑built ‘AI‑ready’ Data Centre campuses essential — whether for regional training clusters or dense inference. What makes a Data Centre AI-ready is the ability to support advanced cooling, predictable scalability and direct access to clouds, networks and partners in a sustainable manner. ... In India, enterprises are rapidly adopting hybrid and multi-cloud architectures as they modernise their digital infrastructure. Domestic enterprises, particularly in BFSI and broking, are moving away from in-house data centres toward third-party colocation facilities to gain scalability, efficient interconnection with their required ecosystem, operational efficiency and access to specialised talent. This shift is being further accelerated by distributed AI, hybrid multi-cloud architectures and a growing focus on sustainability. ... India’s Data Centre market is distinctive because of the scale of its digital consumption, combined with the early stage of ecosystem development. India generates a significant share of global data, yet its installed data centre capacity remains comparatively low, creating strong long-term growth potential. This growth is now being amplified by hyperscalers and AI-led demand. India aims to become a USD 1 T digital economy by 2028. It is already making significant progress, supported by the country’s thriving startup ecosystem, the third largest in the world, and initiatives like Startup India.

Surprise! The One Being Ripped Off by Your AI Agent Is You

It’s now happening all the time: in the sale of location data and browsing histories to brokers who assemble and sell our highly personal profiles, and in DOGE’s and other data grabs across the federal government, where housing, tax, and health information is being weaponized for immigration enforcement or misleading voter fraud “investigations.” With AI agents, it just gets worse. Data betrayal is an even more intimate act. Yet the people who granted OpenClaw access to their accounts were making a reasonable choice—to use a powerful tool on their behalf. ... The data aggregation capabilities of AI add another dimension of risk that rarely gets even a mention, but represent a change in scale that adds up to a sea change, making someone marketed as “productivity” software a menacing vector for data weaponization. The same capabilities that make agents useful—synthesizing enormous amounts of information across sources and acting autonomously across platforms with persistence and memory—make them extraordinarily powerful instruments for state surveillance and targeted repression. An autocratic government could build dossiers on dissidents, journalists, or voters from financial records, social media, location data, and communications metadata, acting in real time: micro-targeting people with persuasion campaigns, swarming targets with coordinated social media attacks, engineering entrapment schemes, or flagging individuals based on patterns no court ever authorized.

What makes Non-Human Identities in AI secure

By aligning security goals with technological advancements, NHIs offer a tangible solution to the challenges posed by AI and cloud-based architectures. Forward-thinking organizations are leveraging this strategic advantage to stay ahead of potential threats, ensuring that their digital remain both protected and resilient. ... Can businesses effectively integrate Non-Human Identities across diverse sectors? Where industries such as financial services, healthcare, and travel become increasingly dependent on digital transformation, the need for securing NHIs is paramount. Each sector presents unique challenges and requirements that necessitate tailored approaches to NHI management. In financial services, for example, the emphasis might be on protecting transactional data, while healthcare organizations focus on safeguarding patient information. Thus, versatile solutions that accommodate varying security demands while maintaining robust protection standards are essential. ... What greater role can NHIs play where emerging technologies unfold? The growing intersection of AI and IoT devices creates a complex web of interactions that requires robust security measures. Non-Human Identities provide a framework for securely managing the myriad connections and transactions occurring between devices. In IoT networks, NHIs authenticate and authorize communication between endpoints, thus safeguarding the integrity of both data and operations.

Daily Tech Digest - February 16, 2026

Quote for the day:

"People respect leaders who share power and despise those who hoard it." -- Gordon Tredgold

TheCUBE Research 2026 predictions: The year of enterprise ROI

Fourteen years into the modern AI era, our research indicates AI is maturing rapidly. The data suggests we are entering the enterprise productivity phase, where we move beyond the novelty of retrieval-augmented-generation-based chatbots and agentic experimentation. In our view, 2026 will be remembered as the year that kicked off decades of enterprise AI value creation. ... Bob Laliberte agreed the prediction is plausible and argued OpenAI is clearly pushing into the enterprise developer segment. He said the consumerization pattern is repeating – consumer adoption often drives faster enterprise adoption – and he viewed OpenAI’s Super Bowl presence as a flag in the ground, with Codex ads and meaningful spend behind them. He said he is hearing from enterprises using Codex in meaningful ways, including cases where as much as three quarters of programming is done with Codex, and discussions of a first 100% Codex-developed product. He emphasized that driving broader adoption requires leaning on early adopters, surfacing use cases, and showing productivity gains so they can be replicated across environments. ... Paul Nashawaty said application development is bifurcating. Lines of business and citizen developers are taking on more responsibility for work that historically sat with professional developers. He said professional developers don’t go away – their work shifts toward “true professional development,” while line of business developers focus on immediate outcomes.

Snowflake CEO: Software risks becoming a “dumb data pipe” for AI

Ramaswamy argues that his company lives with the fear that organizations will stop using AI agents built by software vendors. There must certainly be added value for these specialized agents, for example, that they are more accurate, operate more securely, and are easier to use. For experienced users of existing platforms, this is already the case. A solution such as NetSuite or Salesforce offers AI functionality as an extension of familiar systems, whereby adoption of these features almost always takes place without migration. Ramaswamy believes that customers have the final say on this. If they want to consult a central AI and ignore traditional enterprise apps, then they should be given that option, according to the Snowflake CEO. ... However, the tug-of-war around the center of AI is in full swing. It is not without reason that vendors claim that their solution should be the central AI system, for example because they contain enormous amounts of data or because they are the most critical application for certain departments. So far, AI trends among these vendors have revolved around the adoption of AI chatbots, easy-to-set-up or ready-made agentic workflows, and automatic document generation. During several IT events over the past year, attendees toyed with the idea that old interfaces may disappear because every employee will be talking to the data via AI.

Will LLMs Become Obsolete?

“We are at a unique time in history,” write Ashu Garg and Jaya Gupta at Foundation Capital, citing multimodal systems, multiagent systems, and more. “Every layer in the AI stack is improving exponentially, with no signs of a slowdown in sight. As a result, many founders feel that they are building on quicksand. On the flip side, this flywheel also presents a generational opportunity. Founders who focus on large and enduring problems have the opportunity to craft solutions so revolutionary that they border on magic.” ... “When we think about the future of how we can use agentic systems of AI to help scientific discovery,” Matias said, “what I envision is this: I think about the fact that every researcher, even grad students or postdocs, could have a virtual lab at their disposal ...” ... In closing, Matias described what makes him enthusiastic about the future. “I'm really excited about the opportunity to actually take problems that make a difference, that if we solve them, we can actually have new scientific discovery or have societal impact,” he said. “The ability to then do the research, and apply it back to solve those problems, what I call the ‘magic cycle’ of research, is accelerating with AI tools. We can actually accelerate the scientific side itself, and then we can accelerate the deployment of that, and what would take years before can now take months, and the ability to actually open it up for many more people, I think, is amazing.”

Deepfake business risks are growing – here's what leaders need to know

The risk of deepfake attacks appears to be growing as the technology becomes more accessible. The threat from deepfakes has escalated from a “niche concern” to a “mainstream cybersecurity priority” at “remarkable speed”, says Cooper. “The barrier to entry has lowered dramatically thanks to open source software and automated creation tools. Even low-skilled threat actors can launch highly convincing attacks.” The target pool is also expanding, says Cooper. “As larger corporations invest in advanced mitigation strategies, threat actors are turning their attention to small and medium-sized businesses, which often lack the resources and dedicated cybersecurity teams to combat these threats effectively.” The technology itself is also improving. Deepfakes have already improved “a staggering amount” – even in the past six months, says McClain. “The tech is internalising human mannerisms all the time. It is already widely accessible at a consumer level, even used as a form of entertainment via face swap apps.” ... Meanwhile, technology can be helpful in mitigating deepfake attack risks. Cooper recommends deepfake detection tools that use AI to analyse facial movements, voice patterns and metadata in emails, calls and video conferences. “While not foolproof, these tools can flag suspicious content for human review.” With the risks in mind, it also makes sense to implement multi-factor authentication for sensitive requests.

The Big Shift: From “More Qubits” to Better Qubits

As quantum systems grew, it became clear that more qubits do not always mean more computing power. Most physical qubits are too noisy, unstable, and short-lived to run useful algorithms. Errors pile up faster than useful results, and after a while, the output stops making sense. Adding more fragile qubits now often makes things worse, not better. This realization has led to a shift in thinking across the field. Instead of asking how many qubits fit on a chip, researchers and engineers now ask a tougher question: how many of those qubits can actually be trusted? ... For businesses watching from the outside, this change matters. It is easier to judge claims when vendors talk about error rates, runtimes, and reliability instead of vague promises. It also helps set realistic expectations. Logical qubits show that early useful systems will be small but stable, solving specific problems well instead of trying to do everything. This new way of thinking also changes how we look at risk. The main risk is not that quantum computing will fail completely. Instead, the risk is that organizations will misunderstand early progress and either invest too much because of hype or too little because of old ideas. Knowing how important error correction is helps clear up this confusion. One of the clearest signs of maturity is how failure is handled. In early science, failure can be unclear.

Reimagining digital value creation at Inventia Healthcare

“The business strategy and IT strategy cannot be two different strategies altogether,” he explains. “Here at Inventia, IT strategy is absolutely coupled with the core mission of value-added oral solid formulations. The focus is not on deploying systems, it is on creating measurable business value.” Historically, the pharmaceutical industry has been perceived as a laggard in technology adoption, largely due to stringent regulatory requirements. However, this narrative has shifted significantly over the last five to six years. “Regulators and organisations realised that without digitalisation, it is impossible to reach the levels of efficiency and agility that other industries have achieved,” notes Nandavadekar. “Compliance is no longer a barrier, it is an enabler when implemented correctly.” ... “Digitalisation mandates streamlined and harmonised operations. Once all processes are digital, we can correlate data across functions and even correlate how different operations impact each other,” points out Nandavadekar. ... With expanding digital footprints across cloud, IoT, and global operations, cybersecurity has become a mission-critical priority for Inventia. Nandavadekar describes cybersecurity as an “iceberg,” where visible threats represent only a fraction of the risk landscape. “In the pharmaceutical world, cybersecurity is not just about hackers, it is often a national-level activity. India is emerging as a global pharma hub, and that makes us a strategic target.”

Scaling Agentic AI: When AI Takes Action, the Real Challenge Begins

Organizations often underestimate tool risk. The model is only one part of the decision chain. The real exposure comes from the tools and APIs the agent can call. If those are loosely governed, the agent becomes privileged automation moving faster than human oversight can keep up. “Agentic AI does not just stress models. It stress-tests the enterprise control plane.” ... Agentic AI requires reliable data, secure access, and strong observability. If data quality is inconsistent and telemetry is incomplete, autonomy turns into uncertainty. Leaders need a clear method to select use cases based on business value, feasibility, risk class, and time-to-impact. The operating model should enforce stage gates and stop low-value projects early. Governance should be built into delivery through reusable patterns, reference architectures, and pre-approved controls. When guardrails are standardized, teams move faster because they no longer have to debate the same risk questions repeatedly. ... Observability must cover the full chain, not just model performance. Teams should be able to trace prompts, context, tool calls, policy decisions, approvals, and downstream outcomes. ... Agentic AI introduces failure modes that can appear plausible on the surface. Without traceability and real-time signals, organizations are forced to guess, and guessing is not an operating strategy.

Security at AI speed: The new CISO reality

The biggest shift isn’t tooling, we’ve always had to choose our platforms carefully, it’s accountability. When an AI agent acts at scale, the CISO remains accountable for the outcome. That governance and operating model simply didn’t exist a decade ago. Equally, CISOs now carry accountability for inaction. Failing to adopt and govern AI-driven capabilities doesn’t preserve safety, it increases exposure by leaving the organization structurally behind. The CISO role will need to adopt a fresh mindset and the skills to go with it to meet this challenge. ... While quantification has value, seeking precision based on historical data before ensuring strong controls, ownership, and response capability creates a false sense of confidence. It anchors discussion in technical debt and past trends, rather than aligning leadership around emerging risks and sponsoring a bolder strategic leap through innovation. That forward-looking lens drives better strategy, faster decisions, and real organizational resilience. ... When a large incumbent experiences an outage, breach, model drift, or regulatory intervention, the business doesn’t degrade gracefully, it fails hard. The illusion of safety disappears quickly when you realise you don’t own the kill switches, can’t constrain behaviour in real time, and don’t control the recovery path. Vendor scale does not equal operational resilience.

Why Borderless AI Is Coming to an End

Most countries are still wrestling with questions related to "sovereign AI" - the technical ambition to develop domestic compute, models and data capabilities - and "AI sovereignty" - the political and legal right to govern how AI operates within national boundaries, said Gaurav Gupta, vice president analyst at Gartner. Most national strategies today combine both. "There is no AI journey without thinking geopolitics in today's world," said Akhilesh Tuteja, partner, advisory services and former head of cybersecurity at KPMG. ... Smaller nations, Gupta said, are increasing their investment in domestic AI stacks as they look for alternatives to the closed U.S. model, including computing power, data centers, infrastructure and models aligned with local laws, culture and region. "Organizations outside the U.S. and China are investing more in sovereign cloud IaaS to gain digital and technological independence," said Rene Buest, senior director analyst at Gartner. "The goal is to keep wealth generation within their own borders to strengthen the local economy." ... The practical barriers to AI sovereignty start with infrastructure. The level of investment is beyond the reach of most countries, creating a fundamental asymmetry in the global AI landscape. "One gigawatt new data centers cost north of $50 billion," Gupta said. "The biggest constraint today is availability of power … You are now competing for electricity with residential and other industrial use cases."

Why Data Governance Fails in Many Organizations: The IT-Business Divide

The problem extends beyond missing stewardship roles to a deeper documentation chaos. Organizations often have multiple documents addressing the same concepts, but the language varies depending on which unit you ask, when you ask, and to whom you’re speaking. Some teams call these documents “policies,” while others use terms like “guidelines,” “standards,” or “procedures.” With no clarity on which term means what or whether these documents represent the same authority level. More critically, no one has the responsibility or authority to define which version is the “appropriate” one. Documents get written – often as part of project deliverables or compliance exercises – but no governance process ensures they’re actually embedded into operations, kept current, or reconciled with other documents covering similar ground. ... Without proper governance, a problematic pattern emerges: Technical teams impose technical obligations on business people, requiring them to validate data formats, approve schema changes, or participate in narrow technical reviews, while the real governance questions go unaddressed. Business stakeholders are involved only in a few steps of the data lifecycle, without understanding the whole picture or having authority over business-critical decisions. ... The governance challenges become even more insidious when organizations produce reports that appear identical in format while concealing fundamental differences in their underlying methodology.

Daily Tech Digest - February 14, 2026

Quote for the day:

"Always remember, your focus determines your reality." -- George Lucas

UK CIOs struggle to govern surge in business AI agents

The findings point to a growing governance challenge alongside the rapid spread of agent-based systems across the enterprise. AI agents, which can take actions or make decisions within software environments, have moved quickly from pilots into day-to-day operations. That shift has increased demands for monitoring, audit trails and accountability across IT and risk functions. UK CIOs also reported growing concern about the spread of internally built tools. ... The results suggest "shadow AI" risks are becoming a mainstream issue for large organisations. As AI development tools get easier to use, more staff outside IT can build automated workflows, chatbots and agent-like applications. This trend has intensified questions about data access, model behaviour, and whether organisations can trace decisions back to specific inputs and approvals. ... The findings also suggest governance gaps are already affecting operations. Some 84% of UK CIOs said traceability or explainability shortcomings have delayed or prevented AI projects from reaching production, highlighting friction between the push to deploy AI and the work needed to demonstrate effective controls. For CIOs, the issue also intersects with enterprise risk management and information security. Unmonitored agents and rapidly developed internal apps can create new pathways into sensitive datasets and complicate incident response if an organisation cannot determine which automated process accessed or changed data.

You’ve Generated Your MVP Using AI. What Does That Mean for Your Software Architecture?

While the AI generates an MVP, teams can’t control the architectural decisions that the AI made. They might be able to query the AI on some of the decisions, but many decisions will remain opaque because the AI does not understand why the code that it learned from did what it did. ... From the perspective of the development team, AI-generated code is largely a black-box; even if it could be understood, no one has time to do so. Software development teams are under intense time pressure. They turn to AI to partially relieve this pressure, but in doing so they also increase the expectations of their business sponsors regarding productivity. ... As a result, the nature of the work of architecting will shift from up-front design work to empirical evaluation of QARs, i.e. acceptance testing of the MVA. As part of this shift, the development team will help the business sponsors figure out how to test/evaluate the MVP. In response, development teams need to get a lot better at empirically testing the architecture of the system. ... The team needs to know what trade-offs it may need to make, and they need to articulate those in the prompts to the AI. The AI then works as a very clever search engine to find possible solutions that might address the trade-offs. As noted above, these still need to be evaluated empirically, but it does save the team some time in coming up with possible solutions.

Successful Leaders Often Lack Self-Awareness

As a leader, how do you respond in emotionally charged situations? It's under pressure that emotions can quickly escalate and unexamined behavioral patterns emerge—for all of us. In my work with senior executives, I have seen time and again how these unconscious “go-to” reactions surface when stakes are high. This is why self-awareness is not a one-time achievement but a lifelong practice—and for many leaders, it remains their greatest blind spot. Why? ... Turning inward to develop self-awareness naturally places you in uncomfortable territory. It challenges long-standing assumptions and exposes blind spots. One client came to me because a colleague described her as harsh. She genuinely did not see herself that way. Another sought my help after his CEO told him he struggled to communicate with him. Through our work together, we uncovered how defensively he responded to feedback, often without realizing it. ... As leaders rise to the top, the accolades that propel them forward are rooted in talent, strategic decision-making and measurable outcomes. However, once at the highest levels, leadership expands beyond execution. The role now demands mastery of relationships—within the organization and beyond, with clients, partners and customers. At this level, self-awareness is no longer optional; it becomes essential.

How Should Financial Institutions Prepare for Quantum Risk?

“Post-quantum cryptography is about proactively developing and building capabilities to secure critical information and systems from being compromised through the use of quantum computers,” said Rob Joyce, then director of cybersecurity for the National Security Agency, in an August 2023 statement. In August 2024, NIST published three post-quantum cryptographic standards — ML-KEM, ML-DSA and SLH-DSA — designed to withstand quantum attacks. These standards are intended to secure data across systems such as digital banking platforms, payment processing environments, email and e-commerce. NIST has encouraged organizations to begin implementation as soon as possible. ... A critical first step is conducting an assessment of which systems and data assets are most at risk. The ISACA IT security organization recommends building a comprehensive inventory of systems vulnerable to quantum attacks and classifying data based on sensitivity, regulatory requirements and business impact. For financial institutions, this assessment should prioritize customer PII, transaction data, long-term financial records and proprietary business information. Understanding where the greatest financial, reputational and regulatory exposure exists enables IT leaders to focus mitigation efforts where they matter most. Institutions should also conduct executive briefings, staff training and tabletop exercises to build awareness.

The cure for the AI hype hangover

The way AI dominates the discussions at conferences is in contrast to its slower progress in the real world. New capabilities in generative AI and machine learning show promise, but moving from pilot to impactful implementation remains challenging. Many experts, including those cited in this CIO.com article, describe this as an “AI hype hangover,” in which implementation challenges, cost overruns, and underwhelming pilot results quickly dim the glow of AI’s potential. Similar cycles occurred with cloud and digital transformation, but this time the pace and pressure are even more intense. ... Too many leaders expect AI to be a generalized solution, but AI implementations are highly context-dependent. The problems you can solve with AI (and whether those solutions justify the investment) vary dramatically from enterprise to enterprise. This leads to a proliferation of small, underwhelming pilot projects, few of which are scaled broadly enough to demonstrate tangible business value. In short, for every triumphant AI story, numerous enterprises are still waiting for any tangible payoff. For some companies, it won’t happen anytime soon—or at all. ... Beyond data, there is the challenge of computational infrastructure: servers, security, compliance, and hiring or training new talent. These are not luxuries but prerequisites for any scalable, reliable AI implementation. In times of economic uncertainty, most enterprises are unable or unwilling to allocate the funds for a complete transformation.

4th-Party Risk: How Commercial Software Puts You At Risk

Unlike third-party providers, however, there are no contractual relationships between businesses and their fourth-party vendors. That means companies have little to no visibility into those vendors' operations, only blind spots that are fueling an even greater need to shift from trust-based to evidence-based approaches. That lack of visibility has severe consequences for enterprises and other end-user organizations. ... Illuminating 4th-party blind spots begins with mapping critical dependencies through direct vendors. As you go about this process, don't settle for static lists. Software supply chains are the most common attack vector, and every piece of software you receive contains evidence of its supply chain. This includes embedded libraries, development artifacts, and behavioral patterns. ... Businesses must also implement some broader frameworks that go beyond the traditional options, such as NIST CSF or ISO 27001, which provide a foundation but ultimately fall short by assuming businesses lack control in their fourth-party relationships. This stems from the fact that no contractual relationships exist that far downstream, and without contractual obligations, a business cannot conduct risk assessments, demand compliance documentation, or launch an audit as it might with a third-party vendor. ... Also consider SLSA (Supply Chain Levels for Software Artifacts). These provide measurable security controls to prevent tampering and ensure integrity. For companies operating in regulated industries, consider aligning with emerging requirements.

Geopatriation and sovereign cloud: how data returns to the source

The key to understanding a sovereign cloud, adds Google Cloud Spain’s national technology director Héctor Sánchez Montenegro, is that it’s not a one-size-fits-all concept. “Depending on the location, sector, or regulatory context, sovereignty has a different meaning for each customer,” he says. Google already offers sovereign clouds, whose guarantee of sovereignty isn’t based on a single product, but on a strategy that separates the technology from the operations. “We understand that sovereignty isn’t binary, but rather a spectrum of needs we guarantee through three levels of isolation and control,” he adds. ... One of the certainties of this sovereign cloud boom is it’s closely connected to the context in which organizations, companies, and other cloud end users operate. While digital sovereignty was less prevalent at the beginning of the century, it’s now become ubiquitous, especially as political decisions in various countries have solidified technology as a key geostrategic asset. “Data sovereignty is a fundamental part of digital sovereignty, to the point that in practice, it’s becoming a requirement for employment contracts,” says María Loza ... With the technological landscape becoming more unsure and complex, the goal is to know and mitigate risks where possible, and create additional options. “We’re at a crucial moment,” Loza Correa points out. “Data is a key business asset that must be protected.”

Managing AI Risk in a Non-Deterministic World: A CTO’s Perspective

Drawing parallels to the early days of cloud computing, Chawla notes that while AI platforms will eventually rationalize around a smaller set of leaders, organizations cannot afford to wait for that clarity. “The smartest investments right now are fearlessly establishing good data infrastructure, sound fundamentals, and flexible architectures,” she explains. In a world where foundational models are broadly accessible, Chawla argues that differentiation shifts elsewhere. ... Beyond tooling, Chawla emphasizes operating principles that help organizations break silos. “Improve the quality at the source,” she says. “Bring DevOps principles into DataOps. Clean it up front, keep data where it is, and provide access where it needs to be.” ... Bias, hallucinations, and unintended propagation of sensitive data are no longer theoretical risks. Addressing them requires more than traditional security controls. “It’s layering additional controls,” Chawla says, “especially as we look at agentic AI and agentic ops.” ... Auditing and traceability are equally critical, especially as models are fine-tuned with proprietary data. “You don’t want to introduce new bias or model drift,” she explains. “Testing for bias is super important.” While regulatory environments differ across regions, Chawla stresses that existing requirements like GDPR, data sovereignty, PCI, and HIPAA still apply. AI does not replace those obligations; it intensifies them.

CVEs are set to top 50,000 this year, marking a record high – here’s how CISOs and security teams can prepare for a looming onslaught

"Much like a city planner considering population growth before commissioning new infrastructure, security teams benefit from understanding the likely volume and shape of vulnerabilities they will need to process," Leverett added. "The difference between preparing for 30,000 vulnerabilities and 100,000 is not merely operational, it’s strategic." While the figures may be jarring for business leaders, Kevin Knight, CEO of Talion, said it’s not quite a worst-case scenario. Indeed, it’s the impact of the vulnerabilities within their specific environments that business leaders and CISOs should be focusing on. ... Naturally, security teams could face higher workloads and will be contending with a more perilous threat landscape moving forward. Adding insult to injury, Knight noted that security teams are often brought in late during the procurement process - sometimes after contracts have been signed. In some cases, applications are also deployed without the CISO’s knowledge altogether, creating blind spots and increasing the risk that critical vulnerabilities are being missed. Meanwhile, poor third-party risk management means organizations can unknowingly inherit their suppliers’ vulnerabilities, effectively expanding their attack surface and putting their sensitive data at risk of being breached. "As CVE disclosures continue to rise, businesses must ensure the CISO is involved from the outset of technology decisions," he said.

Data Privacy in the Age of AI

The first challenge stems from the fact that AI systems run on large volumes of customer data. This “naturally increases the risk of data being used in ways that go beyond what customers originally expected, or what regulations allow,” says Chiara Gelmini, financial services industry solutions director at Pegasystems. This is made trickier by the fact that some AI models can be “black boxes to a certain degree,” she says. “So it’s not always clear, internally or to customers, how data is used or how decisions are actually made," she tells SC Media UK. ... AI is “fully inside” the existing data‑protection regime the UK General Data Protection Regulation (UK GDPR) and the Data Protection Act 2018, Gelmini explains. Under these current laws, if an AI system uses personal data, it must meet the same standards of lawfulness, transparency, data minimisation, accuracy, security and accountability as any other processing, she says. Meanwhile, organisations are expected to prove they have thought the area through, typically by carrying out a Data Protection Impact Assessment (DPIA) before deploying high‑risk AI. ... The growing use of AI can pose a risk, but only if it gets out of hand. As AI becomes easier to adopt and more widespread, the practical way to stay ahead of these risks is “strong, AI governance,” says Gelmini. “Firms should build privacy in from the start, mask private data, lock down security, make models explainable, test for bias, and keep a close eye on how systems behave over time."