Daily Tech Digest by Kannan Subbiah: multi-cloud

Showing posts with label multi-cloud. Show all posts

Daily Tech Digest - August 13, 2026

Quote for the day:

“Personal growth is not a matter of learning new information but unlearning old limits.” -- Alan Cohen

🎧 Listen to the audio debrief on YouTube

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

4 RPA lessons that still hold true in the AI boom

As companies rush to adopt new artificial intelligence tools, many are stumbling over the exact same hurdles they faced years ago with robotic process automation. To succeed with AI technology today, organizations should remember four vital lessons from the past. First, they must carefully choose what to automate. Applying new technology to a broken or inefficient process only speeds up the creation of bad results. Every automation project needs a clear, measurable business benefit before it begins. Second, automation is never a project you can simply turn on and ignore. Because artificial intelligence acts quickly and sounds confident, keeping human experts in the loop is essential to prevent small errors from becoming large failures. Third, the quality of the information you feed the system remains critical. While modern tools can read messy data, they can easily misunderstand context, leading to flawed decisions on a massive scale. Finally, managing how people adapt to the changes is the most difficult challenge of all. Most technology projects fail because of people and workflows, not the software itself. Rather than abandoning older, predictable automation methods entirely, smart organizations are combining them with new artificial intelligence to create highly reliable, cost-effective, and highly practical solutions.

The intelligent workplace (part 2): Technology’s next transformation of work

As artificial intelligence takes on a larger role in the modern workplace, organizations must rethink how they manage teams and measure performance. The traditional focus on the sheer volume of tasks completed, such as reports written or cases closed, is no longer effective when automated tools can generate that output almost instantly. Instead, managers need to prioritize the actual quality of work, accuracy, and the ability to solve the right problems. Rather than competing with machines on speed, employees should focus on areas where human judgment remains critical. Furthermore, managers are shifting from simply overseeing daily activity to deliberately designing workflows where people and technology support each other. This change requires establishing clear rules for when employees should rely on automated systems and when they need to step in and override them. Ultimately, accountability must always rest with humans. A major challenge is ensuring junior employees still develop necessary expertise, as the routine tasks they traditionally learned from are now handed off to software. Companies will need to create deliberate opportunities for practice, mentoring, and direct feedback. Finally, successfully integrating these tools relies heavily on trust and transparency. Leaders must maintain human oversight, protect time for learning, and ensure that automated metrics do not replace empathy and open communication.

AI, Digital Twins, and Cybersecurity in Industrial Remote Operations

The second part of this article series explores how artificial intelligence and virtual models—often called digital twins—are fundamentally changing remote industrial operations, while highlighting the serious cybersecurity challenges that come with them. Instead of waiting for machines to break down, AI allows manufacturers to shift from reactive monitoring to predictive maintenance. By analyzing patterns in temperature, vibration, and power use, these systems can spot equipment failures weeks in advance. This capability drastically reduces unplanned downtime and lowers maintenance costs. Meanwhile, digital twins serve as the virtual interface for these physical systems. Engineers can use these exact digital copies to run simulations, test adjustments, and manage entire production lines remotely, achieving a level of oversight that previously required being physically present on the factory floor. However, moving factory controls online introduces major network security risks. Manufacturing remains a prime target for cyberattacks, and every new remote connection is a potential entry point. This risk is complicated by a severe shortage of security professionals who actually understand industrial systems. Ultimately, building a secure foundation is what makes these remote capabilities possible. Organizations that proactively address their network security can safely unlock the very real efficiency and productivity benefits of these modern industrial tools.

Social engineering reshapes financial fraud as attacks scale

Social engineering has rapidly emerged as the primary method for financial fraud, moving away from complex technical hacking toward manipulating human behavior. Recent data reveals that impersonation scams in the United States have more than doubled over the past year. Fraudsters frequently pose as trusted organizations, celebrities, or relatives to deceive individuals into authorizing transactions themselves. Investment scams are currently causing the most financial damage, with criminals using fake websites and fabricated platforms to create a false sense of urgency. This trend is not limited to everyday consumers; major Wall Street firms, including hedge funds and private equity companies, are also defending against sophisticated phone-based attacks targeting their employees. Adding to the challenge is the growing commercial market for these scams. Rather than building malicious systems from the ground up, criminals can now purchase ready-made scam kits online. These affordable packages provide everything needed to launch convincing campaigns, such as fake cryptocurrency presales with personalized elements and countdown timers. By lowering the barrier to entry, these kits allow individuals with minimal technical skills to execute highly professional and persuasive scams. Ultimately, modern financial fraud relies less on defeating security software and more on exploiting human trust through highly convincing deception.

Tokenmaxxing: The strangest developer productivity metric of all time

A concerning trend called "tokenmaxxing" has emerged in software engineering, where developers are evaluated by how much AI computing power they consume rather than the quality of their code. Much like the outdated practice of measuring productivity by lines of code, this metric encourages the wrong behaviors. When companies reward raw token usage, developers are incentivized to generate massive amounts of unrefined code, stuff prompts with unnecessary text, and set up automated systems simply to climb internal leaderboards. This careless approach leads to higher code duplication, less thoughtful refinement, and software that is quickly discarded. Beyond degrading software quality, tokenmaxxing is financially destructive. The blind pursuit of AI usage has caused companies to burn through budgets rapidly, forcing some to restrict their access to these tools. Furthermore, this flawed measurement ignores the most valuable ways developers use AI, such as debugging complex issues or planning architectural designs, because these tasks do not generate high token counts. Ultimately, true software engineering requires careful planning and simplification. AI is a helpful tool for solving problems and learning, but using it effectively means focusing on meaningful outcomes rather than blindly treating the volume of AI interactions as a sign of success.

Architecting Multi-Cloud Networks to Survive Cryptographic Migrations under DORA Rules

The article outlines the critical intersection of the European Union’s Digital Operational Resilience Act, multi-cloud network strategies, and the impending shift toward post-quantum cryptography. Under DORA, financial institutions face strict mandates to ensure continuous operational resilience and to mitigate third-party concentration risks. This effectively makes multi-cloud and cloud-agnostic architectures a necessity rather than a mere option, as organizations can no longer rely on a single cloud provider without a tested, actionable exit strategy. As the financial industry prepares for complex cryptographic migrations to defend against advanced quantum computing threats, these multi-cloud network architectures will be put to the ultimate test. Updating long-lived trust chains, encryption protocols, and digital certificates across sprawling IT environments is an inherently risky process. The text explains that surviving this transition without violating DORA’s strict uptime requirements demands highly decoupled network designs. By strategically distributing workloads and avoiding deep dependencies on provider-specific services, financial entities can safely manage phased cryptographic updates. Ultimately, a well-architected multi-cloud environment is essential not just for avoiding vendor lock-in, but as a robust safety net. It allows institutions to implement sweeping security upgrades smoothly, ensuring total compliance and uninterrupted service delivery in a heavily regulated modern landscape.

The web’s newest weapon against AI scrapers is a font

Designers Isaque Seneda and Gabriel Abrucio have developed a new typeface called ShieldFont, designed to protect online content from unauthorized data extraction by artificial intelligence companies. The core mechanism relies on the traditional ligature feature found in standard typography. While a web page using ShieldFont appears perfectly normal and readable to human visitors, the underlying HTML source code is intentionally altered. When AI scrapers and automated web crawlers attempt to harvest the website text, they encounter only random, meaningless data instead of the actual content. This approach offers web publishers a practical technical method to prevent their work from being absorbed into AI training datasets without permission. Unlike earlier blocking methods that often disrupted the user experience or proved ineffective, ShieldFont specifically targets the data collection process by intentionally ruining the harvested text. Experts note that the success of this method depends on how well the substitution strategy is executed. If the replacements rely on simple patterns, such as direct synonyms or antonyms, advanced algorithms might learn to reverse the alterations. By focusing on random string generation and complex substitutions, ShieldFont aims to safeguard digital ownership and provide a reliable defense against the aggressive scraping tactics currently used across the internet.

Post-Quantum Deadlines Collide With OT Reality

The transition to post-quantum cryptography is becoming an urgent priority as looming regulatory deadlines clash with the practical constraints of operational technology environments. While government agencies and security bodies push for rapid adoption of quantum-resistant algorithms to protect critical infrastructure, the realities of operational technology present significant engineering and logistical hurdles. Unlike standard enterprise networks, operational technology systems like industrial control units, medical devices, and smart grids are built for longevity. They often run on older hardware with limited processing power and minimal memory. These strict constraints make it exceedingly difficult to implement complex new cryptographic standards without disrupting essential services or triggering massive hardware replacement cycles. Furthermore, the threat is not entirely theoretical. Adversaries are actively engaging in "harvest now, decrypt later" campaigns, collecting encrypted data today to break it once quantum computing matures. Consequently, securing these industrial environments requires a nuanced approach rather than a simple software update. Organizations must begin their planning immediately by conducting thorough inventories of their cryptographic assets. They should isolate vulnerable operational systems through strict network segmentation and adopt hybrid security models. Ultimately, building flexible encryption into aging infrastructure is crucial for navigating the tension between ambitious mandates and the slow-moving reality of industrial technology.

Beyond Cyber Protection: How European Companies Can Operate Through Cyber Disruption

European businesses face an evolving threat landscape where preventing cyberattacks entirely is simply no longer a realistic expectation. Driven by integrated supply chains and rapid artificial intelligence adoption, companies remain vulnerable despite heavy investments in traditional security. According to recent research, while many executives expect to recover from incidents like ransomware within days, actual disruptions often take months to resolve. To navigate this reality, leaders must transition their focus from basic protection to true operational resilience. This means acknowledging that some attacks will succeed and designing systems capable of operating under stress. Executives should start by identifying their essential operating core, which includes the critical services, data, and processes that must remain available during a crisis. Additionally, while strict regulations establish important security baselines, compliance should be viewed as a starting point rather than the ultimate goal. True resilience requires engineering robust recovery processes rather than simply hoping for a rapid response. It also demands making resilience a company wide responsibility, extending these practices across the entire value chain, and fully understanding the economic costs of a disruption. By accepting the inevitability of breaches and planning for continuity, organizations can confidently sustain their core functions and protect their stability during a severe disruption.

AI Agents Are Creating a New Identity Security Challenge for Enterprises

Morey Haber outlines the necessity of treating artificial intelligence agents as a unique class of non-human identity that requires strict security controls. Unlike standard software or human users, these agents operate autonomously, make independent decisions, and run on unpredictable schedules. Because they can reason and interact with other systems on their own, traditional access management is simply not enough. Organizations must assign each agent a specific identity tied to an accountable human owner. Instead of relying on permanent passwords, these agents should use temporary security secrets and be granted the absolute minimum access required to complete a specific task. Furthermore, security teams must monitor their behavior constantly rather than just checking their login credentials, looking for unusual activity or excessive data access. Proper management also means tracking an agent from the moment it is created to when it is retired. Crucially, companies need a reliable kill switch to instantly revoke an agent's access if it behaves improperly or is compromised by an attacker. By managing these tools with calm, steady oversight and limiting their permissions, organizations can prevent them from becoming dangerous entry points for cyber threats. Ultimately, an agent should never hold more power than you are prepared for it to misuse.

Daily Tech Digest - July 22, 2026

Quote for the day:

“Identify your problems but give your power and energy to solutions.” -- Tony Robbins

🎧 Listen to the audio debrief on YouTube

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Duration: 20 mins • Perfect for listening on the go.

Context bombing heralds a new AI era of deceptive defense

The article describes a defensive technique called “context bombing,” which uses the weaknesses of malicious AI agents against them. Attackers increasingly rely on autonomous AI models to speed up every stage of a cyberattack, from reconnaissance to exploitation. To counter this, defenders plant decoy files or secrets that contain short, carefully crafted prompts designed to trigger an AI model’s built‑in safety rules. When a rogue agent reads one of these prompts, it often stops executing its task entirely, halting the attack rather than simply alerting defenders. This builds on traditional “canary” techniques, where fake resources signal unauthorized access, but adds an active disruption layer. Tracebit, the firm behind the approach, tested context bombs in an AWS environment and found they reduced attack success rates by up to 90% by causing models to refuse further action . Because AI agents are vulnerable to prompt injection, hidden instructions placed in documents, DNS records, or environment variables can derail them mid‑operation. As one researcher explained, once the refusal enters the model’s context, “the model will often refuse to continue”. Context bombing heralds a new AI era of deceptive defense. The technique doesn’t replace other defenses, but it buys time, limits damage, and turns attackers’ reliance on AI into a practical point of failure.

Reskilling Mid-Career Leaders: What Senior Talent Needs to Stay Relevant

The discussion focuses on how mid‑career leaders can stay relevant as AI reshapes the workplace. Host Isaac Sacolick and guest Dean Cantave talk about the anxiety many senior professionals feel as their long‑held strengths no longer guarantee future opportunities. They emphasize that staying relevant now requires more than collecting certifications; leaders need to show clear, visible proof of their impact through thoughtful communication, public work, and practical results. Critical thinking, collaborative leadership, and strong data governance skills are highlighted as essential, along with understanding how AI agents and automation change decision‑making and team dynamics. The conversation also notes that leadership roles are becoming more cross‑functional, pushing senior talent to adapt their style, learn new tools, and work more fluidly across departments. Participants share personal stories about career transitions, stressing that credibility today comes from demonstrating how one’s experience translates into modern challenges rather than relying on past titles. They encourage leaders to build a recognizable professional presence, articulate their value clearly, and stay open to continuous learning. Overall, the session frames reskilling not as starting over but as evolving deliberately to match the demands of an AI‑driven workplace.

The Resilience Paradox – Why Autonomous Operations Require a New Approach to Governance

The article argues that as organizations move toward autonomous operations, their traditional governance models no longer fit the reality of how modern systems behave. It explains that observability has matured to the point where most companies can detect issues, but the real question now is how much decision‑making they are willing to hand over to AI. As environments grow more complex and produce more telemetry than humans can reasonably process, AIOps becomes essential for filtering noise and spotting patterns. However, each step toward autonomy reduces human workload while increasing the impact of a wrong automated decision. The piece notes that different teams often advance at different speeds, with platform groups embracing automation early while critical business systems remain manually governed. This uneven maturity creates a “resilience paradox”: delegating more to AI can strengthen reliability, but it also introduces new risks that governance frameworks were not designed to handle. The author stresses that resilience is no longer just about detecting problems but about deciding when systems should act on their own. As organizations shift from observation to autonomous action, they must rethink governance to ensure accountability, manage new categories of risk, and maintain trust in systems that increasingly make decisions without human intervention.

Technology moves faster than ecosystems

The article argues that many digital transformation efforts fail because technology evolves far faster than the ecosystems needed to support it. Companies invest heavily in advanced monitoring, automation, and predictive systems, yet execution performance often worsens. As the author notes, unplanned downtime rose to $1.4 trillion even as digital capability increased, revealing a structural gap where “technology advances faster than the ecosystems required to realize its value.” The paper explains that most industries operate across three maturity tiers, from highly digital enterprises to SMEs still dependent on spreadsheets and email. This mismatch means Tier‑1 intelligence layers can detect problems early, but Tier‑2 and Tier‑3 execution layers cannot respond at the same pace. The semiconductor shortage illustrates this clearly: Toyota’s deeper visibility helped for a time, but “the execution layer… still could not respond on the same timescale.” Workforce capability and physical infrastructure add further delays, evolving over years or decades while technology changes in months. To address this, the author proposes four architectural principles: design for graceful degradation, instrument for friction, build coordination layers, and orchestrate across the ecosystem rather than optimizing only within the enterprise. The core message is that digital transformation succeeds only when decision and execution architectures mature together.

SaaS will survive, but lazy SaaS is dead

The article argues that SaaS is not disappearing, but the old model of “lightweight” SaaS — tools that mainly provide a polished interface over simple workflows — is losing its footing. The author describes an internal review of AI meeting‑transcription tools where the products worked fine, yet the team kept asking, “what exactly are we paying for?” . Because they already had a secure AI environment, they could build the same workflow themselves in days and tailor it to their needs. This experience reflects a broader shift: AI and agentic systems have erased the old advantage SaaS once had, where buying was cheaper and faster than building. Large language models can now move data, call APIs, and automate logic with far less engineering effort, collapsing the integration friction that protected many SaaS categories. The SaaS most at risk are the thin workflow layers — dashboards, meeting tools, narrow productivity apps — whose value rested on simplifying implementation. Agents don’t use interfaces, and they don’t care about switching costs, which weakens the stickiness of these products. The SaaS that endures will be the kind that carries real operational burden for customers, such as compliance, regulatory complexity, or domain‑specific liability. In short, SaaS survives, but “lazy SaaS” — tools that exist mainly because integration used to be hard — does not.

Closing the Identity Gaps in Critical Infrastructure Security

Critical infrastructure remains highly vulnerable to identity‑based attacks, and the article explains why closing those gaps is now essential. It uses the Colonial Pipeline ransomware incident as a clear example, where attackers accessed the network through an inactive VPN account without MFA, leading to a shutdown that disrupted fuel supply across the U.S. East Coast . The piece notes that today’s threat actors, including state‑sponsored groups like Volt Typhoon, rely on stolen credentials, compromised devices, and legitimate remote‑access tools to blend into normal activity and maintain long‑term persistence inside critical infrastructure networks. Because these environments combine IT, cloud services, operational technology, and physical systems, implicit trust becomes dangerous. CISA’s guidance stresses that OT systems require careful handling due to safety and legacy constraints, but the article makes clear that business IT systems can be just as damaging when compromised. The core message is that MFA alone is not enough; organizations must verify both user identity and device trust, enforce segmentation, and continuously monitor for abnormal access patterns. Binding identities to trusted devices and eliminating unmanaged endpoints are highlighted as practical steps. Overall, the article urges critical‑infrastructure operators to adopt zero‑trust principles across both IT and OT so attackers cannot quietly enter, persist, and escalate into national‑level disruptions.

When your vehicle outlives its cloud: What happens next?

The article looks at what happens when a car’s cloud‑based features stop working long before the vehicle itself reaches the end of its life. Modern cars rely heavily on connected services for conveniences like remote locking, cabin pre‑conditioning, vehicle status checks, and emergency assistance. As Ars Technica notes, these features have become standard across brands, from HondaLink to BMW ConnectedDrive, and many owners willingly pay subscription fees to keep them active . The problem is that these services depend on backend systems, cellular networks, and telematics hardware that have much shorter lifespans than the vehicles they support. When networks shut down or manufacturers retire older platforms, owners can lose access to features overnight. A related report highlights how 3G shutdowns caused Lexus, Acura, and BMW to discontinue connected services for older models, sometimes leaving drivers with no upgrade path or costly hardware replacements. The mechanical car remains usable, but the digital layer quietly expires. The article suggests that this mismatch will only grow as more vehicles become internet‑dependent. Without modular hardware or long‑term support commitments, many drivers will eventually face a future where the car still runs but the cloud it depends on does not — raising practical questions about reliability, ownership, and the real lifespan of connected technology.

Designing Multi-Cloud Resiliency for Business Continuity

The piece explains why multi‑cloud strategies are becoming essential for business continuity, especially as outages, cyberattacks, and regional disruptions grow more frequent. It argues that relying on a single cloud provider creates a concentration risk: if that provider suffers a failure, the organization’s critical services may go down with it. Multi‑cloud architectures spread workloads across different providers, reducing the chance that one incident can halt operations. The article notes that this approach is not simply about redundancy; it is about designing systems that can operate even when parts of the environment are degraded. That includes planning for data portability, consistent security controls, and clear failover procedures. The author stresses that resilience requires more than technical configuration. Teams must understand how applications behave under stress, test recovery paths regularly, and ensure that governance policies support cross‑cloud operations. Multi‑cloud also introduces complexity, so organizations need strong visibility, shared standards, and disciplined architecture to avoid fragmentation. The core message is that resilience comes from intentional design: distributing risk, preparing for partial failures, and ensuring that critical functions can continue even when one cloud provider experiences trouble. In a world where disruptions are inevitable, multi‑cloud is presented as a practical way to keep essential services running with confidence.

From the bank branch to the mobile phone: India’s core banking journey

The article traces how India’s banking system evolved from branch‑centric operations to today’s mobile‑first experience, showing that this shift was gradual, uneven, and shaped by both technology and policy. It begins with the early core‑banking era, when banks moved from isolated branch systems to centralized platforms that allowed customers to access services from any branch. This foundation enabled nationwide expansion and consistent service delivery. As digital payments grew and smartphones became widespread, banks shifted again—this time from centralized infrastructure to digital channels that could support millions of small, real‑time transactions. The piece highlights how mobile banking, UPI, and app‑based services transformed customer expectations, pushing banks to modernize legacy systems, strengthen cybersecurity, and redesign processes for speed and reliability. It also notes that modernization is not only about technology; banks had to rethink architecture, improve integration, and adopt cloud‑ready platforms to keep pace with rising transaction volumes. The journey reflects India’s broader digital transformation: a move from physical branches to digital ecosystems that reach rural and urban customers alike. The article closes with a reminder that modernization is ongoing, and banks must continue refining their core systems to stay resilient and competitive in a fast‑changing financial landscape.

What is RPA? A revolution in business process automation

The article explains robotic process automation (RPA) in straightforward terms, focusing on what it is, how it works, and why organizations use it. RPA relies on software “bots” that mimic the steps a person takes on a computer—logging in, clicking buttons, copying data, moving files, and completing routine tasks much faster and without human error. These bots are best suited for high‑volume, rule‑based work on structured data, such as invoice processing, claims handling, report generation, and other repetitive back‑office activities. Because RPA operates at the user‑interface level, it works across existing applications without requiring deep system changes or complex integrations, making it practical for organizations with legacy systems. Sources note that RPA frees employees from tedious tasks so they can focus on work that requires judgment or creativity. RPA is not the same as AI; it cannot learn or make decisions outside its predefined workflow, though pairing it with AI enables more advanced “intelligent automation” capable of handling unstructured inputs or basic reasoning. The article also highlights that RPA can run unattended in the background or assist users directly, and its appeal continues to grow as businesses seek speed, accuracy, and consistency in routine operations. Overall, RPA is presented as a practical, dependable way to streamline repetitive digital work.

Daily Tech Digest - March 07, 2026

Quote for the day:

"Be willing to make decisions. That's the most important quality in a good leader." -- General George S. Patton, Jr.

LangChain's CEO argues that better models alone won't get your AI agent to production

contends that achieving production-ready requires more than just utilizing more powerful foundational models. While improved offer better reasoning, Chase emphasizes that agents often fail due to systemic issues rather than model limitations. He advocates for a shift toward "agentic" engineering, where the focus moves from simple prompting to building robust, stateful systems. A critical component of this transition is the move away from "vibe-based" development—relying on subjective successes—toward rigorous evaluation frameworks like . Chase highlights that developers must implement precise control over an agent's logic through tools like LangGraph, which allows for cycles, state management, and human-in-the-loop interactions. These architectural guardrails are essential for managing the inherent unpredictability of LLMs. By treating agent development as a complex systems engineering task, organizations can overcome the "last mile" hurdle, moving beyond impressive demos to reliable, autonomous applications. Ultimately, the maturity of AI agents depends on sophisticated orchestration, detailed observability, and a willingness to architect the environment in which the model operates, rather than expecting a single model to handle every nuance of a complex workflow autonomously.

Where Multi-Factor Authentication Stops and Credential Abuse Starts

This article examines the false sense of security provided by multi-factor authentication (MFA) within Windows-centric environments. While MFA is highly effective for cloud-based applications, the piece argues that traditional paths—such as interactive logons, Remote Desktop Protocol (RDP) sessions, and Server Message Block (SMB) traffic—often bypass modern identity providers, leaving internal networks vulnerable to password-only attacks. The article details seven critical gaps, including the persistence of legacy susceptible to , the abuse of Kerberos tickets, and the risks posed by unmonitored service accounts or local administrator credentials that frequently lack MFA coverage. To mitigate these significant risks, the author recommends that organizations treat Windows authentication as a distinct security surface by enforcing longer passphrases, continuously blocking compromised passwords, and strictly limiting legacy protocols. Furthermore, the text highlights the importance of auditing service accounts and leveraging advanced security tools like Specops Password Policy to bridge the gap between cloud security and on-premises infrastructure. Ultimately, securing a modern enterprise requires moving beyond simple MFA implementation toward a holistic strategy that addresses these often-overlooked internal authentication vulnerabilities and credential reuse habits.

Why enterprises are still bad at multicloud

In this InfoWorld analysis, argues that while most enterprises are technically multicloud by default, they largely fail to operate them as a cohesive business capability. Instead of a unified strategy, multicloud environments often emerge haphazardly through mergers, acquisitions, or localized team decisions, leading to fragmented "technology estates" that function as isolated silos. Each provider—typically AWS, Azure, and Google—is managed with its own native consoles, security protocols, and talent pools, which creates redundant processes, inconsistent governance, and hidden global costs. Linthicum emphasizes that the "" of multicloud is only worth paying if organizations can achieve operational commonality. He advocates for the implementation of common control planes—shared services for identity, policy, and observability—that sit above individual cloud brands to ensure consistent guardrails. To improve maturity, enterprises must shift from viewing cloud adoption as a series of procurement choices to designing a singular operating model. By establishing cross-cloud coordination and relentlessly measuring business value through metrics like recovery speed and unit economics, organizations can move from uncontrolled variety to "controlled optionality," finally leveraging the specialized strengths of different providers without multiplying their operational overhead or fracturing their technical foundations.

The Accidental Orchestrator

This article by O'Reilly Radar examines the profound transformation of the software developer's role in the era of . It posits that developers are transitioning from traditional manual coding to becoming strategic orchestrators of autonomous AI agents. This shift, described as "accidental," occurred as AI tools evolved from simple autocomplete plugins into sophisticated assistants capable of managing complex, end-to-end tasks. Developers now find themselves overseeing a fleet of agents that handle various components of the software lifecycle, including design, implementation, and debugging. This new reality demands a significant pivot in professional skills; instead of focusing primarily on syntax and logic, engineers must now master , agent coordination, and high-level system architecture. The piece emphasizes that while AI significantly boosts productivity, the complexity of managing these interlinked systems introduces critical challenges regarding transparency, security, and long-term reliability. Ultimately, the role of the accidental orchestrator requires a mindset shift where the developer acts as a tactical director of digital workers rather than a lone creator. This evolution suggests that the future of software engineering lies in the quality of the human-AI partnership and the effective orchestration of intelligent agents.

Powering the new age of AI-led engineering in IT at Microsoft

Microsoft Digital is spearheading a transformative shift toward AI-led engineering, fundamentally changing how IT services are designed, built, and maintained. At the heart of this evolution is the integration of and other generative AI tools, which empower developers to automate repetitive "toil" and focus on high-value architectural innovation. By adopting a platform-centric approach, Microsoft standardizes development environments and leverages AI to enhance security, catch bugs earlier, and optimize code quality through sophisticated semantic searches and automated testing. This transition moves beyond simply using AI tools to a holistic culture where AI is woven into the entire software development lifecycle. Key benefits include significantly accelerated deployment cycles, improved developer satisfaction, and a more resilient IT infrastructure. Furthermore, the initiative prioritizes security and compliance by embedding AI-driven checks directly into the engineering pipeline. As Microsoft refines these internal practices, it aims to provide a blueprint for the industry on how to scale enterprise IT operations in an increasingly complex digital landscape. Ultimately, AI-led engineering at Microsoft is not just about speed; it is about fostering a creative environment where engineers solve complex problems with unprecedented efficiency, driving a new standard for modern software development.

Read-Copy-Update (RCU): The Secret to Lock-Free Performance

Read-Copy-Update (RCU) is a sophisticated synchronization mechanism explored in this InfoQ article, primarily utilized within the Linux kernel to handle concurrent data access. Unlike traditional locking methods that can cause significant performance bottlenecks, RCU allows multiple readers to access shared data simultaneously without the overhead of locks or atomic operations. The core concept involves updaters creating a modified copy of the data and then swapping the pointer to the new version, while ensuring that the original data is only reclaimed after a "grace period" when all active readers have finished. This approach ensures that readers always see a consistent, albeit potentially slightly outdated, version of the data without ever being blocked. While RCU offers unparalleled scalability and performance for read-heavy workloads, the article emphasizes that it introduces complexity for developers, particularly regarding memory management and the coordination of update cycles. Updaters must carefully manage the transition between versions to avoid data corruption. Ultimately, RCU represents a fundamental shift in concurrency design, prioritizing reader efficiency at the cost of more intricate update logic, making it an essential tool for high-performance systems where read operations vastly outnumber modifications.

AI transforms ‘dangling DNS’ into automated data exfiltration pipeline

AI-driven automation is fundamentally transforming "dangling DNS" from a common administrative oversight into a sophisticated, high-speed pipeline for automated data exfiltration. Dangling DNS occurs when a Domain Name System record continues to point to a decommissioned cloud resource, such as an abandoned IP address or a deleted storage bucket. While this vulnerability has existed for years, attackers are now utilizing generative AI and advanced scanning scripts to identify these orphaned subdomains across the internet at an unprecedented scale. Once a target is located, AI agents can automatically reclaim the abandoned resource on cloud platforms like AWS or Azure, effectively hijacking the legitimate domain to intercept sensitive traffic, harvest user credentials, or distribute malware through prompt injection attacks. This evolution represents a shift from opportunistic manual exploitation to a systematic, machine-led attack surface management strategy. To counter this, security professionals must move beyond periodic audits, implementing continuous, automated DNS monitoring and lifecycle management. The article underscores that as threat actors leverage AI to weaponize legacy misconfigurations, organizations can no longer afford to leave DNS records unmanaged. Addressing this infrastructure is a critical component of modern cyber defense, requiring the same level of automation that attackers currently use to exploit it.

The New Calculus of Risk: Where AI Speed Meets Human Expertise

The article examines the launch of Crisis24 Horizon, a sophisticated AI-enabled risk management platform designed to address the complexities of a volatile global security landscape. Developed on a modern technology stack, the platform provides a unified "single pane of glass" view, integrating dynamic intelligence with travel, people, and site-specific risk management. By leveraging artificial intelligence to process roughly 20,000 potential incidents daily, Crisis24 Horizon dramatically accelerates threat detection and triage, effectively expanding the capacity of security teams. Key features include "Ask Horizon," a natural language interface for querying risk data; "Latest Event Synopsis," which consolidates fragmented alerts into coherent summaries; and integrated mass notification systems for critical event response. While AI handles massive data aggregation and initial filtering, the platform emphasizes the "human in the loop" approach, where expert analysts provide necessary contextual judgment for high-stakes decisions like emergency evacuations. This synergy of AI speed and human expertise marks a shift from reactive to anticipatory security, allowing organizations to monitor assets in real-time and safeguard operations against interconnected global threats. Ultimately, Crisis24 Horizon empowers leaders to mitigate risks with greater precision, ensuring operational resilience and employee safety amidst geopolitical instability and environmental disasters.

Accelerating AI, cloud, and automation for global competitiveness in 2026

The guest blog post by Pavan Chidella argues that by 2026, the global competitiveness of enterprises will be defined by their ability to transition from AI experimentation to large-scale, disciplined execution. Focusing primarily on the healthcare sector, the author illustrates how the orchestration of AI, cloud-native architectures, and intelligent automation is essential for modernizing legacy processes like claims adjudication, which traditionally suffer from structural latency. In this evolving landscape, technology is no longer an isolated tool but a strategic driver of measurable business outcomes, including improved operational efficiency and enhanced customer transparency. Chidella emphasizes that "responsible acceleration" requires embedding governance, ethical AI monitoring, and regulatory compliance directly into system designs rather than treating them as afterthoughts. By adopting a product-led engineering mindset, organizations can reduce friction and build trust within their ecosystems. Ultimately, the piece asserts that global leadership in 2026 will belong to those who successfully integrate speed and precision with accountability, effectively leveraging hybrid cloud capabilities to process data in real-time. This shift represents a broader competitive imperative to move beyond proof-of-concept stages toward a resilient, automated, and digitally mature infrastructure that can thrive amidst increasing global complexity and regulatory scrutiny.

Engineering for AI intensity: The new blueprint for high-density data centers

This article explores the critical infrastructure evolution required to support the escalating demands of artificial intelligence. As traditional data centers struggle with the unprecedented power and thermal requirements of GPU-heavy workloads, a new engineering paradigm is emerging. This blueprint emphasizes a radical transition from legacy air-cooling systems to advanced liquid cooling technologies, such as direct-to-chip and immersion cooling, which are essential for managing rack densities that now frequently exceed 50kW and can reach up to 100kW per cabinet. Beyond thermal management, the article highlights the necessity of modular, high-voltage power distribution to ensure electrical efficiency and minimize transmission losses across the facility. It also underscores the importance of structural adaptations, including reinforced flooring to support heavier liquid-cooled hardware and overhead cable management to optimize airflow. Furthermore, the blueprint advocates for high-bandwidth, low-latency networking fabrics to facilitate the massive data exchanges inherent in parallel AI training. Ultimately, the piece argues that achieving AI intensity requires a holistic, future-proof design strategy that integrates power scalability, structural flexibility, and sustainable practices, positioning the modern data center as the strategic engine for digital transformation in an AI-first era.

Daily Tech Digest - February 27, 2026

Quote for the day:

"The best leaders build teams that don’t rely on them. That’s true excellence." -- Gordon Tredgold

Ransomware groups switch to stealthy attacks and long-term access

“Ransomware groups no longer treat vulnerabilities as isolated entry points,” says Aviral Verma, lead threat intelligence analyst at penetration testing and cybersecurity services firm Securin. “They assemble them into deliberate exploitation chains, selecting weaknesses not just for severity, but for how effectively they can collapse trust, persistence, and operational control across entire platforms.” AI is now widely accessible to threat actors, but it primarily functions as a force multiplier rather than a driving force in ransomware attacks. ... Vasileios Mourtzinos, a member of the threat team at managed detection and response firm Quorum Cyber, says that more groups are moving away from high-impact encryption towards extortion-led models that prioritize data theft and prolonged, low-noise access. “This approach, popularized by actors such as Cl0p through large-scale exploitation of third-party and supply chain vulnerabilities, is now being mirrored more widely, alongside increased abuse of valid accounts, legitimate administrative tools to blend into normal activity, and in some cases attempts to recruit or incentivize insiders to facilitate access,” Mourtzinos says. ... “For CISOs, the priority should be strengthening identity controls, closely monitoring trusted applications and third-party integrations, and ensuring detection strategies focus on persistence and data exfiltration activity,” Mourtzinos advises.

Expert Maps Identity Risk and Multi-Cloud Complexity to Evolving Cloud Threats

Cavalancia began by noting that cloud adoption has fundamentally altered traditional security boundaries. With 88 percent of organizations now operating in hybrid or multi-cloud environments, the hardened network edge is no longer the primary control point. Instead, identity and privilege determine access across distributed systems. ... Discussing identity risk specifically, he underscored how central privilege is to modern attacks, saying, "If you don't have identity, you don't have identity, you don't have privilege, you don't have privilege, you don't have a threat." Excessive permissions and credential abuse create privilege escalation paths once access is obtained. ... Reducing exploitable attack paths requires prioritizing risk based on business impact. Rather than attempting to address every vulnerability equally, organizations should identify which exposures would cause the greatest operational or financial harm and focus there first. ... Looking ahead, Cavalancia argued that security must be built around continuous monitoring and identity-first principles. "Continuous monitoring, continuous validation, continuous improvement, maybe we should just have the word continuous here," he said. He also cautioned that AI-assisted attacks are already influencing the threat landscape, noting that "90% of the decisions being made by that attack were done solely by AI, no human intervention whatsoever."

Data Centers in Space: Pi in the Sky or AI Hallucination?

Space is a great place for data centers because it solves one of the biggest problems with locating data centers on Earth: power, argues Google’s Senior Director of Paradigms of Intelligence, Travis Beals. ... SpaceX is also on board with the idea of data centers in space. Last month, it filed a request with the Federal Communications Commission to launch a constellation of up to one million solar-powered satellites that it said will serve as data centers for artificial intelligence. ... “Data centers in space can access solar power 24/7 in certain ‘sun-synchronous’ orbits, giving them all the power they need to operate without putting immense strain on power grids here on Earth,” Scherer told TechNewsWorld. “This would alleviate concerns about consumers having to bear the costs of higher energy use.” “There is also less risk of running out of real estate in space, no complex permitting requirements, and no community pushback to new data centers being built in people’s backyards,” he added. ... “By some estimates, energy and land costs are only around 25% of the total cost for a data center,” Yoon told TechNewsWorld. “AI hardware is the real cost driver, and shifting to space only makes that hardware more expensive.” “Hardware cannot be repaired or upgraded at scale in space,” he explained. “Maintaining satellites is extremely hard, especially if you have hundreds of thousands of them. Maintaining a traditional data center is extremely easy.”

Centralized Security Can't Scale. It's Time to Embrace Federation

In a federated model, the organization recognizes that technology leaders, whether from across security, IT, and Engineering, have a deep understanding of the nuances of their assigned units. Their specialized knowledge helps them set strategies that match the goals, technologies, workflows, and risks they need. That in turn leads to benefits that a centralized security authority can't touch. To start with, security decisions happen faster when the people making them are closer to the action. Service and application owners already have the context and expertise to make the right calls based on their scopes. Delegated authority allows companies to seize market opportunities faster, deploy new tools more easily, manage fewer escalations, and reduce friction and delays. ... In practice, that might look like a CISO setting data classification standards, while partner teams take responsibility for implementing these standards via low-friction policies and capabilities at the source of record for the data. Netflix's security team figured this out early. Their "Paved Roads" philosophy offers a collection of secure options that meet corporate guidelines while being the easiest for developers to use. In other words, less saying no, more offering a secure path forward. Outside of engineering, organization-wide standards also need to provide flexibility and avoid becoming overly specific or too narrow.

Linux explores new way of authenticating developers and their code - here's how it works

Today, kernel maintainers who want a kernel.org account must find someone already in the PGP web of trust, meet them face‑to‑face, show government ID, and get their key signed. ... the kernel maintainers are working to replace this fragile PGP key‑signing web of trust with a decentralized, privacy‑preserving identity layer that can vouch for both developers and the code they sign. ... Linux ID is meant to give the kernel community a more flexible way to prove who people are, and who they're not, without falling back on brittle key‑signing parties or ad‑hoc video calls. ... At the core of Linux ID is a set of cryptographic "proofs of personhood" built on modern digital identity standards rather than traditional PGP key signing. Instead of a single monolithic web of trust, the system issues and exchanges personhood credentials and verifiable credentials that assert things like "this person is a real individual," "this person is employed by company X," or "this Linux maintainer has met this person and recognized them as a kernel maintainer." ... Technically, Linux ID is built around decentralized identifiers (DIDs). This is a W3C‑style mechanism for creating globally unique IDs and attaching public keys and service endpoints to them. Developers create DIDs, potentially using existing Curve25519‑based keys from today's PGP world, and publish DID documents via secure channels such as HTTPS‑based "did:web" endpoints that expose their public key infrastructure and where to send encrypted messages.

IT hiring is under relentless pressure. Here's how leaders are responding

The CIO's relationship with the chief human resources officer (CHRO) matters greatly, though historically, they've viewed recruitment through different lenses. HR professionals tend not to be technologists, so their approach to hiring tends to be generic. Conversely, IT leaders aren't HR professionals. Many of them were promoted to management or executive roles for their expert technical skills, not their managerial or people skills. ... The multigenerational workforce can be frustrating for everyone at times, simply because employees' lives and work experiences can be so different. While not all individuals in a demographic group are homogeneous, at a 30,000-foot view, Gen Z wants to work on interesting and innovative projects -- things that matter on a greater scale, such as climate change. They also expect more rapid advancement than previous generations, such as being promoted to a management role after a year or two versus five or seven years, for example. ... Most organizational leaders will tell you their companies have great cultures, but not all their employees would likely agree. Cultural decisions made behind closed doors by a few for the many tend to fail because too many assumptions are made, and not enough hypotheses tested. "Seeing how your job helps the company move forward has been a point of opacity for a long time, and after a certain point, it's like, 'Why am I still here?'" Skillsoft's Daly said.

Generative AI has ushered in a new era of fraud, say reports from Plaid, SEON

“Generative AI has lowered the barrier to creating fake personas, falsifying documents, and impersonating real people at scale,” says a new report from Plaid, “Rethinking fraud in the AI era.” “As a result, fraud losses are projected to reach $40 billion globally within the next few years, driven in large part by AI-enabled attacks.” The warning is familiar. What’s different about Plaid’s approach to the problem is “network insights” – “each person’s unique behavioral footprint across the broader financial and app ecosystem,” understood as a system of relationships and long-standing patterns. In these combined signals, the company says, can be found “a resilient, high-signal lens into intent, risk and legitimacy.” ... “The industry is overdue for its next wave of fraud-fighting innovation,” the report says. “The question is not whether change is needed, but what unique combination of data, insights, and analytics can meet this moment.” The AI era needs its weapon of choice, and it needs to work continuously. “AI driven fraud is exposing the limits of identity controls that were designed for point in time verification rather than continuous assurance,” says Sam Abadir, research director for risk, financial (crime & compliance) at IDC, as quoted in the Plaid report. ... The overarching message is that “AI is real, embedded and widely trusted, but it has not materially reduced the scope of fraud and AML operations.” Fraud continues to scale, enabled by the same AI boom.

The hidden cost of AI adoption: Why most companies overestimate readiness

Walk into enough leadership meetings and you’ll hear the same story told with different accents: “We need AI.” It shows up in board decks, annual strategy documents and that one slide with a hockey-stick curve that magically turns pilot into profit. ... When I talk about the hidden cost of AI adoption, I’m not talking about model pricing or vendor fees. Those are visible and negotiable. The real cost lives in the messy middle: data foundations, integration work, operating model changes, governance, security, compliance and the ongoing effort required to keep AI useful after the demo fades. ... If I had to summarize AI readiness in one sentence, it would be this: AI readiness is your organization’s ability to repeatedly take a business problem, turn it into a well-defined decision or workflow, feed it trustworthy data and ship a solution you can monitor, audit and improve. ... Having data is not the same as having usable data. AI systems amplify quality problems at scale. Until proven otherwise, “we already have the data” usually means duplicated records, inconsistent definitions, missing fields, sensitive data in the wrong places and unclear ownership. ... If it adds friction or produces unreliable outputs, adoption collapses fast. Vendor risk doesn’t disappear either. Pricing changes. Usage spikes. Workflows become coupled to tools you don’t fully control. Without internal ownership, you’re not building capability, you’re renting it.

Overcoming Security Challenges in Remote Energy Operations

The security landscape for remote facilities has shifted "dramatically," and energy providers can no longer rely on isolation for protection, said Nir Ayalon, founder and CEO of Cydome, a maritime and critical infrastructure cybersecurity firm. "These sites are just as exposed as a corporate office - but with far more complex operational challenges," Ayalon said. ... A recent PES Wind report by Cyber Energia found that only 1% of 11,000 wind assets worldwide have adequate cyber protection, while U.K.-based renewable assets face up to 1,000 attempted cyberattacks daily. Trustwave SpiderLabs also reported an 80% rise in ransomware attacks on energy and utilities in 2025, with average costs exceeding $5 million. Ransomware is the most common form of attack. ... Protecting offshore facilities is also costly and a major challenge. Sending a technician for on-site installation can run up to $200,000, including vessel rental. Ayalon said most sites lack specialized IT staff. The person managing the hardware is usually an operator or engineer and not necessarily a certified cybersecurity professional. Limited space for racks and equipment, as well as poor bandwidth poses major challenges, said Rick Kaun, global director of cybersecurity services at Rockwell Automation. ... Designing secure offshore energy systems and shipping vessels is no longer a choice but a necessity. Cybersecurity can't be an afterthought, said Guy Platten, secretary general of the International Chamber of Shipping.

How the CISO’s Role is Evolving From Technologist to Chief Educator

Regardless of structure, modern CISOs are embedded in executive decision-making, legal strategy and supply chain oversight. Their responsibilities have expanded from managing technical defenses to maintaining dynamic risk portfolios, where trade-offs must be weighed across business functions. Stakeholders now include regulators, customers and strategic partners, not just internal IT teams. ... Effective leaders accumulate knowledge and know when to go deep and when to delegate, ensuring subject-matter experts are empowered while key decisions remain aligned to business outcomes. This blend of technical insight and strategic judgment defines the CISO’s value in complex environments. ... As security becomes more embedded in daily operations, cultural leadership plays a defining role in long-term resilience. A positive cybersecurity culture is proactive and free from blame, creating an environment where employees feel safe to speak up and suggest improvements without fear of repercussions. This shift leads to earlier detection, better mitigation and stronger overall security posture. Teams asking for security input during the design phase and employees self-reporting suspicious activity signal a mature culture that understands protection is everyone’s job. ... The modern CISO operates at the intersection of technology, risk, leadership and influence. Leaders must navigate shifting business priorities and complex stakeholder relationships while building a strong security culture across the enterprise.

Daily Tech Digest - February 26, 2026

Quote for the day:

"It is not such a fierce something to lead once you see your leadership as part of God's overall plan for his world." -- Calvin Miller

Boards don’t need cyber metrics — they need risk signals

Decision-makers want to know whether risk is increasing or decreasing, whether controls are effective, and whether the organization can limit damage when prevention fails. Metrics are therefore useful when they clarify those questions. “Time is really the universal metric because everyone can understand time,” Richard Bejtlich, strategist and author in residence at Corelight, tells CSO. “How fast do we detect problems, and how fast do we contain them. Dwell time, containment time. That’s the whole game for me.” Organizations cannot prevent every intrusion, Bejtlich argues, but they can measure how quickly they recognize and contain one. ... Wendy Nather, a longtime CISO who is now an advisor at EPSD, cautions against equating measurement with understanding. “When you are reporting to the board, there are some things you just cannot count that you have to report anyway,” she tells CSO. She points to incidents, near misses, and changes in assumptions as examples. “Anything that changes your assumptions about how you’re managing your security program, you should be bringing those to the board, even if you can’t count them,” Nather says. Regular metrics can create a rhythm of predictability, and that predictability could lull board members into a false sense of security. “Metrics are very seductive,” she says. “They lead us toward things that can be counted, that happen on a regular basis.” The result may be a steady flow of data that obscures structural risk or emerging weaknesses, Nather warns.

The Enterprise AI Postmortem Playbook: Diagnosing Failures at the Data Layer

Your first rule of the playbook is to treat AI incidents as data incidents – until proven otherwise. You should start by tagging the failure type. Document whether it’s a structure issue, retrieval misalignment, conflict with metric definition, or other categories. Ideally, you want to assign the issue to an owner and attach evidence to force some discipline into the review. Try to classify the issue into clearly defined buckets. For example, you can classify into these four buckets: structural failure, retrieval misalignment, definition conflict, or freshness failure. Once this part is clear, the investigation becomes more focused. The goal with this step is to isolate the data fault line. ... The next step is to move one layer deeper. Identify the source table behind the retrieved context. You also want to confirm the timestamp of the last refresh. Check whether any ingestion jobs failed, partially completed, or ran late. Silent failures are common. A job may succeed technically while loading incomplete data. As you go through the playbook continue tracing upstream. Find the transformation job that shaped the dataset. Look at recent schema changes. Check whether any business rules were updated. The idea here is to rebuild the exact path that led to the output. Try to not make any assumptions at this stage about model behavior – simply keep tracing until the process is complete. Don’t be surprised if the model simply worked with what it was given.

Top Attacks On Biometric Systems (And How To Defend Against Them)

Presentation attacks, often referred to as spoofing attacks, occur when an attacker presents a fake biometric sample to a sensor (like a camera or microphone) in an attempt to impersonate a legitimate user. Common examples include printed photos, video replays, silicone masks, prosthetics or synthetic fingerprints. More recently, high-quality deepfake videos have become a powerful new tool in the attacker’s arsenal. ... Passive liveness techniques, which analyze subtle physiological and behavioral signals without requiring user interaction, are particularly effective because they reduce friction while improving security. However, liveness detection must be resilient to unknown attack methods, not just tuned to detect known spoof types. ... Not all biometric attacks happen in front of the sensor. Replay and injection attacks target the biometric data pipeline itself. In these scenarios, attackers intercept, replay or inject biometric data, such as images or templates, directly into the system, bypassing the sensor entirely. ... Defensive strategies must extend beyond the biometric algorithm. Secure transmission, encryption in transit, device attestation, trusted execution environments and validation that data originates from an authorized sensor are all essential. ... Although less visible to end users, attacks targeting biometric templates and databases can pose long-term risks. If biometric templates are compromised, the impact extends far beyond a single breach.

Open-source security debt grows across commercial software

High and critical risk findings remain widespread. Most codebases contain at least one high risk vulnerability, and nearly half contain at least one critical risk issue. Those rates dipped slightly from the prior year even as total vulnerability counts rose. Supply chain attacks add another layer of risk. Sixty five percent of surveyed organizations experienced a software supply chain attack in the past year. ... “As AI reshapes software development, security teams will have to continue to adapt in turn. Security budgets and security guidelines should reflect this new reality. Leaders should continue to invest in tooling and education required to equip teams to manage the drastic increase in velocity, volume, and complexity of applications,” Mackey said. Board level reporting also requires adjustment as vulnerability volumes rise. ... Outdated components appear in nearly every audited environment. More than nine in ten codebases contain components that are several years out of date or show no recent development activity. A large share of components run many versions behind current releases. Only a small fraction operate on the latest available version. This maintenance debt intersects with regulatory obligations. The EU Cyber Resilience Act entered into effect in late 2024, with key reporting requirements taking effect in 2026 and broader enforcement following in 2027.

The agentic enterprise: Why value streams and capability maps are your new governance control plane

The enterprise is currently undergoing a seismic pivot from generative AI, which focuses on content creation, to agentic AI, which focuses on goal execution. Unlike their predecessors, these agents possess “structured autonomy”: the ability to perceive contexts, plan actions and execute across systems without constant human intervention. For the CIO and the enterprise architect, this is not merely an upgrade in automation speed; it is a fundamental shift in the firm’s economic equation. We are moving from labor-centric workflows to digital labor capable of disassembling and reassembling entire value chains. ... In an agentic enterprise, the value stream map is no longer just a diagram; it is the control plane. It must explicitly define the handoff protocols between human and digital agents. In my opinion, Value stream maps must move from static documents stored in a repository to context documents used to drive agentic automation. ... If a value stream does not exist, you cannot automate it. For new agentic workflows, do not map the current human process. Instead, use an outcome-backwards approach. Work backward from the concrete deliverable (e.g., customer onboarded) to identify the minimum viable API calls required. Before granting write access, run the new agentic stream in shadow mode to validate agent decisions against human outcomes.

Beyond compliance: Building a culture of data security in the digital enterprise

Cyber compliance is something organisations across industrial sectors take seriously, especially with new regulations getting introduced and non-compliance having consequences such as hefty penalties. Hence, businesses are placing compliance among their top priorities. However, hyper-focusing only on compliance can lead to tunnel vision, crippling creativity, and innovation. It fails to offer a comprehensive risk assessment due to the checklist approach it follows, exposing organizations to vulnerabilities and fast-evolving threats. Having a compliance-first mindset can lead to incomplete risk assessment, creating blind spots and security gaps in security provisions. ... With businesses relying on data for operations, customer engagement, and decision-making, ensuring data security protects both users and organisations. Data breaches have severe consequences, including financial losses, reputational damage, customer churn, and regulatory penalties. With data moving across on-premises data centers, cloud platforms, third-party ecosystems, remote work environments, and AI-driven applications, there is a need for a holistic, culture-driven approach to cybersecurity. ... Data protection traditionally was focused on safeguarding the perimeter by securing networks and systems within the physical boundaries where data was normally stored.

If you thought RTO battles were bad, wait until AI mandates start taking hold across the industry

With the advent of generative AI and the incessant beating of the drum by executives hellbent on unlocking productivity gains, we could see a revival of the dreaded workforce mandate –- only this time with AI. We’ve already had a glimpse of the same RTO tactics being used with AI over the last year. In mid-2025, Microsoft introduced new rules aimed at boosting AI use across the company, with an internal memo warning staff that “using AI is no longer optional”. ... As with RTO mandates, we’re now reaching a point where upward mobility within the enterprise could be at risk as a result of AI use. It’s a tactic initially touted by Dell in 2024 when enforcing its own hybrid work rules, which prompted a fierce backlash among staff. Forcing workers to use AI or risk losing out on promotions will have the desired effect executives want, namely that employees will use the technology, but that’s missing the point entirely. AI has been framed by many big tech providers as a prime opportunity to supercharge productivity and streamline enterprise efficiency. We’ve all heard the marketing jargon. If business leaders are at the point where they’re forcing staff to use the technology, it begs the question of whether it’s actually having the desired effect, which recent analysis suggests it’s not. ... Recent analysis from CompTIA found roughly one-third of companies now require staff to complete AI training.

In perfect harmony: How Emerald AI is turning data centers into flexible grid assets

At the core of Emerald AI is its Emerald Conductor platform. Described by Sivaram as “an AI for AI,” the system orchestrates thousands of AI workloads across one or more data centers, dynamically adjusting operations to respond to grid conditions while ensuring the facility maintains performance. The system achieves this through a closed-loop orchestration platform comprising an autonomous agent and a digital twin simulator. ... A point keenly pointed out by Steve Smith, chief strategy and regulation officer at National Grid, at the time of the announcement: “As the UK’s digital economy grows, unlocking new ways to flexibly manage energy use is essential for connecting more data centers to our network efficiently.” The second reason was National Grid's transatlantic stature - as an American company active in both the UK and US markets - and its commitment to the technology. “They’ve invested in the program and agreed to a demo, which makes them the ideal partner for our first international launch,” says Sivaram. The final, and most important, factor, notes Sivaram, was the access to the NextGrid Alliance, a consortium of 150 utilities worldwide. By gaining access to such a robust partner network, the deal could serve as a springboard for further international projects. This aligns with the company’s broader partnership approach. Emerald AI has already leveraged Nvidia’s cloud partner network to test its technology across US data centers, laying the groundwork for broader deployment and continued global collaboration.

7 ways to tame multicloud chaos with generative AI

Architects have the difficult job of understanding tradeoffs between proprietary cloud services and cross-cloud platforms. For example, should developers use AWS Glue, Azure Data Factory, or Google Cloud Data Fusion to develop data pipelines on the respective platforms, or should they adopt a data integration platform that works across clouds? ... “Managing multicloud is like learning multiple languages from AWS, Azure, Oracle, and others, and it’s rare to have teams that can traverse these environments fluidly and effectively. Plus, services and concepts are not portable among clouds, especially in cloud-native PaaS services that go beyond IaaS,” says Harshit Omar, co-founder and CTO at FluidCloud. One way to work around this issue is to assign an AI agent to support the developer or architect in evaluating platform selections. ... Standardizing infrastructure and service configurations across different clouds requires expertise in different naming conventions, architecture, tools, APIs, and other paradigms. Look for genAI tools to act as a translator to streamline configurations, especially for organizations that can templatize their requirements. ... CI/CD, infrastructure-as-code, and process automation are key tools for driving efficiency, especially when tasks span multiple cloud environments. Many of these tools use basic flows and rules to streamline tasks or orchestrate operations, which can create boundary cases that cause process-blocking errors.

It’s Time To Reinforce Institutional Crypto Key Management With MPC: Sodot CEO

For years, crypto security operations were almost exclusively focused on finding a way to protect the private keys to crypto wallets. It’s known as the “custody risk,” and it will always be a concern to anyone holding digital assets. However, Sofer believes that custody is no longer the weakest link. Cyberattackers have come to realize that secure wallets, often held in cold storage, are far too difficult to crack. ... Sodot has built a self-hosted infrastructure platform that leverages a pair of cutting-edge security techniques – namely, Multi-Party Computation or MPC and Trusted Execution Environments or TEEs. With Sodot’s platform, API keys are never reassembled in full plaintext, eliminating one of the main weaknesses of traditional secrets managers, which typically expose the entire key to any authenticated machine. Instead, Sodot uses MPC to split each key into multiple “shares” that are held by different partners on different technology stacks, Sofer explained. Distributing risk in this way makes an attacker’s job exponentially more difficult, as it means they would have to compromise multiple isolated systems to gain access. ... “Keys are here to stay, and they will control more value and become more sensitive as technology progresses,” Sofer concluded. “As financial institutions get more involved in crypto, we believe demand for self-hosted solutions that secure them will only grow, driven by performance requirements, operational resilience, and control over security boundaries.”