Daily Tech Digest by Kannan Subbiah: project management

Showing posts with label project management. Show all posts

Daily Tech Digest - April 22, 2026

Quote for the day:

"Any code of your own that you haven't looked at for six or more months might as well have been written by someone else." -- Eagleson's law

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Duration: 18 mins • Perfect for listening on the go.

From pilots to platforms: Industrial IoT comes of age

The article "From Pilots to Platforms: Industrial IoT Comes of Age" explores the transformative shift in India’s manufacturing sector as Industrial IoT (IIoT) matures from isolated experimental pilots into robust, enterprise-wide operational platforms. Historically, IIoT deployments were limited to simple sensor installations for monitoring single machines; however, the current landscape focuses on building a production-grade digital infrastructure that integrates data from across the entire shop floor. This evolution enables a transition from reactive maintenance to proactive operational intelligence, allowing leaders to prioritize measurable outcomes such as increased throughput, energy efficiency, and overall revenue. Experts emphasize that the conversation has moved beyond questioning the technology's viability to addressing the complexities of scaling across multiple facilities and managing "" realities where decades-old equipment must be retrofitted for connectivity. The modern IIoT stack now balances edge and cloud workloads while leveraging to sustain continuous operations. Despite these advancements, robust network design and cybersecurity remain critical challenges that must be addressed to ensure resilience. Ultimately, the success of IIoT in India now hinges on converting vast operational data into repeatable, high-speed decisions that deliver tangible business value across the industrial ecosystem.

Beyond the ‘25 reasons projects fail’: Why algorithmic, continuous scenario planning addresses the root causes

The article "Beyond the '25 reasons projects fail'" argues that high failure rates in enterprise initiatives—highlighted by BCG and Gartner data—are not merely delivery misses but symptoms of a systemic failure in portfolio design and decision logic. While visible symptoms like scope creep and poor communication are real, they represent a deeper "pattern under the pattern" where organizations lack the capacity to calculate the ripple effects of change. The author, John Reuben, posits that modern governance requires "" and "continuous scenario planning" to translate strategic ambition into modeled consequences. Without this discipline, leadership cannot effectively navigate trade-offs or manage dependencies. Furthermore, the piece emphasizes that while AI offers transformative potential, it must be anchored in mathematically sound planning data to avoid magnifying weak assumptions. To address these root causes, CIOs are urged to implement a modern control system for change featuring six essential capabilities: a unified planning model across priorities and budgets, side-by-side scenario comparison, interdependency mapping, early visibility into bottlenecks, continuous recalculation as conditions shift, and executive-facing summaries that turn data into decisions. Ultimately, the solution lies in evolving planning from a static, narrative process into a dynamic, algorithmic discipline capable of seeing and governing complex interactions in real time.

Is AI creating value or just increasing your IT bill?

The Spiceworks article, grounded in the "State of IT 2026" research by Spiceworks Ziff Davis, examines the economic tension between AI’s promise of value and its actual impact on corporate budgets. While AI software expenditures currently appear manageable—with a median spend of only 2.7% of total IT computing infrastructure—the report warns that this represents just the visible portion of a much larger financial commitment. The "hidden" bill for enterprise AI includes critical investments in high-performance servers, specialized storage, and robust networking, which experts estimate can increase the total cost by four to five times the software license fees. This disparity highlights a significant risk: organizations may underestimate the capital required to move from experimentation to full-scale deployment. The article argues that "putting your money where your mouth is" requires a strategic alignment of talent, time, and treasure rather than just following market hype. To achieve a positive return on investment, IT leaders must look beyond software-as-a-service costs and account for the substantial infrastructure upgrades necessary to power modern AI workloads. Ultimately, the path to value depends on a holistic understanding of the total cost of ownership in an increasingly AI-driven landscape.

Cryptographic debt is becoming the next enterprise risk layer

"Cryptographic debt" is emerging as a critical enterprise risk layer, especially within the financial sector, as organizations face the consequences of outdated algorithms, fragmented key management, and encryption deeply embedded in legacy systems. According to Ruchin Kumar of Futurex, this "debt" has long remained invisible to boardrooms because cryptography was historically treated as a technical silo rather than a strategic risk domain. However, the rise of and the impending transition to post-quantum cryptography (PQC) are exposing these structural vulnerabilities. Major hurdles to modernization include a lack of centralized cryptographic visibility, the tight coupling of security logic with application code, and manual, error-prone key management processes. To address these challenges, enterprises must shift toward a "crypto-agile" architecture. This transformation requires centralizing governance through Hardware Security Modules (HSMs), abstracting cryptographic functions via standardized APIs, and automating the entire key lifecycle. Such a horizontal transformation will likely trigger a massive wave of IT spending, comparable to cloud migration. As ecosystems become increasingly interconnected through APIs and fintech partnerships, weak cryptographic governance in any single segment now poses a systemic threat, making unified, architecture-first security essential for long-term business resilience and regulatory compliance.

Practical SRE Habits That Keep Teams Sane

The article "Practical SRE Habits That Keep Teams Sane" outlines essential strategies for Site Reliability Engineering teams to maintain high system availability while safeguarding engineer well-being. Central to these habits is the clear definition of Service Level Objectives (SLOs), which provide a data-driven framework for balancing feature velocity with operational stability. To combat burnout, the piece emphasizes reducing "toil"—repetitive, manual tasks—through targeted automation and the creation of actionable runbooks that lower the cognitive burden during high-pressure incidents. A significant portion of the advice focuses on human-centric operations, advocating for blameless post-mortems that prioritize systemic learning over individual finger-pointing, effectively removing the drama from failure analysis. Furthermore, the article suggests optimizing on-call health by implementing "interrupt buffers" and rotating "shield" roles to protect the rest of the team from productivity-killing context switching. By adopting safer deployment patterns and rigorous backlog hygiene, teams can shift from a chaotic, reactive firefighting mode to a controlled and predictable "boring" operational state. Ultimately, these practical habits aim to create a sustainable culture where reliability is a shared responsibility, ensuring that both the technical infrastructure and the humans who support it remain resilient and efficient in the long term.

From the engine room to the bridge: What the modern leadership shift means for architects like me

The article explores how the evolving role of modern technology leadership, specifically CIOs, necessitates a fundamental shift in the approach of system architects. Traditionally, CIOs focused on uptime and cost efficiency, but today’s leaders prioritize competitive differentiation, workforce transformation, and organizational alignment. Many modernization projects fail not due to technical flaws, but because of "upstream" issues like unresolved stakeholder conflicts or a lack of strategic clarity. Consequently, architects must look beyond sound code and clean implementation to build the "social infrastructure" and trust required for adoption. Modern leadership acts as both navigator and engineer, demanding infrastructure that supports both technical needs—like automated policy enforcement—and business outcomes. Managing technical debt proactively is crucial, as legacy systems often stifle innovation like AI adoption. For architects, this means evolving from purely technical resources into strategic partners who understand the cultural and decision-making constraints of the business. The best architectural designs are ultimately useless unless they resonate with the organizational reality and strategic pressures facing the customer. Bridging the gap between the engine room and the bridge is now the essential mandate for those designing the systems that drive modern business forward.

Are We Actually There? Assessing RPKI Maturity

The article "Are We Actually There? Assessing RPKI Maturity" provides a critical evaluation of the Resource Public Key Infrastructure (RPKI) and its current state of global deployment for securing internet routing. The authors argue that while RPKI adoption is steadily growing, the system is still far from reaching true maturity. Through comprehensive measurements, the research reveals that the effectiveness of RPKI enforcement varies significantly across the internet ecosystem; while large transit networks provide broad protection, the impact of enforcement at Internet Exchange Points remains localized. Furthermore, the paper highlights severe vulnerabilities within the RPKI software ecosystem, identifying over 40 security flaws that could compromise deployments. These issues are often rooted in the immense complexity and vague requirements of the RPKI specifications, which make correct implementation difficult and error-prone. The research also notes dependencies on other protocols like DNSSEC, which itself faces design-flaw vulnerabilities like KeyTrap. Ultimately, the authors conclude that although RPKI is currently the most effective defense against Border Gateway Protocol (BGP) hijacks, achieving a robust and mature architecture requires a fundamental redesign to simplify its structure, clarify specifications, and improve overall efficiency. Until these systemic flaws are addressed, the internet's routing security remains precarious.

Study finds AI fraud losses decline, but the risks are growing

The Javelin Strategy & Research 2026 identity fraud study, "The Illusion of Progress," highlights a deceptive shift in the digital landscape where total monetary losses have decreased while systemic risks continue to escalate. In 2025, combined fraud and scam losses fell to $38 billion, a $9 billion reduction from the previous year, accompanied by a drop in victim numbers to 36 million. This decline was primarily fueled by a 45 percent drop in scam-related losses. However, these improvements are overshadowed by a 31 percent surge in new-account fraud victims, signaling that criminals are pivoting their tactics. Artificial intelligence is at the core of this evolution, as fraudsters adopt advanced tools more rapidly than financial institutions can update their defenses. Lead analyst Suzanne Sando warns that lower loss figures are misleading because scammers are increasingly focused on stealing personal data to seed future, more sophisticated attacks rather than seeking immediate cash. To address this "inflection point," the report stresses that organizations must move beyond one-time security decisions. Instead, they must implement continuous fraud controls and foster deep industry collaboration to stay ahead of AI-powered criminals who operate without the regulatory constraints that often slow down legitimate financial services.

Why identity is the driving force behind digital transformation

In the modern digital landscape, identity has evolved from a simple login mechanism into the fundamental "invisible engine" driving successful digital transformation. As traditional network perimeters dissolve due to cloud adoption and remote work, identity has emerged as the critical new security boundary, utilizing a "never trust, always verify" approach to protect sensitive data. This shift empowers businesses to implement fine-grained access controls that enhance security while streamlining operations. Beyond security, identity systems act as a catalyst for business agility, allowing software teams to navigate complex environments more efficiently. Crucially, centralized identity management enhances the customer experience by unifying disparate data points to provide highly personalized interactions and build brand trust. In high-stakes sectors like finance, identity-centric frameworks are essential for real-time fraud detection and comprehensive risk assessment by linking multiple accounts to a single verified user. To truly leverage identity as a strategic asset, organizations must ensure their systems are real-time, easily integrable, and governed by strict access rules. Ultimately, establishing identity as a core infrastructure is no longer optional; it is the essential foundation for innovation, security, and competitive growth in an increasingly interconnected and complex global digital economy.

From Panic to Playbook: Modernizing Zero‑Day Response in AppSec

In "From Panic to Playbook: Modernizing Zero-Day Response in AppSec," Shannon Davis explores how the increasing frequency and rapid exploitation of zero-day vulnerabilities, such as , necessitate a shift from reactive improvisation to structured, rehearsed workflows. Traditional AppSec cadences—where vulnerabilities are typically addressed through scheduled scans and predictable sprint fixes—fail to meet the urgent demands of zero-day events due to collapsed time-to-exploit windows, high data volatility, and complex transitive dependencies. To bridge this gap, Davis highlights the Mend AppSec Platform’s modernized approach, which emphasizes four critical components: a live, authoritative data feed independent of scan schedules, instant correlation with existing inventory to identify exposure without manual rescanning, a defined 30-day lifecycle for active threats, and a centralized audit trail for cross-team alignment. This framework enables organizations to respond effectively within the vital first 72 hours after disclosure by providing a single source of truth for both human teams and automated tooling. Ultimately, the article argues that organizational resilience during a security crisis depends less on the total size of a security budget and more on the implementation of a proactive, data-driven playbook that transforms chaotic incident response into a sustainable, repeatable, and efficient operational reality.

Daily Tech Digest - March 30, 2026

Quote for the day:

"Leaders who won't own failures become failures." -- Orrin Woodward

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Duration: 14 mins • Perfect for listening on the go.

A practical guide to controlling AI agent costs before they spiral

Managing the financial implications of is becoming a critical priority for IT leaders as these autonomous tools integrate into enterprise workflows. While software licensing fees are generally predictable, costs related to tokens, infrastructure, and management are often volatile due to the non-deterministic nature of AI. To prevent spending from exceeding the generated value, organizations must adopt a strategic framework that balances agent autonomy with fiscal oversight. Key recommendations include selecting flexible platforms that support various models and hosting environments, utilizing lower-cost for less complex tasks, and implementing automated cost-prediction tools. Furthermore, businesses should actively track real-time expenditures, optimize or repeat cost-effective workflows, and employ data caching to reduce redundant token consumption. Establishing hard token quotas can act as a safety net against runaway agents, while periodic reviews help curb agent sprawl similar to SaaS management practices. Ultimately, the goal is to leverage the transformative potential of without allowing unpredictable operational expenses to spiral out of control. By prioritizing flexible architectures and robust monitoring early in the adoption phase, CIOs can ensure that their AI investments deliver measurable productivity gains rather than becoming a financial burden.

Teaching Programmers A Survival Mindset

The article "," published by ACM, argues that the traditional educational focus on pure logic and "happy path" coding is no longer sufficient for the modern digital landscape. As software systems grow increasingly complex and interconnected, the author advocates for a pedagogical shift toward a "survival" or "adversarial" mindset. This approach prioritizes resilience, security, and the anticipation of failure over simple feature delivery. Instead of assuming a controlled environment where inputs are valid and dependencies are stable, programmers must learn to view their code through the lens of potential exploitation and systemic breakdown. The piece emphasizes that a survival mindset involves rigorous defensive programming, a deep understanding of the , and the ability to navigate legacy environments where documentation may be scarce. By integrating these "survivalist" principles into computer science curricula and professional development, the industry can move away from fragile, high-maintenance builds toward robust systems capable of withstanding real-world pressures. Ultimately, the goal is to produce engineers who treat security and stability not as afterthoughts or separate departments, but as foundational elements of the craft, ensuring long-term viability in an increasingly volatile technological ecosystem.

For Financial Services, a Wake-Up Call for Reclaiming IAM Control

Part five of the "Repatriating IAM" series focuses on the strategic necessity of reclaiming Identity and Access Management (IAM) control within the financial services sector. The article argues that while SaaS-based identity solutions offer convenience, they often introduce unacceptable risks regarding operational resilience, regulatory compliance, and concentrated third-party dependencies. For financial institutions, identity is not merely an IT function but a core component of the financial control fabric, essential for enforcing segregation of duties and preventing fraud. By repatriating critical IAM functions—such as authorization decisioning, token services, and machine identity governance—closer to the actual workloads, organizations can achieve deterministic performance and forensic-grade auditability. The author highlights that "waiting out" a cloud provider’s outage is not a viable strategy when market hours and settlement windows are at stake. Instead, moving these high-risk workflows into controlled, hardened environments allows for superior telemetry and real-time responsiveness. Ultimately, the post positions IAM repatriation as a logical evolution for firms needing to balance AI-scale identity demands with the rigorous security and evidentiary standards required by global regulators, ensuring that no single external failure can paralyze essential banking operations or compromise sensitive customer data.

Practical Problem-Solving Approaches in Modern Software Testing

Modern software testing has evolved from a final development checkpoint into a continuous discipline characterized by proactive problem-solving and shared quality ownership. As software architectures grow increasingly complex, traditional testing models often prove inefficient, resulting in high defect costs and sluggish release cycles. To address these challenges, the article highlights four core approaches that prioritize speed, visibility, and accuracy. Shift-left testing embeds quality checks into the earliest design phases, significantly reducing production defect rates by catching requirements issues before they are ever coded. This proactive strategy is complemented by exploratory testing, which utilizes human intuition and AI-driven insights to uncover nuanced edge cases that automated scripts frequently overlook. Furthermore, risk-based testing allows teams to strategically allocate limited resources to high-impact system areas, while continuous testing within CI/CD pipelines provides near-instant feedback on every code change. By moving away from rigid, script-driven protocols toward these integrated methods, organizations can achieve faster feedback loops and lower overall maintenance costs. Ultimately, modern testing requires making failures visible and actionable in real time, transforming quality assurance from a siloed task into a collaborative foundation for reliable software delivery. This holistic strategy ensures that testing keeps pace with rapid development while meeting rising user expectations.

Data centers are war infrastructure now

The article "Data centers are war infrastructure now" explores the paradigm shift of digital hubs from silent commercial utilities to central pillars of national security and modern combat. As warfare becomes increasingly software-defined and data-driven, the facilities housing the world's processing power have transitioned into high-value strategic targets, comparable to energy grids and maritime ports. This evolution is driven by the "infrastructural entanglement" between sovereign states and private hyperscalers, where military operations, intelligence gathering, and essential government services are hosted on the same servers as civilian data. The physical vulnerability of this infrastructure is underscored by rising tensions in critical transit zones like the Red Sea, where undersea cables and landing stations have become active frontlines. Consequently, data centers are no longer viewed as mere business assets but as integral components of a nation's defense posture. This shift necessitates a new approach to physical security, cybersecurity, and international regulation, as the boundary between corporate interests and national sovereignty continues to blur. Ultimately, the piece highlights that in an era where information dominance determines victory, the data center has emerged as the most critical—and vulnerable—ammunition depot of the twenty-first century.

Why delivery drift shows up too late, and what I watch instead

In his article for CIO, James Grafton explores why critical project delivery issues often remain hidden until they escalate into full-blown crises. He argues that traditional governance and status reporting are structurally flawed because they prioritize "smoothed" expectations over the messy reality of execution. To move beyond deceptive "green" status reports, Grafton suggests monitoring three early-warning signals that reflect actual system behavior under load. First, he identifies "," where queues and stretching lead times signal that demand has outpaced capacity at key boundaries. Second, he highlights "rework," which indicates that implicit assumptions or communication gaps are forcing teams to backtrack. Finally, he points to "borrowed capacity," where temporary heroics and reprioritization quietly consume future resilience to protect current metrics. By shifting the governance conversation from performance justifications to identifying system strain, leaders can detect both "erosion"—visible, loud failures—and "ossification"—the quiet drift hidden behind outdated processes. This proactive approach allows organizations to bridge the gap between intent and delivery reality, preserving strategic options before failure becomes inevitable. By observing these behavioral trends rather than focusing on absolute values, CIOs can foster a safer environment for surfacing risks early and making deliberate, rather than reactive, interventions to ensure long-term stability.

Goodbye Software as a Service, Hello AI as a Service

The digital landscape is undergoing a profound transformation as Software as a Service (SaaS) begins to give way to AI as a Service (AIaaS), driven primarily by the emergence of Agentic AI. Unlike traditional SaaS models that rely on manual user navigation through dashboards and interfaces, AIaaS utilizes autonomous agents that execute workflows by directly calling systems and services. This shift transitions software from a primary workspace to an underlying capability, where the focus moves from user-driven inputs to autonomous orchestration. A critical development in this evolution is the rise of agent collaboration, facilitated by frameworks like the Model Context Protocol, which allow multiple agents to pass tasks and data across various platforms seamlessly. Consequently, the role of developers is evolving from building static integrations to designing and supervising agent behaviors within sophisticated governance frameworks. However, this increased autonomy introduces significant operational risks, including data exposure and complexity. Organizations must therefore prioritize robust infrastructure and clear guardrails to ensure accountability and traceability. Ultimately, while AI agents may replace human-driven manual processes, human oversight remains essential to manage decision-making and ensure that these autonomous systems operate within defined ethical and operational boundaries to drive long-term business value.

Scaling industrial AI is more a human than a technical challenge

Industrial AI has transitioned from experimental pilots to practical implementation, yet achieving mature, large-scale adoption remains an elusive goal for most organizations. While technical hurdles such as infrastructure gaps and cybersecurity risks are prevalent, the primary obstacle to scaling is inherently human rather than technological. The core challenge lies in bridging the historical divide between information technology (IT) and operational technology (OT) departments. These two disciplines must operate as a cohesive team to succeed, but many organizations still suffer from siloed structures where nearly half report minimal cooperation. True progress requires a shift from individual convergence to organizational collaboration, where IT experts and OT specialists align their distinct competencies toward shared goals like safety, uptime, and resilience. By fostering trust and establishing clear lines of accountability, leaders can navigate the complexities of AI-driven operations more effectively. Organizations that successfully dismantle these departmental barriers report higher confidence, stronger security postures, and a more ready workforce. Ultimately, the future of industrial AI depends on the ability to forge connected teams that blend digital agility with operational rigor, transforming isolated technological promises into sustained, everyday impact across manufacturing, transportation, and utility sectors.

Building Consumer Trust with IoT

The Internet of Things (IoT) is revolutionizing modern life, with projections suggesting a global value of up to $12.5 trillion by 2030 through innovations like smart cities and environmental monitoring. However, this digital transformation faces a critical hurdle: establishing and maintaining consumer trust. Central to this challenge are ethical concerns surrounding data privacy and security vulnerabilities, as devices often collect sensitive personal information susceptible to cyber threats like DDoS attacks. To foster confidence, organizations must implement transparent data usage policies and proactive security measures, such as real-time traffic monitoring, while adhering to regulatory standards like GDPR. Beyond digital security, the article emphasizes the environmental toll of IoT, noting that energy consumption and electronic waste necessitate a "" approach characterized by sustainable product design. Achieving a trustworthy ecosystem requires a collective commitment to global best practices, including the adoption of IPv6 for scalable connectivity and engagement with open technical communities like RIPE. By integrating ethical considerations throughout a project's lifecycle, developers can ensure that IoT serves the broader well-being of society and the planet. This holistic approach, combining robust security with environmental responsibility and regulatory compliance, is essential for unlocking the full potential of an interconnected world.

Why risk alone doesn’t get you to yes

The article by Chuck Randolph emphasizes that the greatest challenge for security leaders isn't identifying threats, but securing to act upon them. While technical briefs may clearly outline risks, they often fail to compel action because they are not translated into the language of business accountability, such as revenue flow and operational stability. To bridge this gap, security professionals must pivot from presenting dense technical metrics to highlighting tangible business consequences, like manufacturing shutdowns or lost contracts. Randolph notes that effective leaders address objections upfront, align security initiatives with shared strategic outcomes rather than departmental needs, and replace vague warnings with precise, actionable requests. By connecting technical vulnerabilities to "business math"—associating risk with specific financial liabilities—security experts can engage stakeholders like CFOs and COOs more effectively. Ultimately, the piece argues that security leadership is defined by the ability to influence organizational movement through better translation rather than just more data. Influence transforms information into action, ensuring that identified risks are not merely acknowledged but actively mitigated. This strategic shift in communication is essential for protecting the enterprise and achieving a "yes" from decision-makers who prioritize long-term value.

Daily Tech Digest - March 18, 2026

Quote for the day:

"Leadership cannot really be taught. It can only be learned." -- Harold S. Geneen

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

Why hardware + software development fails

In the CIO article "Why hardware + software development fails," Chris Wardman explores the chronic pitfalls that lead complex technical projects to stall or collapse. He argues that failure often stems from a fundamental misunderstanding of the ""—the reality that code is never truly finished and requires continuous refinement. Key contributors to failure include unrealistic timelines that force engineers to cut critical corners and the "mythical man-month" fallacy, where adding more personnel to a slipping project only increases communication overhead and further delays. Additionally, Wardman identifies the premature focus on building a final product rather than first resolving technical unknowns, which account for roughly 80% of total effort. Draconian IT policies and the misuse of simplified frameworks also stifle innovation by creating friction and capping system capabilities. Finally, the author points to inadequate testing strategies that fail to distinguish between hardware, software, and physical environmental issues. To succeed, organizations must foster empowered leadership, set realistic expectations, and prioritize solving core uncertainties before moving to production. By mastering these fundamentals, companies can transform the inherent difficulties of hardware-software integration into a competitive advantage, delivering reliable, value-driven products to the market.

New font-rendering trick hides malicious commands from AI tools

The BleepingComputer article details a sophisticated "," dubbed "" by researchers at LayerX, which exploits the disconnect between how AI assistants and human browsers interpret web content. By utilizing custom font files and CSS styling, attackers can perform character remapping through glyph substitution. This allows them to display a clear, malicious command to a human user while presenting the underlying HTML to an AI scanner as entirely benign or unreadable text. Consequently, when a user asks an AI assistant—such as , Gemini, or Copilot—to verify the safety of a command (like a reverse shell payload), the AI analyzes only the hidden, safe DOM elements and mistakenly provides a reassuring response. Despite the high success rate across multiple popular AI platforms, most vendors initially dismissed the vulnerability as "out of scope" due to its reliance on social engineering, though Microsoft has since addressed the issue. The research underscores a critical blind spot in modern automated security tools that rely strictly on text-based analysis rather than visual rendering. To combat this, experts recommend that LLM developers incorporate visual-aware parsing or optical character recognition to bridge the gap between machine processing and human perception, ensuring that security safeguards cannot be bypassed through creative font manipulation.

More Attackers Are Logging In, Not Breaking In

In the Dark Reading article "More Attackers Are Logging In, Not Breaking In," Jai Vijayan highlights a critical shift in cybercrime where attackers increasingly favor legitimate credentials over technical exploits to infiltrate enterprise networks. Data from Recorded Future reveals that credential theft surged in late 2025, with nearly two billion credentials indexed from malware combo lists. This rapid escalation is fueled by the industrialization of infostealer malware, malware-as-a-service ecosystems, and AI-enhanced social engineering. Most alarmingly, roughly 31% of stolen credentials now include active session cookies, which allow threat actors to bypass multi-factor authentication entirely through session hijacking. Attackers are specifically targeting high-value entry points like , Azure Active Directory, and corporate VPNs to gain stealthy, broad access while avoiding traditional security alarms. Because identity has become the primary attack surface, experts argue that perimeter-centric defenses are no longer sufficient. Organizations are urged to move beyond basic MFA toward continuous identity monitoring, phishing-resistant FIDO2 standards, and behavioral-based conditional access policies. By treating identity as a "Tier-0" asset, businesses can better defend against a landscape where criminals simply log in using valid, stolen data rather than making noise by breaking through technical barriers.

From SAST to “Shift Everywhere”: Rethinking Code Security in 2026

The article "From SAST to 'Shift Everywhere': Rethinking Code Security in 2026" on DZone explores the necessary evolution of software security in response to modern development challenges. It argues that traditional static analysis (SAST) is no longer adequate on its own, advocating instead for a "shift everywhere" approach that integrates security testing throughout the entire software development lifecycle (SDLC). The author emphasizes that true security is not achieved through isolated scans but through continuous risk management, robust architecture, and comprehensive threat modeling. In an era of cloud-native systems and AI-assisted coding, vulnerabilities can spread rapidly across large dependency graphs, making early design decisions more impactful than ever. The text notes that "secure code" is a relative concept defined by an organization's specific threat model and maturity level rather than an absolute state. Key strategies for improvement include fostering developer security literacy, gaining executive commitment, and utilizing AI-driven tools to prioritize findings and reduce alert fatigue. Ultimately, the article suggests that security must become a core property of software systems, evolving into a more analytical and context-driven discipline to effectively combat sophisticated global threats and manage the risks inherent in open-source components.

CISOs rethink their data protection strategi/es

In the contemporary digital landscape, Chief Information Security Officers (CISOs) are fundamentally re-evaluating their data protection strategies, primarily driven by the rapid proliferation of artificial intelligence. According to recent research, the integration of has necessitated a shift in how organizations manage sensitive information, with approximately 90% of firms expanding their privacy programs to address these new complexities. Beyond AI, security leaders are grappling with exponential increases in data volume, expanding attack surfaces, and heightening regulatory pressures that demand greater operational resilience. To combat "data sprawl," CISOs are moving away from traditional perimeter-based defenses toward more sophisticated models that emphasize granular data classification, tagging, and the monitoring of lateral data movement. This evolution involves rethinking legacy tools like Data Loss Prevention (DLP) systems, which often struggle to secure modern, AI-driven environments. Consequently, modern strategies prioritize collaborative risk assessments with executive peers to align security spending with tangible business impact. By adopting automation, exploring passwordless environments, and co-innovating with vendors, CISOs aim to build proactive guardrails that protect data regardless of how it is accessed or used. This strategic pivot reflects a broader transition from reactive compliance to a dynamic, intelligence-driven framework essential for navigating today’s volatile threat landscape.

Storage wars: Is this the end for hard drives in the data center?

The debate over the future of hard disk drives (HDDs) in data centers has intensified, as highlighted by Pure Storage executive Shawn Rosemarin’s bold prediction that HDDs will be obsolete by 2028. This potential shift is primarily driven by the escalating costs and limited availability of electricity, as data centers currently consume approximately three percent of global power. Proponents of an all-flash future argue that solid-state drives (SSDs) offer superior energy efficiency—reducing power consumption by up to ninety percent—while providing the high density and performance required for modern AI and machine learning workloads. Conversely, industry giants like Seagate and Western Digital maintain that HDDs remain the indispensable backbone of the storage ecosystem, currently holding about ninety percent of enterprise data. They contend that the structural cost-per-terabyte advantage of magnetic storage is insurmountable for mass-capacity needs, particularly as AI-driven data growth surges. While flash technology continues to capture performance-sensitive tiers, HDD manufacturers report that their capacity is already sold out through 2026, suggesting that the "end" of spinning disk may be premature. Ultimately, the industry appears to be moving toward a multi-tiered architecture where both technologies coexist to balance performance, power sustainability, and economic scale.

Update your databases now to avoid data debt

The InfoWorld article "Update your databases now to avoid data debt" warns that 2026 will be a pivotal year for database management due to several major end-of-life (EOL) milestones. Popular systems such as MySQL 8.0, PostgreSQL 14, Redis 7.2 and 7.4, and MongoDB 6.0 are all facing EOL status throughout the year, forcing organizations to confront the looming risks of "data debt." While many IT teams historically follow the "if it isn't broken, don't fix it" philosophy, delaying these critical upgrades eventually leads to increased long-term costs, security vulnerabilities, and system instability. Conversely, rushing complex migrations without proper preparation can introduce significant operational failures. To navigate these challenges, the author emphasizes a disciplined planning approach that starts with a comprehensive inventory of all database instances across test, development, and production environments. Migrations should ideally begin with lower-risk test instances to ensure resilience before moving to mission-critical production deployments. A successful transition also requires benchmarking current performance to measure the impact of any changes accurately. Ultimately, gaining organizational buy-in involves highlighting the performance and ease-of-use benefits of modern versions rather than merely focusing on deadlines. By prioritizing proactive updates today, businesses can effectively avoid the technical debt that threatens future scalability.

Data Sovereignty Isn’t a Policy Problem, It’s a Battlefield

Samuel Bocetta’s article, "Data Sovereignty Isn’t a Policy Problem, It’s a Battlefield," argues that has evolved from a simple compliance checklist into a high-stakes geopolitical contest. Bocetta asserts that datasets now carry significant political weight, as their physical and digital locations dictate who can access, subpoena, or monetize information. While governments and cloud providers understand this dynamic, many enterprises view sovereignty merely through the lens of regional settings or slow-moving regulations. However, the reality is that data moves too quickly for traditional laws to maintain control, creating a widening gap where power shifts to those controlling underlying infrastructure rather than legal frameworks. Cloud providers, often perceived as neutral, are active participants in this struggle, where physical location does not guarantee political independence. The article warns that enterprises often fail by treating sovereignty reactively or delegating it as a minor technical detail. Instead, it must be recognized as a core strategic issue impacting risk and procurement. As the digital landscape fragments into competing spheres of influence, businesses must prioritize architectural flexibility and dynamic governance. Ultimately, surviving this battlefield requires moving beyond static compliance to embrace a proactive, defensive posture that anticipates constant shifts in the global data landscape.

A chief AI officer is no longer enough - why your business needs a 'magician' too

As organizations grapple with how to best leverage generative artificial intelligence, a significant debate is emerging over whether to appoint a dedicated Chief AI Officer (CAIO) or pursue alternative leadership structures. While industry data suggests that approximately 60% of companies have already installed a CAIO to oversee governance and security, some leaders argue for a more integrated approach. For instance, the insurance firm Howden has pioneered the role of Director of AI Productivity, a specialist who bridges the gap between technical IT infrastructure and data science teams. This specific role focuses on three primary objectives: ensuring seamless cross-departmental collaboration, maximizing the value of enterprise-grade tools like Microsoft Copilot and ChatGPT, and driving competitive advantage. By appointing a dedicated productivity lead to manage broad tool adoption and user training, senior data leaders are freed to focus on high-value, proprietary machine learning models that differentiate the business. Ultimately, the article suggests that while a CAIO provides high-level oversight, a productivity-focused director acts as a magician who translates complex AI capabilities into tangible daily efficiency gains for employees, ensuring that expensive technology licenses are fully exploited rather than being underutilized by a confused workforce across the global enterprise.

Scientists Harness 19th-Century Optics To Advance Quantum Encryption

Researchers at the University of Warsaw’s Faculty of Physics have developed a groundbreaking quantum key distribution (QKD) system by reviving a 19th-century optical phenomenon known as the Talbot effect. Traditionally, QKD relies on qubits, the simplest units of quantum information, but this method often struggles with the high-bandwidth demands of modern digital communication. To address this, the team implemented high-dimensional encoding using time-bin superpositions of photons, where light pulses exist in multiple states simultaneously. By applying the temporal Talbot effect—where light pulses "self-reconstruct" after traveling through a dispersive medium like optical fiber—the researchers created a setup that is significantly simpler and more cost-effective than current alternatives. Unlike standard systems that require complex networks of interferometers and multiple detectors, this innovative approach utilizes commercially available components and a single photon detector to register multi-pulse superpositions. Although the method currently faces higher measurement error rates, its efficiency is superior because every photon detection event contributes to the cryptographic key. Successfully tested in urban fiber networks for both two-dimensional and four-dimensional encoding, this advancement, supported by rigorous international security analysis, marks a vital step toward making high-capacity, secure quantum communication commercially viable and technically accessible.

Daily Tech Digest - January 20, 2026

Quote for the day:

"The level of morale is a good barometer of how each of your people is experiencing your leadership." -- Danny Cox

The culture you can’t see is running your security operations

Non-observable culture is everything happening inside people’s heads. Their beliefs about cyber risk. Their attitudes toward security. Their values and priorities when security conflicts with convenience or speed. This is where the real decisions get made. You can’t see someone’s belief that “we’re too small to be targeted” or “security is IT’s job, not mine.” You can’t measure their assumption that compliance equals security. You can’t audit their gut feeling that reporting a mistake will hurt their career. But these invisible forces shape every security decision your people make. Non-observable culture includes beliefs about the likelihood and severity of threats. It includes how people weigh security against productivity. It includes their trust in leadership and their willingness to admit mistakes. It includes all the cognitive biases that distort risk perception. ... Implicit culture is the stuff nobody talks about because nobody even realizes it’s there. The unspoken assumptions. The invisible norms. The “way things are done here” that everyone knows but nobody questions. This is the most powerful layer because it operates below conscious awareness. People don’t choose to follow implicit norms. They do. Automatically. Without thinking. Implicit culture includes unspoken beliefs like “security slows us down” or “leadership doesn’t really care about this.” It contains hidden power dynamics that determine who can challenge security decisions and who can’t.

The top 6 project management mistakes — and what to do instead

Project managers are trained to solve project problems. Scope creep. Missed deadlines. Resource bottlenecks. ... Start by helping your teams understand the business context behind the work. What problem are we trying to solve? Why does this project matter to the organization? What outcome are we aiming for? Your teams can’t answer those questions unless you bring them into the strategy conversation. When they understand the business goals, not just the project goals, they can start making decisions differently. Their conversations change to ensure everyone knows why their work matters. ... Right from the start of the project, you need to define not just the business goal but how you’ll measure it was successful in business terms. Did the project reduce cost, increase revenue, improve the customer experience? That’s what you and your peers care about, but often that’s not the focus you ask the project people to drive toward. ... People don’t resist because they’re lazy or difficult. They resist because they don’t understand why it’s happening or what it means for them. And no amount of process will fix that. With an accelerated delivery plan designed to drive business value, your project teams can now turn their attention to bringing people with them through the change process. ... To keep people engaged in the project and help it keep accelerating toward business goals, you need purpose-driven communication designed to drive actions and decisions.

AI has static identity verification in its crosshairs. Now what?

Identity models based on “joiner–mover–leaver” workflows and static permission assignments cannot keep pace with the fluid and temporary nature of AI agents. These systems assume identities are created carefully, permissions are assigned deliberately, and changes rarely happen. AI changes all of that. An agent can be created, perform sensitive tasks, and terminate within seconds. If your verification model only checks identity at login, you’re leaving the entire session vulnerable. ... Securing AI-driven enterprises requires a shift similar to what we saw in the move from traditional firewalls to zero-trust architectures. We didn’t eliminate networks; we elevated policy and verification to operate continuously at runtime. Identity verification for AI must follow the same path. This means building a system that can: Assign verifiable identities to every human and machine actor; Evaluate permissions dynamically based on context and intent; Enforce least privilege at high velocity; Verify actions, not just entry points; ... This is why frameworks like SPIFFE and modern workload identity systems are receiving so much attention. They treat identity as a short-lived, cryptographically verifiable construct that can be created, used, and retired in seconds, exactly the model AI agents require. Human activity is becoming the minority as autonomous systems that can act faster than we can are being spun up and terminated before governance can keep up. That’s why identity verification must shift from a checkpoint to a real-time trust engine that evaluates every action from every actor, human or AI.

AWS European cloud service launch raises questions over sovereignty

AWS established a new legal entity to operate the European Sovereign Cloud under a separate governance and operational model. The new company is incorporated in Germany and run exclusively by EU residents, AWS said. ... “This is the elephant in the room,” said Rene Buest, senior director analyst at Gartner. There are two main concerns regarding the operation of AWS’s European Sovereign Cloud for businesses in Europe. The first relates to the 2018 US Cloud Act, which could require AWS to disclose customer data stored in Europe to the United States, if requested by US authorities. The second involves the possibility of US government sanctions: If a business that uses AWS services is subject to such sanctions, AWS may be compelled to block that company’s access to its cloud services, even if its data and operations are based in Europe. ... It’s an open question at this stage, said Dario Maisto, senior analyst at Forrester. “Cases will have to be tested in court before we can have a definite answer,” he said. “The legal ownership does matter, and this is one of the points that may not be addressed by the current setup of the AWS sovereign cloud.” AWS’s European Sovereign Cloud represents one of several ways that European business can approach the challenge of digital sovereignty. Gartner identifies a spectrum that ranges from global hyperscaler public cloud services through to regional cloud services that are based on non-hyperscaler technology.

Why peripheral automation is the missing link in end-to-end digital transformation?

While organisations have successfully modernized their digital cores, the “last mile” of business operations often remains fragmented, manual, and surprisingly analogue. This gap is why Peripheral Automation is emerging not merely as a tactical correction but as the critical missing link in achieving true, end-to-end digital transformation. ... Peripheral Automation offers a strategic resolution to this paradox. It’s an architectural philosophy that advocates “differential innovation.” Rather than disrupting stable cores to accommodate fleeting business needs, organisations build agile, tailored applications and workflows that sit on top of the core systems. This approach treats the enterprise as a layered ecosystem. The core remains the single source of truth, but the periphery becomes the “system of engagement”. By leveraging modern low-code platforms and composable architecture, leaders can deploy lightweight, purpose-built automation tools that address specific friction points without altering the underlying infrastructure. ... Peripheral automation reduces process latency, manual effort, and rework. By addressing specific pain points rather than attempting broad, multi-year system redesigns, companies unlock measurable efficiency in weeks. This precision improves throughput, reduces cycle times, and frees teams to focus on high-value work.

How does agentic ops transform IT troubleshooting?

AI Canvas introduces a fundamentally different user experience for network troubleshooting. Rather than navigating through multiple dashboards and CLI interfaces, engineers interact with a dynamic canvas that populates with relevant widgets as troubleshooting progresses. You could say that the ‘canvas’ part of the name AI Canvas is the most important part of it. That is, AI Canvas is actually a blank canvas every time you start troubleshooting. It fills the canvas with boxes and on the fly widgets, among other things, during the troubleshooting. Sampath confirms this: “When you ask a question, it’s using and picking the right types of tools that it can go and execute on a specific task and calls agents to be able to effectively take a task to completion and returns a response back.” The system can spin up monitoring agents that continuously provide updated information, creating a living troubleshooting environment rather than static reports. ... AI Canvas doesn’t exist in isolation. It builds on Cisco’s existing automation foundation. The company previously launched Workflows, a no-code network automation engine, and AI assistants with specific skills for network operations. “All of the automations that are already baked into the workflows, the skills that were built inside of the assistants, now manifest themselves inside of the canvas,” Sampath details. This creates a continuum from deterministic workflows to semi-autonomous assistants to fully autonomous agentic operations.

UK government launches industry 'ambassadors' scheme to champion software security improvements

"By acting as ambassadors, signatories are committing to a process of transparency, development and continuous improvement. The implementation of this code of practice will take time and, in doing so, may bring to light issues that need to be addressed," DSIT said in a statement confirming the announcement. "Signatories and policymakers will learn from these issues as well as the successes and challenges for each organization and, where appropriate, will share information to help develop and strengthen this government policy." ... The Software Security Code of Practice was unveiled by the NCSC in May last year, setting out a series of voluntary principles defining what good software security looks like across the entire software lifecycle. Aimed at technology providers and organizations that develop, sell, or procure software, the code offers best practices for secure design and development, build-environment security, and secure deployment and maintenance. The code also emphasizes the importance of transparent communication with customers on potential security risks and vulnerabilities. ... “The code moves software security beyond narrow compliance and elevates it to a board-level resilience priority. As supply chain attacks continue to grow in scale and impact, a shared baseline is essential and through our global community and expertise, ISC2 is committed to helping professionals build the skills needed to put secure-by-design principles into practice.”

Privacy teams feel the strain as AI, breaches, and budgets collide

Where boards prioritize privacy, AI use appears more frequently and follows defined direction. Larger enterprises, particularly those with broader risk and compliance functions, also report higher uptake. In smaller organizations, or those where privacy has limited visibility at the leadership level, AI adoption remains tentative. Teams that apply privacy principles throughout system development report higher use of AI for privacy tasks. In these environments, AI supports ongoing work rather than introducing new approaches. ... Respondents working in organizations where privacy has active board backing report more consistent use of privacy by design. Budget stability shows a similar pattern, with better-funded teams reporting stronger integration of privacy into design and engineering work. The study also shows that privacy by design on its own does not stop breaches. Organizations that experienced breaches report similar levels of design practice as those that did not. The data places privacy by design mainly in a governance and compliance role, with limited connection to incident prevention. ... Governance shapes how teams view that risk. Professionals in organizations where privacy lacks board priority report higher expectations of a breach in the coming year. Gaps between privacy strategy and broader business goals also appear alongside higher breach expectations, suggesting that structural alignment influences outlook as much as technical controls. Confidence remains common, even among organizations that have experienced breaches.

Cyber Insights 2026: Information Sharing

The sheer volume of cyber threat intelligence being generated today is overwhelming. “Information sharing channels often help condense inputs and highlight genuine signals amid industry noise,” says Caitlin Condon, VP of security research at VulnCheck. “The very nature of cyber threat intelligence demands validation, context, and comparison. Information sharing allows cybersecurity professionals to more rigorously assess rising threats, identify new trends and deviations, and develop technically comprehensive guidance.” ... “The importance of the Cybersecurity Information Sharing Act of 2015 for U.S. national security cannot be overstated,” says Crystal Morin, cybersecurity strategist at Sysdig. “Without legal protections, many legal departments would advise security teams to pull back from sharing threat intelligence, resulting in slower, more cautious processes. ...” CISOs have developed their own closed communities where they can discuss current incidents with other CISOs. This is done via channels such as Slack, WhatsApp and Signal. Security of the channels is a concern, but who better than multiple CISOs to monitor and control security? ... “Much of today’s threat intelligence remains reactive, driven by short-lived IoCs that do little to help agencies anticipate or disrupt cyberattacks,” comments BeyondTrust’s Greene. “We need to modernize our information-sharing framework to emphasize behavior-based analytics enriched with identity-centric context,” he continues.

Edge AI: The future of AI inference is smarter local compute

The bump in edge AI goes hand in hand with a broader shift in focus from AI training, the act of preparing machine learning (ML) models with the right data, to inference, the practice of actively using models to apply knowledge or make predictions in production. “Advancements in powerful, energy-efficient AI processors and the proliferation of IoT (internet of things) devices are also fueling this trend, enabling complex AI models to run directly on edge devices,” says Sumeet Agrawal ... “The primary driver behind the edge AI boom is the critical need for real-time data processing,” says David. The ability to analyze data on the edge, rather than using centralized cloud-based AI workloads, helps direct immediate decisions at the source. Others agree. “Interest in edge AI is experiencing massive growth,” says Informatica’s Agrawal. For him, reduced latency is a key factor, especially in industrial or automotive settings where split-second decisions are critical. There is also the desire to feed ML models personal or proprietary context without sending such data to the cloud. “Privacy is one powerful driver,” says Johann Schleier-Smith ... A smaller footprint for local AI is helpful for edge devices, where resources like processing capacity and bandwidth are constrained. As such, techniques to optimize SLMs will be a key area to aid AI on the edge. One strategy is quantization, a model compression technique that reduces model size and processing requirements.

Daily Tech Digest - November 10, 2025

Quote for the day:

"You can only lead others where you yourself are willing to go." -- Lachlan McLean

CISOs must prove the business value of cyber — the right metrics can help

With a foundational , and by aligning metrics to business priorities, cybersecurity leaders can ultimately prove the value of the cyber security function. Useful metrics examples in business terms include maturity, compliance, risk, budget, , and status of (shifting left) adoption, explains. But how does a cybersecurity expert learn what’s important to the business? ... “Boards are faced with complex matters such as impact on interest rates, tariffs, stock price volatility, supply chain issues, profitability, and acquisitions. Then the CISO enters the boardroom with their , patching metrics and ,” Hetner continues. “These metrics are not aligned to what the board is conditioned to reviewing.” ... Rather than just asking “are we secure?” business leaders are asking what metrics their cyber components are using to measure and quantify risk and how they’re spending against those risks. For CISO’s, this goes beyond measuring against frameworks such as NIST, listing a litany of security vulnerabilities they patched, or their mean time to response. “Instead, we can say, ‘This is our potential financial exposure’,” Nolen explains. “So now you’re talking dollars and cents rather than and technical scores that board members don’t care about. What they care about is the bottom line.”

Feeding the AI beast, with some beauty

is placing an unprecedented load on worldwide, and is poised to shoulder a large share of the incremental electricity, real estate, and cooling burden created by rising AI demand. The has estimated a trajectory that AI is accelerating at a rapid pace. Under realistic scenarios, AI workloads alone could require on the order of 1–1.5 GW of continuous IT power—equivalent to 8.8–13 TWh annually—in India by 2030. This translates into a significant new draw on grids, water resources, and capex for cooling and power infrastructure. Recent analyses indicate that while AI’s share of data centre power today stands in the single-digit to low-teens range, it could climb to 20–40 per cent or more by 2030 in some scenarios, fundamentally reshaping the power-consumption profile of digital infrastructure. ... As data centres grow in scale, sustainability is becoming a competitive differentiator—and that’s where Life Cycle Assessments (LCAs) and Environmental Product Declarations (EPDs) play a critical role. An LCA is a systematic method for evaluating the total environmental impact of a product, process, or system across its entire life cycle. For a data centre, this spans both upstream (embodied) impacts—such as construction materials, IT equipment manufacturing, and cooling and power infrastructure including —as well as operational impacts like electricity consumption.

8 IT leadership tips for first-time CIOs

Generally speaking, the first three years can make or break your IT leadership career, given that digital leaders globally tend to stay at one company for just over that length of time on average, according to the 2025 Nash Squared Digital Leadership Report. CIOs looking to sidestep that statistic are taking intentional measures, ensuring they get early wins, and perhaps most importantly, not coming into their role with preconceived ideas about how to lead or assuming what worked in a past job can be replicated. ... The CTO of staffing and recruiting firm Kelly says that “building momentum, finding ways to get quick wins from the low hanging fruit” will help build credibility with the leadership team. Then, you can parlay those into bigger wins and avoid spinning out, he says. ... While making connections and establishing relationships is critical, Lewis stresses the importance of not rushing to change things right away when you’re new to the job. “Let it set for a while,” he says. ... This is especially true of midsize and larger midsize organizations “where the clarity of strategy and clarity of what’s important … isn’t always well documented and well thought out,” Rosenbaum says. Knowing the maturity of your organization is really important, he says. “Some CIO roles are just about keeping the lights on, making sure security is good at a lower level. As the company starts to mature, they start thinking about technology as an enabler, and to that end, they start having maybe a more unified technology strategy.”

Drata’s VP of Data on Rethinking Data Ops for the AI Era: Crawl, Walk, Run — Then Sprint

While may be the shiny new tool, Solomon makes it clear that foundational work around ingestion and transformation is far from trivial. “We live and die by making sure that all the data has been ingested in a fresh manner into the data warehouse,” he explains. He describes the “bread and butter” of the team: synchronizing thousands of from a single-tenant production architecture into the warehouse — closer to real-time. “We do a lot of activities with regard to the , which is just like driving terabytes of data per day.” But the data team isn’t working in isolation. GTM executives return from conferences excited about GenAI. ... Rather than building fully-fledged pipelines from day one, the team prioritizes quick feedback loops — using sandboxes, cloud notebooks, or to test hypotheses. Once business impact is validated, the team gradually introduces cost tracking, governance, and scalability. If a stakeholder’s hypothesis lacks merit, there is no point in building complex data pipelines, governance frameworks, or cost-tracking systems. This shift in mindset, he explains, is something many data teams are grappling with today. Traditionally, data teams were trained to focus on building scalable, robust pipelines from day one — often requiring significant upfront effort. But this often led to cost inefficiencies and delays.

Model Context Protocol Servers: Build or Buy?

"The tension lies in whether you have the sustained capacity to keep pace with protocols that are still being debated by their maintainers," said Rishi Bhargava, co-founder at , a . "Are you prepared to build the plane while it's flying, or would you rather upgrade a finished plane mid-flight?" ... "From a business perspective, the build versus buy decision for boils down to strategic priorities and risk appetite," Jain said. Building MCP servers in-house gives you "complete control," but buying provides "speed, reliability, and lower operational burden," he said. But others think there's no reason to rush your decision. ... "Most companies shouldn't be doing either yet," he said, explaining that companies should first focus on the specific business goals they are trying to achieve, rather than on which existing applications they think should have AI features added. "Build when you have an actual AI application that requires custom data integration and you understand exactly what intelligence you're trying to deploy. If you're simply connecting to your , you don't need MCP at all," Prywata said. ... "It is usually best to build [MCP servers] in-house when compliance, performance tuning, or data sovereignty are key priorities for the business," said Marcus McGehee, founder at The AI Consulting Lab.

Every CIO Fails; The Smart Ones Admit It

There's a "hero CIO" myth deeply rooted in our mindset - the idea that you're the person who makes technology work, no matter what. Admitting failure feels like admitting incompetence, especially in boardrooms where few understand the complexity of IT. Organizational incentives also discourage openness. Many companies punish failure more than they reward learning. I've seen talented CIOs denied promotion because of a single delayed project, even when their broader portfolio delivered value. When institutional memory focuses on what went wrong rather than what was learned, people stop taking risks. The second factor is C-suite politics. In some environments, transparency becomes ammunition. Another team might use a project delay to justify requests for budget increases or to exert influence. And finally, CIOs worry about vendor perception, admitting setbacks could impact pricing, support or their reputation with partners. ... Build your transparency muscle in peacetime, not when something is on fire. By the time a crisis hits, it's too late to establish credibility. Make transparency habitual. Share work in progress, not just results. Celebrate learning, not perfection. Run "pre-mortems" where you assume a project failed and work backwards to identify what could go wrong. And when you make a mistake, own it publicly. The honesty earns you more trust than a polished explanation ever will.

6 proven lessons from the AI projects that broke before they scaled

In analyzing dozens of AI PoCs that sailed on through to full production use — or didn’t — six common pitfalls emerge. Interestingly, it’s not usually the quality of the technology but misaligned goals, poor planning or unrealistic expectations that caused failure. ... Define specific, measurable objectives upfront. Use . For example, aim for “reduce equipment downtime by 15% within six months” rather than a vague “make things better.” Document these goals and align stakeholders early to avoid scope creep. ... Invest in data quality over volume. Use tools like for preprocessing and Great Expectations for data validation to catch issues early. Conduct exploratory data analysis (EDA) with visualizations (like ) to spot outliers or inconsistencies. Clean data is worth more than terabytes of garbage. ... Start simple. Use straightforward algorithms like or from to establish a baseline. Only scale to complex models — — if the problem demands it. Prioritize explainability with tools like to build trust with stakeholders. ... Plan for production from day one. Package models in Docker containers and deploy with for scalability. Use or for efficient inference. Monitor performance with Prometheus and Grafana to catch bottlenecks early. Test under realistic conditions to ensure reliability.

Andela CEO talks about the need for ‘borderless talent’ amid work visa limitation

Globally, three of four IT employers say they lack the tech talent they need, and the outlook will only get more dire as AI creates a demand for high-skilled specialists like data engineers, senior architects, and agentic orchestrators. Visa programs aren’t designed by the laws of supply and demand. They’re defined by policy makers and are updated infrequently. So, they’ll never truly be in sync with the needs of the labor market. ... Brilliant people exist around the world. It’s why they want to sponsor people for . But hiring outside of those traditional pathways — to work with a brilliant machine learning engineer from Cairo or São Paulo, for example — is…a long, painful process that takes months and is inaccessible to them. They don’t know that they can find the right partner, someone who has sorted this all out and vetted talent and developed compliance with global labor and tax laws, etc. Once they understand that those partners exist, the global workforce becomes instantly accessible to them. ... Technical hiring still feels like a gamble, even though software development is, relatively speaking, packed with deterministic skills. There are two main problems. One problem is the data problem. There’s not enough reliable data about what a job actually requires and what a worker is capable of doing. Today, we rely on resumes and job descriptions.

The Overwhelm Epidemic: Why Resilience Begins with You

People have so much to do and not enough time. There’s nothing new with the phenomena of not enough time to do what needs to be done, but today it’s different. Today, it’s unique because this feeling of overwhelm has been continuously expanding since early 2020 as we experienced the pandemic. We’re being overwhelmed to an extent most people are not experienced to deal with. For you in operational resilience, I believe self-care is more critical now than it has ever been. You are only able to help your clients and their systems be resilient to the extent you are taking care of yourself and are resilient. ... Most say something like, “I’m going to double down and focus on this. I’m going to work harder and spend as much time as needed, even if it means cutting into my already precious personal time.” They think working harder is the best approach, but here’s the thing—they are wrong. When you are operating at high-stress levels, introducing more stress by doubling down and working harder, actually reduces your output. ... Bottom line, a thriving, elite mindset is the foundation of personal wellbeing and professional success.  Turning to positive psychology, underlying Martin Seligman‘s model for human flourishing, are 24 positive character strengths. While more research is still needed, the research to date has concluded that of the 24, the best predictor of living a flourishing, thriving life is gratitude.

Ask a Data Ethicist: What Are the Impacts of AI on Creativity, Schools, and Industry?

Generally speaking, if the goal is to reduce the cost of labour by replacing it with equipment (capital – or AI), then assuming the AI tool replaces the labour in a way that is acceptable to drive the desired outputs the business could possibly drive more profit. So that might be construed as positive for the business. However, businesses exist in the bigger context of society. To take an extreme example, if a large section of the population loses their jobs, they can’t buy your products, and that could hurt your organization. It also puts more burdens on society for a social safety net, perhaps resulting in tax increases or some other impacts to business to pay for those services. ... I think it’s important to disclose the use of AI in a process. For video, audio or images – a symbol or some text to say “AI generated” can accomplish that goal. There is also watermarking that content which is a more technical method. For text, it’s trickier. I don’t think everyone needs to be told about every instance of a spellchecker (to use an extreme example) but if the whole thing is generated, then it is important to say that. This is where a policy can be helpful. For example, one might apply the 80/20 rule – if less than 20% is generated, perhaps it’s not necessary to disclose it. That said, there better not be any inaccuracies or errors in the content if you choose NOT to disclose it. See this case in Australia. This is an example of why I think disclosing, overall, is a good idea.