Daily Tech Digest by Kannan Subbiah: cto

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Daily Tech Digest - March 16, 2026

Quote for the day:

"Inspired leaders move a business beyond problems into opportunities." -- Dr. Abraham Zaleznik

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Why many enterprises struggle with outdated digital systems & how to fix them

The article on Express Computer, "Why many enterprises struggle with outdated digital systems & how to fix them," explores the pervasive issue of legacy technical debt. Many organizations remain tethered to aging infrastructure that stifles innovation and hampers agility. The struggle often stems from the prohibitive costs of replacement, the immense complexity of migrating mission-critical processes, and a fundamental fear of business disruption. Governance layers and siloed ownership further exacerbate these challenges, creating compounding "enterprise debt" across processes, data, and talent. To address these bottlenecks, the author advocates for a strategic shift toward a product mindset and incremental modernization instead of high-risk, wholesale replacements. Recommended fixes include mapping system dependencies, quantifying inefficiencies, and following a clear roadmap that progresses from stabilization to systematic optimization. By decoupling tightly integrated components and establishing clear ownership, enterprises can transform their brittle legacy systems into scalable, resilient assets. Fostering a culture of continuous improvement and aligning with core business objectives are equally vital for survival. Ultimately, the piece emphasizes that overcoming outdated digital systems is a strategic necessity in a fast-paced market, requiring a balanced approach to technical remediation and organizational change to ensure long-term competitiveness.

COBOL developers will always be needed, even as AI takes the lead on modernization projects

The article from ITPro explores the enduring necessity of COBOL developers amidst the rise of in legacy modernization projects. While AI is increasingly being marketed as a "silver bullet" for converting ancient COBOL codebases into modern languages like Java, industry experts argue that these digital transformations cannot succeed without human domain expertise. COBOL remains the backbone of global financial and administrative systems, housing decades of intricate business logic that AI often fails to interpret accurately. The piece emphasizes that while can significantly accelerate code translation and documentation, it lacks the contextual understanding required to define what a successful transformation actually looks like. Consequently, veteran developers are essential for overseeing AI-driven migrations, identifying potential risks, and ensuring that the logic preserved in the legacy system is correctly replicated in the new environment. Rather than replacing the workforce, AI acts as a collaborative tool that shifts the developer's role from manual coding to strategic orchestration. Ultimately, the survival of critical infrastructure depends on a hybrid approach that combines the speed of machine learning with the deep-seated knowledge of COBOL specialists, proving that legacy expertise is more valuable than ever in the modern era.

The CTO is dead. Long live the CTO

In the article "The CTO is dead. Long live the CTO" on CIO.com, Marios Fakiolas argues that the traditional role of the as a technical gatekeeper and "human compiler" has become obsolete due to the rise of advanced AI. Modern Large Language Models can now design complex system architectures in minutes, outperforming humans in handling multidimensional constraints and technical interdependencies. Consequently, the new era demands a "multiplier" who shifts focus from providing technical answers to architecting systems that enable continuous organizational intelligence. Today’s CTO is measured not by architectural purity, but by tangible business outcomes such as gross margin, ROI, and operational velocity. This evolution requires leaders to move beyond their "AI comfort zone" of fancy demos and instead tackle difficult structural challenges like cost optimization and team restructuring. The author emphasizes that the modern leader must lead from the front, ruthlessly killing legacy "darlings" and designing for impermanence rather than static stability. Ultimately, the successful CTO must transition from being a bottleneck to becoming an orchestrator of AI agents and human expertise, ensuring that the entire organization can pivot rapidly without trauma. By embracing this proactive mindset, technology leaders can transcend the gatekeeping era and drive meaningful innovation in a fierce, AI-driven market.

When insider risk is a wellbeing issue, not just a disciplinary one

In the article "When insider risk is a wellbeing issue, not just a disciplinary one" on Security Boulevard, Katie Barnett argues for a paradigm shift in how organizations manage insider threats. Moving beyond traditional framing—which often focuses on malicious intent and punitive disciplinary measures—the author highlights that many security incidents are actually the byproduct of employee stress, fatigue, and disengagement. In a modern work environment characterized by digital isolation and economic uncertainty, personal strains such as financial pressure or burnout can erode professional judgment, making individuals more susceptible to manipulation or unintentional policy violations. The piece emphasizes that relying solely on technical controls and monitoring is insufficient; these tools do not address the underlying human factors that lead to risk. Instead, Barnett advocates for a proactive approach where wellbeing is treated as a core pillar of organizational resilience. This involves training managers to recognize early behavioral warning signs, fostering a supportive culture where staff feel safe raising concerns, and creating interdepartmental cooperation between HR and security teams. Ultimately, the article posits that by integrating support and psychological safety into the security strategy, organizations can prevent incidents before they escalate, strengthening their overall security posture through empathy rather than just compliance.

What it takes to win that CSO role

In the CSO Online article "What it takes to win that CSO role," David Weldon explores the transformation of the Chief Security Officer position into a high-stakes C-suite role requiring board-level accountability. No longer a back-office function, the modern CSO operates at the critical intersection of technology, regulatory exposure, revenue continuity, and brand trust. Achieving success in this position demands a shift from being a "cost center" to a "trust center," where security is positioned as a strategic business enabler that supports revenue growth rather than just a preventative measure. Key requirements include deep expertise in identity and access management and a sophisticated understanding of emerging threats like shadow AI, data poisoning, and model risk. Beyond technical prowess, financial acumen is non-negotiable; aspiring CSOs must translate security investments into business value, such as reduced insurance premiums or contractual leverage. Communication is paramount, as the role involves constant negotiation and the ability to translate complex risks for non-technical stakeholders. Ultimately, winning the role requires aligning accountability with authority and demonstrating the operating depth to maintain business resilience during sustained outages. By evolving from a "no" person to a "how" person, successful CSOs ensure that security becomes a foundational pillar of organizational success and customer confidence.

Human-Centered AI Is Becoming A Leadership Imperative

In his Forbes article, "Human-Centered AI Is Becoming A Leadership Imperative," Rhett Power argues that while artificial intelligence offers unprecedented industrial opportunities, its successful implementation depends entirely on a shift from technical obsession to human-centric leadership. Power contends that unchecked AI deployment often fails because it ignores the social and cognitive arrangements necessary for technology to thrive. To bridge the widening gap between technological promise and actual business value, leaders must adopt three foundational principles: prioritizing desired business outcomes over specific tools, evolving training to support role-specific enablement, and treating human-centered design as a core competitive advantage. Power identifies a new leadership paradigm where executives must serve as visionary guides who align AI with human values, ethical guardians who ensure transparency and bias mitigation, and human advocates who prioritize employee experience. By focusing on augmenting rather than replacing human expertise, organizations can transform AI into a seamless collaborative partner that drives long-term resilience and innovation. Ultimately, the article emphasizes that the true value of AI lies in its ability to extend the reach of human judgment, making the integration of empathy and ethical oversight a non-negotiable requirement for modern executive accountability in a rapidly evolving digital landscape.

Employee Experience 2.0: AI as the Performance Engine of the Work Operating System

In the article "Employee Experience 2.0: AI as the Performance Engine of the Work Operating System," Jeff Corbin outlines an essential evolution in workplace management. While the first version of the Employee Experience (EX 1.0) focused on cross-departmental alignment between HR, IT, and Communications, the author argues that human capacity alone is no longer sufficient to manage the modern digital workspace. EX 2.0 introduces artificial intelligence as a "performance layer" that transforms the work operating system from a static framework into a self-optimizing engine. AI addresses critical challenges such as "digital friction"—where employees waste nearly 30% of their day searching through disconnected systems like SharePoint and ServiceNow—by acting as an automated editor for content governance. Beyond cleaning up data, AI-driven EX 2.0 enables hyper-personalization of communications and provides predictive analytics that can identify turnover risks or workflow bottlenecks before they escalate. By integrating AI as a core architectural component, organizations can move beyond manual coordination to create a frictionless environment that boosts engagement and productivity. Ultimately, the piece calls for leaders to upgrade their governance models, positioning AI not just as a tool, but as a collaborative partner that ensures the employee experience remains agile and effective in a technology-driven era.

The Next Era of UX and Analytics, and Merging Conversational AI with Design-to-Code

The article "The Transformation of Software Development: , the Next Era of UX and Analytics" explores the profound shift from static, reactive user interfaces to proactive, intelligent systems. Modern software development is evolving beyond standard component libraries toward "smarter" UI elements that leverage embedded analytics and machine learning to adapt to user behavior in real-time. This transformation allows digital interfaces to anticipate user needs, personalize layouts dynamically, and optimize complex workflows without manual intervention. By integrating sophisticated telemetry directly into front-end components, developers gain granular, actionable insights into performance and engagement, effectively bridging the gap between user experience and technical execution. This evolution significantly impacts the modern DevOps lifecycle, as development teams move from building isolated features to orchestrating continuous learning environments. The article further highlights that these intelligent components reduce the cognitive load for end-users by surfacing relevant information and simplifying intricate navigations. Ultimately, the synergy between advanced data analytics and front-end engineering is setting a new industry standard for digital excellence, where personalization and efficiency are core to the process. Organizations that embrace this era of "smarter" components will deliver highly tailored experiences that drive superior retention and user satisfaction in an increasingly competitive market.

Certificate lifespans are shrinking and most organizations aren’t ready

The article "Certificate lifespans are shrinking and most organizations aren't ready," featured on Help Net Security, outlines the critical challenges businesses face as TLS certificate validity periods compress from one year down to 47 days. John Murray of GlobalSign emphasizes that this rapid shift, driven by browser requirements, necessitates a complete overhaul of traditional manual certificate management. To avoid operational disruptions and outages, organizations must prioritize "discovery" as the foundational step, utilizing tools like GlobalSign's Atlas or LifeCycle X to inventory every certificate and platform. This proactive approach is not only vital for managing shorter lifecycles but also serves as essential preparation for the eventual migration to post-quantum cryptography. Murray suggests that manual spreadsheets are no longer sustainable; instead, businesses should adopt automation protocols like ACME and shift toward flexible, SAN-based licensing models to remove procurement friction. While larger enterprises may have dedicated PKI teams, mid-market and smaller organizations are at a higher risk of being caught off guard. By establishing automated renewal pipelines and closing the specialized knowledge gap in PKI expertise, companies can build a resilient security posture. Ultimately, the window for preparation is closing, and integrating automated lifecycle management is now a strategic imperative rather than a future luxury.

Agoda CTO on why AI still needs human oversight

In the Tech Wire Asia article, , Idan Zalzberg, discusses the essential role of human oversight in an era dominated by artificial intelligence. While AI tools have significantly accelerated developer workflows and boosted productivity—with early experiments at Agoda showing a 27% uplift—Zalzberg emphasizes that these technologies remain supplementary. The primary challenge lies in the inherent unpredictability and non-deterministic nature of generative AI, which differs from traditional software by producing inconsistent outputs. Consequently, Agoda maintains a strict policy where human engineers remain fully accountable for all code, regardless of its origin. Quality control remains rigorous, utilizing the same static analysis and automated testing frameworks applied to human-written scripts. Zalzberg notes that the evolution of the engineering role shifts focus toward critical thinking, strategic decision-making, and "evaluation"—a statistical method for assessing AI performance. Beyond technical management, the article highlights how cultural attitudes toward risk influence AI adoption rates across different regions. Ultimately, Zalzberg argues that AI maturity is defined by a balanced approach: leveraging the speed of automation while ensuring that sensitive decisions—such as pricing or critical architecture—are governed by human judgment and a centralized gateway to manage security and costs effectively.

Daily Tech Digest - January 30, 2026

Quote for the day:

"In my experience, there is only one motivation, and that is desire. No reasons or principle contain it or stand against it." -- Jane Smiley

Crooks are hijacking and reselling AI infrastructure: Report

In a report released Wednesday, researchers at Pillar Security say they have discovered campaigns at scale going after exposed large language model (LLM) and MCP endpoints – for example, an AI-powered support chatbot on a website. “I think it’s alarming,” said report co-author Ariel Fogel. “What we’ve discovered is an actual criminal network where people are trying to steal your credentials, steal your ability to use LLMs and your computations, and then resell it.” ... How big are these campaigns? In the past couple of weeks alone, the researchers’ honeypots captured 35,000 attack sessions hunting for exposed AI infrastructure. “This isn’t a one-off attack,” Fogel added. “It’s a business.” He doubts a nation-state it behind it; the campaigns appear to be run by a small group. ... Defenders need to treat AI services with the same rigor as APIs or databases, he said, starting with authentication, telemetry, and threat modelling early in the development cycle. “As MCP becomes foundational to modern AI integrations, securing those protocol interfaces, not just model access, must be a priority,” he said. ... Despite the number of news stories in the past year about AI vulnerabilities, Meghu said the answer is not to give up on AI, but to keep strict controls on its usage. “Do not just ban it, bring it into the light and help your users understand the risk, as well as work on ways for them to use AI/LLM in a safe way that benefits the business,” he advised.

AI-Powered DevSecOps: Automating Security with Machine Learning Tools

Here's the uncomfortable truth: AI is both causing and solving the same problem. A Snyk survey from early 2024 found that 77% of technology leaders believe AI gives them a competitive advantage in development speed. That's great for quarterly demos and investor decks. It's less great when you realize that faster code production means exponentially more code to secure, and most organizations haven't figured out how to scale their security practice at the same rate. ... Don't try to AI-ify your entire security stack at once. Pick one high-pain problem — maybe it's the backlog of static analysis findings nobody has time to triage, or maybe it's spotting secrets accidentally committed to repos — and deploy a focused tool that solves just that problem. Learn how it behaves. Understand its failure modes. Then expand. ... This is non-negotiable, at least for now. AI should flag, suggest, and prioritize. It should not auto-merge security fixes or automatically block deployments without human confirmation. I've seen two different incidents in the past year where an overzealous ML system blocked a critical hotfix because it misclassified a legitimate code pattern as suspicious. Both cases were resolved within hours, but both caused real business impact. The right mental model is "AI as junior analyst." ... You need clear policies around which AI tools are approved for use, who owns their output, and how to handle disagreements between human judgment and AI recommendations.

AI & the Death of Accuracy: What It Means for Zero-Trust

The basic idea is that as the signal quality degrades over time through junk training data, models can remain fluent and fully interact with the user while becoming less reliable. From a security standpoint, this can be dangerous, as AI models are positioned to generate confident-yet-plausible errors when it comes to code reviews, patch recommendations, app coding, security triaging, and other tasks. More critically, model degradation can erode and misalign system guardrails, giving attackers the opportunity exploit the opening through things like prompt injection. ... "Most enterprises are not training frontier LLMs from scratch, but they are increasingly building workflows that can create self-reinforcing data stores, like internal knowledge bases, that accumulate AI-generated text, summaries, and tickets over time," she tells Dark Reading. ... Gartner said that to combat the potential impending issue of model degradation, organizations will need a way to identify and tag AI-generated data. This could be addressed through active metadata practices (such as establishing real-time alerts for when data may require recertification) and potentially appointing a governance leader that knows how to responsibly work with AI-generated content. ... Kelley argues that there are pragmatic ways to "save the signal," namely through prioritizing continuous model behavior evaluation and governing training data.

The Friction Fix: Change What Matters

Friction is the invisible current that sinks every transformation. Friction isn’t one thing, it’s systemic. Relationships produce friction: between the people, teams and technology. ... When faced with a systemic challenge, our human inclination is to blame. Unfortunately, we blame the wrong things. We blame the engineering team for failing to work fast enough or decide the team is too small, rather than recognize that our Gantt chart was fiction, which is an oversimplification of a complex dynamic. ... The fix is to pause and get oriented. Begin by identifying the core domain, the North Star. What is the goal of the system? For Fedex, it is fast package delivery. Chances are, when you are experiencing counterintuitive behavior, it is because people are navigating in different directions while using the same words. ... Every organization trying to change has that guy: the gatekeeper, the dungeon master, the self-proclaimed 10x engineer who knows where the bodies are buried. They also wield one magic word: No. ... It’s easy to blame that guy’s stubborn personality. But he embodies behavior that has been rewarded and reinforced. ... Refusal to change is contagious. When that guy shuts down curiosity, others drift towards a fixed mindset. Doubt becomes the focus, not experimentation. The organization can’t balance avoiding risk with trying something new. The transformation is dead in the water.

From devops to CTO: 8 things to start doing now

Devops leaders have the opportunity to make a difference in their organization and for their careers. Lead a successful AI initiative, deploy to production, deliver business value, and share best practices for other teams to follow. Successful devops leaders don’t jump on the easy opportunities; they look for the ones that can have a significant business impact. ... Another area where devops engineers can demonstrate leadership skills is by establishing standards for applying genAI tools throughout the software development lifecycle (SDLC). Advanced tools and capabilities require effective strategies to extend best practices beyond early adopters and ensure that multiple teams succeed. ... If you want to be recognized for promotions and greater responsibilities, a place to start is in your areas of expertise and with your team, peers, and technology leaders. However, shift your focus from getting something done to a practice leadership mindset. Develop a practice or platform your team and colleagues want to use and demonstrate its benefits to the organization. Devops engineers can position themselves for a leadership role by focusing on initiatives that deliver business value. ... One of the hardest mindset transitions for CTOs is shifting from being the technology expert and go-to problem-solver to becoming a leader facilitating the conversation about possible technology implementations. If you want to be a CTO, learn to take a step back to see the big picture and engage the team in recommending technology solutions.

The stakes rise for the CIO role in 2026

The CIO's days as back-office custodian of IT are long gone, to be sure, but that doesn't mean the role is settled. Indeed, Seewald and others see plenty of changes still underway. In 2026, the CIO's role in shaping how the business operates and performs is still expanding. It reflects a nuanced change in expectations, according to longtime CIOs, analysts and IT advisors -- and one that is showing up in many ways as CIOs become more directly involved in nailing down competitive advantage and strategic success across their organizations. ... "While these core responsibilities remain the same, the environment in which CIOs operate has become far more complex," Tanowitz added. Conal Gallagher, CIO and CISO at Flexera, said the CIO in 2026 is now "accountable for outcomes: trusted data, controlled spend, managed risk and measurable productivity. "The deliverable isn't a project plan," Gallagher said. "It's proof that the business runs faster, safer and more cost-disciplined because of the operating model IT enables." ... In 2026, the CIO role is less about being the technology owner and more about being a business integrator, Hoang said. At Commvault, that shift places greater emphasis on governance and orchestration across ecosystems. "We're operating in a multicloud, multivendor, AI-infused environment," she said. "A big part of my job is building guardrails and partnerships that enable others to move fast -- safely," she said.

Inside the Shift to High-Density, AI-Ready Data Centres

As density increases, design philosophy must evolve. Power infrastructure, backup systems, and cooling can no longer be treated as independent layers; they have to be tightly integrated. Our facilities use modular and scalable power and cooling architectures that allow us to expand capacity without disrupting live environments. Rated-4 resilience is non-negotiable, even under continuous, high-density AI workloads. The real focus is flexibility. Customers shouldn’t be forced into an all-or-nothing transition. Our approach allows them to move gradually to higher densities while preserving uptime, efficiency, and performance. High-density AI infrastructure is less about brute force and more about disciplined engineering that sustains reliability at scale. ... The most common misconception is that AI data centres are fundamentally different entities. While AI workloads do increase density, power, and cooling demands, the core principles of reliability, uptime, and efficiency remain unchanged. AI readiness is not about branding; it’s about engineering and operations. Supporting AI workloads requires scalable and resilient power delivery, precision cooling, and flexible designs that can handle GPUs and accelerators efficiently over sustained periods. Simply adding more compute without addressing these fundamentals leads to inefficiency and risk. The focus must remain on mission-critical resilience, cost-effective energy management, and sustainability.

Software Supply Chain Threats Are on the OWASP Top Ten—Yet Nothing Will Change Unless We Do

As organizations deepen their reliance on open-source components and embrace AI-enabled development, software supply chain risks will become more prevalent. In the OWASP survey, 50% of respondents ranked software supply chain failures number one. The awareness is there. Now the pressure is on for software manufacturers to enhance software transparency, making supply chain attacks far less likely and less damaging. ... Attackers only need one forgotten open-source component from 2014 that still lives quietly inside software to execute a widespread attack. The ability to cause widespread damage by targeting the software supply chain makes these vulnerabilities alluring for attackers. Why break into a hardened product when one outdated dependency—often buried several layers down—opens the door with far less effort? The SolarWinds software supply chain attack that took place in 2020 demonstrated the access adversaries gain when they hijack the build process itself. ... “Stable” legacy components often go uninspected for years. These aging libraries, firmware blocks, and third-party binaries frequently contain memory-unsafe constructs and unpatched vulnerabilities that could be exploited. Be sure to review legacy code and not give it the benefit of the doubt. ... With an SBOM in hand, generated at every build, you can scan software for vulnerabilities and remediate issues before they are exploited.

What the first 24 hours of a cyber incident should look like

When a security advisory is published, the first question is whether any assets are potentially exposed. In the past, a vendor’s claim of exploitation may have sufficed. Given the precedent set over the past year, it is unwise to rely solely on a vendor advisory for exploited-in-the-wild status. Too often, advisories or exploitation confirmations reach teams too late or without the context needed to prioritise the response. CISA’s KEV, trusted third-party publications, and vulnerability researchers should form the foundation of any remediation programme. ... Many organisations will leverage their incident response (IR) retainers to assess the extent of the compromise or, at a minimum, perform a rudimentary threat hunt for indicators of compromise (IoCs) before involving the IR team. As with the first step, accurate, high-fidelity intelligence is critical. Simply downloading IoC lists filled with dual-use tools from social media will generate noise and likely lead to inaccurate conclusions. Arguably, the cornerstone of the initial assessment is ensuring that intelligence incorporates decay scoring to validate command-and-control (C2) infrastructure. For many, the term ‘threat hunt’ translates to little more than a log search on external gateways. ... The approach at this stage will be dependent on the results of the previous assessments. There is no default playbook here; however, an established decision framework that dictates how a company reacts is key.

NIST’s AI guidance pushes cybersecurity boundaries

For CISOs, what should matter is that NIST is shifting from a broad, principle-based AI risk management framework toward more operationally grounded expectations, especially for systems that act without constant human oversight. What is emerging across NIST’s AI-related cybersecurity work is a recognition that AI is no longer a distant or abstract governance issue, but a near-term security problem that the nation’s standards-setting body is trying to tackle in a multifaceted way. ... NIST’s instinct to frame AI as an extension of traditional software allows organizations to reuse familiar concepts — risk assessment, access control, logging, defense in depth — rather than starting from zero. Workshop participants repeatedly emphasized that many controls do transfer, at least in principle. But some experts argue that the analogy breaks down quickly in practice. AI systems behave probabilistically, not deterministically, they say. Their outputs depend on data that may change continuously after deployment. And in the case of agents, they may take actions that were not explicitly scripted in advance. ... “If you were a consumer of all of these documents, it was very difficult for you to look at them and understand how they relate to what you are doing and also understand how to identify where two documents may be talking about the same thing and where they overlap.”

Daily Tech Digest - October 19, 2025

; Quote for the day:

"The most powerful leadership tool you have is your own personal example." -- John Wooden

How CIOs Can Close the IT Workforce Skills Gap for an AI-First Organization

Deliberately building among existing talent, rather than searching outside the organization for new hires or leaving skills development to chance, can help develop the desired institutional knowledge and build an IT-resilient workforce. is a strategic approach that guides the use of AI technology within an enterprise or a unit within it, with the intention of maximizing the benefits from AI. IT organizations must maintain ongoing skills development to be successful as an AI-first organization. ... In developing the future-state competency map, CIOs must include AI-specific skills and competencies, ensuring each role has measurable expectations aligned with the company’s strategic objectives related to AI. CIO must also partner with to design and establish . While HR leaders are experts in scaling learning initiatives and standardizing tools, CIOs have more insight into foundational AI skills, training, and technical support required in the enterprise. CIOs should regularly review whether their teams’ AI capabilities contribute to faster product launches or improved customer insights. ... Addressing employees’ key concerns is a critical step for any to be successful. AI is fundamentally changing traditional workplace operating models by democratizing access to technology, generating insights, and changing the relationship between people and technology.

20 Strategies To Strengthen Your Crisis Management Playbook

The regular review and refinement of protocols ensures alignment when a scenario arises. At our company, we centralize contacts, prepare for a range of scenarios and set outreach guidelines. This enables rapid response, timely updates and meaningful support, which safeguards trust and strengthens relationships with employees, stakeholders and clients. ... Unintended consequences often arise when are left out of crisis planning. Leaders should bake audience insights into their playbooks early—not after headlines hit. Anticipating concerns builds trust and gives you the clarity and credibility to lead through the tough moments. ... Know when to do nothing. Sometimes the instinct to respond immediately leads to increased confusion and puts your brand even further under the microscope. The best crisis managers know when to stop, see how things play out and respond accordingly (if at all), all while preparing for a variety of scenarios behind the scenes. ... Act like a . A crisis is not an event; it's a stress test of brand, enterprise and reputation infrastructure and resilience. Crisis plans must align with business continuity, incident response and disaster recovery plans. Marketing and communications must co-lead with the exec team, legal, ops and regulatory to guide action before commercial, brand equity and reputation risk escalates.

Abstract or die: Why AI enterprises can't afford rigid vector stacks

Without portability, organizations stagnate. They have from recursive code paths, are hesitant to adopt new technology and cannot move prototypes to production at pace. In effect, the database is a bottleneck rather than an accelerator. Portability, or the ability to move underlying infrastructure without re-encoding the application, is ever more a strategic requirement for enterprises rolling out AI at scale. ... Instead of having application code directly bound to some specific vector backend, companies can compile against an abstraction layer that normalizes operations like inserts, queries and filtering. This doesn't necessarily eliminate the need to choose a backend; it makes that choice less rigid. Development teams can start with or in the lab, then scale up to or for production and ultimately adopt a special-purpose cloud vector DB without having to re-architect the application. ... What's happening in the vector space is one example of a bigger trend: Open-source abstractions as critical infrastructure; In data formats: ; In ML models: ; In orchestration: ; In AI APIs: and other such frameworks. These projects succeed, not by adding new capability, but by removing friction. They enable enterprises to move more quickly, hedge bets and evolve along with the ecosystem. Vector DB adapters continue this legacy, transforming a high-speed, fragmented space into infrastructure that enterprises can truly depend on. ...

AWS's New Security VP: A Turning Point for AI Cybersecurity Leadership?

"As we move forward into 2026, the breadth and depth of AI opportunities, products, and threats globally present a paradigm shift in cyber defense," Lohrmann said. He added that he was encouraged by AWS's recognition of the need for additional focus and attention on these cyberthreats. ... "Agentic AI attackers can now operate with a 'reflection loop' so they are effectively self-learning from failed attacks and modifying their attack approach automatically," said Simon Ratcliffe, fractional CIO at Freeman Clarke. "This means the attacks are faster and there are more of them … putting overwhelming pressure on CISOs to respond." ... "I think the 's role will evolve to meet the broader governance ecosystem, bringing together AI security specialists, , , and ," she said, adding cybersecurity's mantra that AI security is everyone's business. "But it demands dedicated expertise," she said. "Going forward, I hope that organizations treat as integral parts of cybersecurity, not siloed add-ons." ... In Liebig's opinion, the future of cybersecurity leadership looks less hierarchical than it does now. "As for who owns that risk, I believe the CISO remains accountable, but new roles are emerging to operationalize AI integrity -- , AI security architects, and ," he explained. "The CISO's role should expand horizontally, ensuring AI aligns to enterprise trust frameworks, not stand apart from them."

The Top 5 Technology Trends For 2026

In recent years, we've seen industry, governments, education and everyday folk scrambling to adapt to the disruptive impact of AI. But by 2026, we're starting to get answers to some of the big questions around its effect on jobs, business and day-to-day life. Now, the focus shifts from simply reacting to reinventing and reshaping in order to find our place in this brave, different and sometimes frightening new world. ... Rather than simply answering questions and generating content, agents take action on our behalf, and in 2026, this will become an increasingly frequent and normal occurrence in everyday life. From automating business decision-making to managing and coordinating hectic family schedules, AI agents will handle the “busy work” involved in planning and problem-solving, freeing us up to focus on the big picture or simply slowing down and enjoying life. ... Quantum computing harnesses the strange and seemingly counterintuitive behavior of particles at the sub-atomic level to accomplish many complex computing tasks millions of times faster than "classic" computers. For the last decade, there's been excitement and hype over their performance in labs and research environments, but in 2026, we are likely to see further adoption in the real world. While this trend might not appear to noticeably affect us in our day-to-day lives, the impact on business, industry and science will begin to take shape in noticeable ways.

How Successful CTOs Orchestrate Business Results at Every Stage

As companies mature, their technical needs shift from building for the present to a long-term vision, strategic partnerships, and leveraging technology to drive business goals. The combines deep technical acumen with business acumen and a deep understanding of the customer journey. This leader collaborates with other executives on strategic planning, but always through the lens of where customers are heading, not strictly where technology is going. ... For large enterprises with complex ecosystems and large customer bases, stability, security, and operational efficiency are paramount. This is where the safeguards the customer experience through technical excellence.This leader oversees all aspects of technical infrastructure, ensuring the reliability, security, and availability of core technology assets with a clear understanding that every decision directly impacts customer trust. ... While these operational models often align with company growth stages, they aren't rigid. A company's needs can shift rapidly due to market conditions, competitive pressures, or unexpected challenges, and customer expectations can evolve just as quickly. ... The most successful companies create environments where technical leadership evolves in response to changing business needs, empowering technical leaders to pivot their focus from building to strategizing, or from innovating to safeguarding, as circumstances demand.

Financial services seek balance of trust, inclusion through face biometrics advances

Advances in the flexibility of , and cross-sectoral collaboration represent the latest measures against fraud in remote financial services. A digital bank in the is integrating ’s face biometrics and liveness detection, and a partner are entering a sandbox to work on protecting against deepfakes, and an event held by Facephi in Mexico explored the challenges of financial services trying to maintain digital trust while advancing inclusion. ... The Philippine digital bank will deploy advanced liveness detection tools as part of a new risk-based authentication strategy. “Our mission is to uplift the lives of all Filipinos through a secure, trusted, and accessible digital bank for all Filipinos, and that requires deploying resilient infrastructure capable of addressing sophisticated fraud,” said Russell Hernandez, chief information security officer at UnionDigital Bank. “As we shift toward risk-based authentication, we need a flexible and future-ready solution. iProov’s internationally proven ability to deliver ease of use, speed, and high security assurance – backed by reliable vendor support – ensures we can evolve our fraud defenses while sustaining customer trust and confidence.” ... The Mexican government has launched several initiatives to standardize digital identity infrastructure, including — a single sign-on platform for public services — and the forthcoming , designed to harmonize verification across sectors.

Why context, not just data, will define the future of AI in finance

Raw intelligence in AI and its ability to crunch numbers and process data is only one part of the equation. What it fundamentally lacks is wisdom, which comes from context. In areas like personal finance, building powerful models with deep domain knowledge is critical. The challenges range from misinterpretation of data to regulatory oversights that directly affect value for customers. That’s why at , we put “context at the core of AI.” This means moving beyond generic datasets to build specialised Financial Large Language Models (LLMs) trained on decades of anonymised financial expertise. It’s about understanding the interconnected journey of our customers across our ecosystem—from the freelancer managing invoices in to that same individual filing taxes with , to them monitoring their financial health on . ... In the age of GenAI, craftsmanship in engineering is being redefined. It’s no longer just about writing every line of code or building models from scratch, but about architecting robust, extensible systems that empower others to innovate. The very soul of engineering is transcending code to become the art of architecture. The measure of excellence is no longer found in the meticulous construction of every model, but in the visionary design of systems that empower domain experts to innovate. With tools like and abstracting complexity, the engineer’s role isn’t diminished but elevated. They evolve from builders of applications to architects of innovation ecosystems.

The modernization mirage: CIOs must see through it to play the long game

Enterprise architecture, in too many organizations, has been reduced to frameworks: , , . These models provide structure but rarely move capital or inspire investor trust. Boards don’t want frameworks. They want influence. That’s why I developed the — a practical model I use in board and transformation discussions. It rests on three pivots - Outcomes: Every architectural choice must tie directly to board-level priorities — growth, resilience, efficiency. ... Relationships: CIOs must serve as business-technology translators. Express progress not in technical jargon, but in investor language — return on capital, return on innovation, margin expansion and risk mitigation. ... Visible wins: Influence grows through undeniable demonstrations. A system that cuts onboarding time by 40%, an AI model that reduces fraud losses or an audit process that clears in half the time — these visible wins build momentum. ... Technologies rise and fall. Frameworks evolve. Titles shift. But one principle endures: What leaders tolerate defines their legacy. Playing the long game requires CIOs to ask uncomfortable questions:Will we tolerate AI models we cannot explain to regulators? Will we tolerate unchecked cloud sprawl without financial discipline? Will we tolerate compliance as a box-ticking exercise rather than a growth enabler?

What Is Cybersecurity Platformization?

is a strategic response to this complexity. It’s the move from a collection of disparate point solutions to a single, unified platform that integrates multiple security functions. Dickson describes it as the “canned integration of security tools so that they work together holistically to make the installation, maintenance and operation easier for the end customer across various tools in the security stack.” ... The most significant hidden cost of a fragmented, multitool security strategy is labor. Managing disconnected tools is a resource strain on an organization, as it requires individuals with specialized skills for each tool. This includes the labor-intensive task of managing and manually coding “shims,” or integrations to translate data between different tools, which often have separate protocols and proprietary interfaces, Dukes says. Beyond the cost of personnel, there’s the operational complexity. ... One of the most immediate benefits of adopting a platform approach is cost reduction. This includes not only the reduction in licensing fees but also a reduction in the operational complexity and the number of specialized employees needed. ... Another key benefit is the well-worn concept of a “,” a single dashboard that enables IT security teams to have easier management and reporting. Instead of multiple tools with different interfaces and data formats, a unified platform streamlines everything into a single, cohesive view.

Daily Tech Digest - May 06, 2025

Quote for the day:

"Limitations live only in our minds. But if we use our imaginations, our possibilities become limitless." --Jamie Paolinetti

A Primer for CTOs: Taming Technical Debt

Taking a head-on approach is the most effective way to address technical debt, since it gets to the core of the problem instead of slapping a new coat of paint over it, Briggs says. The first step is for leaders to work with their engineering teams to determine the current state of data management. "From there, they can create a realistic plan of action that factors in their unique strengths and weaknesses, and leaders can then make more strategic decisions around core modernization and preventative measures." Managing technical debt requires a long-term view. Leaders must avoid the temptation of thinking that technical debt only applies to legacy or decades old investments, Briggs warns. "Every single technology project has the potential to add to or remove technical debt." He advises leaders to take a cue from medicine's Hippocratic Oath: "Do no harm." In other words, stop piling new debt on top of the old. ... Technical debt can be useful when it's a conscious, short-term trade-off that serves a larger strategic purpose, such as speed, education, or market/first-mover advantage, Gibbons says. "The crucial part is recognizing it as debt, monitoring it, and paying it down before it becomes a more serious liability," he notes. Many organizations treat technical debt as something they're resigned to live with, as inevitable as the laws of physics, Briggs observes.

AI agents are a digital identity headache despite explosive growth

“AI agents are becoming more powerful, but without trust anchors, they can be hijacked or abused,” says Alfred Chan, CEO of ZeroBiometrics. “Our technology ensures that every AI action can be traced to a real, authenticated person—who approved it, scoped it, and can revoke it.” ZeroBiometrics says its new AI agent solution makes use of open standards and technology, and supports transaction controls including time limits, financial caps, functional scopes and revocable keys. It can be integrated with decentralized ledgers or PKI infrastructures, and is suggested for applications in finance, healthcare, logistics and government services. The lack of identity standards suited to AI agents is creating a major roadblock for developers trying to address the looming market, according to Frontegg. That is why it has developed an identity management platform for developers building AI agents, saving them from spending time building ad-hoc authentication workflows, security frameworks and integration mechanisms. Frontegg’s own developers discovered these challenges when building the company’s autonomous identity security agent Dorian, which detects and mitigates threats across different digital identity providers. “Without proper identity infrastructure, you can build an interesting AI agent — but you can’t productize it, scale it, or sell it,” points out Aviad Mizrachi, co-founder and CTO of Frontegg.

Rethinking digital transformation for the agentic AI era

Most CIOs already recognize that generative AI presents a significant evolution in how IT departments can deliver innovations and manage IT services. “Gen AI isn’t just another technology; it’s an organizational nervous system that exponentially amplifies human intelligence,” says Josh Ray, CEO of Blackwire Labs. “Where we once focused on digitizing processes, we’re now creating systems that think alongside us, turning data into strategic foresight. The CIOs who thrive tomorrow aren’t just managing technology stacks; they’re architecting cognitive ecosystems where humans and AI collaborate to solve previously impossible challenges.” IT service management (ITSM) is a good starting point for considering gen AI’s potential. Network operation centers (NOCs) and site reliability engineers (SREs) have been using AIOps platforms to correlate alerts into time-correlated incidents, improve the mean time to resolution (MTTR), and perform root cause analysis (RCA). As generative and agentic AI assists more aspects of running IT operations, CIOs gain a new opportunity to realign IT ops with more proactive and transformative initiatives. ... “Opportunities such as gen AI for hotfix development and predictive AI to identify, correlate, and route incidents for improved incident response are transforming our business, resulting in improved customer satisfaction, revenue retention, and engineering efficiency.”

Strengthening Software Security Under the EU Cyber Resilience Act: A High-Level Guide for Security Leaders and CISOs

One of the hardest CRA areas for organizations to get a handle on is knowing and proving where appropriate controls and configurations are in place vs. where they’re lacking. This lack of visibility often leads to underutilized licenses, unchecked areas of product development, and the potential for unauthorized access into sensitive areas of the development environment. One of the ways security-conscious organizations are combating this is through the creation of “paved pathways” that include very specific technology and security tooling to be utilized across all their development environments, but this often requires extreme vigilance of deviations within those environments and very few ways to automate the adherence to those standards. Legit Security not only automatically inventories and details what and where controls exist within an SDLC so you can ensure 100% coverage of your application portfolio, but we also analyze all of the configurations throughout the entirety of the build process to find any that could allow for supply chain attacks or unauthorized access to SCMs or CI/CD systems. This ensures that your teams are using secure defaults and putting appropriate guardrails into development workflows. This also automates baseline enforcement, configuration management, and quick resets to a known safe state when needed.

Observability 2.0? Or Just Logs All Over Again?

As observability solutions have ostensibly become more mature over the last 15 years, we still see customers struggle to manage their observability estates, especially with the growth of cloud native architectures. So-called “unified” observability solutions bring tools to manage the three pillars, but cost and complexity continue to be major pain points. Meanwhile, the volume of data has kept rising, with 37% of enterprises ingesting more than a terabyte of log data per day. Legacy logging solutions typically deal with the problems of high data volume and cardinality through short retention windows and tiered storage — meaning that data is either thrown away after a fairly short period of time or stored in frozen tiers where it goes dark. Meanwhile, other time series or metric databases take high-volume source data, aggregate it into metrics, then discard the underlying logs. Finally, tracing generates so much data that most traces aren’t even stored in the first place. Head-based sampling retains a small percentage of traces, typically random, while tail-based sampling allows you to filter more intelligently but at the cost of efficient processing. And then traces are typically discarded after a short period of time. There’s a common theme here: While all of the pillars of observability provide different ways of understanding and analyzing your systems, they all deal with the problem of high cardinality by throwing data away.

What it really takes to build a resilient cyber program

A good place to begin is the ‘Identify’ phase from NIST’s Incident Response guide. You need to identify all of your risks, vulnerabilities, and assets. Prioritize them and then determine the best way to protect and detect threats against those assets. Assets not only include physical things like laptops and phones, but also anything that is in a Cloud Service Provider, SaaS applications, and digital items like domain names. Determine the threats, risks and vulnerabilities to those assets. Prioritize them and determine how your organization is going to protect and monitor them. Most organizations don’t have a very good idea of what they actually own, which is why they tend to be reactive and waste time on actions that do not apply to them. How often has a security analyst been asked if a recently disclosed zero-day affects the company? They perform the scans and pull in data manually only to discover they don’t run that piece of software or hardware. ... Many organizations use a red team exercise to try and blame someone or group for a deficiency or even to score an internal political point. That will never end well for anyone. The name of the game is improvement in your security posture and these help identify areas of weakness. There might be things that don’t get fixed immediately, or maybe ever, but knowing that the gap exists is the critical first step.

Top tips for successful threat intelligence usage

“The value of threat intelligence is directly tied to how well it is ingested, processed, prioritized, and acted upon,” wrote Cyware in their report. This means a careful integration into your existing constellation of security tools so you can leverage all your previous investment in your acronyms of SOARs, SIEMs and XDRs. According to the Greynoise report “you have to embed the TIP into your existing security ecosystem, making sure to correlate your internal data and use your vulnerability management tools to enhance your incident response and provide actionable analytics.” The keyword in that last sentence is actionable. Too often threat intel doesn’t guide any actions, such as kicking off a series of patches to update outdated systems, or remediation efforts to firewall a particular network segment or taking offline an offending device. ... Part of the challenge here is to prevent siloed specialty mindsets from making the appropriate remedial measures. “I’ve seen time and time again when the threat intel or even the vulnerability management team will send out a flash notification about a high priority threat only for it to be lost in a queue because the threat team did not chase it up. It’s just as important for resolver groups to act as it is for the threat team to chase it,” Peck blogged.

How empathy is a leadership gamechanger in a tech-first workplace

Empathy isn’t just about creating a feel-good workplace—it’s a powerful driver of innovation and performance. When leaders lead with empathy, they unlock something essential: a work culture where people feel safe to speak up, take risks, and bring their boldest ideas to life. That’s where real progress happens. Empathy also enhances productivity, employees who feel valued and supported are more motivated to perform at their highest potential. Research shows that organisations led by empathetic leaders experience a 20% increase in customer loyalty, underscoring the far-reaching impact of a people-first approach. When employees thrive, so do customer relationships, business outcomes, and overall organisational growth. In India, where workplace dynamics are often shaped by hierarchical structures and collectivist values, empathetic leadership can be transformative. By prioritising open communication, recognition, and personal development, leaders can strengthen employee morale, increase job satisfaction, and drive long-term loyalty. ... In a tech-first world, empathy isn’t a nice-to-have, it’s a leadership gamechanger. When leaders lead with heart and clarity, they don’t just inspire people, they unlock their full potential. Empathy fuels trust, drives innovation, and builds workplaces where people and ideas thrive.

Analyzing the Impact of AI on Critical Thinking in the Workplace

Instead of generating content from scratch, knowledge workers increasingly invest effort in verifying information, integrating AI-generated outputs into their work, and ensuring that the final outputs meet quality standards. What is motivating this behavior? Some explanations for these trends could be to enhance work quality, develop professional AI skills, laziness, and the desire to avoid negative outcomes like errors. For example, someone who is not very proficient in the English language could use GenAI to make their emails sound a lot more natural and avoid any potential misunderstandings. On the flipside, there are some drawbacks to using GenAI. These include overreliance on GenAI for routine or lower-stakes tasks, time pressures, limited awareness of potential AI pitfalls, and challenges in improving AI responses. ... The findings suggest that GenAI tools can reduce the perceived cognitive load for certain tasks. However, they find that GenAI poses risks to workers’ critical thinking skills by shifting their roles from active problem-solvers to AI output overseers who must verify and integrate responses into their workflows. Once again (and this can not be emphasized enough) the study underscores the need for designing GenAI systems that actively support critical thinking. This will ensure that efficiency gains do not come at the expense of developing essential critical thinking skills.

Harnessing Data Lineage to Enhance Data Governance Frameworks

One of the most immediate benefits is improved data quality and troubleshooting. When a data quality issue arises, data lineage’s detailed trail can help you to quickly identify where the problem originated, so that you can fix errors and minimize downtime. Data lineage also enables better planning, since it allows you to run more effective data protection impact analysis. You can map data dependencies to assess how changes like system upgrades or new data integrations might affect your overall data integrity. This is especially valuable during migrations or major updates, as you can proactively mitigate any potential disruptions. Furthermore, regulatory compliance is also greatly enhanced through data lineage. With a complete audit trail documenting every data movement and transformation, organizations can more easily demonstrate compliance with regulations like GDPR, CCPA, and HIPAA. ... Developing a comprehensive data lineage framework can take substantial time, not to mention significant funds. In addition to the various data lineage tools, you might also need to have dedicated hosting servers, depending on the level of compliance needed, or to hire data lineage consultants. Mapping out complex data flows and maintaining up-to-date lineage in a data landscape that’s constantly shifting requires continuous attention and investment.

Daily Tech Digest - March 12, 2025

Quote for the day:

"People may forget what you say, but they won't forget how you made them feel." -- Mary Kay Ash

Rethinking Firewall and Proxy Management for Enterprise Agility

Firewall and proxy management follows a simple rule: block all ports by default and allow only essential traffic. Recognizing that developers understand their applications best, why not empower them to manage firewall and proxy changes as part of a “shift security left” strategy? In practice, however, tight deadlines often lead developers to implement overly broad connectivity – opening up to the complete internet – with plans to refine later. Temporary fixes, if left unchecked, can evolve into serious vulnerabilities. Every security specialist understands what happens in practice. When deadlines are tight, developers may be tempted to take shortcuts. Instead of figuring out the exact needed IP range, they open connectivity to the entire internet with the intention of fixing this later. ... Periodically auditing firewall and proxy rule sets is essential to maintaining security, but it is not a substitute for a robust approval process. Firewalls and proxies are exposed to external threats, and attackers might exploit misconfigurations before periodic audits catch them. Blocking insecure connections on a firewall when the application is already live requires re-architecting the solution, which is costly and time-consuming. Thus, preventing risky changes must be the priority.

Multicloud: Tips for getting it right

It’s obvious that a multicloud strategy — regardless of what it actually looks like — will further increase complexity. This is simply because each cloud platform works with its own management tools, security protocols and performance metrics. Anyone who wants to integrate multicloud into their IT landscape needs a robust management system that can handle the specific requirements of the different environments while ensuring an overview and control across all platforms. This is necessary not only for reasons of handling and performance but also to be as free as possible when choosing the optimal provider for the respective application scenario. This requires cross-platform technologies and tools. The large hyperscalers do provide interfaces for data exchange with other platforms as standard. ... In general, anyone pursuing a multicloud strategy should take steps in advance to ensure that complexity does not lead to chaos but to more efficient IT processes. Security is one of the main issues. And it is twofold: on the one hand, the networked services must be protected in themselves and within their respective platforms. On the other hand, the entire construct with its various architectures and systems must be secure. It is well known that the interfaces are potential gateways for unwelcome “guests”.

FinOps and AI: A Winning Strategy for Cost-Efficient Growth

FinOps is a management approach focused on shared responsibility for cloud computing infrastructure and related costs. ... Companies are attempting to drink from the AI firehose, and unfortunately, they’re creating AI strategies in real-time as they rush to drive revenue and staff productivity. Ideally, you want a foundation in place before using AI in operations. This should include an emphasis on cost management, resource allocation, and keeping tabs on ROI. This is also the focus of FinOps, which can prevent errors and improve processes to further AI adoption. ... To begin, companies should create a budget and forecast the AI projects they want to take on. This planning is a pillar of FinOps and should accurately assess the total cost of initiatives, emphasizing resource allocation (including staffing) and eliminating billing overruns. Cost optimization can also help identify opportunities and reduce expenses. The new focus on AI services in the cloud could drive scalability and cost efficiency as they are much more sensitive to overruns and inefficient usage. Even if organizations are not implementing AI into end-user workloads, there is still an opportunity to craft internal systems utilizing AI to help identify operational efficiencies and implement cost controls on existing infrastructure.

3 Signs Your Startup Needs a CTO — But Not As a Full-Time Hire

CTO as a service provides businesses with access to experienced technical leadership without the commitment of a full-time hire. This model allows startups to leverage specialized expertise on an as-needed basis. ... An on-demand expert can bridge this gap by offering leadership that goes beyond programming. This model provides access to strategic guidance on technology choices, project architecture and team dynamics. During a growth phase, mistakes in management won't be forgiven. ... Hiring a full-time CTO can strain tight budgets, diverting funds from critical areas like product development and market expansion. However, with the CTO as a service model, companies can access top-tier expertise tailored to their financial capabilities. This flexibility allows startups to engage a tech strategist on a project basis, paying only for the high-quality leadership they need when they need it (and if needed). ... Engaging outsourced expertise offers a viable solution, providing a fresh perspective on existing challenges at a cost that remains accessible, even amid resource constraints. This strategic move allows businesses to tap into a wealth of external knowledge, leveraging insights gained from diverse industry experiences. Such an external viewpoint can be invaluable, especially when navigating complex technical hurdles, ensuring that projects not only survive but thrive.

How to Turn Developer Team Friction Into a Positive Force

Developer team friction, while often seen as a negative trait, can actually become a positive force under certain conditions, McGinnis says. "Friction can enhance problem-solving abilities by highlighting weaknesses in current processes or solutions," he explains. "It prompts the team to address these issues, thereby improving their overall problem-solving skills." Team friction often occurs when a developer passionately advocates a new approach or solution. ... Friction can easily spiral out of control when retrospectives and feedback focus on individuals instead of addressing issues and problems jointly as a team. "Staying solution-oriented and helping each other achieve collective success for the sake of the team, should always be the No. 1 priority," Miears says. "Make it a safe space." As a leader it's important to empower every team member to speak up, Beck advises. Each team member has a different and unique perspective. "For instance, you could have one brilliant engineer who rarely speaks up, but when they do it’s important that people listen," he says. "At other times, you may have an outspoken member on your team who will speak on every issue and argue for their point, regardless of the situation."

Enterprise Architecture in the Digital Age: Navigating Challenges and Unleashing Transformative Potential

EA is about crafting a comprehensive, composable, and agile architecture-aligned blueprint that synchronizes an organization’s business processes, workforce, and technology with its strategic vision. Rooted in frameworks like TOGAF, it transcends IT, embedding itself into the very heart of a business. ... In this digital age, EA’s role is more critical than ever. It’s not just about maintaining systems; it’s about equipping organizations—whether agile startups or sprawling, successful enterprises—for the disruptions driven by rapid technological evolution and innovation. ... As we navigate inevitable future complexities, Enterprise Architecture stands as a critical differentiator between organizations that merely survive digital disruption and those that harness it for competitive advantage. The most successful implementations of EA share common characteristics: they integrate technical depth with business acumen, maintain adaptable governance frameworks, and continuously measure impact through concrete metrics. These aren’t abstract benefits—they represent tangible business outcomes that directly impact market position and financial performance. Looking forward, EA will increasingly focus on orchestrating complex ecosystems rather than simply mapping them.

Generative AI Drives Emphasis on Unstructured Data Security

As organizations pivot their focus, the demand for vendors specializing in security solutions, such as data classification, encryption and access control, tailored to unstructured data is expected to increase. This increased demand reflects the necessity for robust and adaptable security measures that can effectively protect the vast and varied types of unstructured data organizations now manage. In tandem with this shift, the rising significance of unstructured data in driving business value and innovation compels organizations to develop expertise in unstructured data security. ... Organizations should prioritize investment in security controls specifically designed for unstructured data. This includes tools with advanced capabilities such as rapid data classification, entitlement management and unclassified data redaction. Solutions that offer prompt engineering and output filtering can also further enhance data security measures. ... Building a knowledgeable team is crucial for managing unstructured data security. Organizations should invest in staffing, training and development to cultivate expertise in this area. This involves hiring data security professionals with specialized skills and providing ongoing education to ensure they are equipped to handle the unique challenges associated with unstructured data.

Quantum Pulses Could Help Preserve Qubit Stability, Researchers Report

The researchers used a model of two independent qubits, each interacting with its own environment through a process called pure dephasing. This form of decoherence arises from random fluctuations in the qubit’s surroundings, which gradually disrupt its quantum state. The study analyzed how different configurations of PDD pulses — applying them to one qubit versus both — affected the system’s evolution. By employing mathematical models that calculate the quantum speed limit based on changes in quantum coherence, the team measured the impact of periodic pulses on the system’s stability. When pulses were applied to both qubits, they observed a near-complete suppression of dephasing, while applying pulses to just one qubit provided partial protection. Importantly, the researchers investigated the effects of different pulse frequencies and durations to determine the optimal conditions for coherence preservation. ... While the study presents promising results, the effectiveness of PDD depends on the ability to deliver precise, high-frequency pulses. Practical quantum computing systems must contend with hardware limitations, such as pulse imperfections and operational noise, which could reduce the technique’s efficiency.

Disaster Recovery Plan for DevOps

While developing your disaster recovery Plan for your DevOps stack, it’s worth considering the challenges DevOps face in this view. DevOps ecosystems always have complex architecture, like interconnected pipelines and environments (e., GitHub and Jira integration). Thus, a single failure, whether due to a corrupted artifact or a ransomware attack, can cascade through the entire system. Moreover, the rapid development of DevOps creates constant changes, which can complicate data consistency and integrity checks during the recovery process. Another issue is data retention policies. SaaS tools often impose limited retention periods – usually, they vary from 30 to 365 days. ... your backup solution should allow you to:Automate your backups, by scheduling them with the most appropriate interval between backup copies, so that no data is lost in the event of failure,

Provide long-term or even unlimited retention, which will help you to restore data from any point in time. Apply the 3-2-1 backup rule and ensure replication between all the storages, so that in case one of the backup locations fails, you can run your backup from another one. Ransomware protection, which includes AES encryption with your own encryption key, immutable backups, restore and DR capabilities

The state of ransomware: Fragmented but still potent despite takedowns

“Law enforcement takedowns have disrupted major groups like LockBit but newly formed groups quickly emerge akin to a good old-fashioned game of whack-a-mole,” said Jake Moore, global cybersecurity advisor at ESET. “Double and triple extortion, including data leaks and DDoS threats, are now extremely common, and ransomware-as-a-service models make attacks even easier to launch, even by inexperienced criminals.” Moore added: “Law enforcement agencies have struggled over the years to take control of this growing situation as it is costly and resource heavy to even attempt to take down a major criminal network.” ... Meanwhile, enterprises are taking proactive measures to defend against ransomware attacks. These include implementing zero trust architectures, enhancing endpoint detection and response (EDR) solutions, and conducting regular exercises to improve incident response readiness. Anna Chung, principal researcher at Palo Alto Networks’ Unit 42, told CSO that advanced tools such as next-gen firewalls, immutable backups, and cloud redundancies, while keeping systems regularly patched, can help defend against cyberattacks. Greater use of gen AI technologies by attackers is likely to bring further challenges, Chung warned.