Daily Tech Digest by Kannan Subbiah: low code

Showing posts with label low code. Show all posts

Daily Tech Digest - March 26, 2026

Quote for the day:

"Appreciate the people who can change their mind when presented with true information that contradicts their beliefs." -- Vala Afshar

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Understanding DoS and DDoS attacks: Their nature and how they operate

In the modern digital landscape, understanding Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks is critical for maintaining organizational resilience. While a DoS attack originates from a single source to overwhelm a system, a DDoS attack leverages a global of compromised devices, making it significantly more complex to detect and mitigate. These cyber threats aim to disrupt essential services, leading to severe functional obstacles and financial consequences, with downtime costs potentially reaching over six thousand dollars per minute. High-availability networks are particularly vulnerable, as massive traffic volumes can bypass redundancy, trigger failovers, and degrade the overall user experience. To counter these evolving threats, the article emphasizes a multi-layered defense strategy incorporating proactive traffic monitoring, rate limiting, and Web Application Firewalls. Specialized solutions like scrubbing centers—which filter malicious packets from legitimate traffic—and Content Delivery Networks are also vital for absorbing large-scale assaults. Ultimately, the article argues that business continuity depends on shifting from reactive measures to advanced, scalable security frameworks that protect both infrastructure and brand reputation. By adopting these robust defenses, organizations can navigate an increasingly hostile environment and ensure that their core digital operations remain accessible and reliable despite sustained cyber-attack conditions.

Low code, no fear

The article "Low code, no fear" explores how CIOs are increasingly adopting low-code/no-code (LCNC) platforms to accelerate digital transformation and address developer shortages. While these tools empower citizen developers and enhance business agility, they introduce significant security risks, such as accidental data exposure and misconfigurations. To mitigate these threats, the author argues that LCNC development must be integrated into the broader IT ecosystem through a . This involves establishing rigorous governance standards, version controls, and automated security guardrails early in the development lifecycle. Specific strategies include implementing policy-as-code templates, automated CI/CD pipeline scanning, and "shift-left" vulnerability testing like SAST and DAST. Additionally, organizations should employ runtime monitoring and data loss prevention measures to prevent sensitive information leaks. By treating low-code projects with the same discipline as traditional software engineering, leaders can ensure that speed does not compromise security. Ultimately, the goal is to foster a culture where innovation and robust security coexist, preventing LCNC from becoming a dangerous form of "shadow IT" within the enterprise. Maintaining clear metrics on deployment frequency and remediation velocity is essential for balancing rapid delivery with effective risk management across all application development activities.

SANS: Top 5 Most Dangerous New Attack Techniques to Watch

At the , the SANS Institute revealed its annual list of the "Top 5 Most Dangerous New Attack Techniques," which are now almost entirely powered by artificial intelligence. The first technique highlights the rise of AI-generated zero-days, which has shattered the barrier to entry for high-level exploits by making vulnerability discovery both cheap and accessible to a wider range of threat actors. Secondly, software supply chain risks have intensified, shifting the industry focus toward the "entire ecosystem of suppliers" and the cascading dangers of third-party dependencies. The third threat identifies an "accountability crisis" in operational technology (OT) and industrial control systems, where a critical lack of forensic visibility prevents investigators from determining if infrastructure failures are mere accidents or sophisticated cyberattacks. Fourth, experts warned against the "dark side of AI" in digital forensics, cautioning that using AI as a primary decision-maker without human oversight leads to flawed incident responses. Finally, the report emphasizes the necessity of "autonomous defense" to counter AI-driven attacks that move forty-seven times faster than traditional methods. By leveraging tools like Protocol SIFT, defenders aim to accelerate human analysis and close the widening speed gap. Together, these techniques underscore a transformative era where AI dictates the pace and complexity of modern cyber warfare.

Why services have become the true differentiator in critical digital infrastructure

The article argues that in the rapidly evolving landscape of critical digital infrastructure, hardware alone no longer provides a competitive edge; instead, comprehensive services have become the primary differentiator. As data centers face increasing complexity driven by AI, high-density computing, and hybrid architectures, the focus has shifted from initial equipment acquisition to long-term operational excellence. Technological parity among major manufacturers means that physical products are often comparable, placing the burden of performance on lifecycle management and expert support. This transition is further fueled by a global skills shortage, leaving many organizations without the internal expertise required to maintain sophisticated power and cooling systems. Consequently, service partnerships that offer proactive maintenance, remote monitoring, and rapid emergency response are essential for ensuring maximum uptime and mitigating the exorbitant costs of downtime. Moreover, the article emphasizes that tailored services play a vital role in achieving sustainability goals by optimizing energy efficiency throughout the asset's lifespan. Ultimately, the true value of infrastructure is realized not through the hardware itself, but through the specialized services that ensure reliability, scalability, and efficiency in an increasingly demanding digital economy, making the choice of a service partner more critical than the equipment specifications.

AI SOC vendors are selling a future that production deployments haven’t reached yet

The article "AI SOC vendors are selling a future that production deployments haven't reached yet" examines the significant gap between marketing promises and the operational reality of AI in Security Operations Centers. While vendors champion autonomous threat investigation and "humanless" operations, actual market adoption remains stagnant at roughly one to five percent. Research indicates that most organizations are trapped in "pilot purgatory," utilizing AI only for low-risk tasks like alert enrichment or report drafting rather than critical decision-making. The authors argue that vendors systematically misattribute this slow uptake to buyer resistance or psychological barriers, whereas the true cause is product immaturity. In live production environments, AI often struggles with non-linear attack paths and lacks the contextual awareness found in custom-built internal tools. Furthermore, reliance on probabilistic AI outputs can inadvertently degrade analyst judgment and obscure operational risks through misleading alert reduction metrics. Experts advocate for a shift in vendor strategy, moving away from "prophetic" claims of total automation toward developing narrow, reliable tools that serve as capability amplifiers. Ultimately, for AI SOC solutions to achieve enterprise readiness, vendors must prioritize transparency, deterministic logic, and verifiable evidence over aspirational marketing narratives.

Meshery 1.0 debuts, offering new layer of control for cloud-native infrastructure

The debut of Meshery 1.0 marks a significant milestone in cloud-native management, introducing a crucial governance layer for complex and multi-cloud environments. As organizations struggle with "YAML sprawl" and the rapid influx of AI-generated configurations, Meshery provides a visual management platform that transitions operations from static text files to a collaborative "Infrastructure as Design" model. At the heart of this release is the Kanvas component, featuring a generally available drag-and-drop Designer for infrastructure blueprints and a beta Operator for real-time cluster monitoring. These tools allow engineering teams to visualize resource relationships, identify configuration conflicts, and automate validation through an embedded Open Policy Agent engine. Beyond visualization, Meshery 1.0 offers over 300 integrations and a built-in load generator, Nighthawk, for performance benchmarking. By offering a shared workspace where architectural decisions are documented and verified, the platform directly addresses the challenges of tribal knowledge and configuration drift. As one of the Cloud Native Computing Foundation's highest-velocity projects, Meshery’s move to version 1.0 signals its maturity as a standard for expressing and deploying portable infrastructure designs while preparing for future AI-driven governance integrations.

What is the Log4Shell vulnerability?

The Log4Shell vulnerability, officially designated as , represents one of the most significant cybersecurity threats in recent history, primarily due to the ubiquity of the Apache Log4j 2 logging library. Discovered in late 2021, this critical zero-day flaw earned a maximum CVSS severity score of 10/10 because it enables remote code execution with minimal effort from attackers. By sending a specially crafted string to a server—often through common inputs like web headers or chat messages—malicious actors can trigger a Java Naming and Directory Interface (JNDI) lookup to a rogue server, allowing them to execute arbitrary code and gain complete system control. The article emphasizes that the vulnerability's impact is vast, affecting everything from cloud services like Apple iCloud to popular games like Minecraft. Identifying every instance of the flawed library remains a major challenge for IT teams because Log4j is often embedded deep within complex software dependencies. Consequently, patching is described as non-negotiable, with organizations urged to upgrade to the latest secure versions of the library immediately. This security crisis underscores the inherent risks found in widely used open-source components and the urgent need for robust supply chain security.

Software-first mentality brings India into future: Industry 4.0 barometer

The eighth edition of the , published by MHP and LMU Munich, highlights how a "software-first" mentality is propelling India to the forefront of the global industrial landscape. Ranking third internationally behind the United States and China, India demonstrates remarkable investment readiness and strategic ambition in adopting digital technologies. The study reveals that 61 percent of surveyed Indian companies already utilize artificial intelligence in production, while 68 percent leverage in logistics. This rapid digitization is anchored in Software-Defined Manufacturing (SDM), where production excellence is increasingly dictated by software, data, and integrated IT/OT architectures. Unlike the DACH region, where only 17 percent of respondents expect fundamental industry change from software-driven approaches, 44 percent of Indian leaders are convinced of such transformation. This discrepancy underscores India’s proactive willingness to evolve, moving beyond traditional manufacturing to embrace a future where smart algorithms and solid data infrastructures are central. Ultimately, the report emphasizes that consistent integration of software and production control is no longer optional but a critical factor for maintaining global relevance, positioning India as a formidable leader in the ongoing digital revolution of industrial production.

Facial age estimation adoption puts pressure on ecosystem

The article "Facial age estimation adoption puts pressure on ecosystem" highlights the rapid integration of biometric age verification technologies amidst intensifying global legal mandates and shifting regulatory responsibilities. As adoption accelerates, the industry faces a critical bottleneck: the demand for system evaluation and testing capacity is currently outstripping available methodologies. This surge has prompted stakeholders, including the European Association for Biometrics, to address the complexities of training algorithms, which require vast, diverse datasets to ensure accuracy across demographics. Technical hurdles remain significant, particularly regarding "bias to the mean," where systems frequently overestimate the age of younger users while underestimating older individuals. Additionally, traditional Presentation Attack Detection struggles with sophisticated spoofs, such as aging makeup, which mimics live facial features effectively. The piece also references real-world applications like Australia’s Age Assurance Technology Trial, noting that while privacy concerns caused some to opt out, peer participation eventually boosted engagement. Ultimately, effective implementation now depends on refining confidence-range metrics rather than relying on absolute age estimates. The future of the ecosystem relies on the emergence of more rigorous, fine-grained standards and fusion techniques to maintain integrity in an increasingly scrutinized and legally demanding digital environment.

Streamline physical security to enable data center growth in the era of AI

The rapid proliferation of artificial intelligence is driving a monumental expansion in data center capacity, creating a "space race" where physical security must evolve from a tactical necessity into a strategic competitive advantage. As colocation and hyperscale providers face unprecedented demand, Andrew Corsaro argues that traditional project-based approaches are no longer sufficient; instead, organizations must adopt a programmatic mindset characterized by repeatable processes, standardized designs, and the intelligent reuse of institutional knowledge. Scaling at AI speed requires a transition where approximately 95 percent of security implementation is standardized, allowing teams to focus on the 5 percent of truly novel challenges, such as airborne drone threats or the physical implications of advanced cooling technologies. Furthermore, the integration of automation, digital twin modeling, and strategic partnerships is essential to maintain precision without sacrificing quality. By embedding security experts into the early stages of the development lifecycle, providers can navigate dynamic regulatory shifts and emerging threat vectors effectively. Ultimately, those who successfully streamline their physical security frameworks will be best positioned to achieve sustainable, high-speed growth in the AI era, transforming potential operational chaos into a disciplined, resilient, and highly scalable delivery engine.

Daily Tech Digest - January 05, 2026

Quote for the day:

"Great leaders do not desire to lead but to serve." -- Myles Munroe

How to make AI agents reliable

Easier said than done. After all, the way genAI works, we’re trying to build deterministic software on top of probabilistic models. Large language models (LLMs), cool though they may be, are non-deterministic by nature. Chaining them together into autonomous loops amplifies that randomness. If you have a model that is 90% accurate, and you ask it to perform a five-step chain of reasoning, your total system accuracy drops to roughly 59%. That isn’t an enterprise application; it’s a coin toss—and that coin toss can cost you. Whereas a coding assistant can suggest a bad function, an agent can actually take a bad action. ... Breunig highlights “context poisoning” as a major reliability killer, where an agent gets confused by its own history or irrelevant data. We tend to treat the context window like a magical, infinite scratchpad. It isn’t. It is a database of the agent’s current state. If you fill that database with garbage (unstructured logs, hallucinated prior turns, or unauthorized data), you get garbage out. ... Finally, we need to talk about the user. One reason Breunig cites for the failure of internal agent pilots is that employees simply don’t like using them. A big part of this is what I call the rebellion against robot drivel. When we try to replace human workflows with fully autonomous agents, we often end up with verbose, hedging, soulless text, and it’s increasingly obvious to the recipient that AI wrote it, not you. And if you can’t be bothered to write it, why should they bother to read it?

Three Cybersecurity predictions that will define the CISO agenda in 2026

Different tools report different versions of “critical” risk. One team escalates an issue while another deprioritises it based on alternative scoring models. Decisions become subjective, slow and inconsistent without a coherent strategy - and critical attack paths remain open. If cyber risk is not presented consistently in the context of business impact, it’s nearly impossible to align cybersecurity with broader business objectives. In 2026, leaders will no longer tolerate this ambiguity. Boards and executives don’t want more dashboards. ... Social engineering campaigns are already more convincing, more personalised and harder for users to detect. Messages sound legitimate. Voices and content appear authentic. The line between real and fake is blurring at scale. In 2026, mature organisations will take a more disciplined approach. They will map AI initiatives to business objectives, identify which revenue streams and operational processes depend on them, and quantify the value at risk. This allows CISOs to demonstrate where existing investments meaningfully reduce exposure — and where they don’t — while maintaining operational integrity and trust. ... AI agents will take over high-volume, repetitive tasks — continuously analysing vast streams of telemetry, correlating signals across environments, and surfacing the handful of risks that truly matter. They will identify the needle in the haystack. Humans will remain firmly in the loop.

The Hidden Costs of Silent Technology Failures

"Most CIOs see failures as negative experiences that undermine their credibility, effectiveness and ultimate growth within the organization," Koeppel said. Under those conditions, escalation is rationally delayed. CIOs attempt recovery first, including new baseline plans, renegotiations of vendor commitments and a narrower scope before formally declaring failure. ... CIOs, Dunkin noted, frequently underplay failure to shield their teams from blame. Few leaders want finger-pointing to cascade through already strained organizations. But Dunkin pointed out that the same instincts are shaped by fear of job loss, budget erosion or internal power shifts. And, she warns, bad news does not age well. Beyond politics and incentives, decision-making psychology compounds the problem. Jim Anderson, founder of Blue Elephant Consulting, describes how sunk-cost bias distorts executive judgment. Admitting a mistake publicly opens leaders to criticism, so past decisions are defended rather than reassessed. ... But not all organizations respond this way. Koeppel said that in his experience, boards and CEOs are receptive to clear, concise explanations when technology initiatives deviate from plan. Over time, disclosure improves because consequences change. Sethi described the shift to openness that followed a major outage in one organization. It resulted in mandatory, blameless post-mortem reviews that focused on systemic and process breakdowns rather than individual fault.

2026 Low-Code/No-Code Predictions

The promise of low-code platforms will finally materialize by the end of 2026. AI will let business users create bespoke applications without writing code, while professional developers guide standards, security, and integration. The line between "developer" and "user" will blur as agentic systems become part of daily work. ... No code's extinction: No code's on its last legs — it's being snuffed out by vibe coding. AI-driven development tools will be the final knell for no code as we know it, with its remit curtailed in this new coding landscape. In this future, the focus will transition entirely to model orchestration and high-level knowledge work, where humans express their intent and expertise through abstract models rather than explicit code. The human role becomes centered on the plan to build. Specifically, ensuring the problem is correctly scoped and defined. ... In 2026, low-code/no-code interfaces will rapidly shift from drag and drop canvases to natural language interfaces, as user expectations rapidly adopt to the changing landscape. As this transition occurs, application vendors will struggle to provide transparency into how the application has interpreted the users' intent. ... While it's proved remarkable for supercharging development speed and allowing non-technical individuals to produce functional software, its outputs are less than perfect. This year, we've continued to uncover that much of AI-generated code turns out fragile or flat-out wrong once it faces real workflows or customers.

AI security risks are also cultural and developmental

The research shows that AI systems increasingly shape cultural expression, religious understanding, and historical narratives. Generative tools summarize belief systems, reproduce artistic styles, and simulate cultural symbols at scale. Errors in these representations influence trust and behavior. Communities misrepresented by AI outputs disengage from digital systems or challenge their legitimacy. In political or conflict settings, distorted cultural narratives contribute to disinformation, polarization, and identity-based targeting. Security teams working on information integrity and influence operations encounter these risks directly. The study positions cultural misrepresentation as a structural condition that adversaries exploit rather than an abstract ethics issue. ... Systems designed with assumptions of reliable connectivity or standardized data pipelines fail in regions where those conditions do not hold. Healthcare, education, and public service applications show measurable performance drops when deployed outside their original development context. These failures expose organizations to cascading risks. Decision support tools generate flawed outputs. Automated services exclude segments of the population. Security monitoring systems miss signals embedded in local language or behavior. ... Models operate on statistical patterns and lack awareness of missing data. Cultural knowledge, minority histories, and local practices often remain absent from training sets. This limitation affects detection accuracy.

The Board’s Duty in the Age of the Black Box

Today, when this Board approves the acquisition of a Generative AI startup or authorizes a billion-dollar investment in GPU infrastructure, you are acquiring a Black Box. You are purchasing a system defined not by logical rules, but by billions of specific weights, biases, and probabilistic outcomes. These systems are inherently unstable; they “hallucinate,” they drift, and they contain latent biases that no static audit can fully reveal. They are closer to biological organisms than to traditional software. ... Critics may argue that applying financial volatility models to operational AI risk is a conceptual leap. There is no perfect mathematical bridge between “Model Drift” and “WACC” (Weighted Average Cost of Capital). However, in the absence of a liquid market for “Algorithm Liability Insurance” or standardized auditing protocols, the Board must rely on empirical proxies to gauge risk. ... The single largest destroyer of capital in the current AI cycle is the misidentification of a “Wrapper” as a “Moat.” The Board must rigorously interrogate the strategic durability of the asset. ... The Risk Committee’s role is shifting from passive monitoring to active defense. The risks associated with AI are “Fat-Tailed”—meaning that while day-to-day operations might be smooth, the rare failure modes are catastrophic. ... For the Chief Information Officer (CIO), the concept of “Model Risk” translates directly into operational reality. It is critical to differentiate between “Valuation Risk” and “Maintenance Cost.”

Cybersecurity leaders’ resolutions for 2026

Any new initiative will start with a clear architectural plan and a deep understanding of end-to-end dependencies and potential points of failure. “By taking a thoughtful, engineering-driven approach — rather than reacting to outages or disruptions — we aim to strengthen the stability, scalability, and reliability of our systems,” he says. “This foundation enables the business to move with confidence, knowing our technology and security investments are built to endure and evolve.” ... As new attack surfaces emerge with AI-driven applications and systems, Piekarski’s priorities will focus on defending and hardening the environment against AI-enabled threats and tactics. ... In practice, SaaS management and discovery tools will be used to get a handle on shadow IT and unsanctioned AI usage. Automation for compliance and reporting will be important as customer and regulatory requirements around ESG and security continue to grow, along with threat intelligence feeds and vulnerability management solutions that help Gallagher and the team stay ahead of what’s happening in the wild. “The common thread is visibility and control; we need to know what’s in our environment, how it’s being used, and that we can respond quickly when things change,” he tells CSO. ... “Quantum computing poses significant cyber risks by potentially breaking current encryption methods, impacting data security, and enabling new attack vectors,” says Piekarski.

Enterprise Digital Twin: Why Your AI Doesn’t Understand Your Organization

Agentic AI systems are moving from research papers to production pilots, taking critical business actions such as processing invoices, scheduling meetings, drafting communications, and coordinating workflows across teams. They operate with increasing autonomy. When an agent misunderstands organizational context, it does not just give a wrong answer. It takes wrong actions, such as approving expenses that violate policy, scheduling meetings with people who should not be in the room, routing decisions to the wrong authority, and creating compliance exposure at machine speed. The industry is catching up to this reality. ... An AI system reviewing a staffing request might confirm that the budget exists, the policy allows the hire, and the hiring manager has authority. All technically correct. But without Constraint Topology, the system does not know that HR cannot process new hires until Q2 due to a systems migration, that the only approved vendor for background checks has a six-week backlog, or that three other departments have competing requisitions for the same job grade and only two can be filled this quarter. ... Most AI frameworks focus on making models smarter. CTRS focuses on making organizations faster. Technically correct outputs that do not translate into action are not actually useful. The bottleneck is not AI capability. It is the distance between what AI recommends and what the organization can execute.

The agentic infrastructure overhaul: 3 non-negotiable pillars for 2026

If 2025 was about the brain (the LLM), 2026 must be about the nervous system. You cannot bolt a self-correcting, multi-step agent onto a 2018 ERP and expect it to function. To move from isolated pilots to enterprise-wide autonomous workflows, we must overhaul our architectural blueprint. We are moving from a world of rigid, synchronous commands to a world of asynchronous, event-driven fluidity. ... We build dashboards with red and green lights so a DevOps engineer can identify a spike in latency. However, an AI agent cannot “look” at a Grafana dashboard. If an agent encounters an error mid-workflow, it needs to understand why in a format it can digest. ... Stop “bolting on” agents to legacy REST APIs. Instead, build an abstraction layer — an “agent gateway” — that converts synchronous legacy responses into asynchronous events that your agents can subscribe to. ... The old mantra was “Data is the new oil.” In 2026, data is just the raw material; Metadata is the fuel. Businesses have spent millions “cleaning” data in snowflakes and lakes, but clean data lacks the intent that agents require to make decisions. ... Invest in a data catalog that supports semantic tagging. Ensure your data engineers are not just moving rows and columns, but are defining the “meaning” of those rows in a way that is accessible via your RAG pipelines. ... The temptation in 2026 will be to build “bespoke” agents for every department — a HR agent, a finance agent, a sales agent. This is a recipe for a new kind of “shadow IT” and massive technical debt.

The New Front Line Of Digital Trust: Deepfake Security

AI-generated deepfakes are ruining the way we perceive one another, as well as undermining institutions’ ways of ensuring identity, verifying intent and maintaining trust. For CISOs and IT security risk leaders, this is a new and pressing frontier for us to focus on: defending against attacks not on systems but on beliefs. ... Deepfakes are coming to the forefront just as CISOs have more risk to manage than ever. Here are some of the other key pressures driving the financial cybersecurity environment today: Multicloud misconfigurations and API exposure; Ransomware shift to triple extortion; Expanding third-party and fourth-party dependencies; Insider threats facing hybrid workforces; Barriers to zero-trust implementation and Regulatory fragmentation. ... Deepfake security isn’t a fringe issue anymore; it’s now a foremost challenge to digital trust and systemic financial resilience. In today’s world, where synthetic voices can create markets and fake identities can trigger transactions, authenticity reigns as the currency of banking. Tomorrow’s front-runners will be those building the next-generation financial systems—secured, transparent and globally trusted. Those systems will include reconfigured trust frameworks, deepfake detection, AI governance that drives model integrity and a resilient-by-design approach. In this world, where anyone can create an AI-generated identity, the ultimate competitive differentiator is proving what’s real.

Daily Tech Digest - November 21, 2025

Quote for the day:

“You live longer once you realize that any time spent being unhappy is wasted.” -- Ruth E. Renkl

DPDP Rules and the Future of Child Data Safety

Most obligations for , including verifiable parental consent, security safeguards, breach notifications, data minimisation, and processing restrictions for children’s data, come into force after 18 months. This means that although the law recognises children’s rights today, full legal protection will not be enforceable until the culmination of the 18-month window. ... Parents’ awareness of data rights, online safety, and responsible technology is the backbone of their informed participation. The government needs to undertake a nationwide with the help of , modelled on literacy and health awareness drives. ... schools often outsource digital functions to vendors without due diligence. Over the next 18 months, they must map where the student data is collected and where it flows, renegotiate contracts with vendors, ensure secure data storage, and train teachers to spot data risks. Nationwide teacher-training programmes should embed digital pedagogy, data privacy, and ethical use of technology as core competencies. ... effective implementation will be contingent on the autonomy, resourcefulness, and accessibility of the . The regulator should include specialised talent such as and privacy engineers. It should be supported by building an in-house digital forensics unit, capable of investigating leaks, tracing unauthorised access, and examining algorithmic profiling.

5 best practices for small and medium businesses (SMEs) to strengthen cybersecurity

First, begin with good access control which would entail restricting employees to only the permissions that they specifically require. It is also important to have multi-factor authentication in place, and regularly audit user accounts, particularly when roles shift or personnel depart. Second, keep systems and software current by immediately patching operating systems, applications, and security software to close vulnerabilities before they can be exploited by attackers. Similarly, updates should be automated to avoid human error. The staff are usually at the front line of the defence, so the third essential practice is the continuous ongoing training of employees in identifying , suspicious links, and social engineering methods, making them active guardians of corporate data and effectively cutting the risk of a . Fourth is the safeguarding your data which can be implemented by having regular backups stored safely in multiple places and by complementing them with an explicit disaster recovery strategy, so that you are able to restore operations promptly, reduce downtime, and constrain losses in the event of a cyber attack. Fifth and finally, companies should embrace the layered security paradigm using antivirus tools, firewalls, endpoint protection, encryption, and safe networks. Each of those layers complement each other, creating a resilient defence that protects your digital ecosystem and strengthens trust with partners, customers, and stakeholders.

How Artificial Intelligence is Reshaping the Software Development Life Cycle (SDLC)

With , workflows become faster and more efficient, giving engineers more time to concentrate on creative innovation and tackling complex challenges. As these models advance, they can better grasp context, learn from previous projects, and adapt to evolving needs. ... AI streamlines software design by speeding up prototyping, automating routine tasks, optimizing with predictive analytics, and strengthening security. It generates design options, translates business goals into technical requirements, and uses fitness functions to keep code aligned with architecture. This allows architects to prioritize strategic innovation and boosts development quality and efficiency. ... AI is shifting developers’ roles from manual coding to strategic "code orchestration." Critical thinking, business insight, and ethical decision-making remain vital. AI can manage routine tasks, but human validation is necessary for security, quality, and goal alignment. Developers skilled in AI tools will be highly sought after. ... AI serves to augment, not replace, the contributions of human engineers by managing extensive data processing and pattern recognition tasks. The synergy between AI's computational proficiency and human analytical judgment results in outcomes that are both more precise and actionable. Engineers are thus empowered to concentrate on interpreting AI-generated insights and implementing informed decisions, as opposed to conducting manual data analysis.

Innovative Approaches To Addressing The Cybersecurity Skills Gap

In a talent-constrained world, forward-leaning organizations aren’t hiring more analysts—they’re deploying agentic AI to generate continuous, cryptographic proof that controls worked when it mattered. This defensible automation reduces breach impact, insurer friction and boardroom risk—no headcount required. ... Create an architecture and engineering review board (AERB) that all current and future technical designs are required to flow through. Make sure the AERB comprises a small group of your best engineers, developers, network engineers and security experts. The group should meet multiple times a year, and all technical staff should be required to rotate through to listen and contribute to the AERB. ... Build security into product design instead of adding it in afterward. Embed industry best practices through predefined controls and policy templates that enforce protection automatically—then partner with trusted experts who can extend that foundation with deep, domain-specific insight. Together, these strategies turn scarce talent into amplified capability. ... Rather than chasing scarce talent, companies should focus on visibility and context. Most breaches stem from unknown identities and unchecked access, not . By strengthening identity governance and access intelligence, organizations can multiply the impact of small security teams, turning knowledge, not headcount, into their greatest defense.

The Configurable Bank: Low‑Code, AI, and Personalization at Scale

What does the present day modern banking system look like: The answer depends on where you stand. For customers, need to be instant, invisible, and intuitive – a seamless tap, a scan, a click. For banks, it’s an ever-evolving race to keep pace with rising expectations. ... What was once a luxury i.e. speed and dependability – has become the standard. Yet, behind the sleek mobile apps and fast payments, many banks are still anchored to quarterly release cycles and manual processes that slow innovation. To thrive in this landscape, banks don’t need to rip out their core systems. What they need is configurability – the ability to re-engineer services to be more agile, composable, and responsive. By making their systems configurable rather than fixed, banks can launch products faster, adapt policies in real time, and reduce the cost and complexity of change. ... The idea of the Configurable Bank is built on this shift – where technology, powered by and AI, transforms banking into a living, adaptive platform. One that learns, evolves, and personalizes at scale – not by replacing the core, but by reimagining how it connects with everything around it. ... This is not just a technology shift; it’s a strategic one. With low-code, innovation is no longer the privilege of IT alone. Business teams, product leaders, and even customer-facing units can now shape and deploy digital experiences in near real time.

Deepfake crisis gets dire prompting new investment, calls for regulation

, Doppel’s CEO, says that organizations are not prepared for the flood of AI-generated deception coming at them. “Over the past few months, what’s gotten significantly better is the ability to do real-time, synchronous in an intelligent manner. I can chat with my own deepfake in real-time. It’s not scripted, it’s dynamic.” Tian tells Fortune that Doppel’s mission is not to stamp out , but “to stop social engineering attacks, and the malicious use of deepfakes, traditional impersonations, copycatting, fraud, phishing – you name it.” The firm says its R&D team has “just scratched the surface” of innovations it plans to bring to existing and upcoming products, notably in (SED). The Series C funds will “be used to invest in the core Doppel gang to meet the exponential surge in demand.” ... Advocating for “laws that prioritize human dignity and protect democracy,” the piece points to the EU’s AI Act and as models, and specifically to new copyright legislation in Denmark, which bans the creation of deepfakes without a subject’s consent. In the authors’ words, Denmark’s law would “legally enshrine the principle that you own you.” ... “The rise of deepfake technology has shown that voluntary policies have failed; companies will not police themselves until it becomes too expensive not to do so,” says the piece.

The what, why and how of agentic AI for supply chain management

To be sure, software and automation are nothing new in the supply chain space. Businesses have long used digital tools to help track inventories, manage fleet schedules and so on as a way of boosting efficiency and scalability. Agentic AI, however, goes further than traditional SCM software tools, offering capabilities that conventional systems lack. For instance, because agents are guided by AI models, they are capable of identifying novel solutions to challenges they encounter. can’t do this because they rely on pre-scripted options and don’t know what to do when they encounter a scenario no one envisioned beforehand. AI can also automate multiple, interdependent SCM processes, as I mentioned above. Traditional SCM tools don’t usually do this; they tend to focus on singular tasks that, although they may involve multiple steps, are challenging to automate fully because conventional tools can’t reason their way through unforeseen variables in the way AI agents do. ... Deploying agents directly into production is enormously risky because it can be challenging to predict what they’ll do. Instead, begin with a proof of concept and use it to validate agent features and reliability. Don’t let agents touch production systems until you’re deeply confident in their abilities. ... For high-stakes or particularly complex workflows, it’s often wise to keep a human in the loop.

How AI can magnify your tech debt - and 4 ways to avoid that trap

The survey, conducted in September, involved 123 executives and managers from large companies. There are high hopes that AI will help cut into and clear up issues, along with cost reduction. At least 80% expect productivity gains, and 55% anticipate AI will help reduce technical debt. However, the large segment expecting AI to increase technical debt reflects "real anxiety about security, legacy integration, and black-box behavior as AI scales across the stack," the researchers indicated. Top concerns include security vulnerabilities (59%), legacy integration complexity (50%), and loss of visibility (42%). ... "Technical debt exists at many different levels of the technology stack," Gary Hoberman, CEO of Unqork, told ZDNET. "You can have the best 10X engineer or the best AI model writing the most beautiful, efficient code ever seen, but that code could still be running on runtimes that are themselves filled with technical debt and security issues. Or they may also be relying on open-source libraries that are no longer supported." ... AI presents a new raft of problems to the tech debt challenge. The rising use of AI-assisted code risks "unintended consequences, such as runaway maintenance costs and increasing tech debt," Hoberman continued. IT is already overwhelmed with current system maintenance.

The State and Current Viability of Real-Time Analytics

Data managers now prefer real-time analytical capabilities built within their applications and systems, rather than a separate, standalone, or bolted-on project. Interest in real-time analytics as a standalone effort has dropped from 50% to 32% during the past 2 years, a recent survey of 259 data managers conducted by Unisphere Research finds ... So, the question becomes: Are real-time analytics ubiquitous to the point in which they are automatically integrated into any and all applications? By now, the use of real-time analytics should be a “standard operating requirement” for customer experience, said , founder and CTO at Aerospike. This is where the rubber meets the road—where “the majority of the advances in real-time applications have been made in consumer-oriented enterprises,” he added. Along these lines, the most prominent use cases for real-time analytics include “risk analysis, fraud detection, recommendation engines, user-based dynamic pricing, dynamic billing and charging, and customer 360,” Srinivasan continued. “For over a decade, these systems have been using AI and machine learning [ML], inferencing for improving the quality of real-time decisions to improve customer experience at scale. The goal is to ensure that the first customer and the hundred-millionth customer have the same vitality of customer experience.” ... “Within industries such as energy, life sciences, and chemicals, the next decade of real-time analytics will be driven by more autonomous operations,” said David Streit

You Down with EDD? Making Sense of LLMs Through Evaluations

We're facing a major infrastructure maturity gap in AI development — the same gap the software world faced decades ago when applications grew too complex for informal testing and crossed fingers. Shipping fast with user feedback works early on, but when done at scale with rising stakes, "vibes" break down and developers demand structure, predictability, and confidence in their deployments. ... AI engineering teams are turning to an emerging solution: evaluation-driven development (EDD), the probabilistic cousin to TDD. An evaluation looks similar to a traditional software test. You have an assertion, a response, and pass-fail criteria, but instead of asking "Does this function return 42?" you're asking "Does this legal AI application correctly flag the three highest-risk clauses in this nightmare of a merger agreement?" Our trust in AI systems comes from our trust in the evaluations themselves, and if you never see an evaluation fail, you're not testing the right behaviors. The practice of Evaluation-Driven Development (EDD) is about repeatedly testing these evaluations. ... The technology for EDD is ready. Modern AI platforms provide solid evaluation frameworks that integrate with existing development workflows, but the challenge facing wide adoption is cultural. Teams need to embrace the discipline of writing evaluations before changing systems, just like they learned to write tests before shipping code. It requires a mindset shift from "move fast and break things," to "move deliberately and measure everything."

Daily Tech Digest - June 21, 2025

Quote for the day:

“People are not lazy. They simply have important goals – that is, goals that do not inspire them.” -- Tony Robbins

AI in Disaster Recovery: Mapping Technical Capabilities to Real Business Value

Despite its promise, AI introduces new challenges, including security risks and trust deficits. Threat actors leverage the same AI advancements, targeting systems with more precision and, in some cases, undermining AI-driven defenses. In the Zerto–IDC survey mentioned earlier, for instance, only 41% of respondents felt that AI is “very” or “somewhat” trustworthy; 59% felt that it is “not very” or “not at all” trustworthy. To mitigate these risks, organizations must adopt AI responsibly. For example, combining AI-driven monitoring with robust encryption and frequent model validation ensures that AI systems deliver consistent and secure performance. Furthermore, organizations should emphasize transparency in AI operations to maintain trust among stakeholders. Successful AI deployment in DR/CR requires cross-functional alignment between ITOps and management. Misaligned priorities can delay response times during crises, exacerbating data loss and downtime. Additionally, the ongoing IT skills shortage is still very much underway, with a different recent IDC study predicting that 9 out of 10 organizations will feel an impact by 2026, at a cost of $5.5 trillion in potential delays, quality issues, and revenue loss across the economy. Integrating AI-driven automation can partially mitigate these impacts by optimizing resource allocation and reducing dependency on manual intervention.

The Quantum Supply Chain Risk: How Quantum Computing Will Disrupt Global Commerce

Whether its API’s, middleware, firmware embedded devices or operational technology, they’re all built on the same outdated encryption and systems of trust. One of the biggest threats from quantum computing will be on all this unseen machinery that powers global digital trade. These systems handle the backend of everything from routing to cargo to scheduling deliveries and clearing large shipments, but they were never designed to withstand the threat of quantum. Attackers will be able to break in quietly — injecting malicious code into control software, ERP systems or impersonating suppliers to communicate malicious information and hijack digital workflows. Quantum computing won’t necessarily affect the industries on its own, but it will corrupt the systems that power the global economy. ... Some of the most dangerous attacks are being staged today, with many nation-states and bad actors storing encrypted data, from procurement orders to shipping records. When quantum computers are finally able to break those encryption schemes, attackers will be able to decrypt them in what’s coined a Harvest Now Decrypt Later (HNDL) attack. These attacks, although retroactive in nature, represent one of the biggest threats to the integrity of cross-border commerce. Global trade depends on digital provenance or handling goods and proving where they came from.

Securing OT Systems: The Limits of the Air Gap Approach

Aside from susceptibility to advanced techniques, tactics, and procedures (TTPs) such as thermal manipulation and magnetic fields, more common vulnerabilities associated with air-gapped environments include factors such as unpatched systems going unnoticed, lack of visibility into network traffic, potentially malicious devices coming on the network undetected, and removable media being physically connected within the network. Once the attack is inside OT systems, the consequences can be disastrous regardless of whether there is an air gap or not. However, it is worth considering how the existence of the air gap can affect the time-to-triage and remediation in the case of an incident. ... This incident reveals that even if a sensitive OT system has complete digital isolation, this robust air gap still cannot fully eliminate one of the greatest vulnerabilities of any system—human error. Human error would still hold if an organization went to the extreme of building a faraday cage to eliminate electromagnetic radiation. Air-gapped systems are still vulnerable to social engineering, which exploits human vulnerabilities, as seen in the tactics that Dragonfly and Energetic Bear used to trick suppliers, who then walked the infection right through the front door. Ideally, a technology would be able to identify an attack regardless of whether it is caused by a compromised supplier, radio signal, or electromagnetic emission.

How to Lock Down the No-Code Supply Chain Attack Surface

A core feature of no-code development, third-party connectors allow applications to interact with cloud services, databases, and enterprise software. While these integrations boost efficiency, they also create new entry points for adversaries. ... Another emerging threat involves dependency confusion attacks, where adversaries exploit naming collisions between internal and public software packages. By publishing malicious packages to public repositories with the same names as internally used components, attackers could trick the platform into downloading and executing unauthorized code during automated workflow executions. This technique allows adversaries to silently insert malicious payloads into enterprise automation pipelines, often bypassing traditional security reviews. ... One of the most challenging elements of securing no-code environments is visibility. Security teams struggle with asset discovery and dependency tracking, particularly in environments where business users can create applications independently without IT oversight. Applications and automations built outside of IT governance may use unapproved connectors and expose sensitive data, since they often integrate with critical business workflows.

Securing Your AI Model Supply Chain

Supply chain Levels for Software Artifacts (SLSA) is a comprehensive framework designed to protect the integrity of software artifacts, including AI models. SLSA provides a set of standards and practices to secure the software supply chain from source to deployment. By implementing SLSA, organizations can ensure that their AI models are built and maintained with the highest levels of security, reducing the risk of tampering and ensuring the authenticity of their outputs. ... Sigstore is an open-source project that aims to improve the security and integrity of software supply chains by providing a transparent and secure way to sign and verify software artifacts. Using cryptographic signatures, Sigstore ensures that AI models and other software components are authentic and have not been tampered with. This system allows developers and organizations to trace the provenance of their AI models, ensuring that they originate from trusted sources. ... The most valuable takeaway for ensuring model authenticity is the implementation of robust verification mechanisms. By utilizing frameworks like SLSA and tools like Sigstore, organizations can create a transparent and secure supply chain that guarantees the integrity of their AI models. This approach helps build trust with stakeholders and ensures that the models deployed in production are reliable and free from malicious alterations.

Data center retrofit strategies for AI workloads

AI accelerators are highly sensitive to power quality. Sub-cycle power fluctuations can cause bit errors, data corruption, or system instability. Older uninterruptible power supply (UPS) systems may struggle to handle the dynamic loads AI can produce, often involving three MW sub-cycle swings or more. Updating the electrical distribution system (EDS) is an opportunity that includes replacing dated UPS technology, which often cannot handle the dynamic AI load profile, redesigning power distribution for redundancy, and ensuring that power supply configurations meet the demands of high-density computing. ... With the high cost of AI downtime, risk mitigation becomes paramount. Energy and power management systems (EPMS) are capable of high-resolution waveform capture, which allows operators to trace and address electrical anomalies quickly. These systems are essential for identifying the root cause of power quality issues and coordinating fast response mechanisms. ... No two mission-critical facilities are the same regarding space, power, and cooling. Add the variables of each AI deployment, and what works for one facility may not be the best fit for another. That said, there are some universal truths about retrofitting for AI. You will need engineers who are well-versed in various equipment configurations, including cooling and electrical systems connected to the network.

Is it time for a 'cloud reset'? New study claims public and private cloud balance is now a major consideration for companies across the world

Enterprises often still have some kind of a cloud-first policy, he outlined, but they have realized they need some form of private cloud too, typically due to the fact that some workloads do not meet the needs, mainly around cost, complexity and compliance. However the problem is that because public cloud has taken priority, infrastructure has not grown in the right way - so increasingly, Broadcom’s conversations are now with customers realizing they need to focus on both public and private cloud, and some on-prem, Baguley says, as they're realizing, “we need to make sure we do it right, we're doing it in a cost-effective way, and we do it in a way that's actually going to be strategically sensible for us going forward.” "In essence - they've realised they need to build something on-prem that can not only compete with public cloud, but actually be better in various categories, including cost, compliance and complexity.” ... In order to help with these concerns, Broadcom has released VMware Cloud Foundation (VCF) 9.0, the latest edition of its platform to help customers get the most out of private cloud. Described by Baguely as, “the culmination of 25 years work at VMware”, VCF 9.0 offers users a single platform with one SKU - giving them improved visibility while supporting all applications with a consistent experience across the private cloud environment.

Cloud in the age of AI: Six things to consider

This is an issue impacting many multinational organizations, driving the growth for regional- and even industry clouds. These offer specific tailored compliance, security, and performance options. As organizations try to architect infrastructure that supports their future states, with a blend of cloud and on-prem, data sovereignty is an increasingly large issue. I hear a lot from IT leaders about how they must consider local and regional regulations, which adds a consideration to the simple concept of migration to the cloud. ... Sustainability was always the hidden cost of connected computing. Hosting data in the cloud consumes a lot of energy. Financial cost is most top of mind when IT leaders talk about driving efficiency through the cloud right now. It’s also at the root of a lot of talk about moving to the edge and using AI-infused end user devices. But expect sustainability to become an increasingly important factor in cloud: geo political instability, the cost of energy, and the increasing demands of AI will see to that. ... The AI PC pitch from hardware vendors is that organizations will be able to build small ‘clouds’ of end user devices. Specific functions and roles will work on AI PCs and do their computing at the edge. The argument is compelling: better security and efficient modular scalability. Not every user or function needs all capabilities and access to all data.

Creating a Communications Framework for Platform Engineering

When platform teams focus exclusively on technical excellence while neglecting a communication strategy, they create an invisible barrier between the platform’s capability and its business impact. Users can’t adopt what they don’t understand, and leadership won’t invest in what they can’t measure. ... To overcome engineers’ skepticism of new tools that may introduce complexity, your communication should clearly articulate how the platform simplifies their work. Highlight its ability to reduce cognitive load, minimize context switching, enhance access to documentation and accelerate development cycles. Present these advantages as concrete improvements to daily workflows, rather than abstract concepts. ... Tap into the influence of respected technical colleagues who have contributed to the platform’s development or were early adopters. Their endorsements are more impactful than any official messaging. Facilitate opportunities for these champions to demonstrate the platform’s capabilities through lightning talks, recorded demos or pair programming sessions. These peer-to-peer interactions allow potential users to observe practical applications firsthand and ask candid questions in a low-pressure environment.

Why data sovereignty is an enabler of Europe’s digital future

Data sovereignty has broad reaching implications with potential impact on many areas of a business extending beyond the IT department. One of the most obvious examples is for the legal and finance departments, where GDPR and similar legislation require granular control over how data is stored and handled. The harsh reality is that any gaps in compliance could result in legal action, substantial fines and subsequent damage to longer term reputation. Alongside this, providing clarity on data governance increasingly factors into trust and competitive advantage, with customers and partners keen to eliminate grey areas around data sovereignty. ... One way that many companies are seeking to gain more control and visibility of their data is by repatriating specific data sets from public cloud environments over to on-premise storage or private clouds. This is not about reversing cloud technology; instead, repatriation is a sound way of achieving compliance with local legislation and ensuring there is no scope for questions over exactly where data resides. In some instances, repatriating data can improve performance, reduce cloud costs and it can also provide assurance that data is protected from foreign government access. Additionally, on-premise or private cloud setups can offer the highest levels of security from third-party risks for the most sensitive or proprietary data.

Daily Tech Digest - March 23, 2025

Quote for the day:

"Law of Leadership: A successful team with 100 members has 100 leaders." -- Lance Secretan

Citizen Development: The Wrong Strategy for the Right Problem

The latest generation of citizen development offenders are the low-code and no-code platforms that promise to democratize software development by enabling those without formal programming education to build applications. These platforms fueled enthusiasm around speedy app development — especially among business users — but their limitations are similar to the generations of platforms that came before. ... Don't get me wrong — the intentions behind citizen development come from a legitimate place. More often than not, IT needs to deliver faster to keep up with the business. But these tools promise more than they can deliver and, worse, usually result in negative unintended consequences. Think of it as a digital house of cards, where disparate apps combine to create unscalable systems that can take years and/or millions of dollars to fix. ... Struggling to keep up with business demands is a common refrain for IT teams. Citizen development has attempted to bridge the gap, but it typically creates more problems than solutions. Rather than relying on workarounds and quick fixes that potentially introduce security risks and inefficiency — and certainly rather than disintermediating IT — businesses should embrace the power of GenAI to support their developers and ultimately to make IT more responsive and capable.

Researchers Test a Blockchain That Only Quantum Computers Can Mine

The quantum blockchain presents a path forward for reducing the environmental cost of digital currencies. It also provides a practical incentive for deploying early quantum computers, even before they become fully fault-tolerant or scalable. In this architecture, the cost of quantum computing — not electricity — becomes the bottleneck. That could shift mining centers away from regions with cheap energy and toward countries or institutions with advanced quantum computing infrastructure. The researchers also argue that this architecture offers broader lessons. ... “Beyond serving as a proof of concept for a meaningful application of quantum computing, this work highlights the potential for other near-term quantum computing applications using existing technology,” the researchers write. ... One of the major limitations, as mentioned, is cost. Quantum computing time remains expensive and limited in availability, even as energy use is reduced. At present, quantum PoQ may not be economically viable for large-scale deployment. As progress continues in quantum computing, those costs may be mitigated, the researchers suggest. D-Wave machines also use quantum annealing — a different model from the quantum computing platforms pursued by companies like IBM and Google.

Enterprise Risk Management: How to Build a Comprehensive Framework

Risk objects are the human capital, physical assets, documents and concepts (e.g., “outsourcing”) that pose risk to an organization. Stephen Hilgartner, a Cornell University professor, once described risk objects as “sources of danger” or “things that pose hazards.” The basic idea is that any simple action, like driving a car, has associated risk objects – such as the driver, the car and the roads. ... After the risk objects have been defined, the risk management processes of identification, assessment and treatment can begin. The goal of ERM is to develop a standardized system that not only acknowledges the risks and opportunities in every risk object but also assesses how the risks can impact decision-making. For every risk object, hazards and opportunities must be acknowledged by the risk owner. Risk owners are the individuals managerially accountable for the risk objects. These leaders and their risk objects establish a scope for the risk management process. Moreover, they ensure that all risks are properly managed based on approved risk management policies. To complete all aspects of the risk management process, risk owners must guarantee that risks are accurately tied to the budget and organizational strategy.

Choosing consequence-based cyber risk management to prioritize impact over probability, redefine industrial security

Nonetheless, the biggest challenge for applying consequence-based cyber risk management is the availability of holistic information regarding cyber events and their outcomes. Most companies struggle to gauge the probable damage of attacks based on inadequate historical data or broken-down information systems. This has led to increased adoption of analytics and threat intelligence technologies to enable organizations to simulate the ‘most likely’ outcome of cyber-attacks and predict probable situations. ... “A winning strategy incorporates prevention and recovery. Proactive steps like vulnerability assessments, threat hunting, and continuous monitoring reduce the likelihood and impact of incidents,” according to Morris. “Organizations can quickly restore operations when incidents occur with robust incident response plans, disaster recovery strategies, and regular simulation exercises. This dual approach is essential, especially amid rising state-sponsored cyberattacks.” ... “To overcome data limitations, organizations can combine diverse data sources, historical incident records, threat intelligence feeds, industry benchmarks, and expert insights, to build a well-rounded picture,” Morris detailed. “Scenario analysis and qualitative assessments help fill in gaps when quantitative data is sparse. Engaging cross-functional teams for continuous feedback ensures these models evolve with real-world insights.”

The CTO vs. CMO AI power struggle - who should really be in charge?

An argument can be made that the CTO should oversee everything technical, including AI. Your CTO is already responsible for your company's technology infrastructure, data security, and system reliability, and AI directly impacts all these areas. But does that mean the CTO should dictate what AI tools your creative team uses? Does the CTO understand the fundamentals of what makes good content or the company's marketing objectives? That sounds more like a job for your creative team or your CMO. On the other hand, your CMO handles everything from brand positioning and revenue growth to customer experiences. But does that mean they should decide what AI tools are used for coding or managing company-wide processes or even integrating company data? You see the problem, right? ... Once a tool is chosen, our CTO will step in. They perform their due diligence to ensure our data stays secure, confidential information isn't leaked, and none of our secrets end up on the dark web. That said, if your organization is large enough to need a dedicated Chief AI Officer (CAIO), their role shouldn't be deciding AI tools for everyone. Instead, they're a mediator who connects the dots between teams.

Why Cyber Quality Is the Key to Security

To improve security, organizations must adopt foundational principles and assemble teams accountable for monitoring safety concerns. Cyber resilience and cyber quality are two pillars that every institution — especially at-risk ones — must embrace. ... Do we have a clear and tested cyber resilience plan to reduce the risk and impact of cyber threats to our business-critical operations? Is there a designated team or individual focused on cyber resilience and cyber quality? Are we focusing on long-term strategies, targeted at sustainable and proactive solutions? If the answer to any of these questions is no, something needs to change. This is where cyber quality comes in. Cyber quality is about prioritization and sustainable long-term strategy for cyber resilience, and is focused on proactive/preventative measures to ensure risk mitigation. This principle is not a marked checkbox on controls that show very little value in the long run. ... Technology alone doesn't solve cybersecurity problems — people are the root of both the challenges and the solutions. By embedding cyber quality into the core of your operations, you transform cybersecurity from a reactive cost center into a proactive enabler of business success. Organizations that prioritize resilience and proactive governance will not only mitigate risks but thrive in the digital age.

ISO 27001: Achieving data security standards for data centers

Achieving ISO 27001 certification is not an overnight process. It’s a journey that requires commitment, resources, and a structured approach in order to align the organization’s information security practices with the standard’s requirements. The first step in the process is conducting a comprehensive risk assessment. This assessment involves identifying potential security risks and vulnerabilities in the data center’s infrastructure and understanding the impact these risks might have on business operations. This forms the foundation for the ISMS and determines which security controls are necessary. ... A crucial, yet often overlooked, aspect of ISO 27001 compliance is the proper destruction of data. Data centers are responsible for managing vast amounts of sensitive information and ensuring that data is securely sanitized when it is no longer needed is a critical component of maintaining information security. Improper data disposal can lead to serious security risks, including unauthorized access to confidential information and data breaches. ... Whether it's personal information, financial records, intellectual property, or any other type of sensitive data, the potential risks of improper disposal are too great to ignore. Data breaches and unauthorized access can result in significant financial loss, legal liabilities, and reputational damage.

Understanding code smells and how refactoring can help

Typically, code smells stem from a failure to write source code in accordance with necessary standards. In other cases, it means that the documentation required to clearly define the project's development standards and expectations was incomplete, inaccurate or nonexistent. There are many situations that can cause code smells, such as improper dependencies between modules, an incorrect assignment of methods to classes or needless duplication of code segments. Code that is particularly smelly can eventually cause profound performance problems and make business-critical applications difficult to maintain. It's possible that the source of a code smell may cause cascading issues and failures over time. ... The best time to refactor code is before adding updates or new features to an application. It is good practice to clean up existing code before programmers add any new code. Another good time to refactor code is after a team has deployed code into production. After all, developers have more time than usual to clean up code before they're assigned a new task or a project. One caveat to refactoring is that teams must make sure there is complete test coverage before refactoring an application's code. Otherwise, the refactoring process could simply restructure broken pieces of the application for no gain.

Handling Crisis: Failure, Resilience And Customer Communication

Failure is something leaders want to reduce as much as they can, and it’s possible to design products with graceful failure in mind. It’s also called graceful degradation and can be thought of as a tolerance to faults or faulting. It can mean that core functions remain usable as parts or connectivity fails. You want any failure to cause as little damage or lack of service as possible. Think of it as a stopover on the way to failing safely: When our plane engines fail, we want them to glide, not plummet. ... Resilience requires being on top of it all: monitoring, visibility, analysis and meeting and exceeding the SLAs your customers demand. For service providers, particularly in tech, you can focus on a full suite of telemetry from the operational side of the business and decide your KPIs and OKRs. You can also look at your customers’ perceptions via churn rate, customer lifetime value, Net Promoter Score and so on. ... If you are to cope with the speed and scale of potential technical outages, this is essential. Accuracy, then speed, should be your priorities when it comes to communicating about outages. The more of both, the better, but accuracy is the most important, as it allows customers to make informed choices as they manage the impact on their own businesses.

Approaches to Reducing Technical Debt in Growing Projects

Technical debt, also known as “tech debt,” refers to the extra work developers incur by taking shortcuts or delaying necessary code improvements during software development. Though sometimes these shortcuts serve a short-term goal — like meeting a tight release deadline — accumulating too many compromises often results in buggy code, fragile systems, and rising maintenance costs. ... Massive rewrites can be risky and time-consuming, potentially halting your roadmap. Incremental refactoring offers an alternative: focus on high-priority areas first, systematically refining the codebase without interrupting ongoing user access or new feature development. ... Not all parts of your application contribute to technical debt equally. Concentrate on elements tied directly to core functionality or user satisfaction, such as payment gateways or account management modules. Use metrics like defect density or customer support logs to identify “hotspots” that accumulate excessive technical debt. ... Technical debt often creeps in when teams skip documentation, unit tests, or code reviews to meet deadlines. A clear “definition of done” helps ensure every feature meets quality standards before it’s marked complete.