Daily Tech Digest by Kannan Subbiah: low code

Showing posts with label low code. Show all posts

Daily Tech Digest - September 18, 2026

Quote for the day:

“An investment in knowledge pays the best interest.” -- Benjamin Franklin

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Duration: 24 mins • Perfect for listening on the go.

Brevo supply-chain attack injected ClickFix scripts on customer sites

Brevo, a popular digital marketing and customer management platform, recently experienced a security breach affecting its website and tools embedded on customer sites. On September 14, attackers used a compromised Cloudflare API key, which had been mistakenly left inside the company's application code, to alter the platform's web traffic. For about five and a half hours, the attackers injected malicious scripts into Brevo's web forms and chat tools. When visitors loaded a website using these tools, they saw a fake verification screen urging them to run a harmful command, a technique known as a ClickFix attack. Additionally, if the visitor was logged into a WordPress site as an administrator, the script secretly attempted to install a hidden backdoor plugin called Web Media Optimizer. Security researchers estimate this incident may have affected up to one hundred thousand websites. Once Brevo identified the issue, the company quickly removed the unauthorized access, deleted the harmful files, and confirmed that core systems like email delivery and customer data remained secure. Website administrators who were logged in during the attack window are advised to carefully check their plugin lists for any unauthorized additions and update their passwords to ensure their systems remain completely safe.

Abandoned IoT apps keep sending sensitive data to broken servers

A recent study by the University of Massachusetts Amherst highlights the significant security risks posed by abandoned Internet of Things (IoT) companion apps. These apps, used to control smart devices like thermostats and cameras, often remain on users' phones long after developers stop updating them. The researchers analyzed over 61,500 abandoned Android IoT apps and found that a staggering number contained software dependencies linked to known vulnerabilities. Many of these apps were still being downloaded by millions of users, despite not receiving an update in over two years. Furthermore, these apps often bundle old software libraries and hard-coded web addresses, many of which no longer function or belong to entirely different owners. This creates a dangerous scenario where sensitive data, gathered through permissions like camera and location access, is sent to broken or potentially malicious endpoints. While the study found similar rates of known vulnerabilities in both abandoned and actively maintained apps, the real issue lies in the destination of the data. Over 40% of the data sinks in abandoned apps were associated with unreachable or vulnerable endpoints, compared to less than 1% in active apps. This research underscores the need for users to regularly review and uninstall abandoned IoT apps to minimize their security exposure.

Is your low code security keeping up with business speed?

Low code development platforms have transformed how organizations build applications, often leading to a misconception that they are as unstructured as vibe coding — the practice of relying entirely on artificial intelligence to generate software from casual prompts. However, while low code environments provide more structure and included guardrails than AI generated code, they still present significant security challenges that teams cannot ignore. Because these platforms empower everyday users to assemble functional applications quickly using visual interfaces, they introduce risks related to improper data handling, misconfigured permissions, and poor access controls. Included security features within low code platforms offer a baseline of protection, ensuring that development is not merely a chaotic environment, but they are not a complete safety net. To maintain a secure environment, IT departments must establish clear governance policies and conduct regular audits of user created applications. Without proper oversight, everyday builders might unintentionally expose sensitive company information or create software vulnerabilities that external attackers could exploit. Ultimately, organizations must strike a careful balance between enabling rapid, accessible software creation and maintaining strict security standards across the board. Relying solely on a platform's default protections is a risky approach; continuous monitoring and proactive management remain essential to keeping your business data truly safe.

Prioritise on the best governance, not the best model

The article from FutureCISO highlights that by mid-2026, the deployment of AI agents in Asia Pacific enterprises has significantly outpaced governance capabilities. Research shows that active AI agents have nearly tripled in a year, while the time to create them has halved. Gartner predicts that 40% of enterprise applications will feature embedded task-specific AI agents by the end of 2026. However, this rapid adoption has led to a rise in "shadow AI," with security incidents doubling year over year, according to IBM. The core issue is a lack of visibility; many organizations do not know what AI agents they have deployed. Lavy Stokhamer from Standard Chartered emphasizes that organizations need the same accountability and visibility for AI agents as they do for human employees, applications, and privileged accounts. A real-time inventory is crucial to understanding what each agent is authorized to do, the data it can access, and who is accountable. This comprehensive inventory of agent identities and permissions is fundamentally the "organizational chart for a digital workforce." Without knowing what digital actors exist and their authority, it is impossible to govern, secure, or manage risk at scale, leading to significant challenges in trust, resilience, and economics.

Malicious JavaScript Evaded VirusTotal in Seven of Eight E-Commerce Storefront Attacks

A recent cybersecurity investigation has revealed that traditional malware scanners are struggling to detect sophisticated e-commerce storefront attacks. Security researchers identified four distinct malicious JavaScript operations actively targeting online retailers. Across these campaigns, they found eight unique payloads designed to run quietly in a shopper's browser. Remarkably, when these payloads were tested against standard security tools, seven of the eight completely evaded detection by VirusTotal, and none were flagged as malicious by URLScan. These attacks succeed because they do not break the website. A modern storefront can look and function perfectly normally while the hidden script secretly siphons affiliate revenue, hijacks clicks, manipulates analytics, or opens a backdoor for remote access. To avoid detection, the malicious code uses clever evasion tactics, such as waiting for specific mobile devices, operating only during certain hours, or staying dormant until particular product buttons load on the page. Because these scripts only execute under exact conditions, traditional signature-based scanners often miss them during routine checks. This incident underscores a critical shift in e-commerce security. Relying solely on standard vendor trust or basic scans is no longer enough. Protecting online storefronts now requires advanced, behavior-based monitoring to catch these elusive threats in live traffic.

Rethinking Disaster Recovery Planning Using Optimized Sequencing

This article from Disaster Recovery Journal focuses on how organizations can improve their IT disaster recovery plans by optimizing their recovery sequences. When a widespread system outage occurs, simply restoring applications one by one based on a static list isn't always effective. Systems rely on each other—for example, an essential business app might need its database and identity services to be brought back online first. The author argues that companies need to look at multiple factors when deciding what to restore first. These include technical dependencies, recovery time objectives, and the potential impact on revenue and critical services. Because tech environments are always changing, with new applications and integrations being added, a fixed recovery sequence can quickly become outdated. To handle this, organizations can adopt recovery optimization. This approach uses existing data on dependencies and business priorities to compute the best recovery sequence for a specific situation. It allows teams to adjust their strategy based on current needs, whether that means prioritizing strict recovery timelines or protecting revenue. Ultimately, using an explainable, data-driven method helps teams make better decisions during a crisis and improves the value of their disaster recovery exercises.

Zombie Workloads Haunt Data Center Efficiency Efforts

Zombie workloads, such as unused applications or abandoned storage volumes, are creating notable challenges in data center efficiency. According to recent findings from the International Data Center Authority, up to 13% of US cloud usage is attributed to these idle workloads. The issue stems from scenarios like incomplete post-merger integrations and employees leaving apps active. The problem is becoming more critical with the rise of AI and GPUs, as the cost of idle time rises steeply compared to traditional CPU workloads. To address this, organizations are relying on Cloud FinOps tools and observability tools that find inactive resources. While features like scale-to-zero in serverless architectures offer some relief, they bring challenges like cold starts. The complexities of AI workloads also make hunting for zombies difficult, because they introduce issues like abandoned GPUs and mid-flight pipeline crashes. Effective management is built upon having sound policies. Clear guidelines, automated reminders, and routine scans are important in curbing zombie workloads. The cost of failing to decommission these idle assets has severe implications.

A Framework for Taming Unstructured Data at Scale

The provided article from CDO Magazine discusses the critical need for a framework to manage unstructured data, which constitutes 80% to 90% of corporate information. This "dark matter" includes emails, PDFs, and Teams messages, often lacking visibility and posing significant risks. The author, Lana DeMaria, highlights two main drivers for this urgency: the rise of "shadow AI," where employees might unknowingly feed sensitive data into public models, and the evolution of ransomware into "double extortion" tactics that target valuable unstructured data. Traditional governance methods, such as manual classification and reliance on regular expressions, fail because they are not scalable and treat governance as a one-time event rather than a continuous process. To address these challenges, the article proposes a cyclical, automated framework centered on three layers: Discovery (indexing data in place), Classification (using AI for semantic analysis), and Continuous Compliance (automating lifecycle management, including defensible deletion). By leveraging AI, organizations can better understand their data, manage risks, and ensure that governance scales effectively. Ultimately, implementing this framework allows leaders to turn unstructured data from a liability into a strategic asset for the enterprise.

The Standard BI Playbook Wasn't Built for the Physical Economy

The standard business intelligence approach often fails when applied to the physical economy, which includes industrial distribution, manufacturing, and marine transportation. These sectors do not suffer from a lack of information but rather struggle with making that information accessible across the organization. Traditional advice assumes data is already organized in a central location, but industrial companies typically rely on fragmented legacy systems, isolated applications, and numerous manual spreadsheets. To make any meaningful progress, companies must first do the practical work of gathering this scattered data into one unified platform. Furthermore, the typical strategy assumes teams are eager for new reports and have dedicated analysts ready to use them. In reality, operational teams are deep domain experts who are often overwhelmed by manual reporting tasks and naturally skeptical of new tools. They need immediate, reliable answers to handle their daily operations, not long-term analytical deep dives. Success in this environment should not be measured by how many reports are created, but by how many hours of manual work are eliminated. By focusing on centralizing information, sharing knowledge across departments, and automating tedious processes, industrial organizations can give employees their time back and significantly improve how they operate on a daily basis.

You Can’t Patch Cybersecurity Burnout: Joe Marshall’s Human Incident Response Framework

The provided article details Joe Marshall's Human Incident Response Framework, introduced during his CYBR.SEC.CON. 2026 keynote. Inspired by his grueling experience fighting the VPNFilter botnet in 2018, Marshall argues the cybersecurity industry expertly manages technical incident response but fails to support the human defenders. His framework provides a playbook to address occupational stress by first differentiating "burnout" into four specific injuries: actual burnout (workload exhaustion), secondary traumatic stress, vicarious trauma, and moral injury. Because they stem from different causes, they require distinct responses beyond just taking time off. The framework challenges the notion that stress merely comes from long hours, highlighting six exposure factors like content type and secrecy that make different cybersecurity roles uniquely taxing. It adapts military and emergency medicine concepts, classifying human strain into four zones: Ready, Reacting, Injured, and Crisis. Crucially, it replaces passive "open-door policies" with structured peer check-ins designed to establish baselines and recognize when a colleague is struggling. While offering practical tools like a 43-page Field Guide and a two-page Playbook, Marshall stresses the framework is a detection aid, not a clinical replacement. It aims to give the industry a shared vocabulary to recognize human distress and properly escalate issues without turning support into surveillance.

Daily Tech Digest - June 25, 2026

Quote for the day:

“If we are growing, we are always going to be out of our comfort zone.” -- John C. Maxwell

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Duration: 22 mins • Perfect for listening on the go.

When IT loses sight of enterprise low-code

When information technology departments lose oversight of low code development, organizations often face significant operational risks. Low code platforms are designed to let everyday employees build applications quickly, which can improve efficiency and solve immediate business problems. However, without proper technical supervision, this newfound freedom can lead to a heavily fragmented digital environment. Employees might create software that handles sensitive data without following standard security protocols, exposing the company to serious breaches and costly compliance failures. Furthermore, these independently built applications often overlap in function, creating unnecessary complexity and increasing ongoing maintenance costs. When employees eventually leave the company, the specialized tools they built can easily become unsupported and difficult to fix, leaving critical business processes vulnerable to disruption. To effectively manage these persistent challenges, technical teams must maintain a strong guiding role in all low code initiatives. By establishing clear rules and providing structured, reliable support, IT can help employees build useful tools safely. This collaborative approach ensures that new applications integrate smoothly with existing systems and adhere strictly to company standards. Ultimately, balancing employee autonomy with technical oversight allows businesses to benefit from faster software creation without compromising their security, stability, or long term operational health.

Hacking the Simulation: The Anti-Gravity Exploit

The article outlines a theoretical framework and engineering approach known as Observer-Patch Holography, which treats the physical world as a highly structured, interactive system rather than a static container. According to this framework, fundamental elements like space, time, and gravity are not absolute background features but emergent properties that arise from the consistency between different observational perspectives. By understanding the underlying mechanics of this shared reality, the author argues that it is possible to interact with the universe much like a hardware program. The core thesis is that reality can be directly manipulated by exerting control over small, bounded physical areas called patches. Engineers could theoretically use specialized devices to adjust boundary data and stabilize these patches into desired states. This process allows them to effectively rewrite the local rules of physics by managing how information and observations synchronize. Specifically, the engineering note proposes that this method of hacking reality provides a practical, low-cost pathway for achieving localized control over gravity and inertia. By manipulating the consensus of information at a micro-level, engineers could produce macroscopic effects, potentially paving the way for advanced technologies like hoverboards and hoverbikes.

Choosing your AI stack: The benefits of vendor lock-in

In the past, IT departments could easily mix and match different hardware and software, but modern artificial intelligence systems require a different approach. Because AI demands immense computing power, technology providers now build hardware and software that work strictly together to maximize efficiency. This tight integration means organizations must commit to complete ecosystems rather than choosing individual components, leading to a modern form of vendor lock-in. While switching platforms might seem simple on paper, it brings serious hidden costs, including wasted engineering effort, deep system dependencies, and poor timing during critical growth phases. As a result, IT leaders need to shift their perspective. Instead of viewing vendor lock-in as a failure to avoid at all costs, they should see it as a strategic choice that can deliver a crucial performance advantage. The most effective organizations understand that openness is not always better than lock-in. They treat platform commitment as a dynamic issue, weighing where raw performance matters most against where flexibility is needed. True leaders do not run from vendor lock-in; they carefully decide when to embrace it, limit it, or move past it before market pressures force their hand.

Why CIOs should be prioritising stability as the foundation for transformation

As local governments face significant structural changes and reorganizations, chief information officers often feel pressured to use the opportunity for immediate, widespread digital overhauls. However, this approach can be risky. The real priority during these transitions must be operational stability. When a new authority takes over, residents expect basic services, like trash collection and benefit processing, to continue working exactly as they did before. Managing technology in local government is already complicated by older systems and disjointed applications. Merging these environments adds another layer of difficulty. Instead of rushing to rebuild every system or process right away, technology leaders should focus on keeping current operations running smoothly. A practical first step is to map out how services actually function today, identifying where delays or manual tasks exist. This clear understanding allows teams to stabilize the foundation and maintain service continuity. By prioritizing resilience and control, councils can reduce the risk of service failures during the transition. Once the foundational systems are secure and the new organizational structure is clear, leaders will have the breathing room needed to implement thoughtful, long-term improvements. Success comes from stabilizing first, then changing at a measured pace.

Cybersecurity is no longer about protection. It’s about survival

Cybersecurity strategy must evolve from a mindset of pure prevention to one focused on organizational survival. While traditional defenses like firewalls, multi-factor authentication, and patching remain necessary, relying solely on keeping attackers out is no longer a realistic strategy in an era where breaches are inevitable. The rapid advancement of artificial intelligence and the increasing complexity of supply chains have dramatically expanded the attack surface, meaning defenses will eventually fail. Therefore, the core objective of modern security is to ensure an organization can continue to function during and after an attack. This shift requires a deep commitment to resilience, business continuity, and rapid recoverability. True security means knowing precisely which systems are critical, isolating the impact of a breach, and having a tested plan to rebuild cleanly. Furthermore, this survival approach cannot be confined to the IT department. It demands active involvement and clear accountability from the board, executive leadership, legal, engineering, and human resources. Ultimately, an organization that collapses the moment its protective walls are breached was never truly secure. Success is now defined by the ability to absorb systemic shocks and recover quickly.

The uptime questions every engineering leader should ask this week

In a recent interview, Mattias Geniar, CTO at Oh Dear, discusses practical strategies for preventing system outages and improving uptime. He observes that engineering teams often monitor isolated metrics and absolute numbers, which leads to alert fatigue and unnecessary middle-of-the-night wake-up calls. Instead, he advises monitoring actual user outcomes—such as the ability to log in or complete a purchase—and establishing baselines to detect meaningful changes over time. Geniar highlights that while front-facing issues are easily tracked, sudden outages frequently stem from unmonitored internal DNS misconfigurations and expired TLS certificates buried deep within complex systems. To manage reliance on third-party vendors, he recommends developing clear failover alternatives to contain the impact of external failures. He cautions that tired engineers are highly prone to making mistakes during late-night incident responses. To mitigate this risk, recovery processes must be thoroughly tested until they become entirely routine and predictable. Finally, Geniar urges leaders to ask their teams direct questions to uncover hidden vulnerabilities. This includes identifying the most fragile infrastructure, ensuring backups are fully tested by actually restoring them, confirming that monitoring catches errors before customers do, and removing dependencies on a single indispensable team member.

Bridging the Divide: How Data Centers Are Addressing Community Concerns

As the development of data centers accelerates to unprecedented scales, developers are facing increased scrutiny from local municipalities and residents. Communities are raising valid concerns regarding the substantial impact these facilities have on power grids, water resources, and local infrastructure. In an era of high inflation and rising utility bills, residents are particularly skeptical of tech companies receiving large tax incentives while household expenses continue to climb. Recognizing these tensions, industry leaders are acknowledging that their traditional approach of operating quietly behind the scenes is no longer effective. Instead, they must proactively engage with the public to dispel misinformation and highlight the tangible benefits these facilities offer, such as high-paying union jobs, infrastructure improvements, and increased tax revenues. However, developers also point to significant challenges, including slow permitting processes and outdated zoning laws that struggle to accommodate modern, large-scale projects. Moving forward, overcoming this divide will require a coordinated effort. Developers, policymakers, and government entities at all levels must collaborate to create cohesive regulations, streamline development processes, and ensure that new projects deliver clear, measurable value to the communities that host them.

AI security doesn’t require a brand-new architecture

The rapid adoption of artificial intelligence brings new security challenges, from rogue applications to invisible software agents, but keeping your organization safe does not require building a completely new architecture. Instead of looking for magical fixes, security experts suggest returning to core fundamentals like granting minimal access and designing systems securely from the start. Rather than blocking AI adoption out of fear, companies can build on their existing tools to detect threats and manage access rights in real time. Because attackers now use automation to find network flaws instantly, defenders must also use artificial intelligence to quickly identify and isolate vulnerabilities before permanent patches are ready. At the same time, internal policy approval needs to speed up; waiting several weeks for permission is simply no longer practical. By writing policies directly into the system code, organizations can safely match the pace of modern technology. Employee education also remains vital, requiring clear guidelines on how to interact with new tools responsibly. Finally, keeping costs manageable is a critical part of a safe deployment. By using existing platforms and combining cloud resources with local hardware, companies can effectively protect both their data and their budgets.

Beyond CLEAN and MVP: Architecting an Offline-first Reactive Data Layer in Android

The provided article introduces the Reactive Data Layer Architecture (RDLA), a practical approach designed to improve data management in Android applications. Traditional structures, such as Model-View-Presenter and Clean Architecture, often create unnecessary complexity or struggle with the continuous updates required by modern mobile interfaces. RDLA addresses these challenges by establishing the local device storage as the single, reliable source of truth. Instead of forcing the user interface to request data repeatedly, RDLA uses a continuous stream that automatically pushes updates to the screen whenever the underlying data changes. This design is particularly useful for applications that must function without an internet connection, such as health tracking tools. When a user makes a change, the application instantly updates the local interface while silently scheduling the network synchronization in the background. By relying on tools built into the Android system, these background tasks are guaranteed to finish even if the user closes the app. Furthermore, RDLA simplifies the testing process. It separates the database and network configurations, allowing engineers to verify their core logic without relying on fragile mock setups. Ultimately, this architecture provides a more reliable foundation for complex mobile applications.

Agentic AI Security: Wrong Context, Wrong Decisions at Machine Speed

The effectiveness of automated artificial intelligence in cybersecurity fundamentally depends on the quality of its context. While organizations are looking to these advanced systems to manage the rapid volume of modern threats, these tools can only make accurate decisions if they possess a complete and updated view of the environment. When fed incomplete or inaccurate data, the artificial intelligence will make incorrect decisions at machine speed, carrying out flawed actions with unwavering confidence. Security leaders caution that any automation system lacking verified context is simply a faster way to make widespread mistakes. For instance, an automated security operations center might shut down a critical device to isolate a threat, completely unaware of the disastrous business impact because it lacked the broader operational context. Given these significant risks, experts suggest that artificial intelligence is not yet mature enough for fully independent action. Instead of allowing the system to execute automated responses, the current best practice involves using it to quickly gather relevant context across various security tools and provide clear, reasoned recommendations. Ultimately, human experts must remain in the loop to make final decisions until context gathering methods become significantly more reliable over time.

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

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."