Daily Tech Digest by Kannan Subbiah: digital workplace

Showing posts with label digital workplace. Show all posts

Daily Tech Digest - April 19, 2026

Quote for the day:

“In the end, it is important to remember that we cannot become what we need to be by remaining what we are.” -- Max De Pree

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Beyond the degree: What education must become in the age of AI

The Firstpost opinion piece titled "" explores the fundamental disruption of traditional academic structures caused by rapid artificial intelligence advancements. It argues that the era where a degree served as a definitive lifelong credential is coming to an end, replaced by a pressing need for continuous, skill-based learning. As AI increasingly automates technical and administrative tasks, the article posits that the uniquely human advantage now lies in higher-order cognitive and ethical functions. Specifically, education must evolve to prioritize the ability to formulate the right questions, critically evaluate AI-generated outputs, and maintain firm personal accountability for decisions that impact society. Rather than focusing on rote memorization—which has been rendered redundant by ubiquitous digital tools—future curricula should nurture curiosity, empathy, and cross-disciplinary thinking. The author highlights that while AI democratizes knowledge through personalized learning, it also necessitates a profound shift in how we value intelligence, moving away from rigid institutional metrics toward adaptable, lifelong expertise. Ultimately, the piece concludes that the most successful individuals in an automated economy will be those who combine technological proficiency with the critical judgment and human-centric values required to guide AI responsibly. By fostering these unique human traits, the educational system can better prepare students for a complex, technology-driven future.

Context Lakes: The Infrastructure Layer AI Agents Need That Doesn't Exist Yet

In her article, addresses a critical architectural flaw in modern AI agent infrastructure: the lack of "." Current systems typically fragment critical data across relational databases, feature stores, and vector databases, with each component operating without a shared transactional boundary. This fragmentation creates a "seam problem" where agents retrieve inconsistent, disparate views of reality—such as current account balances paired with stale behavioral signals or outdated semantic embeddings. Consequently, agents may make incorrect, irreversible decisions, particularly in high-concurrency environments like financial transaction approvals or resource allocation. To bridge this gap, Zhao introduces the concept of the "Context Lake," a system class specifically designed to enforce Decision Coherence. Unlike traditional decoupled stacks, a Context Lake integrates episodic events, semantic transformations, and procedural rules within a single transactional scope. This ensures that every decision-making context is internally consistent, semantically enriched, and strictly bounded in freshness. By moving semantic computations—like embedding generation—inside the system boundary, the Context Lake eliminates the asynchronous delays that plague existing architectures. Based on research by Xiaowei Jiang, this emerging infrastructure layer is essential for production-grade AI agents that manage fast-changing, shared states and require absolute correctness to avoid costly operational failures or system-wide logic errors.

The Algorithmic Arms Race: Navigating the Age of Autonomous Attacks

In the article "The Algorithmic Arms Race," Kannan Subbiah explores the paradigm shift from human-led cyberattacks to the rise of autonomous Cyber Reasoning Systems. This transition marks an evolution from traditional automated scripts to cognitive AI agents capable of independent reasoning, real-time adaptation, and executing the entire cyber kill chain at machine speed. Subbiah details the anatomy of these autonomous attacks, highlighting how they compress reconnaissance, weaponization, and lateral movement into rapid, self-directed sequences that outpace human intervention. Through case studies like and the Shai-Hulud supply chain siege, the author illustrates a future where malware independently manages its own obfuscation and identifies obscure vulnerabilities. To counter these sophisticated threats, the article advocates for a "fighting fire with fire" strategy, urging organizations to deploy Autonomous Security Operations Centers, Moving Target Defense, and hyper-segmented Zero Trust architectures. Furthermore, Subbiah emphasizes the necessity of integrated risk analytics, mandatory Software Bill of Materials, and adversarial red teaming where AI systems challenge one another. Ultimately, the narrative stresses that in an era of machine-speed conflict, human-centric defense models are no longer sufficient; instead, organizations must embrace autonomous, resilient infrastructures while maintaining human oversight as a final ethical and operational kill switch.

Workplace stress in 2026 is still worse than before the pandemic

The from Help Net Security highlights a concerning trend: employee stress remains significantly higher than pre-pandemic levels, with global engagement hitting a five-year low. According to ’s latest findings, roughly 40% of workers worldwide experience daily stress, while negative emotions like anger and sadness persist at elevated rates. This lack of engagement is not just a cultural issue but a massive economic burden, costing the global economy approximately $10 trillion in lost productivity, or 9% of global GDP. The report indicates that managers and leaders are bearing the brunt of this emotional weight, reporting higher levels of loneliness and stress compared to individual contributors. Demographic disparities are also evident, as women and workers under the age of 35 report higher stress levels than their peers. Geographically, the United States and Canada lead the world in daily stress at 50%. Interestingly, the study finds that work location plays a role, with hybrid and remote-capable employees experiencing more stress than those in fully remote or strictly on-site roles. Ultimately, the data suggests that organizational success is deeply tied to emotional wellbeing, as engaged leaders are far more likely to thrive and mitigate the negative impacts of workplace pressure.

Most enterprises can't stop stage-three AI agent threats, VentureBeat survey finds

According to a recent VentureBeat survey, a significant security gap exists as enterprises struggle to defend against "stage-three" AI agent threats. The survey identifies a three-stage maturity model: Stage 1 focuses on observation, Stage 2 on enforcement via Identity and Access Management (IAM), and Stage 3 on isolation through sandboxed execution. While monitoring investment has surged to 45% of security budgets, most organizations remain trapped at the observation stage, leaving them vulnerable to sophisticated agentic failures where traditional guardrails prove insufficient. Data from Gravitee and the Cloud Security Alliance underscores this readiness gap, noting that only 21.9% of teams treat AI agents as distinct identity-bearing entities, while 45.6% still rely on shared API keys. This structural weakness allows for rapid lateral movement and unauthorized actions, which 72% of CISOs identify as their top priority. Despite the high demand for robust permissioning, current enterprise infrastructure often lacks the necessary runtime enforcement to contain a "blast radius" when agents go rogue. The survey highlights that while agents are already operating with privileged access to siloed data, security teams are lagging behind in providing the isolation required to stop the next wave of autonomous exploits and supply-chain breaches.

Empty Attestations: OT Lacks the Tools for Cryptographic Readiness

Operational technology (OT) systems face a critical security gap as regulators increasingly demand attestations of post-quantum cryptographic readiness despite a severe lack of specialized auditing tools. Unlike IT environments, which prioritize confidentiality and can be regularly updated, OT infrastructure focuses primarily on availability and often relies on decades-old legacy hardware with minimal processing power. This makes the implementation of modern cryptographic standards exceptionally difficult, as many devices lack the memory to execute post-quantum algorithms or have encryption hard-coded into immutable firmware. Consequently, asset owners are often forced to treat security compliance as a box-ticking exercise, producing paperwork that provides a false sense of assurance rather than genuine protection. This vulnerability is compounded by "harvest now, decrypt later" tactics and the risk of stolen firmware signing keys, which allow adversaries to maintain long-term access and potentially push malicious updates. Without OT-specific frameworks and instrumentation, these systems remain exposed to sophisticated threats like Volt Typhoon. To truly secure critical infrastructure, industry leaders and regulators must acknowledge that current IT-centric assessment models are insufficient, requiring a shift toward developing practical tools that account for the unique operational constraints and long life cycles inherent in industrial environments.

Business Risk: How It’s Changing In The Digital Economy

The digital economy has fundamentally transformed the landscape of business risk, shifting focus from traditional financial and operational concerns toward complex, technology-driven vulnerabilities. According to experts from the Forbes Business Council, risk is no longer a separate "balance sheet" issue but is now embedded in every design choice and organizational decision. Key emerging threats include data vulnerability, algorithmic bias, and cyber risks that extend across entire supply chains via sophisticated social engineering. Notably, the rapid adoption of artificial intelligence introduces "invisible" risks, such as business models quietly becoming obsolete or conflicting AI agents causing critical system outages. Furthermore, companies face unprecedented challenges regarding digital visibility and public perception; in an oversaturated market, being unseen or suffering from viral reputation damage can be as detrimental as direct financial loss. Managing these dynamic parameters requires a shift from reactive detection to proactive, upstream governance and a focus on organizational adaptability. Ultimately, the modern definition of risk centers on a firm's ability to match its cognitive capabilities with the increasing speed and non-linearity of the digital environment. To survive, leaders must move beyond standard business formulas, integrating real-time intelligence and human-centered context to navigate the uncertainty inherent in a data-driven world.

Building your cryptographic inventory: A customer strategy for cryptographic posture management

As approaches, Microsoft emphasizes that the primary challenge for organizations is not selecting new algorithms, but discovering existing cryptographic assets. This Microsoft Security blog post outlines a strategy for building a cryptographic inventory as the foundation of Cryptography Posture Management (CPM). A cryptographic inventory is defined as a dynamic catalog encompassing certificates, keys, protocols, and libraries used across an enterprise. To manage these effectively, Microsoft proposes a continuous six-stage lifecycle: discovery, normalization, risk assessment, prioritization, remediation, and ongoing monitoring. This approach spans four critical domains—code, network, runtime, and storage—ensuring visibility into everything from source code primitives to active network sessions. Organizations can leverage existing tools like GitHub Advanced Security for code analysis, Microsoft Defender for Endpoint for runtime signals, and Azure Key Vault for centralized key management to simplify this process. Rather than a one-time project, CPM requires clear ownership and documented policy baselines to maintain security hygiene and achieve "crypto agility." By establishing these practices now, businesses can proactively identify vulnerabilities, comply with emerging global regulations, and ensure a resilient transition to a quantum-safe future. Through strategic integration of Microsoft capabilities and partner solutions, teams can transform complex cryptographic landscapes into manageable, risk-informed systems.

The Rise of Intelligent Automation: How Technology Is Redefining Work and Efficiency

The rise of intelligent automation (IA) is fundamentally reshaping the financial landscape by blending artificial intelligence with robotic process automation to create more agile, efficient, and strategic work environments. According to Global Banking & Finance Review, this shift is not merely about replacing manual labor but about redefining the nature of work itself. By automating repetitive and high-volume tasks—such as data entry, reconciliation, and compliance checks—organizations can significantly reduce human error and operational costs while accelerating processing speeds. Beyond mere efficiency, IA empowers financial institutions to leverage advanced analytics for real-time decision-making and hyper-personalized customer experiences, such as tailored loan products and instant virtual assistance. This technological evolution allows human professionals to pivot from mundane administrative roles toward high-value activities like strategic planning and creative problem-solving. Furthermore, IA enhances risk management through proactive fraud detection and seamless regulatory adherence, providing a robust framework for digital transformation. As the industry moves toward autonomous financial operations, embracing these intelligent systems becomes a competitive necessity. Ultimately, the integration of intelligent automation fosters a culture of innovation, ensuring that financial services remain resilient, secure, and customer-centric in an increasingly complex and data-driven global market.

World targets central IDV, AI agent management role with selfie biometrics

World has unveiled a major strategic expansion aimed at becoming the primary identity verification (IDV) layer for an economy increasingly dominated by agentic AI. Central to this update is the introduction of "," a face biometric and liveness detection service that provides a lower-assurance alternative to its high-level iris-based verification. This shift positions World as a versatile IDV provider, allowing apps to pay for proof of personhood to combat bots and deepfakes. Key features include the "Deep Face" tool, which integrates with platforms like Zoom to offer hardware-backed "root of trust" for real-time presence verification. Beyond individual authentication, the new introduces AI agent management and delegation tools, supported by partnerships with industry leaders such as AWS, Okta, and Shopify. These updates represent a comprehensive reengineering of the World stack, incorporating privacy-enhancing technologies like multi-party entropy and key rotation to keep user data unlinkable. By diversifying its verification methods and focusing on the governance of autonomous digital agents, World seeks to monetize its infrastructure as a global trust anchor. This evolution reflects a broader market push to align biometric credentials with the evolving demands of AI-driven interactions, securing human identity in an increasingly automated world.

Daily Tech Digest - January 23, 2026

Quote for the day:

"Strong convictions precede great actions." -- James Freeman Clarke

90% of companies are woefully unprepared for quantum security threats

Companies shouldn't wait, warned, pointing to rapid progress made by , , and other industry leaders on this front. "At a certain threshold, will be able to easily and quickly break asymmetric cryptography protocols such as Rivest-Shamir-Adelman (RSA), Diffie-Hellman (DH), and elliptic-curve cryptography (ECC) and reduce the time required, weakening symmetric cryptography such as advanced encryption standard (AES) and ," ... The highest impact will be on secure keys and tokens, , , data encrypted at rest, and even network security and identity access management (IAM) tools. Essentially, anything currently relying on encryption. Beyond that, quantum computing could supercharge and make it easier to identify and weaponize "zero day" flaws, Bain warned. Another risk highlighted by security experts is "steal now, crack later" techniques, whereby threat actors harvest data now to decrypt later. ... Companies need a board-led – and funded – roadmap to consider across their business decision making, ensuring quantum resilience across their own suppliers, existing technology, and even their products. But so far, the revealed only 12% of companies are considering quantum readiness as a key factor in procurement and risk assessments.

The New Rules of Work: What a global HR leader reveals about modern talent

The impact of AI on the workforce is a subject Sonia has thought deeply about, especially as it relates to entry-level talent. “There’s always been a question about repetitive engineering tasks—whether these should be done by engineers or by diploma holders. Now, with AI in the picture, many of these tasks will be automated,” she says. Rather than seeing this as a threat, Kutty believes it frees up human talent to focus on innovation and problem-solving. “Our true value at comes from leveraging innovation to solve the toughest engineering problems. AI will allow us to do more of this meaningful work.” ... While the company offers AI-based courses and certifications, Kutty emphasises the importance of fostering a mindset of adaptability and systems thinking. “We call it nurturing ‘’—professionals who can think broadly, adapt to new challenges, and learn continuously,” she says. ... As the engineering and R&D sector prepares for rapid growth, Kutty identifies leadership development as her biggest challenge—and her greatest responsibility. “We need strong leaders who understand this industry and are ready to step up when the time comes. Planning for leadership succession keeps me up at night. It’s critical for our continued success.” On the other hand, client expectations have evolved alongside technological advances. “In the past, clients would tell us exactly what they wanted. Now, they expect us to tell them what’s possible with AI and technology. They see us as partners in innovation, not just service providers,” Kutty observes.

Work-from-office mandate? Expect top talent turnover, culture rot

There is value in cross-functional teams working together in person, says , CTO at marketing firm . “When teams meet for architecture sessions, design sprints, or incident response, the pace of progress, as well as the level of clarity, may increase simply because being in-person caters to the way most people in the business interact,” he says. However, there are potential downsides for IT leaders, with strict work-from-office policies making it more difficult to attract and retain top IT talent. ... Despite possible resistance, it makes sense for some IT jobs to be tied to an office, says , founder and CEO of culture and leadership advisory firm . Some IT roles, including device provisioning, network operations, and conference room IT support, are better done in person, she notes. She sees some other benefits in specific situations. “In-person work is genuinely valuable for onboarding and mentoring early-career technologists, especially when learning how the organization actually operates, not just how the codebase works,” McDearmid says. “It’s also powerful when teams need to think together in high-bandwidth ways: whiteboards, war rooms, architecture reviews, incident response, or when solving messy, cross-functional problems.” ... IT leaders enforcing in-person work mandates can also focus on making the workplace a real place to collaborate, she adds. CIOs can align office space, meeting schedules, and in-office days so they reinforce the goals of collaboration and knowledge sharing, Wettemann adds.

Rethinking IT leadership to unlock the agility of ‘teamship’

Rather than waiting for the leader to set the pace, the best teams coach one another, challenge one another, co-elevate one another, and move faster, because they and their leaders have built cultures where candor is a shared responsibility. For CIOs navigating the messy middle of AI, modernization, and talent transformation, this shift from leadership to what calls “teamship” may be the most important upgrade of all. ... The No. 1 shift is to move from leadership to teamship. That means stop thinking of leadership as a hub and spoke. Don’t think aboutwhat you need to give feedback on, how you need to hold people accountable, how you need to do this or that. Instead, think about, how do you get your team to step up and meet each other, to give each other feedback, to hold each other’s energy up. Get out of the center and expect your team to step up. ... To be effective, stress testing needs to be positioned as a service to the person who’s giving the project update. We’re not trying to make them look bad or catch them in what they’re doing wrong. The feedback should be offered and received as data, with no presumption that they have to act on it. ... That fear is rooted in a misunderstanding of how high-performing teams actually work. In traditional leadership models, accountability flows upward: People worry about what the boss will think. In teamship, accountability flows sideways: People worry about letting their peers down.

The Upside Down is Real: What Stranger Things Teaches Us About Modern Cybersecurity

The Upside Down’s danger lies in the unseen portals – the gates and rifts – that allow its monstrous inhabitants, like the and , to cross over and wreak havoc in the seemingly safe, familiar world of . Today, nearly every business’s hidden reality is its extended attack surface. It’s the sprawling, complex, and often unmanaged network of IT, , , medical, cloud systems and beyond that modern organizations rely on. ... For the and security team, this translates directly to the need for full, continuous visibility across every single connected device and system to protect the entire attack surface and manage their organization’s cyber risk exposure in real time. Like the analogies the kids use to understand the creatures and their tactics, security teams rely on context and intelligence – risk scoring, vulnerability prioritization, and threat analysis – to understand how an asset is connected, why it is vulnerable, and what the most effective countermeasure is. ... First and foremost, cybersecurity requires teamwork, particularly through the fusion of IT, OT, security and business leadership so that they work from a unified view of any risks at hand. It also demands persistence from the dedicated security professionals protecting our digital infrastructure. Most of all, cybersecurity needs to be a proactive and preemptive effort where risk exposures are continuously monitored and threats can be stopped before they ever fully manifest.

Shadow AI: The emerging enterprise risk that can no longer be ignored

With regulatory frameworks tightening and emerging national standards, unsanctioned AI activity can quickly become a governance liability. Instead of reactive controls, organisations are now moving toward multi-layered visibility frameworks: monitoring external AI calls, classifying enterprise assets by sensitivity and tracking unmanaged AI usage. Forward-looking teams are even translating these metrics into financial exposure scores, linking AI misuse to operational, reputational and regulatory impact. Assigning monetary value to Shadow AI risk has proven effective for prioritising mitigation at leadership levels. ... A structured foundation is essential, comprised of trusted assessment frameworks, tested architectural blueprints and scalable AI operating models. Some organisations are pairing these with comprehensive training programs to build AI-literate leaders and teams, ensuring governance evolves alongside capability. This reflects a broader shift: responsible AI has now become the foundation of durable competitive advantage. ... Regulators, global partners and enterprise clients are seeking evidence of formal AI governance models, not just intent. For example, as per the Digital India Act, sectoral data localisation rules and global regulatory momentum are prompting enterprises to strengthen AI auditability, model documentation and workforce training. For many organisations, AI governance has moved from an operational task to a board-level agenda.

Ireland to make age checks through government app mandatory for social media

The plan is unprecedented among governments legislating online safety, in that it makes downloading the app, designed by the Government’s chief information officer, mandatory for age assurance. Per the Extra report, “if adults refuse to download the digital wallet, they will no longer be able to access their existing social media accounts.” “Mr. O’Donovan said the process of downloading the app might inconvenience someone for ‘three or four minutes’ but this was a small ask in order to protect children online.” O’Donovan has called the harmful effects of social media and other online content on youth a “severe public health issue.” ... Concerns about age assurance technology persist among privacy rights activists. Since age verification and facial age estimation often involves the processing of biometrics, the potential for sensitive data to be exposed is high. And requiring the process to run through a government product is likely to agitate fears about mass surveillance. O’Donovan says the risk to Ireland’s youth is higher. ... “At the end of the day, if the companies have a social conscience and are interested in the protection of children online, I don’t see why anybody who wouldn’t be trading in Ireland, not just domiciled in Ireland, wouldn’t adopt the format that we’re proposing,” he says. “Some of them do have, you know, something bordering on a social conscience, which is to be welcomed. But others don’t.”

Secure networking: the foundation for the AI era

Global networks have been under siege for years, but recent attacks are more sophisticated and move at unprecedented speed. Many organizations are still relying on outdated infrastructure, with Cisco research revealing that 48% of network assets worldwide are aging or obsolete. This creates vulnerabilities that attackers eagerly exploit. It’s no longer enough to patch and maintain; a fundamental shift in strategy is required. ... Modern networks typically span solutions and services from a range of different vendors, creating layers of complexity that can quickly overwhelm even experienced IT teams. This complexity often translates into vulnerability, especially when secure configurations aren’t consistently implemented or maintained. For many, simplicity and automation are now mission critical. Businesses increasingly need networks where secure configurations, protocols, and features are enabled by default and adapt automatically. ... Organizations now face the challenge of not only detecting threats quickly, but also responding before vulnerabilities can be exploited. There is an urgent need to reduce the attack surface, remove legacy insecure features, and introduce advanced capabilities for detection and response. ... The next generation of security requires networks to seamlessly provide identity management, deep visibility, integrated detection and protection, and streamlined management, while also incorporating advanced technologies like post-quantum cryptography.

Ransomware gang’s slip-up led to data recovery for 12 US firms

Researchers at Florida-based said Thursday they took advantage of a lapse in operational security by the gang: They found artifacts left behind by , an legitimate open source backup utility the gang uses to encrypt and exfiltrate victim data into cloud storage environments it controls. Assuming the gang regularly re-uses Restic-based infrastructure led to finding an unnamed cloud storage provider where stolen data was dumped. ... While Restic wasn’t used for exfiltration in this particular attack, Cyber Centaurs suspected the gang regularly used it, based on patterns seen in other incidents. It also suspected the infrastructure the crooks used was unlikely to be dismantled even after negotiations ended or payments were made by corporate victims. With that in mind, the incident response team developed a custom enumeration script to identify certain patterns that identify S3-style cloud bucket infrastructure that the stolen data might be going to. The script ran through a curated list of candidate repository identifiers derived from previously observed Restic artifacts. For each candidate, environment variables were set to match the configuration style used by the threat actor, including the repository endpoint and encryption password. Restic was then instructed to list available snapshots in a structured format, enabling investigators to analyze results without interacting with the underlying data.

The Real Attack Surface Isn’t Code Anymore — It’s Business Users

Traditional programs are optimized for code stored in repositories, pushed through pipelines, and deployed through CI/CD, not for no-code apps, connectors, and automations created on platforms like , , , and . Meanwhile, most organizations assume business-user automations are simple, low-risk, and limited in scope. The reality is more complex. Citizen developers now outnumber traditional software developers by an order of magnitude. Plus, they are wiring together data sources, triggering multi-system workflows, and calling APIs, not just building basic macros or departmental utilities. Because these automations are created outside engineering governance, traditional monitoring tools never see them. ... What emerges is a that sits entirely outside the boundaries of traditional AppSec, , and identity programs. As long as ownership remains fragmented and discovery elusive, security debt continues to grow unchecked. ... We’re entering an era where the most dangerous vulnerabilities aren’t in the code AppDev teams write, but in the thousands of workflows and automations business users build on their own. The sooner organizations recognize and confront the invisible no-code estate, the faster they can reduce the security debt accumulating inside their infrastructure.

Daily Tech Digest - December 08, 2025

Quote for the day:

"You don't build business, you build people, and then people build the business." -- Zig Ziglar

CIOs shift from ‘cloud-first’ to ‘cloud-smart’

The is being influenced by better on-prem technology, longer hardware cycles, ultra-high margins with , and the typical hype cycles of the industry, according to . All favor hybrid infrastructure approaches. However, “AI has added another major wrinkle with siloed data and compute,” he adds. “Many organizations aren’t interested in or able to build high-performance , and need to use the cloud. But if they’ve been conservative or cost-averse, their data may be in the on-prem component of their hybrid infrastructure.” These variables have led to complexity or unanticipated costs, either through migration or data egress charges, McElroy says. ... IT has parsed out what should be in a private cloud and what goes into a public cloud. “Training and fine-tuning large models requires strong control over customer and telemetry data,” explains. “So we increasingly favor hybrid architectures where inference and data processing happen within secure, private environments, while orchestration and non-sensitive services stay in the public cloud.” starts with data classification and workload profiling. Anything with customer-identifiable information, diagnostic traces, and model feedback loops are processed within regionally compliant private clouds, he says. ... “Many organizations are wrestling with cloud costs they know instinctively are too high, but there are few incentives to take on the risky work of repatriation when a CFO doesn’t know what savings they’re missing out on,” he says.

Harmonizing EU's Expanding Cybersecurity Regulations

Aligning , and is difficult, since each framework approaches risks differently, which creates overlapping obligations for reporting, controls and vendor oversight, leading to areas that require careful interpretation. Given these overlapping requirements, organizations should establish an integrated governance model that consolidates risk management to report workflows and third-party oversight across all relevant EU frameworks. Strengthening internal coordination - especially between legal, compliance, cybersecurity and executive teams - helps ensure consistent interpretation of obligations and reduces fragmentation in implementation. ... Developers must build safeguards into AI systems, including adversarial testing, robust access controls and monitoring for unexpected behavior. Transparent development practices and collaboration with cybersecurity teams help prevent AI models from being exploited for malicious purposes. ... A trust-based ecosystem depends on transparency, consistent governance and strong cybersecurity practices across all stakeholders. Key elements still missing include harmonized standards, comprehensive regulatory guidance, and mechanisms to verify compliance and foster confidence among users and businesses. ... Ethical frameworks guide responsible decision-making by balancing societal impact, individual right and technological innovation. Organizations can apply them through policies, AI oversight and risk assessments that incorporate principles from , , and into everyday operations and strategic planning.

Invisible IT is becoming the next workplace priority

defines as support that runs in the background and prevents problems before employees notice them. The report highlights two areas that bring this approach to life. The first is . Eighty three percent of leaders say this approach is essential, but only 21 percent have achieved it. With that monitor telemetry data across devices, support teams can detect early signs of failure and trigger automated fixes. If a fix requires human involvement, the repair can happen before the user experiences downtime. This reduces disruptions and shifts support teams away from repetitive tasks that slow down operations. The second area is hyper personalization. Many organizations personalize support by role or seniority, but the study argues this does not reflect how people work. AI systems can now create personas based on individual usage patterns. This lets support teams tailor responses and rollouts to real conditions rather than assumptions. ... Although interest in invisible IT is high, most companies are still using manual processes. Sixty five percent detect issues only when users contact support. Fifty five percent resolve them through manual interventions. Hyper personalization is also limited, with 51 percent of organizations offering standard support for all employees. Barriers are widespread. Fifty one percent cite fragmented systems as their top challenge. Another 47 percent point to cost concerns or uncertain return on investment. Limited AI capabilities and skills gaps also slow progress, along with slow upgrade cycles and a lack of time for planning.

Why AI coding agents aren’t production-ready: Brittle context windows, broken refactors, missing operational awareness

AI agents have demonstrated a critical lack of awareness regarding OS machine, command-line and environment installations. This deficiency can lead to frustrating experiences, such as the agent attempting to execute Linux commands on PowerShell, which can consistently result in ‘unrecognized command’ errors. Furthermore, agents frequently exhibit inconsistent ‘wait tolerance’ on reading command outputs, prematurely declaring an inability to read results before a command has even finished, especially on slower machines. ... Working with AI coding agents often presents a longstanding challenge of , or incorrect or incomplete pieces of information (such as small code snippets) within a larger set of changesexpected to be fixed by a developer with trivial-to-low effort. However, what becomes particularly problematic is when incorrect behavior is repeated within a single thread, forcing users to either start a new thread and re-provide all context, or intervene manually to “unblock” the agent. ... Agents may not consistently leverage the latest , instead generating more verbose and harder-to-maintain implementations. ... Despite the allure of autonomous coding, the reality of AI agents in enterprise development often demands constant human vigilance. Instances like an agent attempting to execute Linux commands on PowerShell, false-positive safety flags or introduce inaccuracies due to domain-specific reasons highlight critical gaps; developers simply cannot step away.

Offensive security takes center stage in the AI era

Now a growing percentage of see as a must-have and, as such, are building up offensive capabilities and integrating them into their security processes to ensure the information revealed during offensive exercises leads to improvements in their overall security posture. ... sees several buckets of activities involved in offensive security, starting with at the bottom end of the maturity scale, and then moving up to and , to threat hunting and , such as . “Then there’s the concept of where the organization looks at an attack scenario and what were the defenses that should have alerted but didn’t and how to rectify those,” he says. ... Many CISOs also have had team members with specific offensive security skills for many years. In fact, the Offensive Security Certified Professional (OSCP), the Offensive Security Experienced Penetration Tester (OSEP), and the Offensive Security Certified Expert (OSCE) certifications from are all credentials that have been in demand for years. ... Another factor that keeps CISOs from incorporating more offensive security into their strategies is concern about exposing vulnerabilities they don’t have the ability to address, Mellen adds. “They can’t unknow that they have those vulnerabilities if they’re not able to do something about them, although the hackers are going to find them whether or not you identify them,” he says.

Securing AI for Cyber Resilience: Building Trustworthy and Secure AI Systems

Attackers increasingly target the - poisoning training data, manipulating models, or exploiting vulnerabilities during deployment and operations. When an AI system or model is compromised, it can quietly skew decisions. This poses significant risks for autonomous systems or analytics engines. Thus, it is important that we embed security and resilience into our AI systems, ensuring robust protection from design to deployment and operations. ... Visibility is key. You can’t protect what you can’t see. Without visibility into data flows, model behavior and system interactions, threats can remain undetected until it is too late. Continuous validation and monitoring help surface anomalies and early, enabling timely interventions. Explainability is just as pivotal. Detecting an anomaly is one thing, but understanding why it happened drives true resilience. Explainability clarifies the reasoning behind AI systems and their decisions, helps verify threats, traces manipulations, makes AI systems auditable, and strengthens trust. Assurance must be continuous. ... Attackers are exploiting AI-specific security weaknesses, such as , , and adversarial manipulations. As AI adoption accelerates, its threats will follow in equal sophistication and scale. The rapid proliferation of AI systems across industries not only drives innovation but also expands the attack surface, drawing the attention from both state-sponsored and criminal actors.

From silos to strategy: What the era of cloud 'coopetition' means for CIOs

This week, historic competitors announced the launch of a cross-cloud interconnect service, effectively tearing down the digital iron curtain that once separated their ecosystems. With expected to join this framework in 2026, the cloud industry is pivoting toward "coopetition"-- a strategic truce driven by the modern enterprise's embrace of multi-cloud. ... One of the primary drivers accelerating AWS and Google's cross-cloud interconnect service is AI. The potential of enterprise AI has been hampered by data silos, with fragmented pockets of information trapped in different systems, which then prevents the training of comprehensive models. found that integration challenges are a leading cause of stalled AI initiatives, with nearly 95% of 1,050 IT leaders surveyed citing connectivity issues as a major hurdle. A cross-cloud partnership is a critical tool for dismantling these barriers -- one that could even eliminate the challenge of data silos, according to . ... However, coopetition is not a silver bullet. It also introduces new friction points where the complexity of managing multiple environments can outweigh the benefits if not addressed properly. Peterson warned that there may not be sufficient value when workloads are "highly dependent and intertwined, requiring low-latency communication across different providers".

Simplicity, speed & scalability are the key pillars of our AI strategy: Siddharth Sureka, Motilal Oswal Financial Services

AI is here to stay, and will transform all industries. Naturally, the sector tends to be on the leading edge of this journey, following closely behind pure technology companies. However, rather than viewing this purely through a technology lens, we approached it from an end-to-end organisational transformation lens. ... The first pillar is simplicity. To reach tier two, three, and four cities, we must make the financial experience intuitive. Simplicity is driven by personalisation, which means how we curate the information delivered to clients and ensure their digital journey is frictionless. The second pillar is speed. We are in the business of providing the right insights at the speed of the market. As an event occurs, we must be able to serve our clients with immediate insights. A prime example of this is our ‘’ product. As news arrives, the system measures the sentiment and analyses how it may impact the market, and then serves that insight directly to the client instantly. The third vertical is scalability. Once we have achieved simplicity and speed, our focus is to scale this architecture to reach the deeper pockets of the country. This scalability is essential for the financial inclusion journey we are embarked upon, ensuring that investors in tier three and four cities can take full advantage of the markets. ... In software engineering, you are delivering a . However, when you move into the domain of AI, the outcomes become or probabilistic in nature. As leaders, we must understand the use cases we are working on and, crucially, the ‘cost of getting it wrong’.

Observability at the Edge: A Quiet Shift in Reliability Thinking

Most organizations still don’t really know what’s happening inside their own digital systems. A survey found that 84% of companies struggle with , the basic ability to understand if their systems are working as they should. The reasons are familiar: monitoring tools are expensive, their architectures clumsy, and when scaled across thousands of locations, the complexity often overwhelms the promise. The cost of that opacity is not abstract. Every minute of downtime is lost revenue. Every unnoticed glitch is a frustrated customer. And every delay in diagnosis erodes trust. In this sense, observability is not just a matter for engineers; it’s central to how modern businesses function. ... When systems fail, the speed of diagnosis becomes critical. In fact, organizations can lose an average of $1 million per hour during unplanned downtime, a striking testament to the high cost of delays. The standard approach, engineers combing through logs, traces, and deployment histories, often slows response when time is most precious. ... What stands out is not only the design of these solutions but their uptake elsewhere. The edge observability model first proven in retail has been mirrored in other industries, including banking. The approach has been picked up by financial services firms seeking to sharpen digital performance. And the reflects a broader shift toward embedding AI into reliability practices. Industry peers have described the work as “innovative, cost-effective, and cloud-native.”

2026 DevOps Predictions - Part 1

In 2026, software teams will begin challenging the rising complexity of their own development environments, shifting from simply executing work to questioning why that work exists in the first place. After years of accumulating tools, rituals, and dependencies, developers will increasingly pause to ask whether a feature, deadline, or workflow actually warrants the effort. ... Death of agile as we used to know it: Agile methodologies have dominated software development for the past 20+ years. However, most organizations still "do agile" rather than be agile: they have adopted the agile practices and rituals that foster team collaboration and have become somewhat faster in both executing and reacting to changes. Meanwhile, AI agents have entered the stage. The speed of getting things done is multiplying and a single developer can sometimes replace a whole team. This means on one hand that the traditional human-centered agile practices become less relevant and on the other hand, that agile may become easier to scale. The death of Agile as we used to know it is a positive thing: now we become agile rather than keep doing agile. ... The momentum is shifting from "" to what's becoming known as "": instead of placing specialized responsibilities on developers, organizations are building development platforms that present opinionated paths and implement best practices by default. That change in momentum is bound to accelerate in 2026.

Daily Tech Digest - October 11, 2025

Quote for the day:

“The only real mistake is the one from which we learn nothing.” -- Henry Ford

CIOs turn to AI to assist with IT purchasing decisions

“AI promptly evaluates product documentation, reviews, and market reports, cutting the time it takes to evaluate vendors from weeks to days and unearthing compatibility problems that go unnoticed by human reviewers,” he says. Like 8×8, Thrive uses a “trust but verify” approach that treats AI output as inputs for its decision-making processes, not final answers, Whittaker says. “AI is great for comparing technical specs, but it can’t help you much with assessing non-technical aspects such as quality of support, cultural fit, etc.” Thrive plans to enhance its future AI models to predict defects in products, foresee deployment challenges, and monitor vendor performance, Whittaker says. ... “When you are negotiating a contract, let’s say you received an order form, or you received a large legal contract, and it’s all unstructured data,” he says. “AI is really good at guiding you on what kind of commercial terms you should be careful with. It can look at your existing contracts and compare them with this new one and say, ‘This one has some anomalies.’” The company’s use of AI is giving the IT team time to work on other priorities instead of spending extra time researching potential products, Johar says. “If you look at how an IT organization works, we are buying software all the time, and sometimes it leaves you very little time to focus on real evaluation and piloting the software, because you just end up spending so much time on all these RFP processes, legal processes, and research,” he says.

Deepfake Awareness High at Orgs, But Cyber Defenses Badly Lag

"The deepfake threat landscape looks, above all else, dynamic," he says. "While email threats and static imagery are still the most commonly encountered vectors, there is a wide diversity of other forms of deepfakes that are quickly growing in prevalence. In fact, we're seeing more and more of every kind of deepfake in the wild." ... Attackers are using a variety of AI techniques to enhance their attack pipeline. Human digital twins can be trained on public information about a person to help create more realistic phishing attacks, which, combined with voice samples, could create convincing audio deepfakes. Concerns over misuse of AI caused Microsoft to mostly scuttle a voice cloning technology feature that it could have integrated into various apps, such as Teams, and allow a user — or an attacker — to hijack someone's voice for all kinds of fraud attempts. ... "The challenge now is that AI can be used to reduce the skill barrier to entry and speed up production to a higher quality," she says. "Since the sophistication of deepfakes are getting harder to detect, it is imperative to turn to AI-augmented tools for detection, as people alone cannot be the last line of defense." Companies should continue to train their employees and create good policies that reduce the impact that one person — even a top executive — can have for the company, says Ironscales' Benishti. "Develop policies that make it impossible for a single employee's bad decision to result in compromise," he says.

Powering Data in the Age of AI: Part 1 – Energy as the Ultimate Bottleneck

“Demand for electricity around the world from data centres is on course to double over the next five years, as information technology becomes more pervasive in our lives,” Birol said in a statement released with the IEA’s 2024 Energy and AI report. “The impact will be especially strong in some countries — in the United States, data centres are projected to account for nearly half of the growth in electricity demand; in Japan, over half; and in Malaysia, one-fifth.” ... Unlike older mainframe workloads that spiked and dropped with changing demand, modern AI systems operate close to full capacity for days or even weeks at a time. ... It’s not a benchmark like FLOPS, but it now influences nearly every design decision. Chipmakers promote performance per watt as their most important competitive edge, because speed doesn’t matter if the grid can’t handle it. ... That dynamic is also reshaping the economics of AI. Cloud providers are starting to charge for workloads based not just on runtime but on the power they draw, forcing developers to optimize for energy throughput rather than latency. Data center architects now design around megawatt budgets instead of square footage, while governments from the U.S. to Japan are issuing new rules for energy-efficient AI systems.

How Artificial Intelligence is Shaping the Future of Secure, Compliant, and Efficient Data Practices

Understanding the journey of data—where it originates, how it transforms, and who accesses it—is critical for both governance and compliance. Generative AI excels at mapping data lineage by automatically tracing data flows across systems, applications, and processes. Consider a scenario where an organisation needs to demonstrate how customer information moves from collection to storage and reporting. AI-powered lineage tools can generate visual maps showing every touchpoint, transformation, and user interaction. This automation not only accelerates audits and compliance reporting but also provides actionable insights to improve data handling practices. ... Organisations often grapple with choosing between centralised and autonomous (decentralised) data management models. Centralised approaches offer uniformity and control, while autonomous models empower individual teams with flexibility. Generative AI supports both paradigms. In centralised settings, AI enforces global policies, ensures consistency, and manages data assets from a single point of control. In autonomous environments, AI agents can be embedded within business units, tailoring governance and security measures to local needs while maintaining alignment with overarching standards. This hybrid capability ensures organisations remain agile without compromising data integrity or compliance.

Cloud Observability Challenges At Scale (And How To Solve Them)

Concentration risk from a cloud customer can be a challenge for hyperscalers. This is especially true when key customers concentrate their load in a single region; they can saturate the shared physical resources faster than the hyperscaler’s auto-scaling can respond. ... At hyperscale, observability requires keeping vast telemetry data like logs, metrics and traces usable and cost-efficient. Storing it under one roof in an accessible, scalable and performant fashion lets organizations run AI and analytics directly from their telemetry data, spotting anomalies, problem areas and threats while future-proofing their infrastructure for data-intensive workloads. ... The complexity of managing microservices doesn’t scale linearly with the number of microservices—it scales exponentially. Mitigation requires a multipronged strategy: Limit the number of microservices; use traditional approaches where a sufficient observability strategy should be robust, yet lightweight; democratize observability-based ops, tools and skills in the organization; and exploit AI for heavy lifting and ops automation. ... One challenge is ephemeral dependency drift. At hyperscale, microservices vanish fast, breaking dependency maps and hiding failure roots. It’s like chasing ghosts in a storm. Fix it with real-time dependency snapshots and AI to predict drift patterns. Teams see the true service web, catch issues early and keep apps humming, no matter how wild the cloud gets.

AI meets EQ: Reimagining HR for the industry 5.0 workplace

The stakes couldn't be higher. The World Economic Forum surveyed over 1,000 global employers and found that nearly half of them said they’ll reduce their workforce in the next five years and replace those jobs with AI. However, paradoxically, the same technologies could create 2.73 million jobs by 2028 in India alone. It depends entirely on how well organisations manage the transition. It's not just about having the right technology; it's about having the right human strategy to deploy it. Consider the emergence of "cobots", which are collaborative robots designed to work alongside humans rather than replace them. ... Perhaps the most insidious challenge is AI bias, which can perpetuate discrimination based on race, gender, age, etc. and erode the trust that is essential for successful human-machine collaboration. When AI systems reflect historical prejudices or systemic inequalities, they undermine the very foundations of inclusive workplaces that Industry 5.0 promises to create. HR leaders must become guardians of algorithmic fairness, ensuring that AI systems used in recruitment, performance evaluation, and career development are transparent, equitable, and regularly audited. This requires building diverse AI development teams, implementing robust data governance frameworks, and maintaining human oversight in critical decision-making processes.

Exploring the Unintended Consequences of Automation in Software

The substitution myth refers to the flawed assumption that automation can simply replace human functions in a system without fundamentally altering how the system or human work operates. This misconception is built on assumptions like HABA-MABA ("Humans Are Better At / Machines Are Better At"), which assume that human and machine strengths are fixed, and system design is merely a matter of allocating tasks accordingly ... When an automated system fails, the amount of knowledge required to make things right again is likely greater than that required during normal operations. This creates immediate, new, and numerous items of work. Because the designers of automation can’t fully automate the human "parts", the human is left to cope with what’s left after the automated parts don’t behave as expected, leaving more complexity in their wake. ... In highly interdependent tasks like software operations, we can only plan our actions effectively when we can accurately anticipate the actions of others. Skilled teams achieve this predictability through shared knowledge and their own coordination mechanisms that are developed over time through extensive collaboration. Despite the common refrain of "human error" in incidents, in general, humans are quite predictable in their work, and we have established means for checking if something seems unpredictable

Observability is the weapon against complex hybrid IT chaos

To understand why observability is taking off, it is important to see the difference with traditional monitoring. Whereas traditional monitoring has been limited for years to servers, networks, and memory statistics, observability goes a step further. Monitoring mainly records what is happening, while observability shows why it is happening. It establishes connections between systems, shows how components interact with each other, and provides insight into the impact on the end user. ... The complexity of modern IT environments requires knowledge and capacity that is not available everywhere. Jean-Bastien outlines the dilemma. “If a customer had to employ someone full-time to manage everything, a capacity and knowledge problem would quickly arise. Many organizations therefore call on us to ensure continuity.” With a team of dozens of engineers, Cegeka can easily scale up, even during peak loads or holidays. In this way, they take care of the operational side of things, while customers retain the insight and reporting they need to bring their IT and business together. ... Nevertheless, there are limits to what observability can achieve. Legacy systems, such as monolithic applications in C++ or COBOL on mainframes, are difficult to instrument with modern agents. This poses a challenge in some sectors, particularly for banks that still rely heavily on older core systems.

AI Becomes a Force Multiplier for Short-Staffed Security Teams

“The skills shortage creates a paradox that limits AI’s potential in cybersecurity,” asserted Tim Freestone, chief strategy officer for Kiteworks, a provider of a secure platform for exchanging private data, in San Mateo, Calif. “Organizations lack personnel with the expertise needed to properly deploy, manage, and optimize AI-powered security tools, meaning the very solution designed to alleviate staffing pressures remains underutilized,” he told TechNewsWorld. “This gap is particularly acute because effective AI implementation requires dual competencies — both operating AI systems and defending against AI-powered attacks — skills that are in even shorter supply than traditional cybersecurity expertise. “Without trained professionals who can configure AI tools appropriately, interpret their outputs accurately, and integrate them effectively into security operations, organizations risk deploying AI systems that fail to reach their defensive potential or, worse, introduce new vulnerabilities through improper management,” he said. ... “Certifications can provide reassurance that candidates meet a certain standard and help organizations demonstrate credibility to clients and regulators,” she told TechNewsWorld, “but the reliance on credentials also has drawbacks.

The CIA triad is dead — stop using a Cold War relic to fight 21st century threats

The CIA triad is both too broad and too narrow. It lacks the vocabulary and context to handle today’s realities. In trying to retrofit authenticity, accountability, privacy, and safety into its rigid structure, we leave gaps that attackers exploit. ... Treating ransomware as a simple “availability” failure misses the point. Being “up” or “down” is irrelevant when your systems are locked and business halted. What matters is resilience: the engineered ability to absorb damage, fail gracefully, and restore from immutable backups. Availability is binary; resilience is survival. Without it, you’re unprepared. ... A fraudulent deepfake of your CEO authorizing a wire transfer may have perfect technical integrity — checksums intact, file unaltered. But its authenticity is destroyed. The CIA triad has no language to capture this breakdown, leaving organizations exposed to fraud and reputational chaos. ... A successful model must explicitly encompass the principles that the triad overlooked — such are authenticity, accountability, and resilience. Those principles must be added as foundational pillars. Furthermore, the model should have the capability to help CISOs and their teams navigate the veritable forest of frameworks, harmonize regulatory demands, and eliminate duplicate work, while also giving them a way to speak to their boards in terms of resilience, accountability, and trust, rather than just uptime and firewalls.