Daily Tech Digest by Kannan Subbiah: analytics

Showing posts with label analytics. Show all posts

Daily Tech Digest - March 16, 2026

Quote for the day:

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

🎧 Listen to this digest on YouTube Music

▶ Play Audio Digest

Duration: 23 mins • Perfect for listening on the go.

Why many enterprises struggle with outdated digital systems & how to fix them

The article on Express Computer, "Why many enterprises struggle with outdated digital systems & how to fix them," explores the pervasive issue of legacy technical debt.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 Ultimately, the piece emphasizes that overcoming outdated digital systems is a strategic necessity in a fast-paced market, requiring a balanced approach to technical remediation and organizational change to ensure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

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

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

The CTO is dead. Long live the CTO

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

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

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

What it takes to win that CSO role

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

Human-Centered AI Is Becoming A Leadership Imperative

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

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

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

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

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

Certificate lifespans are shrinking and most organizations aren’t ready

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

Agoda CTO on why AI still needs human oversight

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

Daily Tech Digest - February 11, 2026

Quote for the day:

"What you do has far greater impact than what you say." -- Stephen Covey

Predicting the future is easy — deciding what to do is the hard part

The prescriptive analysis assists in developing strategies to optimize operations, increase profitability, and reduce risks. Traditionally, linear and non-linear programming models are used for resource allocation, supply chain management, and portfolio optimization. ... In enterprise decision-making, both predictive and prescriptive analytics play an important role. Predictive analytics enables forecasting possible business outcomes, while prescriptive analytics uses these forecasts to create a strategy to maximize business profits. However, enterprises often fail to integrate these two analytics techniques in an effective way for their own benefit. ... The integration of AI agents in predictive and prescriptive analytics workflows has not been explored much by data science professionals. However, a consolidated AI agentic framework can be developed that makes integrated use of predictive and prescriptive analytics in a combined way. ... On implementing the AI agentic framework, the industries experienced better forecasts through efficient predictive analytics. On the other hand, prescriptive analytics helped businesses in making their workflows more adaptable. Despite this success, high computational costs and explainability still remain a major challenge. To overcome these setbacks, an enterprise can further invest in developing multi-modal predictive-prescriptive AI agents and neuro-symbolic agents.

Agile development might be 25 years old, but it’s withstood the test of time – and there’s still more to come in the age of AI

Key focus areas of the Agile Manifesto helped drastically simplify software development, Reynolds noted. By moving teams to smaller more regular releases, for example, this “shortened feedback loops” typically associated with Waterfall and improved flexibility throughout the development lifecycle. “That reduced risk made it easier to respond to customer and business needs, and genuinely improved software quality,” he told ITPro. “Smaller changes meant testing could happen continuously, rather than being bolted on at the end.” The longevity of Agile methodology is testament to its impact, and research shows it’s still highly popular. ... According to Kern, AI and Agile are “a match made in heaven” and the advent of the technology means this approach is no longer optional, albeit with a notable caveat. “You need it more than ever,” he said. “You can build so much more in less time, which can also magnify potential pitfalls if you’re not careful. The speed of delivery with AI can easily outpace feedback, but that’s an exciting opportunity, not a flaw.” Reynolds echoed those comments, noting that while Agile can be a force multiplier for teams, there are still risks – particularly with the influx of AI-generated code in software development. “Those gains are often offset downstream, creating more bugs, higher cloud costs, and greater security exposure. The real value comes when AI is extended beyond code creation into testing, quality assurance, and deployment,” he said.

CISOs must separate signal from noise as CVE volume soars

“While the number of vulnerabilities goes up, what really matters is which of these are going to be exploited,” Michael Roytman, co-founder and CTO of Empirical Security, tells CSO. “And that’s a different process. It does not depend on the number of vulnerabilities that are out there because sometimes an exploit is written before the CVE is even out there.” What FIRST’s forecast highlights instead is a growing signal-to-noise problem, one that strains already overburdened security teams and raises the stakes for prioritization, automation, and capacity planning rather than demanding that organizations patch more flaws exponentially. ... Despite the scale of the forecast, experts stress that vulnerability volume alone is a poor proxy for enterprise risk. “The risk to an enterprise is not directly related to the number of vulnerabilities released,” Empirical Security’s Roytman says. “It is a separate process.” ... For CISOs, the implication is that patching strategies are now more about scaling decision-making processes that were already under strain. ... The cybersecurity industry is not facing an explosion of exploitable weaknesses so much as an explosion of information. For CISOs, success in 2026 will depend less on reacting faster and more on deciding better — using automation and context to ensure that rising vulnerability counts do not translate into rising risk. “It hasn’t been a human-scale problem for some time now,” Roytman says.

Strengthening a modern retail cybersecurity strategy

Enterprises might declare robust cybersecurity strategies yet fail to adequately address the threats posed by complex supply chains and aggressive digital transformation efforts. To bridge this gap, at Groupe Rocher, we have chosen to integrate cybersecurity into the core business strategy, ensuring that security measures are not only reactive but also predictive, leveraging threat intelligence to anticipate and mitigate risks effectively. ... It’s also important to remember that vulnerabilities aren’t always about technology. Often, they come from poor practices, like using weak passwords, having too much access, or not using multi-factor authentication (MFA). Criminals might use phishing or social engineering attacks to steal access from their victims. ... Additionally, fostering open communication and collaboration with vendors can help identify potential vulnerabilities early. We regularly organize workshops and joint security drills that can enhance mutual understanding and preparedness. By building strong partnerships and emphasizing shared security goals, brands can create a resilient network that not only protects their interests but also strengthens the entire ecosystem against evolving threats. ... As both regulators and consumers become less accepting of business models that prioritize data above all else, retail and beauty brands need to change how they protect data, focusing more on privacy and transparency.

OT Attacks Get Scary With 'Living-off-the-Plant' Techniques

For a number of reasons, ransomware against IT is affecting OT," Derbyshire explains. "This can occur due to, for example, convergences within the IT environment, that the OT simply cannot function without relying upon. Or a complete lack of trust in security controls or network architecture from the IT or OT security teams, so they voluntarily shut down the OT systems or sever the connection to kind of prevent the spread [of an IT attack]. Colonial Pipeline style. ... With a holistic understanding of how OT works, and knowledge of how a given OT site works, suddenly new threat vectors come into focus, which can blend with operational systems as elegantly as LotL attacks do Windows or Linux systems. For instance, Derbyshire plans to demonstrate at RSAC how an attacker can weaponize S7comm, Siemens' proprietary protocol for communication between programmable logic controllers (PLCs). He'll show how, by manipulating frequently overlooked configuration fields in S7comm, an attacker could potentially leak sensitive data and transmit attacks across devices. He calls it "an absolute brain melter." ... there are plenty of resources attackers can turn to to understand OT products better, be they textbooks, chatbots, or even just buying a PLC on a secondhand marketplace. "It still takes a bit of investment or a bit of time going out of your way to find these obscure things. But it's never been impossible and it's only getting easier," Derbyshire says.

The missing layer between agent connectivity and true collaboration

Today's AI challenge is about agent coordination, context, and collaboration. How do you enable them to truly think together, with all the contextual understanding, negotiation, and shared purpose that entails? It's a critical next step toward a new kind of distributed intelligence that keeps humans firmly in the loop. ... While protocols like MCP and A2A have solved basic connectivity, and AGNTCY tackles the problems of discovery, identity management to inter-agent communication and observability, they've only addressed the equivalent of making a phone call between two people who don't speak the same language. But Pandey's team has identified something deeper than technical plumbing: the need for agents to achieve collective intelligence, not just coordinated actions. ... "We have to mimic human evolution,” Pandey explained. “In addition to agents getting smarter and smarter, just like individual humans, we need to build infrastructure that enables collective innovation, which implies sharing intent, coordination, and then sharing knowledge or context and evolving that context.” ... Guardrails remain a central challenge in deploying multi-functional agents that touch every part of an organization's system. The question is how to enforce boundaries without stifling innovation. Organizations need strict, rule-like guardrails, but humans don't actually work that way. Instead, people operate on a principle of minimal harm, or thinking ahead about consequences and making contextual judgments.

Cyber firms face ‘verification crisis’ on real risk

Continuous Threat Exposure Management, commonly referred to as CTEM, has become more widely adopted as a way to structure security work around an organisation's exposure to attack. Even so, only 33% of organisations measure whether exploitable risk is actually reduced over time, according to the report. Instead, most programmes continue to track metrics focused on discovery and volume, such as coverage gaps, asset counts and alert volume. These measures can show rising activity and expanding scope, but they do not necessarily show whether the organisation has reduced the likelihood of a successful attack. "Security programs keep adding tools and expanding scope, but outcomes aren't improving," said Rogier Fischer, CEO and co-founder of Hadrian. ... According to the report, these vulnerabilities were not unknown. They were identified and recorded, but competed for attention as security teams dealt with new alerts, new tickets and the ongoing output of multiple tools. In organisations with complex technology estates, this can create a persistent backlog in which older issues remain unresolved while new potential risks continue to surface. "Security teams can move fast, but too many tools and unverified alerts make it difficult to maintain focus on what actually matters," Fischer said. The report calls for earlier validation of exploitability and success measures that focus on reducing real exposure rather than the number of findings generated.

Trust and Compliance in the Age of AI: Navigating the Risks of Intelligent Software Development

One of the most pressing challenges is trust in AI-generated outputs: Many teams report minimal productivity gains despite operational deployment, citing issues such as hallucinated code, misleading suggestions, and a lack of explainability. This trust gap is amplified by the opaque nature of many AI systems; developers often report struggling to understand how models arrive at decisions, making it difficult for them to validate outputs or debug errors. This lack of transparency, known as black box AI, puts teams at risk of accepting flawed code or test cases, potentially introducing vulnerabilities or performance regressions. ... AI's reliance on data introduces significant compliance risks, especially when proprietary documentation or sensitive datasets are used to train models. Continuing to conduct business the old-fashioned way is not the answer because traditional compliance frameworks often lag behind AI innovation, and governance models built for deterministic systems struggle with probabilistic outputs and autonomous decision-making. ... Another risk with potentially serious consequences: AI-generated code often lacks context. It may not align with architectural patterns, business rules, or compliance requirements, and without rigorous review, these changes can degrade system integrity and increase technical debt. It also must be noted that faster code generation does not equal better code. There is a risk of "bloated" or unsecure code being generated, requiring rigorous validation.

The Cost of AI Slop in Lines of Code

Before we can get to the problem of excessive lines of code, we need to understand how LLMs arrived at the generation of code with unnecessary lines. The answer is in the training dataset and how that dataset was sourced from publicly accessible places, including open repositories on Github and coding websites. These sources lack any form of quality control, and therefore the code the LLMs learned on is of varying quality. ... In the quest to get as much training data as possible, there was little effort available to vet the training data to ensure that it was good training data. The result LLMs outputting the kind of code written by a first-year developer – and that should be concerning to us. ... Some of the common vulnerabilities that we’ve known about for decades, including cross-site scripting, SQL injection, and log injection, are the kinds of vulnerabilities that AI introduces into the code – and it generates this code at rates that are multiples of what even junior developers produce. In a time when it’s important that we be more cautious about security, AI can’t do it. ... Today, we have AI generating bloated code that creates maintenance problems, and we’re looking the other way. It can’t structure code to minimize code duplication. It doesn’t care that there are two, three, four, or more implementations of basic operations that could be made into one generic function. The code it was trained on didn’t generate the abstractions to create the right functions, so it can’t get there.

Why Jurisdiction Choice Is the Newest AI Security Filter

AI moves exponentially faster than legislation and regulations ever could. By the time that sector regulators or governing bodies have drafted frameworks, held consultations, and passed laws through their incumbent democratic processes, the technology has already evolved and scaled far ahead. Not to be too hyperbolic, but the rules could prove irrelevant for a widely-adopted technology and solution that's far outpaced them. This creates what's been dubbed the "speed of instinct" challenge. In essence, how can you possibly regulate something that reinvents itself regularly? ... Rather than attempting to codify every possible and conceivable AI scenario into law, Gibraltar developed a principles-based framework, emphasizing clarity, proportionality, and innovation. Essentially, the framework recognizes that AI regulations must be adaptive and not binary. ... While frameworks exist at both ends of the spectrum—with some enforcing strict rules and others encouraging innovation with AI technology—neither solution is inherently superior. The EU model provides more certainty and protection for humans, but the agile model has merit with responsive governance and the encouragement of rapid innovation. For cybersecurity teams deploying AI, the smart strategy is understanding both standpoints and choosing jurisdictions strategically and with informed processes. Scale and implications matter profoundly; a customer chatbot may have fewer jurisdictional considerations than an internal threat intelligence platform.

Daily Tech Digest - November 21, 2025

Quote for the day:

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

DPDP Rules and the Future of Child Data Safety

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

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

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

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

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

Innovative Approaches To Addressing The Cybersecurity Skills Gap

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

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

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

Deepfake crisis gets dire prompting new investment, calls for regulation

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

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

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

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

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

The State and Current Viability of Real-Time Analytics

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

You Down with EDD? Making Sense of LLMs Through Evaluations

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

Daily Tech Digest - August 08, 2025

Quote for the day:

“Every adversity, every failure, every heartache carries with it the seed of an equal or greater benefit.” -- Napoleon Hill

Major Enterprise AI Assistants Can Be Abused for Data Theft, Manipulation

In the case of Copilot Studio agents that engage with the internet — over 3,000 instances have been found — the researchers showed how an agent could be hijacked to exfiltrate information that is available to it. Copilot Studio is used by some organizations for customer service, and Zenity showed how it can be abused to obtain a company’s entire CRM. When Cursor is integrated with Jira MCP, an attacker can create malicious Jira tickets that instruct the AI agent to harvest credentials and send them to the attacker. This is dangerous in the case of email systems that automatically open Jira tickets — hundreds of such instances have been found by Zenity. In a demonstration targeting Salesforce’s Einstein, the attacker can target instances with case-to-case automations — again hundreds of instances have been found. The threat actor can create malicious cases on the targeted Salesforce instance that hijack Einstein when they are processed by it. The researchers showed how an attacker could update the email addresses for all cases, effectively rerouting customer communication through a server they control. In a Gemini attack demo, the experts showed how prompt injection can be leveraged to get the gen-AI tool to display incorrect information.

Who’s Leading Whom? The Evolving Relationship Between Business and Data Teams

As the data boom matured, organizations realized that clear business questions weren’t enough. If we wanted analytics to drive value, we had to build stronger technical teams, including data scientists and machine learning engineers. And we realized something else: we had spent years telling business leaders they needed a working knowledge of data science. Now we had to tell data scientists they needed a working knowledge of the business. This shift in emphasis was necessary, but it didn’t go perfectly. We had told the data teams to make their work useful, usable, and used, and they took that mandate seriously. But in the absence of clear guidance and shared norms, they filled in the gap in ways that didn’t always move the business forward. ... The foundation of any effective business-data partnership is a shared understanding of what actually counts as evidence. Without it, teams risk offering solutions that don’t stand up to scrutiny, don’t translate into action, or don’t move the business forward. A shared burden of proof makes sure that everyone is working from the same assumptions about what’s convincing and credible. This shared commitment is the foundation that allows the organization to decide with clarity and confidence.

A new worst coder has entered the chat: vibe coding without code knowledge

A clear disconnect then stood out to me between the vibe coding of this app and the actual practiced work of coding. Because this app existed solely as an experiment for myself, the fact that it didn’t work so well and the code wasn’t great didn’t really matter. But vibe coding isn’t being touted as “a great use of AI if you’re just mucking about and don’t really care.” It’s supposed to be a tool for developer productivity, a bridge for nontechnical people into development, and someday a replacement for junior developers. That was the promise. And, sure, if I wanted to, I could probably take the feedback from my software engineer pals and plug it into Bolt. One of my friends recommended adding “descriptive class names” to help with the readability, and it took almost no time for Bolt to update the code. ... The mess of my code would be a problem in any of those situations. Even though I made something that worked, did it really? Had this been a real work project, a developer would have had to come in after the fact to clean up everything I had made, lest future developers be lost in the mayhem of my creation. This is called the “productivity tax,” the biggest frustration that developers have with AI tools, because they spit out code that is almost—but not quite—right.

From WAF to WAAP: The Evolution of Application Protection in the API Era

The most dangerous attacks often use perfectly valid API calls arranged in unexpected sequences or volumes. API attacks don't break the rules. Instead, they abuse legitimate functionality by understanding the business logic better than the developers who built it. Advanced attacks differ from traditional web threats. For example, an SQL injection attempt looks syntactically different from legitimate input, making it detectable through pattern matching. However, an API attack might consist of perfectly valid requests that individually pass all schema validation tests, with the malicious intent emerging only from their sequence, timing, or cross-endpoint correlation patterns. ... The strategic value of WAAP goes well beyond just keeping attackers out. It's becoming a key enabler for faster, more confident API development cycles. Think about how your API security works today — you build an endpoint, then security teams manually review it, continuous penetration testing (link is external) breaks it, you fix it, and around and around you go. This approach inevitably creates friction between velocity and security. Through continuous visibility and protection, WAAP allows development teams to focus on building features rather than manually hardening each API endpoint. Hence, you can shift the traditional security bottleneck into a security enablement model.

Scrutinizing LLM Reasoning Models

Assessing CoT quality is an important step towards improving reasoning model outcomes. Other efforts attempt to grasp the core cause of reasoning hallucination. One theory suggests the problem starts with how reasoning models are trained. Among other training techniques, LLMs go through multiple rounds of reinforcement learning (RL), a form of machine learning that teaches the difference between desirable and undesirable behavior through a point-based reward system. During the RL process, LLMs learn to accumulate as many positive points as possible, with “good” behavior yielding positive points and “bad” behavior yielding negative points. While RL is used on non-reasoning LLMs, a large amount of it seems to be necessary to incentivize LLMs to produce CoT, which means that reasoning models generally receive more of it. ... If optimizing for CoT length leads to confused reasoning or inaccurate answers, it might be better to incentivize models to produce shorter CoT. This is the intuition that inspired researchers at Wand AI to see what would happen if they used RL to encourage conciseness and directness rather than verbosity. Across multiple experiments conducted in early 2025, Wand AI’s team discovered a “natural correlation” between CoT brevity and answer accuracy, challenging the widely held notion that the additional time and compute required to create long CoT leads to better reasoning outcomes.

4 regions you didn't know already had age verification laws – and how they're enforced

Australia’s 2021 Online Safety Act was less focused on restricting access to adult content than it was on tackling issues of cyberbullying and online abuse of children, especially on social media platforms. The act introduced a legal framework to allow people to request the removal of hateful and abusive content, ... Chinese law has required online service providers to implement a real-name registration system for over a decade. In 2012, the Decision on Strengthening Network Information Protection was passed, before being codified into law in 2016 as the Cybersecurity Law. The legislation requires online service providers to collect users’ real names, ID numbers, and other personal information. ... As with the other laws we’ve looked at, COPPA has its fair share of critics and opponents, and has been criticized as being both ineffective and unconstitutional by experts. Critics claim that it encourages users to lie about their age to access content, and allows websites to sidestep the need for parental consent. ... In 2025, the European Commission took the first steps towards creating an EU-wide strategy for age verification on websites when it released a prototype app for a potential age verification solution called a mini wallet, which is designed to be interoperable with the EU Digital Identity Wallet scheme.

The AI-enabled company of the future will need a whole new org chart

Let’s say you’ve designed a multi-agent team of AI products. Now you need to integrate them into your company by aligning them with your processes, values and policies. Of course, businesses onboard people all the time – but not usually 50 different roles at once. Clearly, the sheer scale of agentic AI presents its own challenges. Businesses will need to rely on a really tight onboarding process. The role of the agent onboarding lead creates the AI equivalent of an employee handbook: spelling out what agents are responsible for, how they escalate decisions, and where they must defer to humans. They’ll define trust thresholds, safe deployment criteria, and sandbox environments for gradual rollout. ... Organisational change rarely fails on capability – it fails on culture. The AI Culture & Collaboration Officer protects the human heartbeat of the company through a time of radical transition. As agents take on more responsibilities, human employees risk losing a sense of purpose, visibility, or control. The culture officer will continually check how everyone feels about the transition. This role ensures collaboration rituals evolve, morale stays intact, and trust is continually monitored — not just in the agents, but in the organisation’s direction of travel. It’s a future-facing HR function with teeth.

The Myth of Legacy Programming Languages: Age Doesn't Define Value

Instead of trying to define legacy languages based on one or two subjective criteria, a better approach is to consider the wide range of factors that may make a language count as legacy or not. ... Languages may be considered legacy when no one is still actively developing them — meaning the language standards cease receiving updates, often along with complementary resources like libraries and compilers. This seems reasonable because when a language ceases to be actively maintained, it may stop working with modern hardware platforms. ... Distinguishing between legacy and modern languages based on their popularity may also seem reasonable. After all, if few coders are still using a language, doesn't that make it legacy? Maybe, but there are a couple of complications to consider. One is that measuring the popularity of programming languages in a highly accurate way is impossible — so just because one authority deems a language to be unpopular doesn't necessarily mean developers hate it. The other challenge is that when a language becomes unpopular, it tends to mean that developers no longer prefer it for writing new applications. ... Programming languages sometimes end up in the "legacy" bin when they are associated with other forms of legacy technology — or when they lack associations with more "modern" technologies.

From Data Overload to Actionable Insights: Scaling Viewership Analytics with Semantic Intelligence

Semantic intelligence allows users to find reliable and accurate answers, irrespective of the terminology used in a query. They can interact freely with data and discover new insights by navigating massive databases, which previously required specialized IT involvement, in turn, reducing the workload of already overburdened IT teams. At its core, semantic intelligence lays the foundation for true self-serve analytics, allowing departments across an organization to confidently access information from a single source of truth. ... A semantic layer in this architecture lets you query data in a way that feels natural and enables you to get relevant and precise results. It bridges the gap between complex data structures and user-friendly access. This allows users to ask questions without any need to understand the underlying data intricacies. Standardized definitions and context across the sources streamlines analytics and accelerates insights using any BI tool of choice. ... One of the core functions of semantic intelligence is to standardize definitions and provide a single source of truth. This improves overall data governance with role-based access controls and robust security at all levels. In addition, row- and column-level security at both user and group levels can ensure that access to specific rows is restricted for specific users.

Why VAPT is now essential for small & medium business security

One misconception, often held by smaller companies, is that they are less likely to be targeted. Industry experts disagree. "You might think, 'Well, we're a small company. Who'd want to hack us?' But here's the hard truth: Cybercriminals love easy targets, and small to medium businesses often have the weakest defences," states a representative from Borderless CS. VAPT combines two different strategies to identify vulnerabilities and potential entry points before malicious actors do. A Vulnerability Assessment scans servers, software, and applications for known problems in a manner similar to a security walkthrough of a physical building. Penetration Testing (often shortened to pen testing) simulates real attacks, enabling businesses to understand how a determined attacker might breach their systems. ... Borderless CS maintains that VAPT is applicable across sectors. "Retail businesses store customer data and payment info. Healthcare providers hold sensitive patient information. Service companies often rely on cloud tools and email systems that are vulnerable. Even a small eCommerce store can be a jackpot for the wrong person. Cyber attackers don't discriminate. In fact, they often prefer smaller businesses because they assume you haven't taken strong security measures. Let's not give them that satisfaction."

Daily Tech Digest - July 31, 2025

Quote for the day:

"Listening to the inner voice & trusting the inner voice is one of the most important lessons of leadership." -- Warren Bennis

AppGen: A Software Development Revolution That Won't Happen

There's no denying that AI dramatically changes the way coders work. Generative AI tools can substantially speed up the process of writing code. Agentic AI can help automate aspects of the SDLC, like integrating and deploying code. ... Even when AI generates and manages code, an understanding of concepts like the differences between programming languages or how to mitigate software security risks is likely to spell the difference between the ability to create apps that actually work well and those that are disasters from a performance, security, and maintainability standpoint. ... NoOps — short for "no IT operations" — theoretically heralded a world in which IT automation solutions were becoming so advanced that there would soon no longer be a need for traditional IT operations at all. Incidentally, NoOps, like AppGen, was first promoted by a Forrester analyst. He predicted that, "using cloud infrastructure-as-a-service and platform-as-a-service to get the resources they need when they need them," developers would be able to automate infrastructure provisioning and management so completely that traditional IT operations would disappear. That never happened, of course. Automation technology has certainly streamlined IT operations and infrastructure management in many ways. But it has hardly rendered IT operations teams unnecessary.

Middle managers aren’t OK — and Gen Z isn’t the problem: CPO Vikrant Kaushal

One of the most common pain points? Mismatched expectations. “Gen Z wants transparency—they want to know the 'why' behind decisions,” Kaushal explains. That means decisions around promotions, performance feedback, or even task allocation need to come with context. At the same time, Gen Z thrives on real-time feedback. What might seem like an eager question to them can feel like pushback to a manager conditioned by hierarchies. Add in Gen Z’s openness about mental health and wellbeing, and many managers find themselves ill-equipped for conversations they’ve never been trained to have. ... There is a growing cultural narrative that managers must be mentors, coaches, culture carriers, and counsellors—all while delivering on business targets. Kaushal doesn’t buy it. “We’re burning people out by expecting them to be everything to everyone,” he says. Instead, he proposes a model of shared leadership, where different aspects of people development are distributed across roles. “Your direct manager might help you with your day-to-day work, while a mentor supports your career development. HR might handle cultural integration,” Kaushal explains. ... When asked whether companies should focus on redesigning manager roles or reshaping Gen Z onboarding, Kaushal is clear: “Redesign manager roles.”

New AI model offers faster, greener way for vulnerability detection

Unlike LLMs, which can require billions of parameters and heavy computational power, White-Basilisk is compact, with just 200 million parameters. Yet it outperforms models more than 30 times its size on multiple public benchmarks for vulnerability detection. This challenges the idea that bigger models are always better, at least for specialized security tasks. White-Basilisk’s design focuses on long-range code analysis. Real-world vulnerabilities often span multiple files or functions. Many existing models struggle with this because they are limited by how much context they can process at once. In contrast, White-Basilisk can analyze sequences up to 128,000 tokens long. That is enough to assess entire codebases in a single pass. ... White-Basilisk is also energy-efficient. Because of its small size and streamlined design, it can be trained and run using far less energy than larger models. The research team estimates that training produced just 85.5 kilograms of CO₂. That is roughly the same as driving a gas-powered car a few hundred miles. Some large models emit several tons of CO₂ during training. This efficiency also applies at runtime. White-Basilisk can analyze full-length codebases on a single high-end GPU without needing distributed infrastructure. That could make it more practical for small security teams, researchers, and companies without large cloud budgets.

Building Adaptive Data Centers: Breaking Free from IT Obsolescence

The core advantage of adaptive modular infrastructure lies in its ability to deliver unprecedented speed-to-market. By manufacturing repeatable, standardized modules at dedicated fabrication facilities, construction teams can bypass many of the delays associated with traditional onsite assembly. Modules are produced concurrently with the construction of the base building. Once the base reaches a sufficient stage of completion, these prefabricated modules are quickly integrated to create a fully operational, rack-ready data center environment. This “plug-and-play” model eliminates many of the uncertainties in traditional construction, significantly reducing project timelines and enabling customers to rapidly scale their computing resources. Flexibility is another defining characteristic of adaptive modular infrastructure. The modular design approach is inherently versatile, allowing for design customization or standardization across multiple buildings or campuses. It also offers a scalable and adaptable foundation for any deployment scenario – from scaling existing cloud environments and integrating GPU/AI generation and reasoning systems to implementing geographically diverse and business-adjacent agentic AI – ensuring customers achieve maximum return on their capital investment.

‘Subliminal learning’: Anthropic uncovers how AI fine-tuning secretly teaches bad habits

Distillation is a common technique in AI application development. It involves training a smaller “student” model to mimic the outputs of a larger, more capable “teacher” model. This process is often used to create specialized models that are smaller, cheaper and faster for specific applications. However, the Anthropic study reveals a surprising property of this process. The researchers found that teacher models can transmit behavioral traits to the students, even when the generated data is completely unrelated to those traits. ... Subliminal learning occurred when the student model acquired the teacher’s trait, despite the training data being semantically unrelated to it. The effect was consistent across different traits, including benign animal preferences and dangerous misalignment. It also held true for various data types, including numbers, code and CoT reasoning, which are more realistic data formats for enterprise applications. Remarkably, the trait transmission persisted even with rigorous filtering designed to remove any trace of it from the training data. In one experiment, they prompted a model that “loves owls” to generate a dataset consisting only of number sequences. When a new student model was trained on this numerical data, it also developed a preference for owls.

How to Build Your Analytics Stack to Enable Executive Data Storytelling

Data scientists and analysts often focus on building the most advanced models. However, they often overlook the importance of positioning their work to enable executive decisions. As a result, executives frequently find it challenging to gain useful insights from the overwhelming volume of data and metrics. Despite the technical depth of modern analytics, decision paralysis persists, and insights often fall short of translating into tangible actions. At its core, this challenge reflects an insight-to-impact disconnect in today’s business analytics environment. Many teams mistakenly assume that model complexity and output sophistication will inherently lead to business impact. ... Many models are built to optimize a singular objective, such as maximizing revenue or minimizing cost, while overlooking constraints that are difficult to quantify but critical to decision-making. ... Executive confidence in analytics is heavily influenced by the ability to understand, or at least contextualize, model outputs. Where possible, break down models into clear, explainable steps that trace the journey from input data to recommendation. In cases where black-box AI models are used, such as random forests or neural networks, support recommendations with backup hypotheses, sensitivity analyses, or secondary datasets to triangulate your findings and reinforce credibility.

GDPR’s 7th anniversary: in the AI age, privacy legislation is still relevant

In the years since GDPR’s implementation, the shift from reactive compliance to proactive data governance has been noticeable. Data protection has evolved from a legal formality into a strategic imperative — a topic discussed not just in legal departments but in boardrooms. High-profile fines against tech giants have reinforced the idea that data privacy isn’t optional, and compliance isn’t just a checkbox. That progress should be acknowledged — and even celebrated — but we also need to be honest about where gaps remain. Too often GDPR is still treated as a one-off exercise or a hurdle to clear, rather than a continuous, embedded business process. This short-sighted view not only exposes organisations to compliance risks but causes them to miss the real opportunity: regulation as an enabler. ... As organisations embed AI deeper into their operations, it’s time to ask the tough questions around what kind of data we’re feeding into AI, who has access to AI outputs, and if there’s a breach – what processes we have in place to respond quickly and meet GDPR’s reporting timelines. Despite the urgency, there’s still a glaring gap of organisations that don’t have a formal AI policy in place, which exposes organisations to privacy and compliance risks that could have serious consequences. Especially when data loss prevention is a top priority for businesses.

CISOs, Boards, CIOs: Not dancing Tango. But Boxing.

CISOs overestimate alignment on core responsibilities like budgeting and strategic cybersecurity goals, while boards demand clearer ties to business outcomes. Another area of tension is around compliance and risk. Boards tend to view regulatory compliance as a critical metric for CISO performance, whereas most security leaders view it as low impact compared to security posture and risk mitigation. ... security is increasingly viewed as a driver of digital trust, operational resilience, and shareholder value. Boards are expecting CISOs to play a key role in revenue protection and risk-informed innovation, especially in sectors like financial services, where cyber risk directly impacts customer confidence and market reputation. In India’s fast-growing digital economy, this shift empowers security leaders to influence not just infrastructure decisions, but the strategic direction of how businesses build, scale, and protect their digital assets. Direct CEO engagement is making cybersecurity more central to business strategy, investment, and growth. ... When it comes to these complex cybersecurity subjects, the alignment between CXOs and CISOs is uneven and still maturing. Our findings show that while 53 per cent of CISOs believe AI gives attackers an advantage (down from 70 per cent in 2023), boards are yet to fully grasp the urgency.

Order Out of Chaos – Using Chaos Theory Encryption to Protect OT and IoT

It turns out, however, that chaos is not ultimately and entirely unpredictable because of a property known as synchronization. Synchronization in chaos is complex, but ultimately it means that despite their inherent unpredictability two outcomes can become coordinated under certain conditions. In effect, chaos outcomes are unpredictable but bounded by the rules of synchronization. Chaos synchronization has conceptual overlaps with Carl Jung’s work, Synchronicity: An Acausal Connecting Principle. Jung applied this principle to ‘coincidences’, suggesting some force transcends chance under certain conditions. In chaos theory, synchronization aligns outcomes under certain conditions. ... There are three important effects: data goes in and random chaotic noise comes out; the feed is direct RTL; there is no separate encryption key required. The unpredictable (and therefore effectively, if not quite scientifically) unbreakable chaotic noise is transmitted over the public network to its destination. All of this is done at the hardware – so, without physical access to the device, there is no opportunity for adversarial interference. Decryption involves a destination receiver running the encrypted message through the same parameters and initial conditions, and using the chaos synchronization property to extract the original message.

5 ways to ensure your team gets the credit it deserves, according to business leaders

Chris Kronenthal, president and CTO at FreedomPay, said giving credit to the right people means business leaders must create an environment where they can judge employee contributions qualitatively and quantitatively. "We'll have high performers and people who aren't doing so well," he said. "It's important to force your managers to review everyone objectively. And if they can't, you're doing the entire team a disservice because people won't understand what constitutes success." ... "Anyone shying away from measurement is not set up for success," he said. "A good performer should want to be measured because they're comfortable with how hard they're working." He said quantitative measures can be used to prompt qualitative debates about whether, for example, underperformers need more training. ... Stephen Mason, advanced digital technologies manager for global industrial operations at Jaguar Land Rover, said he relies on his talented IT professionals to support the business strategy he puts in place. "I understand the vision that the technology can help deliver," he said. "So there isn't any focus on 'I' or 'me.' Every session is focused on getting the team together and giving the right people the platform to talk effectively." Mason told ZDNET that successful managers lean on experts and allow them to excel.