Daily Tech Digest by Kannan Subbiah: accoutability

Showing posts with label accoutability. Show all posts

Daily Tech Digest - March 15, 2026

Quote for the day:

"A leader must inspire or his team will expire." -- Orrin Woodward

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

The Last Frontier: Navigating the Dawn of the Brain-Computer Interface Era

In the article "The Last Frontier: Navigating the Dawn of the Brain-Computer Interface Era," Kannan Subbiah explores the transformative rise of Brain-Computer Interfaces (BCIs) as they move from science fiction to strategic reality. BCIs function by bypassing traditional neural pathways to establish a direct communication link between the brain's electrical signals and external hardware. By 2026, the technology has transitioned from clinical trials—aimed at restoring mobility and sensory perception for the paralyzed—into the enterprise sector, where it is used to monitor cognitive load and optimize worker productivity. However, this deep integration between biological and digital intelligence introduces profound risks, including physical inflammation from invasive implants, cybersecurity threats like "brain-jacking," and ethical concerns regarding the erosion of personal agency. To address these vulnerabilities, a global movement for "neurorights" has emerged, led by frameworks from UNESCO and pioneer legislation in nations like Chile to protect mental privacy and integrity. Subbiah argues that while the potential for human augmentation is immense, society must establish rigorous ethical standards to ensure thoughts are treated as expressions of human dignity rather than mere harvestable data. Ultimately, navigating this frontier requires balancing rapid innovation with a "hybrid mind" philosophy that prioritizes psychological continuity and user autonomy.

Is your AI agent a security risk? NanoClaw wants to put it in a virtual cage

In the article "Is your AI agent a security risk? NanoClaw wants to put it in a virtual cage" on ZDNet, Charlie Osborne discusses the newly announced partnership between NanoClaw and Docker, designed to tackle the escalating security concerns surrounding autonomous AI agents. NanoClaw emerged as a lightweight, security-first alternative to OpenClaw, boasting a tiny codebase of fewer than 4,000 lines compared to its predecessor's massive 400,000. This simplicity allows for easier auditing and reduced risk. The integration enables NanoClaw agents to run within Docker Sandboxes, which utilize MicroVM-based, disposable isolation zones. Unlike traditional containers that share a kernel with the host, these MicroVMs provide a "hard boundary," ensuring that even if an agent misbehaves or is compromised, it remains contained and cannot access or damage the host system. This "secure-by-design" approach addresses critical enterprise obstacles, such as the potential for agents to accidentally delete files or leak sensitive credentials. By providing a controlled environment where agents can independently install tools and execute workflows without constant human oversight, the collaboration unlocks greater productivity while maintaining rigorous enterprise-grade safeguards. Ultimately, the partnership shifts the security paradigm from trusting an agent's behavior to enforcing OS-level isolation, making it safer for organizations to deploy powerful AI agents in production.

Banks Turn to Unified Data Platforms to Manage Risk Intelligence

In the article "Banks Turn to Unified Data Platforms to Manage Risk Intelligence," Sandhya Michu explores how financial institutions are addressing the complexities of digital banking by consolidating fragmented data environments into strategic unified platforms. The rapid growth of digital transactions has scattered operational and customer data across mobile apps and backend systems, creating a "brittle" infrastructure that often hinders the scalability of AI and analytics initiatives. To overcome this, leading banks are building centralized data lakes and unified digital layers to aggregate structured and unstructured information. These centralized environments empower business, compliance, and risk departments with shared datasets, significantly improving regulatory reporting and customer analytics. Additionally, unified platforms enhance operational observability by enabling faster incident analysis through log correlation across diverse systems. Beyond reliability, these data frameworks are revolutionizing credit risk management by providing real-time underwriting capabilities and early warning systems that ingest external market data. By digitizing legacy archives and investing in real-time data stores, banks are creating a robust foundation for advanced generative AI applications and continuous analytics. Ultimately, this shift toward a unified data architecture is essential for maintaining transparency, regulatory oversight, and enterprise-wide decision-making in an increasingly volatile and data-intensive financial landscape.

Why nobody cares about laptop touchscreens anymore

In the article "Why nobody cares about laptop touchscreens anymore," author Chris Hoffman argues that the once-coveted feature has become a neglected afterthought for both hardware manufacturers and Microsoft. While touchscreens remain prevalent on Windows 11 devices, they are rarely showcased in marketing because the industry has shifted focus toward performance, battery life, and AI integration. Hoffman posits that the initial appeal of touchscreens was largely a workaround for the poor-quality trackpads found on older Windows 10 machines. With the advent of highly responsive, "precision" touchpads across modern laptops, the functional necessity of reaching for the screen has vanished. Furthermore, Windows 11 lacks a truly optimized touch interface, and the ecosystem of touch-first applications has stagnated since the Windows 8 era. Even on 2-in-1 convertible devices, the "tablet mode" is described as an imperfect compromise with awkward ergonomics and watered-down software gestures. Unless a user specifically requires pen input for digital art or note-taking, Hoffman suggests that a touchscreen is now a "check-box" feature that adds little real-world value. Ultimately, the piece advises consumers to prioritize other specifications, as the current Windows environment remains firmly a mouse-and-keyboard-first experience, leaving the touchscreen as a redundant relic of past design ambitions.

How AI is changing your mind

In the Computerworld article "How AI is changing your mind," Mike Elgan warns that the widespread adoption of artificial intelligence is fundamentally altering human cognition and social interaction. Drawing on recent research from institutions like Cornell and USC, Elgan identifies two primary dangers: behavioral manipulation and the homogenization of thought. Studies show that biased AI autocomplete tools can successfully shift user opinions on controversial topics—even when individuals are warned of the bias—because the interactive nature of co-writing makes the influence feel internal. Simultaneously, the reliance on a few dominant Large Language Models (LLMs) is erasing linguistic and cultural diversity, nudging global expression toward a bland, Western-centric "hive mind" through a feedback loop of generic training data. These chatbots act as "co-reasoners," fostering sycophancy and simulated validation that can distort reality, particularly for isolated individuals. To combat this cognitive erosion, Elgan suggests practical strategies: disabling autocomplete, writing without AI to preserve individuality, and treating chatbots as intellectual sparring partners rather than authority figures. Ultimately, the piece argues that while AI offers immense utility, users must consciously protect their mental autonomy from being subtly rewritten by algorithms that prioritize consensus and efficiency over authentic human perspective and diversity of thought.

The value of reducing middle office emissions for ESG

In the Information Age article "The value of reducing middle-office emissions for ESG," Danielle Price explores how the modernization of middle-office functions—such as reconciliation, trade matching, and risk management—can significantly advance corporate sustainability. Historically, these processes have been energy-intensive, running continuously on legacy on-premise servers at peak capacity. As ESG performance increasingly influences a bank’s cost of capital, CIOs must view the middle office as a strategic asset for decarbonization. Migrating these data-heavy workloads to public, cloud-native infrastructure can reduce operational emissions by 60% to 80% without requiring fundamental changes to business processes. This transition is becoming essential as Pillar 3 disclosures demand more granular ESG reporting and evidence of measurable year-on-year reductions. Financially, high ESG scores are linked to lower credit spreads and reduced regulatory capital charges, making infrastructure efficiency a direct factor in a firm’s financial health. Furthermore, the shift to cloud-native platforms creates a powerful network effect; when shared systems lower their carbon footprint, the entire counter-party ecosystem benefits. Ultimately, the article argues that aligning operational efficiency with ESG objectives is no longer optional, but a strategic imperative that combines environmental stewardship with enhanced financial competitiveness in today's global capital markets.

New European Emissions Regs Include Cybersecurity Rules

The article from Data Breach Today details the integration of new cybersecurity requirements into the European Union's "Euro 7" emissions regulations, marking a significant shift in automotive compliance. Prompted by the "Dieselgate" scandal, these rules mandate that gas-powered vehicles feature on-board systems to monitor emissions data, which must be protected from tampering, spoofing, and unauthorized over-the-air updates. While the regulations primarily target malicious external hackers, they also aim to prevent corporate fraud. However, a major point of contention has emerged: the potential conflict with the "right-to-repair" movement. The same secure gateway technologies used to prevent unauthorized modifications to engine control units could effectively lock out independent mechanics, who require access to diagnostic data for legitimate repairs. Automotive experts warn that while most passenger vehicle manufacturers are prepared, the commercial sector lags behind, and the industry faces an immense architectural challenge in balancing security with equitable data access. Furthermore, as cars become increasingly connected, broader risks—including remote takeovers and sensitive data leaks—remain a concern for EU public safety, suggesting that current type-approval regimes may need to evolve to address nation-state threats and organized cybercrime.

Why Data Governance Fails in Many Organizations: The Accountability Crisis and Capability Gaps

In the article "Why Data Governance Fails in Many Organizations," Stanyslas Matayo explores the critical factors behind the high failure rate of data governance initiatives, specifically highlighting the "accountability crisis" and "capability gaps." Despite significant investments, many organizations engage in "governance theater," where committees exist on paper but lack the executive authority, seniority, and enforcement mechanisms to drive change. This accountability gap is exacerbated when governance roles report to mid-level IT rather than leadership, rendering them expendable scribes rather than strategic governors. Simultaneously, a "capability deficit" arises when initiatives are treated as purely technical projects. Teams often overlook essential non-technical skills like change management, ethics, and learning design, assuming technical expertise alone is sufficient for organizational transformation. To combat these failures, the author references the DMBOK framework, advocating for four pillars: formal role clarification (e.g., Data Owners and Stewards), governed metadata, explicit quality mechanisms, and aligned communication flows. Ultimately, success requires moving beyond technical delivery to establish a business-led discipline where data is managed as a strategic asset through senior-level sponsorship and a holistic integration of diverse organizational capabilities, ensuring that governance structures possess the actual power to resolve conflicts and enforce standards.

AI coding agents keep repeating decade-old security mistakes

The Help Net Security article "AI coding agents keep repeating decade-old security mistakes" details a 2026 study by DryRun Security that evaluated the security performance of Claude Code, OpenAI Codex, and Google Gemini. Researchers discovered that despite their rapid software generation capabilities, these AI agents introduced vulnerabilities in 87% of the pull requests they created. The study identified ten recurring vulnerability categories across all three agents, with broken access control, unauthenticated sensitive endpoints, and business logic failures being the most prevalent. For example, agents frequently failed to implement server-side validation for critical actions or neglected to wire authentication middleware into WebSocket handlers. While OpenAI Codex generally produced the fewest vulnerabilities, all agents struggled with secure JWT secret management and rate limiting. The report emphasizes that traditional regex-based static analysis tools often miss these complex logic and authorization flaws, as they cannot reason about data flows or trust boundaries effectively. Consequently, the study recommends that development teams scan every pull request, incorporate security reviews into the initial planning phase, and utilize contextual security analysis tools. Ultimately, while AI agents significantly accelerate development, their lack of inherent security-centric reasoning necessitates rigorous human oversight and advanced scanning to prevent the recurrence of foundational security errors.

Impact of Artificial Intelligence (AI) in Enterprise Architecture (EA) Discipline

The article "Impact of Artificial Intelligence (AI) in Enterprise Architecture (EA) Discipline" examines how AI is fundamentally reshaping the traditional responsibilities of enterprise architects. By integrating advanced AI tools into the EA framework, organizations can automate labor-intensive tasks such as data mapping and technical documentation, allowing architects to focus on higher-value strategic initiatives that drive business value. AI-driven analytics provide architects with deeper, real-time insights into complex system dependencies, enabling more accurate predictive modeling and significantly faster decision-making across the enterprise. This technological shift encourages a transition away from static, reactive architectures toward dynamic, proactive ecosystems that can autonomously adapt to rapid market changes and emerging digital threats. However, the author emphasizes that this transition is not without its hurdles; it necessitates a robust foundation in data governance, careful ethical considerations regarding AI bias, and a long-term commitment to upskilling the existing workforce. Ultimately, the fusion of AI and EA facilitates much better alignment between high-level business goals and underlying IT infrastructure, driving continuous innovation and operational efficiency. As the discipline evolves, the most successful enterprise architects will be those who leverage AI as a sophisticated collaborative partner to manage organizational complexity and provide strategic foresight in an increasingly competitive digital landscape.

Daily Tech Digest - February 17, 2026

Quote for the day:

"If you want to become the best leader you can be, you need to pay the price of self-discipline." -- John C. Maxwell

6 reasons why autonomous enterprises are still more a vision than reality

"AI is the first technology that allows systems that can reason and learn to be integrated into real business processes," Vohra said. ... Autonomous organizations, he continued, "are built on human-AI agent collaboration, where AI handles speed and scale, leaving judgment and strategy up to humans." They are defined by "AI systems that go beyond just generating insights in silos, which is how most enterprises are currently leveraging AI," he added. Now, the momentum is toward "executing decisions across workflows with humans setting intent and guardrails." ... The survey highlighted that work is required to help develop agents. Only 3% of organizations -- and 10% of leaders -- are actively implementing . "This limited adoption signals that orchestration is still an emerging discipline," the report stated. "The scarcity of orchestration is a litmus test for both internal capability and external strategic positioning. Successful orchestration requires integrating AI into workflows, systems, and decision loops with precision and accountability." ... Workforce capability gaps continue to be the most frequently cited organizational constraint to AI adoption, as reported by six in 10 executives -- yet only 45% say their organizations offer AI training for all employees. ... As AI takes on more execution and pattern recognition, human value increasingly shifts toward system design, integration, governance, and judgment -- areas where trust, context, and accountability still sit firmly with people.

Finding the key to the AI agent control plane

Agents change the physics of risk. As I’ve noted, an agent doesn’t just recommend code. It can run the migration, open the ticket, change the permission, send the email, or approve the refund. As such, risk shifts from legal liability to existential reality. If a hallucinates, you get a bad paragraph. ... Every time an AI system makes a mistake that a human has to clean up, the real cost of that system goes up. The only way to lower that tax is to stop treating governance as a policy problem and start treating it as architecture. That means least privilege for agents, not just humans. It means separating “draft” from “send.” It means making “read-only” a first-class capability, not an afterthought. It means auditable action logs and reversible workflows. It means designing your agent system as if it will be attacked because it will be. ... Right now, permissions are a mess of vendor-specific toggles. One platform has its own way of scoping actions. Another bolts on an approval workflow. A third punts the problem to your identity and access management team. That fragmentation will slow adoption, not accelerate it. Enterprises can’t scale agents until they can express simple rules. We need to be able to say that an agent can read production data but not write to it. We need to say an agent can draft emails but not send them. We need to say an agent can provision infrastructure only inside a sandbox, with quotas, or that it must request human approval before any destructive action.

PAM in Multi‑Cloud Infrastructure: Strategies for Effective Implementation

The "Identity Gap" has emerged as the leading cause of cloud security breaches. Traditional vault-based Privileged Access Management (PAM) solutions, designed for static server environments, are inadequate for today’s dynamic, API-driven cloud infrastructure. ... PAM has evolved from an optional security measure to an essential and fundamental requirement in multi-cloud environments. This shift is attributed to the increased complexity, decentralized structure, and rapid changes characteristic of modern cloud architectures. As organizations distribute workloads across AWS, Azure, Google Cloud, and on-premises systems, traditional security perimeters have become obsolete, positioning identity and privileged access as central elements of contemporary security strategies. ... Fragmented identity systems hinder multi‑cloud PAM. Centralizing identity and federating access resolves this, with a Unified Identity and Access Foundation managing all digital identities—human or machine—across the organization. This approach removes silos between on-premises, cloud, and legacy applications, providing a single control point for authentication, authorization, and lifecycle management. ... Cloud providers deliver robust IAM tools, but their features vary. A strong PAM approach aligns these tools using . RBAC assigns permissions by job role for easy scaling, while ABAC uses user and environment attributes for tight security.

Giving AI ‘hands’ in your SaaS stack

If an attacker manages to use an — hiding malicious instructions in a calendar invite or a web page the agent reads — that agent essentially becomes a confused deputy. It has the keys to the kingdom. It can delete opportunities, export customer lists or modify pricing configurations. ... For AI agents, this means we must treat them as non-human identities (NHIs) with the same or greater scrutiny than we apply to employees. ... The industry is coalescing around the model context protocol (MCP) as a standard for this layer. It provides a universal USB-C port for connecting AI models to your data sources. By using an MCP server as your gateway, you ensure the agent never sees the credentials or the full API surface area, only the tools you explicitly allow. ... We need to treat AI actions with the same reverence. My rule for autonomous agents is simple: If it can’t dry run, it doesn’t ship. Every state-changing tool exposed to an agent must support a dry_run=true mode. When the agent wants to update a record, it first calls the tool in dry-run mode. The system returns a diff — a preview of exactly what will change . This allows us to implement a human-in-the-loop approval gate for high-risk actions. The agent proposes the change, the human confirms it and only then is the live transaction executed. ... As CIOs and IT leaders, our job isn’t to say “no” to AI. It’s to build the invisible rails that allow the business to say “yes” safely. By focusing on gateways, identity and transactional safety, we can give AI the hands it needs to do real work, without losing our grip on the wheel.

AI-fuelled supply chain cyber attacks surge in Asia-Pacific

Exposed credentials, source code, API keys and internal communications can provide detailed insight into business processes, supplier relationships and technology stacks. When combined with brokered access, that information can support impersonation, targeted intrusion and fraud activity that blends in with legitimate use. One area of concern is open-source software distribution, where widely used libraries can spread malicious code at scale. ... The report points to AI-assisted phishing campaigns that target OAuth flows and other single sign-on mechanisms. These techniques can bypass multi-factor authentication where users approve malicious prompts or where tokens are stolen after login. ... "AI did not create supply chain attacks, it has made them cheaper, faster, and harder to detect," Mr Volkov added. "Unchecked trust in software and services is now a strategic liability." The report names a range of actors associated with supply-chain-focused activity, including , Scattered Spider, HAFNIUM, DragonForce and 888, as well as campaigns linked to Shai-Hulud. It said these groups illustrate how criminal organisations and state-aligned operators are targeting similar platforms and integration layers. ... The report's focus on upstream compromise reflects a broader trend in cyber risk management, where organisations assess not only their own exposure but also the resilience of vendors and technology supply chains.

Automation cannot come at the cost of accountability; trust has to be embedded into the architecture

is actively working with issuers, merchants, and payment aggregators to roll out authentication mechanisms based on global standards. “Consumers want payments to be invisible,” Chhabra adds. “They want to enjoy the shopping experience, not struggle through the payment process.” plays a critical role in enabling this vision. By replacing sensitive card details with unique digital tokens, Visa has created a secure foundation for tap-and-pay, in-app purchases, and cross-border transactions. In India alone, nearly half a billion cards have already been tokenised. “Once is in place, device-based payments and seamless commerce become possible,” Chhabra explains. “It’s the bedrock of frictionless payments.” Fraud prevention, however, is no longer limited to card-based transactions. With real-time and account-to-account payments gaining momentum, Visa has expanded its scope through strategic acquisitions such as Featurespace. The UK-based firm specialises in behavioural analytics for real-time fraud detection, an area Chhabra describes as increasingly critical. “We don’t just want to detect fraud on the Visa network. We want to help prevent fraud across payment types and networks,” he says. Before deploying such capabilities in India, Visa conducts extensive back-testing using localised data and works closely with regulators. “Global intelligence is powerful, but it has to be adapted to local behaviour. You can’t simply overfit global models to India’s unique payment patterns.”

Most ransomware playbooks don't address machine credentials. Attackers know it.

The gap between ransomware threats and the defenses meant to stop them is getting worse, not better. found that the preparedness gap widened by an average of 10 points year over year across every threat category the firm tracks. ... The accompanying Ransomware Playbook Toolkit walks teams through four phases: containment, analysis, remediation, and recovery. The credential reset step instructs teams to ensure all affected user and device accounts are reset. Service accounts are absent. So are API keys, tokens, and certificates. The most widely used playbook framework in enterprise security stops at human and device credentials. The organizations following it inherit that blind spot without realizing it. ... “Although defenders are optimistic about the promise of AI in cybersecurity, Ivanti’s findings also show companies are falling further behind in terms of how well prepared they are to defend against a variety of threats,” said Daniel Spicer, Ivanti’s Chief Security Officer. “This is what I call the ‘Cybersecurity Readiness Deficit,’ a persistent, year-over-year widening imbalance in an organization’s ability to defend their data, people, and networks against the evolving threat landscape.” ... You can’t reset credentials that you don’t know exist. Service accounts, API keys, and tokens need ownership assignments mapped pre-incident. Discovering them mid-breach costs days.

CISO Julie Chatman offers insights for you to take control of your security leadership role

In a few high-profile cases, security leaders have faced criminal charges for how they handled breach disclosures, and civil enforcement for how they reported risks to investors and regulators. The trend is toward holding CISOs personally accountable for governance and disclosure decisions. ... You’re seeing the rise of , heads of IT security instead of full CISO titles. It’s a lot harder to hold a fractional CISO personally liable. This is relatively new. The liability conversation really intensified after some high-profile enforcement actions, and now we’re seeing the market respond. ... First, negotiate protection upfront. When you’re thinking about accepting a CISO role, explicitly ask about D&O insurance coverage. If the CISO is not considered a director or an officer of the company and can’t be given D&O coverage, will the company subsidize individual coverage? There are companies now selling CISO-specific policies. Make this part of your compensation negotiation. Second, do your job well but understand the paradox. Sometimes when you do your job properly, you’re labeled ‘the office of no,’ you’re seen as ‘difficult,’ and you last 18 months. It’s a catch-22. Real liability protection is changing how your organization thinks about risk ownership. Most organizations don’t have a unified view of risk or the vocabulary to discuss it properly. If you can advance that as a CISO, you can help the business understand that risk is theirs to accept, not yours.

The AI bubble will burst for firms that can’t get beyond demos and LLMs

Even though the discussion of a potential bubble is ubiquitous, what’s going on is more nuanced than simple boom-and-bust chatter, said Francisco Martin-Rayo, CEO of Helios AI. “What people are really debating is the gap between valuation and real-world impact. Many companies are labeled ‘AI-driven,’ but only a subset are delivering measurable value at scale,” Martin-Rayo said. Founders confuse fundraising with progress, which comes only when they are solving real problems for real clients, said Nacho De Marco, founder of BairesDev. “Fundraising gives you dopamine, but real progress comes from customers,” De Marco said. “The real value of a $1B valuation is customer validation.” ... The AI shakeout has already started, and the tenor at WEF “feels less like peak hype and more like the beginning of a sorting process,” Martin-Rayo said. ... Companies that survive the coming shakeout will be those willing to rebuild operations from the ground up rather than throwing AI into existing workflows, said Jinsook Han, chief agentic AI officer at Genpact. ”It’s not about just bolting some AI into your existing operation,” Han said. “You have to really build from ground up — it’s a complete operating model change.” Foundational models are becoming more mature and can do more of what startups sell. As a result, AI providers that don’t offer distinct value will have a tough time surviving, Han said.

What could make the EU Digital Identity Wallets fail?

Large-scale digital identity initiatives rarely fail because the technology does not work. They fail because adoption, incentives, trust, and accountability are underestimated. The EU Digital Identity Wallet could still fail, or partially fail, succeeding in some countries while struggling or stagnating in others. ... A realistic risk is fragmented success. Some member states are likely to deliver robust wallets on time. Others may launch late, with limited functionality, or without meaningful uptake. A smaller group may fail to deliver a convincing solution at all, at least in the first phase. From the perspective of users and service providers, this fragmentation already undermines cross border usage. If wallets differ significantly in capabilities, attributes, and reliability across borders, the promise of a seamless European digital identity weakens. ... While EU Digital Identity Wallets offer significantly higher security than current solutions, they will not eliminate fraud entirely. There will still be cases of wallets issued to the wrong individual, phishing attempts, and wallet takeovers. If early fraud cases are poorly handled or publicly misunderstood, trust in the ecosystem could erode quickly. The wallet’s strong privacy architecture introduces real trade-offs. One uncomfortable but necessary question worth asking is: are we going too far with privacy? ... The EU Digital Identity Wallet will succeed only if policymakers, wallet providers, and service providers treat trust, economics, and usability as core design principles, not secondary concerns.

Daily Tech Digest - January 11, 2026

Quote for the day:

"Courage doesn't mean you don't get afraid. Courage means you don't let fear stop you." -- Bethany Hamilton

From Coder to Catalyst: What They Don’t Teach About Technical Leadership

The best technical leaders don’t just solve harder problems – they multiply their impact by solving different kinds of problems. What follows is the three-tier evolution most engineers never see coming, and the skills you’ll need that no computer science program ever taught you. ... You’ll have moments of doubt. When you’re starting out, if a junior engineer falls behind, your instinct is to jump in and solve the problem yourself. You might feel like a hero, but this is bad leadership. You’re not holding the junior engineer accountable, and worse, you’re breaking trust—signaling that you don’t believe they can handle the challenge. ... When projects drift off track, you’re cutting scope, reallocating people, and making key decisions at crossroads. But there’s something more critical: risk management. You need to think one step ahead of the projects, identify key risks before they materialize, and mitigate them proactively. ... Additionally, there’s one more thing nobody mentions: managing stakeholders. Not just your team, but peers across the organization and leaders above you. Technical leadership isn’t just downward – it’s omnidirectional. ... The learning curve never ends. You never stop feeling like you’re figuring it out as you go, and that’s the point. Technical leadership is continuous adaptation. The best leaders stay humble enough to admit they’re still learning. The real measure of success isn’t in your commit history. You’re succeeding when your team can execute without you. When people you hired are better than you at things you used to do.

In an AI-perfect world, it’s time to prove you’re human

Being yourself in all communication is not only about authenticity, but individuality. By communicating in a way that only you can communicate, you increase your appeal and value in a world of generic, faceless, zero-personality AI content. For marketing communications, this goes double. The public will increasingly assume what they see is AI-generated, and therefore cheap garbage. ... Not only will the public reject what they assume to be AI, the social algorithms will increasingly reward and boost content offering the signals of authenticity. In fact, Mosseri said that within Meta there is a push to prioritize “original content” over “templated“ or “generic“ AI content that is easy to churn out at a massive scale. ... Rather than thinking of AI as a tool that replaces work and workers, we should think of it as a “scaffolding for human potential,” a way to magnify our cognitive capabilities, not replace them. In other words, instead of viewing AI as something that writes and creates pictures so we don’t have to or writes code so we don’t have to — meaning we don’t even have to learn how to code — we need to use AI to become great at writing, creating images and coding. From now on, everyone will assume everyone else has and uses AI. Content and communications will always exist on a spectrum from fully AI-generated to zero-AI human communication. The further toward the human any bit of content gets, the more valuable it will feel to both the receivers of the content and to the gatekeepers.

How to Build a Robust Data Architecture for Scalable Business Growth

As early in the process as possible, you should begin engaging with stakeholders like IT teams, business and data analysts, executives, administrators, and any other group within your organization that regularly interacts with data. Get to know their data practices and goals, which will provide insight into the requirements for your new data architecture, ensuring you have a deep well of information to draw from. ... After communicating with stakeholders and researching your organization’s current data landscape, you can determine exactly what your data architecture will need now and into the future. Some requirements you will need to precisely define the volume of data your architecture will handle, how fast data needs to move through your organization, and how secure the data needs to be. All this data about your data will guide you toward better decisions in designing and building your data architecture. ... The exact construction of your data architecture will depend largely upon the needs you outlined during the previous step, but some solutions are more advantageous for businesses looking to expand. ... While there is plenty of healthy debate regarding the merits of horizontal scaling versus vertical scaling, the truth is that the best database architectures use both. Horizontal scaling, or using multiple servers to distribute data and processes, allows an organization to have many nodes within a system so the system can dedicate resources to specific data tasks.

The Quiet Shift Changing UX

Right now, three big transformations collide. Designers are moving away from static screens, leaning into building full flows and shaping behaviours. Conversational AI redefines the user experiences from the ground up. Plus, with Gen-AI tools and mature design systems, designers shift from pixel movers to curators of experiences. All these transformations quietly reshape UX at its core. ... Back in the day, UX ‌design focused mainly on interfaces. Think pages and layouts, breakpoints, all the components, yeah, that defined the work. We’d talk about flows, sure, but really, we just built out sequences of screens. But now, that way of doing things is changing. Products are now changing and adapting depending on what’s happening around them, what the user has done before and what’s happening right now. One thing you do can lead to completely different results depending on how the user uses the system or what they know about it. Screens are becoming temporary; what really matters is what’s happening underneath and how the system changes. ... Designers now focus on curating, refining and shaping the final results, which is a strategic and decisive role. This shift does come with some risks. Sometimes, we settle for ‘good enough’ design, which can mask more serious issues. The design might look good on the surface, but it could be acting strangely beneath the surface.

What does the drought at Stack Overflow teach us?

“AI developer tools seem to be taking attention away from static question-and-answer solutions, replacing Stack Overflow with generated code without the middleman… and without waiting for a question to be answered,” said Walls. “Interestingly, AI tools lack the reputational metadata that Stack Overflow relied on: i.e. when was this solution posted and who posted it… and do they have a lot of prior answers? Developers are conferring trust to LLMs that human-sourced sites had to build over years and fight to retain. It’s much easier for developers to ask an agent for some code to accomplish a task and click accept, regardless of the provenance of that code.” ... “Today we know that LLMs like ChatGPT are already pretty good at answering common questions, which are the bulk of the questions asked at StackOverflow. Additionally, LLMs can respond in real time, so it is not a surprise that people were shifting away from StackOverflow. It might be not the only reason though – some people also reported StackOverflow moderators being rather hostile and unwelcoming towards new users, which had additional impact,” said Zaitsev. “Why would you deal with what you see as bad treatment, if an alternative exists?” ... “With AI now available directly in IDEs, engineers naturally turn to quick, contextual support as they work,” said Jackson.

Ready or Not, AI is Rewriting the Rules for Software Testing

Etan Lightstone, a product design leader at Domino Data Lab, argues that building trust in agents requires applying familiar operational principles. He suggests that for an enterprise with mature MLOps capabilities, trusting an agent is not enormously different from trusting a human user, because the same pillars of governance are in place: Robust logging of every action, complete auditability to trace what happened and the critical ability to roll back any action if something goes wrong. This product-centric mindset also extends to how we design and test the MCP tools before they ever reach production. Lightstone proposes a novel approach he calls “usability testing for AI.” Just as a product team would run usability tests with human beings to uncover design flaws before a release, he advises that MCP servers should be tested with sample AI agents. This is an effective way to discover issues in how a tool’s functions are documented and described — which is critical, since this documentation effectively becomes part of the prompt that the AI agent uses. Furthermore, he suggests we need to build “support links” for AI agents acting on our behalf. When a user gets stuck, they can often click a link to get help or submit feedback. Lightstone argues that AI agents need similar recovery mechanisms. This could be an MCP-exposed feedback tool that an agent can call if it cannot recover from an error or a dedicated function to get help from a documentation search.

Defending at Scale: The Importance of People in Data Center Security

In the tech world, the mantra of “move fast and break things” has become a badge of innovation. For cases like social platforms or mobile apps, where “breaking things” translates to inconveniences rather than catastrophes, it can work quite well. But when it comes to building critical infrastructure that supports essential functions and drives the future of society, companies must take the time to ensure they build safely and sustainably. Establishing robust physical security is already challenging, and implementing strong policies and processes to support those controls is even more difficult. Often, the core risk lies in the human layer that determines whether controls are applied consistently. ... With the promise of AI-powered efficiency gains, there’s increased pressure to move faster. When organizations take shortcuts in the name of speed, however, those shortcuts often come at the cost of consistent and thorough security. This could include gaps in training for guards, technicians, and vendors, unclear policies for after-hours access, frequent contractor changes, poorly defined emergency protocols, or procedures that only exist on paper. ... As businesses rush to meet the demand for AI, the data center boom is expected to continue rising. In all this rush, it's easy to overlook that moving fast without first establishing and reliably executing proper processes increases risk. Building too quickly without a strong security culture can lead to expensive problems down the line.

Industrial cyber governance hits inflection point, shifts toward measurable resilience and executive accountability

For industrial operators, the harder task is converting cyber exposure into defensible investment decisions. Quantified risk approaches, promoted by the World Economic Forum, are gaining traction by linking potential downtime, safety impact, and financial loss to capital planning and insurance strategy. ... “Governance should shift to a unified IT/OT risk council where safety engineers and CISOs share a common language of operational impact,” Paul Shaver, global practice leader at Mandiant’s Industrial Control Systems/Operational Technology Security Consulting practice, told Industrial Cyber. “Organizations should integrate OT-specific safety metrics into the standard IT risk framework to ensure cybersecurity decisions are made with production uptime in mind. This evolution requires aligning IT’s data confidentiality goals with OT’s requirement for high availability and human safety. ... Organizations need to move from siloed governance to a risk-first model that prioritizes the most critical threats, whether cyber or operational, and updates policies dynamically based on risk assessments, Jacob Marzloff, president and co-founder at Armexa, told Industrial Cyber. “A shared risk matrix across teams enables consistent trade-offs for safety and cybersecurity. Oversight should be centralized through a cross-functional Risk Committee rather than a single leader, ensuring expertise from IT, engineering, and operations. This committee creates a feedback loop between real-world risks and governance, building resilience.”

A Reality Check on Global AI Adoption

"AI is diffusing at extraordinary speed, but not evenly," the report said. Advanced digital economies are integrating AI into everyday work far faster than emerging markets. The findings underscore a shift in the AI race from model development to real-world deployment in which diffusion, not innovation alone, determines who benefits most. Microsoft CEO Satya Nadella in a recent blog said, "The next phase of the AI will be defined by execution at scale rather than discovery. The industry is moving from model breakthroughs to the harder work of building systems that deliver real-world value." ... Microsoft defines AI diffusion as the proportion of working-age individuals who have used generative AI tools within a defined period. This usage-based measurement shifts attention from venture funding, compute ownership or research output to real-world interaction including how AI is entering daily workflows, from coding and analysis to communication and content creation. ... Infrastructure gaps persist, language limitations reduce the effectiveness of many generative AI systems, and skills shortages constrain adoption when education and workforce training have not kept pace. Institutional capacity also plays a role, influencing trust, governance and public-sector deployment. At the same time, the diffusion metric captures breadth, not depth. A one-time interaction with a chatbot is measured the same as embedding AI into mission-critical enterprise systems.

The Hidden Resilience Gap: Why Most Organizations Are One Vendor Failure Away from Crisis

The most striking finding: when vendors lack business continuity or IT recovery plans, 43% of organizations simply ask them to create one and resubmit later. Another 32% do nothing at all. Only 13% provide structured questionnaires to actually help vendors develop meaningful plans. This means 75% of enterprises are essentially hoping their vendors figure it out on their own. ... Here’s another uncomfortable truth: 43% of organizations don’t have any system for combining operational and cyber risk indicators into a unified vendor resilience score. Another 22% track separate indicators but never connect the dots. That means nearly two-thirds of organizations can’t answer a simple question: “Which of our vendors pose the highest operational risk right now?” ... But compliance alone won’t fix this. Organizations need vendor resilience programs that actually reduce operational risk, not just check regulatory boxes. That requires moving beyond point-in-time assessments toward continuous intelligence. It means combining cyber indicators, financial health signals, operational metrics, and recovery evidence into coherent risk profiles. It demands bringing business owners, procurement teams, and risk functions into the same system with the same data. ... whatever you prioritize, make it measurable, make it continuous, and make it integrated. Fragmented data creates fragmented decisions. Point-in-time assessments create point-in-time confidence. Manual processes create manual failure modes. The organizations that crack this will have competitive advantage.

Daily Tech Digest - December 21, 2025

Quote for the day:

"Don't worry about being successful but work toward being significant and the success will naturally follow." -- Oprah Winfrey

Is it Possible to Fight AI and Win?

What’s the most important thing security teams need to figure out? Organizations must stop talking about AI like it’s a death star of sorts. AI is not a single, all-powerful, monolithic entity. It’s a stack of threats, behaviors, and operational surfaces and each one has its own kill chain, controls, and business consequences. We need to break AI down into its parts and conduct a real campaign to defend ourselves. ... If AI is going to be operationalized inside your business, it should be treated like a business function. Not a feature or experiment, but a real operating capability. When you look at it that way, the approach becomes clearer because businesses already know how to do this. There is always an equivalent of HR, finance, engineering, marketing, and operations. AI has the same needs. ... Quick fixes aren’t enough in the AI era. The bad actors are innovating at machine speed, so humans must respond at machine speed with appropriate human direction and ethical clarity. AI is a tool. And the side that uses it better will win. If that isn’t enough, AI will force another reality that organizations need to prepare for. Security and compliance will become an on-demand model. Customers will not wait for annual reports or scheduled reviews. They will click into a dashboard and see your posture in real time. Your controls, your gaps, and your response discipline will be visible when it matters, not when it is convenient.

Cybersecurity Budgets are Going Up, Pointing to a Boom

Nearly all of the security leaders (99%) in the 2025 KPMG Cybersecurity Survey plan on upping their cybersecurity budgets in the two-to-three years to come, in preparation for what may be the upcoming boom in cybersecurity. More than half (54%) say budget increases will fall between 6%-10%. “The data doesn’t just point to steady growth; it signals a potential boom. We’re seeing a major market pivot where cybersecurity is now a fundamental driver of business strategy,” Michael Isensee, Cybersecurity & Tech Risk Leader, KPMG LLP, said in a release. “Leaders are moving beyond reactive defense and are actively investing to build a security posture that can withstand future shocks, especially from AI and other emerging technologies. This isn’t just about spending more; it’s about strategic investment in resilience.” ... The security leaders recognize AI is amassing steam as a dual catalyst—38% are challenged by AI-powered attacks in the coming three years, with 70% of organizations currently committing 10% of their budgets to combating such attacks. But they also say AI is their best weapon to proactively identify and stop threats when it comes to fraud prevention (57%), predictive analytics (56%) and enhanced detection (53%). But they need the talent to pull it off. And as the boom takes off, 53% just don’t have enough qualified candidates. As a result, 49% are increasing compensation and the same number are bolstering internal training, while 25% are increasingly turning to third parties like MSSPs to fill the skills gap.

How Neuro-Symbolic AI Breaks the Limits of LLMs

While AI transforms subjective work like content creation and data summarization, executives rightfully hesitate to use it when facing objective, high-stakes determinations that have clear right and wrong answers, such as contract interpretation, regulatory compliance, or logical workflow validation. But what if AI could demonstrate its reasoning and provide mathematical proof of its conclusions? That’s where neuro-symbolic AI offers a way forward. The “neuro” refers to neural networks, the technology behind today’s LLMs, which learn patterns from massive datasets. A practical example could be a compliance system, where a neural model trained on thousands of past cases might infer that a certain policy doesn’t apply in a scenario. On the other hand, symbolic AI represents knowledge through rules, constraints, and structure, and it applies logic to make deductions. ... Neuro-symbolic AI introduces a structural advance in LLM training by embedding automated reasoning directly into the training loop. This uses formal logic and mathematical proof to mechanically verify whether a statement, program, or output used in the training data is correct. A tool such as Lean,4 is precise, deterministic, and gives provable assurance. The key advantage of automated reasoning is that it verifies each step of the reasoning process, and not just the final answer.

Three things they’re not telling you about mobile app security

With the realities of “wilderness survival” in mind, effective mobile app security must be designed for specific environmental exposures. You may need to wear some kind of jacket at your office job (web app), but you’ll need a very different kind of purpose-built jacket as well as other clothing layers, tools, and safety checks to climb Mount Everest (mobile app). Similarly, mobile app development teams need to rigorously test their code for potential security issues and also incorporate multi-layered protections designed for some harsh realities. ... A proactive and comprehensive approach is one that applies mobile application security at each stage of the software development lifecycle (SDLC). It includes the aforementioned testing in the stages of planning, design, and development as well as those multi-layered protections to ensure application integrity post-release. ... Whether stemming from overconfidence or just kicking the can down the road, inadequate mobile app security presents an existential risk. A recent survey of developers and security professionals found that organizations experienced an average of nine mobile app security incidents over the previous year. The total calculated cost of each incident isn’t just about downtime and raw dollars, but also “little things” like user experience, customer retention, and your reputation.

Cybersecurity in 2026: Fewer dashboards, sharper decisions, real accountability

The way organisations perceive risk is one of the most important changes predicted in 2026. Security teams spent years concentrating on inventory, which included tracking vulnerabilities, chasing scores and counting assets. The model is beginning to disintegrate. Attack-path modelling, on the other hand, is becoming far more useful and practical. These models are evolving from static diagrams to real-world settings where teams may simulate real attacks. Consider it a cyberwar simulation where defenders may test “what if” scenarios in real time, comprehend how a threat might propagate via systems and determine whether vulnerabilities truly cause harm to organisations. This evolution is accompanied by a growing disenchantment with abstract frameworks that failed to provide concrete outcomes. The emphasis is shifting to risk-prioritized operations, where teams start tackling the few problems that actually provide attackers access instead than responding to clutter. Success in 2026 will be determined more by impact than by activities. ... Many companies continue to handle security issues behind closed doors as PR disasters. However, an alternative strategy is gaining momentum. Communicate as soon as something goes wrong. Update frequently, share your knowledge and acknowledge your shortcomings. Post signs of compromise. Allow partners and clients to defend themselves. Particularly in the middle of disorder, this seems dangerous.

AI and Latency: Why Milliseconds Decide Winners and Losers in the Data Center Race

Many traditional workloads can tolerate latency. Batch processing doesn’t care if it takes an extra second to move data. AI training, especially at hyperscale, can also be forgiving. You can load up terabytes of data in a data center in Idaho and process it for days without caring if it’s a few milliseconds slower. Inference is a different beast. Inference is where AI turns trained models into real-time answers. It’s what happens when ChatGPT finishes your sentence, your banking AI flags a fraudulent transaction, or a predictive maintenance system decides whether to shut down a turbine. ... If you think latency is just a technical metric, you’re missing the bigger picture. In AI-powered industries, shaving milliseconds off inference times directly impacts conversion rates, customer retention, and operational safety. A stock trading platform with 10 ms faster AI-driven trade execution has a measurable financial advantage. A translation service that responds instantly feels more natural and wins user loyalty. A factory that catches a machine fault 200 ms earlier can prevent costly downtime. Latency isn’t a checkbox, it’s a competitive differentiator. And customers are willing to pay for it. That’s why AWS and others have “latency-optimized” SKUs. That’s why every major hyperscaler is pushing inference nodes closer to urban centers.

Why developers need to sharpen their focus on documentation

“One of the bigger benefits of architectural documentation is how it functions as an onboarding resource for developers,” Kalinowski told ITPro. “It’s much easier for new joiners to grasp the system’s architecture and design principles, which means the burden’s not entirely on senior team members’ shoulders to do the training," he added. “It also acts as a repository of institutional knowledge that preserves decision rationale, which might otherwise get lost when team members move to other projects or leave the company." ... “Every day, developers lose time because of inefficiencies in their organization – they get bogged down in repetitive tasks and waste time navigating between different tools,” he said. “They also end up losing time trying to locate pertinent information – like that one piece of documentation that explains an architectural decision from a previous team member,” Peters added. “If software development were an F1 race, these inefficiencies are the pit stops that eat into lap time. Every unnecessary context switch or repetitive task equals more time lost when trying to reach the finish line.” ... “Documentation and deployments appear to either be not routine enough to warrant AI assistance or otherwise removed from existing workflows so that not much time is spent on it,” the company said. ... For developers of all experience levels, Stack Overflow highlighted a concerning divide in terms of documentation activities.

AI Pilots Are Easy. Business Use Cases Are Hard

Moving from pilot to purpose is where most AI journeys lose momentum. The gap often lies not in the model itself, but in the ecosystem around it. Fragmented data, unclear ROI frameworks and organizational silos slow down scaling. To avoid this breakdown, an AI pilot must be anchored to clear business outcomes - whether that's cost optimization, data-led infrastructure or customer experience. Once the outcomes are defined, the organization can test the system with the specific data and processes that will support it. This focus sets the stage for the next 10 to 14 months of refinement needed to ready the tool for deeper integration. When implementation begins, workflows become self-optimizing, decisions accelerate and frontline teams gain real-time intelligence. As AI moves beyond pilots, systems begin spotting patterns before people do. Teams shift from retrospective analysis to live decision-making. Processes improve themselves through constant feedback loops. These capabilities unlock efficiency and insight across businesses, but highly regulated industries such as banking, insurance, and healthcare face additional hurdles. Compliance, data privacy and explainability add layers of complexity, making it essential for AI integration to include process redesign, staff retraining and organizationwide AI literacy, not just within technical teams.

Why your next cloud bill could be a trap

“AI-ready” often means “AI–deeply embedded” into your data, tools, and runtime environment. Your logs are now processed through their AI analytics. Your application telemetry routes through their AI-based observability. Your customer data is indexed for their vector search. This is convenient in the short term. In the long term, it shifts power. The more AI-native services you consume from a single hyperscaler, the more they shape your architecture and your economics. You become less likely to adopt open source models, alternative GPU clouds, or sovereign and private clouds that might be a better fit for specific workloads. You are more likely to accept rate changes, technical limits, and road maps that may not align with your interests, simply because unwinding that dependency is too painful. ... For companies not prepared to fully commit to AI-native services from a single hyperscaler or in search of a backup option, these alternatives matter. They can host models under your control, support open ecosystems, or serve as a landing zone for workloads you might eventually relocate from a hyperscaler. However, maintaining this flexibility requires avoiding the strong influence of deeply integrated, proprietary AI stacks from the start. ... The bottom line is simple: AI-native cloud is coming, and in many ways, it’s already here. The question is not whether you will use AI in the cloud, but how much control you will retain over its cost, architecture, and strategic direction.

IT and Security: Aligning to Unlock Greater Value

While many organisations have made strides in aligning IT and security, communication breakdowns can remain a challenge. Historically, friction between these two departments was driven by a lack of communication and competing priorities. For the CISO or head of the security team, reducing the company’s attack surface, limiting access privileges, or banning apps that might open their organisation up to unnecessary, additional risks are likely to be core focus areas. ... The good news is, there are more opportunities now than ever before for IT and security operations to naturally converge – in endpoint management, patch deployment, identity and access management, you name it. It can help to clearly document IT and security’s roles and responsibilities and practice scenarios with tabletop exercises to get everyone on the same page and identify coverage gaps. ... In addition to building versatile teams, organisations should focus on consolidating IT and security toolkits by prioritising solutions that expedite time to value and boost visibility. We’ve said this in security for a long time: you can’t protect (or defend against) what you can’t see. With shared visibility through integrated platforms and consolidated toolkits, both IT and security teams can gain real-time insights into infrastructure, threats, vulnerabilities, and risks before they can impact business. Solutions that help IT and security teams rapidly exchange critical information, accelerate response to incidents, and document the triaging process will make it easier to address similar instances in the future.

Daily Tech Digest - February 17, 2025

Quote for the day:

"Hardships often prepare ordinary people for an extraordinary destiny." -- C.S. Lewis

Like it or not, AI is learning how to influence you

We need to consider the psychological impact that will occur when we humans start to believe that the AI agents giving us advice are smarter than us on nearly every front. When AI achieves a perceived state of “cognitive supremacy” with respect to the average person, it will likely cause us to blindly accept its guidance rather than using our own critical thinking. This deference to a perceived superior intelligence (whether truly superior or not) will make agent manipulation that much easier to deploy. I am not a fan of overly aggressive regulation, but we need smart, narrow restrictions on AI to avoid superhuman manipulation by conversational agents. Without protections, these agents will convince us to buy things we don’t need, believe things that are untrue and accept things that are not in our best interest. It’s easy to tell yourself you won’t be susceptible, but with AI optimizing every word they say to us, it is likely we will all be outmatched. One solution is to ban AI agents from establishing feedback loops in which they optimize their persuasiveness by analyzing our reactions and repeatedly adjusting their tactics. In addition, AI agents should be required to inform you of their objectives. If their goal is to convince you to buy a car, vote for a politician or pressure your family doctor for a new medication, those objectives should be stated up front.

Leveraging AI for Business Continuity and Disaster Recovery in the Work-From-Home Era

AI-driven tools can monitor the health and performance of hardware and predict hardware failure before it happens using anomaly detection algorithms. For example, if a hard drive is starting to fail or there’s unusual network activity, AI systems can flag the activity/potential problem early and send an email to alert the WFH user or corporate IT staff, allowing businesses to take preventative action. ... AI can detect anomalies in network traffic or access patterns which may indicate a cyberattack (e.g., ransomware, phishing, or data breach). AI-powered cybersecurity tools, such as intrusion detection systems (IDS) and endpoint protection software, can respond automatically to threats by isolating affected systems or rolling back malicious changes. ... Small businesses may not have reliable or frequent data backups or rely on manual processes (e.g., external hard drives) that aren’t automated or secure. It may be difficult to recover without a proper backup strategy if critical data is lost due to hardware failure, cyber-attacks, or natural disasters. ... AI-assisted BC and DR solutions offer a range of benefits, particularly for SOHO and WFH users. These offerings are becoming essential as businesses of all sizes seek to maintain operational resilience in an ever-changing technological landscape.

GenAI can make us dumber — even while boosting efficiency

“A key irony of automation is that by mechanizing routine tasks and leaving exception-handling to the human user, you deprive the user of the routine opportunities to practice their judgement and strengthen their cognitive musculature, leaving them atrophied and unprepared when the exceptions do arise,” the study found. Overall, workers’ confidence in genAI’s abilities correlates with less effort in critical thinking. The focus of critical thinking shifts from gathering information to verifying it, from problem-solving to integrating AI responses, and from executing tasks to overseeing them. The study suggests that genAI tools should be designed to better support critical thinking by addressing workers’ awareness, motivation, and ability barriers. ... As Agentic AI becomes common, people may come to rely on it for problem-solving — but how will we know it’s doing things correctly, Gold said. People might accept its results without questioning, potentially limiting their own skills development by allowing technology to handle tasks. Lev Tankelevitch, a senior researcher with Microsoft Research, said not all genAI use is bad. He said there’s clear evidence in education that it can enhance critical thinking and learning outcomes.

How to harness APIs and AI for intelligent automation

APIs are the steady bridges connecting diverse systems and data sources. This reliable technology, which emerged in the 1960s and matured during the noughties ecommerce boom, is bridging today’s next-gen technologies. APIs allow data transfer to be automated, which is essential for training AI models efficiently. Rather than building complex integrations from scratch, they standardize data flow to ensure the data that feeds AI models is accurate and reliable. ... Data preprocessing is the critical step before training any AI model. APIs can ensure that AI applications and models only receive preprocessed data. This minimizes manual errors which smoothes the AI training pipeline. With a direct interface to standardized data, developers can focus on refining the model architecture rather than spending excessive time on data cleanup. Real-time evaluation keeps AI models in check in dynamic environments. By feeding real-time performance data back into the system, developers can quickly adjust parameters to improve the model. ... As your data volumes and transaction rates increase, your APIs must scale accordingly. Performance issues like latency or downtime can disrupt AI training and real-time processing. To be responsive under heavy loads, design APIs with load balancing, caching, and built-in redundancy to maintain consistent performance during peak use.

Applying Behavioral Economics to Phishing and Social Engineering Attacks

It’s all about deeply and thoroughly understanding human behavior and how these behaviors are impacted by influences that use cognitive biases, emotions, social influences, and contextual factors to drive decisions. Bad actors in the world of cybersecurity also prey upon these human tendencies to drive actions that put organizations at risk. ... Humans are social creatures that trust those they believe are authorities. They’re driven by fear, greed, and curiosity that can cloud their judgement. And they’re prone to cognitive shortcuts—biases that often drive behaviors. Understanding the power of these drivers can help organizations put strategies into place to thwart them. ... Here are some important steps that can help employees make better decisions:Training employees about the threat of cyberattacks, the form these attacks generally take, and their role in helping to avert them is an important first step. Training should be ongoing, not a single instance or once a year event. Phishing simulations have proven to be a very effective way to tangibly reduce security breakdowns. These simulations serve to test employee awareness and identify areas of opportunity for improvement. Strong authentication measures can help keep accounts secure by requiring two or more methods of identification and verification—muti-factor authentication—before allowing access to information or systems.

Why Digital Projects Need Transparency and Accountability

As a CIO, it is easy to underestimate the time it will take to build forward. In the public sector, this takes longer due to inherent risk aversion. In my first few months at DWP, I felt I was making a difference, but after the first few months, the size of the prize began to take its toll and the risk factors of going forward began to set in. As CIOs, it is our role to persuade, influence and keep in mind where we are trying to get to. We landed that vision with the senior team but DWP's size and geographic spread made it harder to get the spokes of the business to hear the same story and grasp the same benefits. If I had my time again, I would spend more time with the business, less at the center and try to build momentum that was unstoppable. As I completed my first 100 days in the CIO role at Segro, one of the key takeaways from DWP was making sure the digital leadership team knew how to act together. In my new role, I am able to replicate that at a faster pace. Brand identity matters. At Segro, we are not known as the digital team, and I am striving to change that. The organization will benefit from unifying its understanding of technology, transformation and data.

Navigating Europe’s AI Code of Practice Before the Clock Runs Out

The Code of Practice for general-purpose AI demonstrates a sincere effort to get the details right. Yet, in a rush to cover every contingency, it risks overlooking the bigger picture: spurring the next generation of AI-driven breakthroughs that can speed up drug discovery, modernize public services, and let small farmers use new predictive tools for planting and harvesting. Innovation is a delicate process, especially in emerging areas like large-scale language models or real-time climate analytics. Europe possesses the scientific expertise and market size to shape a future where these tools become transformative assets in every corner of the continent. But that future hinges on how carefully policymakers, industry players, and civil society calibrate the rules. ... Europe’s AI revolution will not happen on autopilot. Real progress demands revamping processes, investing in talent, and scaling up what works. The public sector must also move faster if Europe is to modernize healthcare, education, and core government services. Tangled or rigid rules risk derailing Europe’s ambitions. Europe’s digital regulations already weigh heavily on businesses. Over the past 25 years, the number of economy-wide laws doubled, and the EU has rolled out close to 100 tech-focused laws. High-minded ideals often mix with fragmented enforcement and overlapping rules.

Seven Common Reasons Why Data Science Projects Fail

Large organizations may own hundreds of data assets spread across sprawling, multi-faceted IT infrastructures. Unless they have a detailed, continuously updated data catalog in place that tracks all of those assets – which many don’t – simply finding the data that the team needs to complete a project can present a major challenge. Here again, however, tools and techniques are available that can help. The major solution is data discovery software, which can automatically identify data resources, including those that are not documented. ... Too often, businesses decide that they want to do something with their data, but they don’t know exactly what. For example, they might establish a high-level goal like using data-derived insights to grow revenue, without determining exactly which types of revenue-related challenges they want to solve with help from data. Avoiding this pitfall is simple: You need to articulate precise deliverables and outcomes at the start of your project. There’s always room to adjust the details a bit once a project is underway, but you should know from the beginning what the overarching outcomes of the project should be. ... A final key challenge that can thwart data science project success is the failure to understand what the goals of data science are, and which methodologies and resources data science requires.

What’s changing the rules of enterprise AI adoption for IT leaders

As model costs fall and the value from AI migrates up to the application layer, enterprises are going to have even greater choice in business solutions, either from third parties or those developed inhouse. For CIOs with access to the right resources, building applications internally is now a more realistic proposition. This becomes increasingly attractive in the context of complex business processes that may be unique to enterprises. As the costs of running models fall to near zero, the ROI equation shifts dramatically. According to Forrester Research, the ability to run hyper-efficient models like DeepSeek locally on PCs opens up a new era of edge intelligence, which businesses can deploy across organizations. “The real value in AI isn’t just in building bigger models, but innovating on top of them and in implementing them efficiently,” says Devesh Mishra, president of CoreAI at digital transformation specialists Keystone. “Companies that pair foundation model advancements with deep business and operational expertise will lead the next phase of AI-driven ROI.” This deep understanding of industry verticals and their specific issues and needs will define success for many vendors as they increasingly compete with inhouse development teams.

Rowing in the Same Direction: 6 Tips for Stronger IT and Security Collaboration

Due to market dominance, many software vendors focus on Windows, but IT fleets today include a mix of Chromebooks, Linux systems and Apple devices. Security and IT teams must recognize that the weakest endpoint determines the overall defense posture. By ensuring IT and security teams are aligned on what’s in the environment, you can break down silos and work together toward shared security goals, such as zero-trust implementation. ... Security and IT teams should collaborate to ensure policies protect the overall business mission, not just the bottom line. For example, if security requires an agent to collect telemetry for advanced analysis (e.g., CrowdStrike, Halcyon, etc.), what’s the performance impact on endpoints? If the agent is running AI/ML workloads, how is it optimized for performance on XPU and non-XPU systems? IT fleet leaders care about security BUT they also demand top performance and battery life from devices. Both security and IT teams together can align solutions that offer best-in-class security without degrading fleet performance. ... Ownership in IT and security is one of the hardest challenges to solve. In many cases, responsibility over cloud workloads, applications and ephemeral systems isn’t always clearly defined.