Daily Tech Digest by Kannan Subbiah: algorithms

Showing posts with label algorithms. Show all posts

Daily Tech Digest - May 07, 2026

Quote for the day:

"You learn more from failure than from success. Don't let it stop you. Failure builds character." -- Unknown

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Duration: 21 mins • Perfect for listening on the go.

Designing front-end systems for cloud failure

In the InfoWorld article "Designing front-end systems for cloud failure," Niharika Pujari argues that frontend resilience is a critical yet often overlooked aspect of engineering. Since cloud infrastructure depends on numerous moving parts, failures are frequently partial rather than absolute, manifesting as temporary network instability or slow downstream services. To maintain a usable and calm user experience during these hiccups, developers should adopt a strategy of . This begins with distinguishing between critical features, which are essential for core tasks, and non-critical components that provide extra richness. When non-essential features fail, the interface should isolate these issues—perhaps by hiding sections or displaying cached data—to prevent a total system outage. Technical implementation involves employing controlled retries with exponential backoff and jitter to manage transient errors without overwhelming the backend. Additionally, protecting user work in form-heavy workflows is vital for maintaining trust. Effective failure handling also requires a shift in communication; specific, reassuring error messages that explain what still works and provide a clear recovery path are far superior to generic "something went wrong" alerts. Ultimately, resilient frontend design focuses on isolating failures, rendering partial content, and ensuring that the interface remains functional and informative even when underlying cloud dependencies falter.

Scaling AI into production is forcing a rethink of enterprise infrastructure

The article "Scaling AI into production is forcing a rethink of enterprise infrastructure" explores the critical shift from AI experimentation to large-scale deployment across real business environments. As organizations move beyond proofs of concept, executives Tarkan Maner and Thomas Cornely argue that the emergence of is a primary driver of this transformation. Agentic systems introduce complex, autonomous, multi-step workflows that traditional infrastructures are often unequipped to handle efficiently. These sophisticated agents require real-time orchestration and secure, on-premises data access to protect sensitive enterprise information. While many organizations initially utilized the public cloud for rapid experimentation, the transition to production highlights serious concerns regarding ongoing cost, strict governance, and data control, prompting a significant shift toward private or hybrid environments. The article emphasizes that AI is designed to augment human capability rather than replace it, seeking a harmonious integration between human decision-making and automated agentic workflows. Practical applications are already emerging across various sectors, from retail’s cashier-less checkouts and targeted marketing to healthcare’s remote diagnostic tools. Ultimately, scaling AI successfully necessitates a foundational rethink of how modern enterprises coordinate their underlying infrastructure, data, and security protocols to support unpredictable workloads while maintaining overall operational stability and long-term cost efficiency.

Why ransomware attacks succeed even when backups exist

The BleepingComputer article "Why ransomware attacks succeed even when backups exist" explains that modern ransomware operations have evolved into sophisticated campaigns that systematically target and destroy an organization's backup infrastructure before deploying encryption. Rather than just locking files, attackers follow a predictable sequence: gaining initial access, stealing administrative credentials, moving laterally across the network, and then identifying and deleting backups. This includes wiping , hypervisor snapshots, and cloud repositories to ensure no easy recovery path remains. Several common organizational failures contribute to this vulnerability, such as the lack of network isolation between production and backup environments, weak access controls like shared admin credentials or missing multi-factor authentication, and the absence of . Furthermore, many organizations suffer from untested recovery processes or siloed security tools that fail to detect attacks on backup systems. To combat these threats, the article emphasizes the necessity of integrated cyber protection, featuring immutable backups with enforced retention locks, dedicated credentials, and continuous monitoring. By neutralizing the traditional "safety net" of backups, ransomware gangs effectively force victims into paying ransoms. This strategic shift highlights that basic, unprotected backups are no longer sufficient in the face of modern, targeted ransomware tactics.

Document as Evidence vs. Data Source: Industrial AI Governance

In the article "Document as Evidence vs. Data Source: Industrial AI Governance," Anthony Vigliotti highlights a critical distinction in how organizations manage information for . Most current programs utilize a "data source" model, where documents are treated as raw material; data is extracted, and the original document is archived or orphaned. This terminal approach severs the link between data and its context, creating significant governance risks, particularly in brownfield manufacturing where legacy records carry decades of operational history. Conversely, the "evidence" model treats documents as permanent artifacts with ongoing legal and operational standing. This framework ensures documents are preserved with high fidelity, validated before downstream use, and permanently linked to any derived data through a navigable citation trail. By adopting an evidence-based posture, organizations can build a robust "Accuracy and Trust Layer" that makes AI-driven decisions defensible and auditable. This is essential for safety-critical operations and regulatory compliance, where being able to prove the provenance of data is as vital as the accuracy of the AI output itself. Transitioning from a throughput-focused extraction mindset to one centered on trust allows industrial enterprises to scale AI safely while mitigating the long-term governance debt associated with disconnected data silos.

Method for stress-testing cloud computing algorithms helps avoid network failures

Researchers at MIT have developed a groundbreaking method called to stress-test cloud computing algorithms, helping prevent large-scale network failures and service outages that impact millions of users. In massive cloud environments, engineers often rely on ""—simplified shortcut algorithms that route data quickly but can unexpectedly break down under unusual traffic patterns or sudden demand spikes. Traditionally, stress-testing these heuristics involved manual, time-consuming simulations using human-designed test cases, which frequently missed critical "blind spots" where the algorithm might fail. MetaEase revolutionizes this evaluation process by utilizing to analyze an algorithm’s source code directly. By mapping out every decision point within the code, the tool automatically searches for and identifies worst-case scenarios where performance gaps and underperformance are most significant. This automated approach allows engineers to proactively catch potential failure modes before deployment without requiring complex mathematical reformulations or extensive manual labor. Beyond standard networking tasks, the researchers highlight MetaEase’s potential for auditing risks associated with AI-generated code, ensuring these systems remain resilient under unpredictable real-world conditions. In comparative experiments, this technique identified more severe performance failures more efficiently than existing state-of-the-art methods. Moving forward, the team aims to enhance MetaEase’s scalability and versatility to process more complex data types and applications.

Hacker Conversations: Joey Melo on Hacking AI

In the SecurityWeek article "Hacker Conversations: Joey Melo on Hacking AI," Principal Security Researcher Joey Melo shares his journey and methodology within the evolving field of artificial intelligence red teaming. Melo, who developed a passion for manipulating software environments through childhood gaming, now applies that curiosity to "" and "data poisoning" AI models. Unlike traditional penetration testing, focuses on bypassing sophisticated guardrails without altering source code. Melo describes jailbreaking as a process of "liberating" bots via complex context manipulation—such as tricking an LLM into believing it is operating in a future where current restrictions no longer apply. Furthermore, he explores data poisoning, where researchers test if models can be influenced by malicious prompt ingestion or untrustworthy web scraping. Despite possessing the skills to exploit these vulnerabilities for personal gain, Melo emphasizes a commitment to ethical, responsible disclosure. He views his work as a vital contribution to an ongoing "cat-and-mouse game" aimed at hardening machine learning defenses against increasingly creative threats. Ultimately, Melo believes that while AI security will continue to improve, the constant evolution of technology ensures that red teaming will remain a necessary, creative endeavor to identify and mitigate emerging risks.

Global Push for Digital KYC Faces a Trust Problem

The global movement toward digital Know Your Customer (KYC) frameworks is gaining significant momentum, as evidenced by the United Arab Emirates’ recent launch of a standardized national platform designed to streamline onboarding and bolster anti-money laundering efforts. While domestic systems are becoming increasingly sophisticated, the concept of portable, cross-border KYC remains largely elusive due to a fundamental lack of trust between international regulators. Governments and financial institutions are eager to reduce duplication and speed up compliance processes to match the rapid growth of instant payments and digital banking. However, significant hurdles persist because KYC extends beyond simple identity verification to include complex assessments of ownership structures and risk profiles, which are heavily influenced by local market contexts and legal frameworks. National regulators often prioritize sovereign control and data protection, making them hesitant to rely on third-party verification performed in different jurisdictions. Consequently, even when countries share broad anti-money laundering goals, their divergent definitions of adequate due diligence and monitoring requirements create a fragmented landscape. Ultimately, the transition to a unified digital identity ecosystem depends less on technological innovation and more on establishing mutual recognition and trust among global supervisory bodies, ensuring that sensitive identity data can be securely and reliably shared across borders.

How To Ensure Business Continuity in the Midst of IT Disaster Recovery

The content provided by the Disaster Recovery Journal (DRJ) at the specified URL serves as a foundational guide for professionals navigating the complexities of organizational stability through the lens of business continuity (BC) and disaster recovery (DR) planning. The material emphasizes that while these two disciplines are closely interconnected, they serve distinct roles in safeguarding an organization. Business continuity is presented as a holistic, high-level strategy focused on maintaining essential operations across all departments during a crisis, ensuring that personnel, facilities, and processes remain functional. In contrast, disaster recovery is defined as a specialized technical subset of BC, primarily concerned with the restoration of information technology systems, critical data, and infrastructure following a disruptive event. A primary theme of the planning process is the requirement for a structured lifecycle, which begins with a rigorous Business Impact Analysis (BIA) and Risk Assessment to identify vulnerabilities and prioritize critical functions. By defining clear Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO), organizations can create targeted response strategies that minimize operational downtime. Furthermore, the resource highlights that modern planning must evolve to address contemporary challenges, such as cyber threats, hybrid work environments, and artificial intelligence integration. Regular testing, cross-functional collaboration, and plan maintenance are essential to transform static documentation into a dynamic, resilient framework capable of withstanding diverse disasters.

The Agentic AI Challenge: Solve for Both Efficiency and Trust

According to the article from The Financial Brand, agentic artificial intelligence represents the next inevitable evolution in banking, marking a fundamental shift from reactive generative AI chatbots to autonomous, proactive systems. While nearly all financial institutions are currently exploring agentic technology, a significant "execution gap" persists; most organizations remain stuck in the pilot phase due to legacy infrastructure, fragmented data silos, and outdated governance frameworks. Unlike traditional AI that merely offers recommendations, agentic systems are designed to act—executing complex workflows, coordinating multi-step transactions, and managing customer financial health in real time with minimal human intervention. The report emphasizes that while banks have historically prioritized low-value applications like back-office automation and fraud prevention, the true potential of agentic AI lies in fulfilling broader ambitions for hyper-personalization and revenue growth. As fintech competitors increasingly rebuild their transaction stacks for real-time execution and autonomous validation, traditional banks face a critical strategic choice. They must modernize their leadership mindset and core technical architecture to support the "self-driving bank" model or risk being permanently outpaced. Ultimately, embracing agentic AI is not merely a technological upgrade but a necessary structural evolution required for banks to remain competitive in an increasingly automated financial ecosystem.

Multi-model AI is creating a routing headache for enterprises

According to F5’s 2026 State of Application Strategy Report, enterprises are rapidly transitioning AI inference into core production environments, with 78% of organizations now operating their own inference services. As 77% of firms identify inference as their primary AI activity, the focus has shifted from experimentation to operational integration within hybrid multicloud infrastructures. Organizations currently manage or evaluate an average of seven distinct AI models, reflecting a diverse landscape where no single model fits every use case. This multi-model approach creates significant architectural complexities, turning AI delivery into a sophisticated traffic management challenge and AI security into a rigorous governance priority. Companies are increasingly adopting identity-aware infrastructure and centralized control planes to manage the routing, observability, and protection of inference workloads. To mitigate operational strain and rising costs, enterprises are integrating shared protection systems and cross-model observability tools. Furthermore, the convergence of AI delivery and security around inference highlights the necessity of managing multiple services to ensure availability and compliance. Ultimately, the report emphasizes that successful AI adoption depends on treating inference as a managed workload subject to the same delivery and resilience requirements as traditional enterprise applications, ensuring faster and safer operational execution.

Daily Tech Digest - May 06, 2026

Quote for the day:

"Little minds are tamed and subdued by misfortune; but great minds rise above it." -- Washington Irving

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Duration: 21 mins • Perfect for listening on the go.

The Architect Reborn

In "The Architect Reborn," Paul Preiss argues that the technology architecture profession is experiencing a significant resurgence after fifteen years of structural decline. He explains that the rise of and the "three-in-a-box" delivery model—comprising product owners, tech leads, and scrum masters—mistakenly rendered the architect role as a redundant expense or a "tax" on speed. This industry shift led many senior developers to pivot toward "engineering" titles while neglecting essential cross-cutting concerns, resulting in massive technical debt and systemic instabilities, exemplified by high-profile failures like the . However, the current explosion of AI-generated code has created a critical need for human oversight that automated tools cannot replicate. Organizations are rediscovering that they require skilled architects to manage complex quality attributes—such as security, reliability, and maintainability—and to bridge the gap between business strategy and technical execution. By leveraging the five pillars of the Business Technology Architecture Body of Knowledge (BTABoK), the reborn architect ensures that systems are designed with long-term viability and strategic purpose in mind. Ultimately, Preiss suggests that as AI disrupts traditional coding roles, the architect’s unique ability to provide business context and disciplined design is becoming the most vital asset in the modern technology landscape.

Supply-chain attacks take aim at your AI coding agents

The emergence of autonomous AI coding agents has introduced a sophisticated new frontier in software supply chain security, as evidenced by recent attacks targeting these systems. Security researchers from ReversingLabs have identified a campaign dubbed "," attributed to the North Korean threat group "Famous Chollima." Unlike traditional social engineering that targets human developers, these adversaries utilize "LLM Optimization" (LLMO) and "knowledge injection" to manipulate AI agents. By crafting persuasive documentation and bait packages on registries like and , attackers increase the likelihood that an agent will autonomously select and integrate malicious dependencies into its projects. This threat is further exacerbated by "slopsquatting," where attackers register package names that AI agents frequently hallucinate. Once installed, these malicious components can grant attackers remote access through SSH keys or facilitate the exfiltration of sensitive codebases. Because AI agents often operate with high-level system privileges, the risk of rapid, automated compromise is significant. To mitigate these vulnerabilities, organizations must implement rigorous security controls, including mandatory developer reviews for all AI-suggested dependencies and the adoption of comprehensive Software Bill of Materials (SBOM) practices. Ultimately, while AI agents offer productivity gains, their integration into development pipelines requires a "trust but verify" approach to prevent large-scale supply chain poisoning.

Why disaster recovery plans fail in geopolitical crises

In "Why Disaster Recovery Plans Fail in Geopolitical Crises," Lisa Morgan explains that traditional disaster recovery (DR) strategies are increasingly inadequate against the cascading disruptions of modern warfare and global instability. Historically, DR plans have relied on "known knowns" like localized hardware failures or natural disasters, but the blurring line between private enterprise and nation-state conflict has introduced unprecedented risks. Recent drone strikes on data centers in the Middle East demonstrate that physical infrastructure is no longer immune to military action. Furthermore, the rise of "techno-nationalism" and strict data sovereignty laws significantly complicates geographic failover, as transiting data across borders can now lead to legal and regulatory violations. Modern resilience requires CIOs to shift from static IT playbooks to cross-functional business capabilities involving legal, risk, and compliance teams. The article also highlights how AI-driven resource constraints, particularly in energy and silicon, exacerbate these vulnerabilities. It is critical that organizations move beyond simple redundancy toward adaptive architectures that can withstand simultaneous infrastructure failures and prioritize employee safety in conflict zones. Ultimately, today’s CIOs must adopt the mindset of military strategists, conducting robust tabletop exercises that challenge existing assumptions and prepare for the total, non-linear disruptions characteristic of the current geopolitical climate.

The immutable mountain: Understanding distributed ledgers through the lens of alpine climbing

The article "The Immutable Mountain" utilizes the high-stakes environment of alpine climbing on Ecuador’s to explain the sophisticated mechanics of distributed ledgers. Moving away from traditional centralized command-and-control structures, which often represent single points of failure, the author illustrates how expedition rope teams function as autonomous nodes. Each team possesses the authority to make critical, real-time decisions, mirroring the decentralized nature of blockchain technology. This structure ensures that information is not merely passed down a hierarchy but is synchronized across a collective network, fostering operational resilience and organizational agility. Key technical concepts like consensus are framed through the lens of climbers reaching a shared agreement on route safety, while immutability is compared to the permanent, unalterable nature of a daily trip report. By adopting this "composable authoritative source," modern enterprises can achieve radical transparency and maintain a singular, verifiable version of the truth across disparate departments and external partners. Ultimately, the piece argues that the true power of a distributed ledger lies not in its complex code, but in a foundational philosophy of collective trust. This paradigm shift allows organizations to navigate volatile global markets with the same discipline and absolute reliability required to survive the "death zone" of a mountain summit.

Train like you fight: Why cyber operations teams need no-notice drills

The article "Train like you fight: Why cyber operations teams need no-notice drills" argues that traditional, scheduled tabletop exercises fail to prepare cybersecurity teams for the intense psychological stress of a real-world incident. While planned exercises satisfy compliance, they lack the "threat stimulus" necessary to engage the sympathetic nervous system, which can suppress executive function when a genuine crisis occurs. Drawing on medical training at Level 1 trauma centers and research by psychologist Donald Meichenbaum, the author advocates for "no-notice" drills as a form of stress inoculation. This approach, rooted in the Yerkes-Dodson principle, shifts incident response from a document-heavy process to a conditioned physiological response by raising the threshold at which stress impairs performance. By surprising teams with realistic anomalies, organizations can uncover critical operational gaps—such as communication breakdowns, cross-functional latency, or outdated escalation contacts—that remain hidden during predictable tests. Furthermore, these drills foster psychological safety and trust, as teams learn to navigate ambiguity together without fear of blame through blameless post-mortems. Ultimately, the article maintains that the temporary discomfort of a surprise drill is a necessary investment, as failing during practice is far less damaging than failing during a real breach when the damage clock is already running.

The Art of Lean Governance: Developing the Nerve Center of Trust

Steve Zagoudis’s article, "The Art of Lean Governance: Developing the Nerve Center of Trust," explores the transformation of data governance from a static, policy-driven framework into a dynamic, continuous control system. He argues that the foundation of modern data integrity lies in data reconciliation, which should be elevated from a mere back-office correction mechanism to the primary control for enterprise data risk. By embedding reconciliation directly into data architecture, organizations can establish a "nerve center of trust" that operates at the same cadence as the data itself. This shift is particularly crucial for AI readiness, as the effectiveness of artificial intelligence is fundamentally defined by whether data can be trusted at the moment of use. Without this systemic trust, AI risks accelerating organizational errors rather than providing a competitive advantage. Zagoudis critiques traditional governance for being too episodic and manual, advocating instead for a lean approach that provides automated, evidence-based assurance. Ultimately, lean governance fosters a culture where data is a reliable asset for defensible decision-making. By operationalizing trust through disciplined execution and architectural integration, institutions can move beyond conceptual alignment to achieve genuine agility and accuracy in an increasingly data-driven landscape, ensuring that their technological investments yield meaningful results.

Narrative Architecture: Designing Stories That Survive Algorithms

The Forbes Business Council article, "Narrative Architecture: Designing Stories That Survive Algorithms," critiques the modern trend of platform-first storytelling, where brands prioritize distribution and algorithmic trends over substantive identity. This reactionary approach often leads to "identity erosion," as content becomes ephemeral and dependent on shifting digital environments. To combat this, the author introduces "" as a vital strategic asset. This framework acts as a brand's "home base," grounding all content in a coherent core story that defines the organization’s history, values, and fundamental purpose. Rather than letting algorithms dictate their messaging, brands should use them as tools to inform a pre-established narrative. By shifting focus from fleeting visibility to deep-rooted credibility, companies can build lasting trust with audiences, investors, and potential employees. The article argues that stories built on solid narrative architecture possess a unique longevity that extends far beyond digital platforms, manifesting in conference invitations, earned media coverage, and consistent internal brand alignment. Ultimately, while platform-optimized content might gain temporary engagement, a well-architected story ensures a brand remains relevant and respected even as algorithms evolve, securing long-term reputation and sustainable business success in an increasingly crowded digital landscape.

Zero Trust in OT: Why It's Been Hard and Why New CISA Guidance Changes Everything

The Nozomi Networks blog post titled "Zero Trust in OT: Why It’s Been Hard and Why New CISA Guidance Changes Everything" examines the historic friction and recent transformative shifts in applying Zero Trust (ZT) principles to operational technology. While ZT has matured within IT, extending it to industrial environments like SCADA systems and critical infrastructure has long been hindered by significant technical and cultural hurdles. Traditional IT security controls—such as active scanning, encryption, and aggressive network isolation—often disrupt real-time industrial processes, posing severe risks to safety, system uptime, and equipment integrity. However, the author emphasizes that the April 2026 release of CISA’s "Adapting Zero Trust Principles to Operational Technology" guide marks a pivotal turning point. This collaborative framework, developed alongside the DOE and FBI, validates unique industrial constraints by prioritizing physical safety and availability over mere data protection. By advocating for specialized, "OT-safe" strategies—including passive monitoring, protocol-aware visibility, and operationally-aware segmentation—the guidance removes years of ambiguity for practitioners. Ultimately, the blog argues that Zero Trust has evolved from an IT concept forced onto the factory floor into a practical, resilient framework designed to protect the physical processes essential to modern society without sacrificing operational integrity.

The expensive habits we can't seem to break

The article "The Expensive Habits We Can't Seem to Break" explores critical management failures that continue to hinder organizational success, focusing on three persistent mistakes. First, it critiques the tendency to treat culture as a mere communications exercise. Instead of relying on glossy value statements, the author argues that culture is defined by lived experiences and managerial responses during crises. Second, the piece highlights the costly underinvestment in the middle manager layer. With research showing that a significant portion of voluntary turnover is preventable through better management, the author notes that managers are often overextended and undersupported, lacking the necessary tools for "people stewardship." Finally, the article addresses the confusion between flexibility and autonomy. The return-to-office debate often misses the mark by focusing on location rather than trust. Organizations that dictate mandates rather than co-creating norms risk losing critical talent who seek agency over their work. Ultimately, bridging these gaps requires a move away from superficial fixes toward deep-seated changes in leadership behavior and employee trust. By addressing these "expensive habits," HR leaders can foster psychologically safe environments that drive retention and long-term performance, ensuring that organizational values are authentically integrated into the daily reality of the workforce.

The tech revolution that wasn’t

The MIT News article "The tech revolution that wasn't" explores Associate Professor Dwai Banerjee’s book, Computing in the Age of Decolonization: India's Lost Technological Revolution. It details India’s early, ambitious attempts to achieve technological sovereignty following independence, exemplified by the 1960 creation of the at the Tata Institute of Fundamental Research. Despite being a state-of-the-art machine built with minimal resources, the TIFRAC never reached mass production. Banerjee examines how India’s vision of becoming a global hardware manufacturing powerhouse was derailed by geopolitical constraints, limited knowledge sharing from the U.S., and a pivotal domestic shift in the 1970s and 1980s toward the private software services sector. This transition favored quick profits through outsourcing over the long-term investment required for R&D and manufacturing. Consequently, India became a leader in offshoring talent rather than a primary innovator in computer hardware. Banerjee challenges the common "individual genius" narrative of tech history, emphasizing instead that large-scale global capital and institutional support are the true determinants of success. Ultimately, the book uses India’s experience to illustrate the enduring, unequal power structures that continue to shape technological advancement in post-colonial nations, where the promise of a sovereign digital revolution was traded for a role in the global services economy.

Daily Tech Digest - February 13, 2026

Quote for the day:

"If you want teams to succeed, set them up for success—don’t just demand it." -- Gordon Tredgold

Hackers turn bossware against the bosses

Huntress discovered two incidents using this tactic, one late in January and one early this month. Shared infrastructure, overlapping indicators of compromise, and consistent tradecraft across both cases make Huntress strongly believe a single threat actor or group was behind this activity. ... CSOs must ensure that these risks are properly catalogued and mitigated,” he said. “Any actions performed by these agents must be monitored and, if possible, restricted. The abuse of these systems is a special case of ‘living off the land’ attacks. The attacker attempts to abuse valid existing software to perform malicious actions. This abuse is often difficult to detect.” ... Huntress analyst Pham said to defend against attacks combining Net Monitor for Employees Professional and SimpleHelp, infosec pros should inventory all applications so unapproved installations can be detected. Legitimate apps should be protected with robust identity and access management solutions, including multi-factor authentication. Net Monitor for Employees should only be installed on endpoints that don’t have full access privileges to sensitive data or critical servers, she added, because it has the ability to run commands and control systems. She also noted that Huntress sees a lot of rogue remote management tools on its customers’ IT networks, many of which have been installed by unwitting employees clicking on phishing emails. This points to the importance of security awareness training, she said.

Why secure OT protocols still struggle to catch on

“Simply having ‘secure’ protocol options is not enough if those options remain too costly, complex, or fragile for operators to adopt at scale,” Saunders said. “We need protections that work within real-world constraints, because if security is too complex or disruptive, it simply won’t be implemented.” ... Security features that require complex workflows, extra licensing, or new infrastructure often lose out to simpler compensating controls. Operators interviewed said they want the benefits of authentication and integrity checks, particularly message signing, since it prevents spoofing and unauthorized command execution. ... Researchers identified cost as a primary barrier to adoption. Operators reported that upgrading a component to support secure communications can cost as much as the original component, with additional licensing fees in some cases. Costs also include hardware upgrades for cryptographic workloads, training staff, integrating certificate management, and supporting compliance requirements. Operators frequently compared secure protocol deployment costs with segmentation and continuous monitoring tools, which they viewed as more predictable and easier to justify. ... CISA’s recommendations emphasize phased approaches and operational realism. Owners and operators are advised to sign OT communications broadly, apply encryption where needed for sensitive data such as passwords and key exchanges, and prioritize secure communication on remote access paths and firmware uploads.

SaaS isn’t dead, the market is just becoming more hybrid

“It’s important to avoid overgeneralizing ‘SaaS,’” Odusote emphasized . “Dev tools, cybersecurity, productivity platforms, and industry-specific systems will not all move at the same pace. Buyers should avoid one-size-fits-all assumptions about disruption.” For buyers, this shift signals a more capability-driven, outcomes-focused procurement era. Instead of buying discrete tools with fixed feature sets, they’ll increasingly be able to evaluate and compare platforms that are able to orchestrate agents, adapt workflows, and deliver business outcomes with minimal human intervention. ... Buyers will likely have increased leverage in certain segments due to competitive pressure among new and established providers, Odusote said. New entrants often come with more flexible pricing, which obviously is an attraction for those looking to control costs or prove ROI. At the same time, traditional SaaS leaders are likely to retain strong positions in mission-critical systems; they will defend pricing through bundled AI enhancements, he said. So, in the short term, buyers can expect broader choice and negotiation leverage. “Vendors can no longer show up with automatic annual price increases without delivering clear incremental value,” Odusote pointed out. “Buyers are scrutinizing AI add-ons and agent pricing far more closely.”

When algorithms turn against us: AI in the hands of cybercriminals

Cybercriminals are using AI to create sophisticated phishing emails. These emails are able to adapt the tone, language, and reference to the person receiving it based on the information that is publicly available about them. By using AI to remove the red flag of poor grammar from phishing emails, cybercriminals will be able to increase the success rate and speed with which the stolen data is exploited. ... An important consideration in the arena of cyber security (besides technical security) is the psychological manipulation of users. Once visual and audio “cues” can no longer be trusted, there will be an erosion of the digital trust pillar. The once-recognizable verification process is now transforming into multi-layered authentication which expands the amount of time it takes to verify a decision in a high-pressure environment. ... AI’s misuse is a growing problem that has created a paradox. Innovation cannot stop (nor should it), and AI is helping move healthcare, finance, government and education forward. However, the rate at which AI has been adopted has surpassed the creation of frameworks and/or regulations related to ethics or security. As a result, cyber security needs to transition from a reactive to a predictive stance. AI must be used to not only react to attacks, but also anticipate future attacks.

Those 'Summarize With AI' Buttons May Be Lying to You

Put simply, when a user visits a rigged website and clicks a "Summarize With AI" button on a blog post, they may unknowingly trigger a hidden instruction embedded in the link. That instruction automatically inserts a specially crafted request into the AI tool before the user even types anything. ... The threat is not merely theoretical. According to Microsoft, over a 60-day period, it observed 50 unique instances of prompt-based AI memory poisoning attempts for promotional purposes. ... AI recommendation poisoning is a sort of drive-by technique with one-click interaction, he notes. "The button will take the user — after the click — to the AI domain relevant and specific for one of the AI assistants targeted," Ganacharya says. To broaden the scope, an attacker could simply generate multiple buttons that prompt users to "summarize" something using the AI agent of their choice, he adds. ... Microsoft had some advice for threat hunting teams. Organizations can detect if they have been affected by hunting for links pointing to AI assistant domains and containing prompts with certain keywords like "remember," "trusted source," "in future conversations," and "authoritative source." The company's advisory also listed several threat hunting queries that enterprise security teams can use to detect AI recommendation poisoning URLs in emails and Microsoft Teams Messages, and to identify users who might have clicked on AI recommendation poisoning URLs.

EU Privacy Watchdogs Pan Digital Omnibus

The commission presented its so-called "Digital Omnibus" package of legal changes in November, arguing that the bloc's tech rules needed streamlining. ... Some of the tweaks were expected and have been broadly welcomed, such as doing away with obtrusive cookie consent banners in many cases, and making it simpler for companies to notify of data breaches in a way that satisfies the requirements of multiple laws in one go. But digital rights and consumer advocates are reacting furiously to an unexpected proposal for modifying the General Data Protection Regulation. ... "Simplification is essential to cut red tape and strengthen EU competitiveness - but not at the expense of fundamental rights," said EDPB chair Anu Talus in the statement. "We strongly urge the co-legislators not to adopt the proposed changes in the definition of personal data, as they risk significantly weakening individual data protection." ... Another notable element of the Digital Omnibus is the proposal to raise the threshold for notifying all personal data breaches to supervisory authorities. As the GDPR currently stands, organizations must notify a data protection authority within 72 hours of becoming aware of the breach. If amended as the commission proposes, the obligation would only apply to breaches that are "likely to result in a high risk" to the affected people's rights - the same threshold that applies to the duty to notify breaches to the affected data subjects themselves - and the notification deadline would be extended to 96 hours.

The Art of the Comeback: Why Post-Incident Communication is a Secret Weapon

Although technical resolutions may address the immediate cause of an outage, effective communication is essential in managing customer impact and shaping public perception—often influencing stakeholders’ views more strongly than the issue itself. Within fintech, a company's reputation is not built solely on product features or interface design, but rather on the perceived security of critical assets such as life savings, retirement funds, or business payrolls. In this high-stakes environment, even brief outages or minor data breaches are perceived by clients as threats to their financial security. ... While the natural instinct during a crisis (like a cyber breach or operational failure) is to remain silent to avoid liability, silence actually amplifies damage. In the first 48 hours, what is said—or not said—often determines how a business is remembered. Post-incident communication (PIC) is the bridge between panic and peace of mind. Done poorly, it looks like corporate double-speak. Done well, it demonstrates a level of maturity and transparency that your competitors might lack. ... H2H communication acknowledges the user’s frustration rather than just providing a technical error code. It recognizes the real-world impact on people, not just systems. Admitting mistakes and showing sincere remorse, rather than using defensive, legalistic language, makes a company more relatable and trustworthy. Using natural, conversational language makes the communication feel sincere rather than like an automated, cold response.

Why AI success hinges on knowledge infrastructure and operational discipline

Many organisations assume that if information exists, it is usable for GenAI, but enterprise content is often fragmented, inconsistently structured, poorly contextualised, and not governed for machine consumption. During pilots, this gap is less visible because datasets are curated, but scaling exposes the full complexity of enterprise knowledge. Conflicting versions, missing context, outdated material, and unclear ownership reduce performance and erode confidence, not because models are incapable, but because the knowledge they depend on is unreliable at scale. ... Human-in-the-loop processes struggle to keep pace with scale. Successful deployments treat HITL as a tiered operating structure with explicit thresholds, roles, and escalation paths. Pilot-style broad review collapses under volume; effective systems route only low-confidence or high-risk outputs for human intervention. ... Learning compounds over time as every intervention is captured and fed back into the system, reducing repeated manual review. Operationally, human-in-the-loop teams function within defined governance frameworks, with explicit thresholds, escalation paths, and direct integration into production workflows to ensure consistency at scale. In short, a production-grade human-in-the-loop model is not an extension of BPO but an operating capability combining domain expertise, governance, and system learning to support intelligent systems reliably.

Why short-lived systems need stronger identity governance

Consider the lifecycle of a typical microservice. In its journey from a developer’s laptop to production, it might generate a dozen distinct identities: a GitHub token for the repository, a CI/CD service account for the build, a registry credential to push the container, and multiple runtime roles to access databases, queues and logging services. The problem is not just volume; it is invisibility. When a developer leaves, HR triggers an offboarding process. Their email is cut, their badge stops working. But what about the five service accounts they hardcoded into a deployment script three years ago? ... In reality, test environments are often where attackers go first. It is the path of least resistance. We saw this play out in the Microsoft Midnight Blizzard attack. The attackers did not burn a zero-day exploit to break down the front door; they found a legacy test tenant that nobody was watching closely. ... Our software supply chain is held together by thousands of API keys and secrets. If we continue to rely on long-lived static credentials to glue our pipelines together, we are building on sand. Every static key sitting in a repo—no matter how private you think it is—is a ticking time bomb. It only takes one developer to accidentally commit a .env file or one compromised S3 bucket to expose the keys to the kingdom. ... Paradoxically, by trying to control everything with heavy-handed gates, we end up with less visibility and less control. The goal of modern identity governance shouldn’t be to say “no” more often; it should be to make the secure path the fastest path.

India's E-Rupee Leads the Secure Adoption of CBDCs

India has the e-rupee, which will eventually be used as a legal tender for domestic payments as well as for international transactions and cross-border payments. Ever since RBI launched the e-rupee, or digital rupee, in December 2022, there has been between INR 400 to 500 crore - or $44 to $55 million - in circulation. Many Indian banks are participating in this pilot project. ... Building broad awareness of CBDCs as a secure method for financial transactions is essential. Government and RBI-led awareness campaigns highlighting their security capability can strengthen user confidence and drive higher adoption and transaction volumes. People who have lost money due to QR code scams, fake calls, malicious links and other forms of payment fraud need to feel confident about using CBDCs. IT security companies are also cooperating with RBI to provide data confidentiality, transaction confidentiality and transaction integrity. E-transactions will be secured by hashing, digital signing and [advanced] encryption standards such as AES-192. This can ensure that the transaction data is not tampered with or altered. ... HSMs use advanced encryption techniques to secure transactions and keys. The HSM hardware [boxes] act as cryptographic co-processors and accelerate the encryption and decryption processes to minimize latency in financial transactions.

Daily Tech Digest - January 18, 2026

Quote for the day:

"Surround yourself with great people; delegate authority; get out of the way" -- Ronald Reagan

Data sovereignty: an existential issue for nations and enterprises

Law-making bodies have in recent years sought to regulate data flows to strengthen their citizens’ rights – for example, the EU bolstering individual citizens’ privacy through the General Data Protection Regulation (GDPR). This kind of legislation has redefined companies’ scope for storing and processing personal data. By raising the compliance bar, such measures are already reshaping C-level investment decisions around cloud strategy, AI adoption and third-party access to their corporate data. ... Faced with dynamic data sovereignty risks, enterprises have three main approaches ahead of them: First, they can take an intentional risk assessment approach. They can define a data strategy addressing urgent priorities, determining what data should go where and how it should be managed - based on key metrics such as data sensitivity, the nature of personal data, downstream impacts, and the potential for identification. Such a forward-looking approach will, however, require a clear vision and detailed planning. Alternatively, the enterprise could be more reactive and detach entirely from its non-domestic public cloud service providers. This is riskier, given the likely loss of access to innovation and, worse, the financial fallout that could undermine their pursuit of key business objectives. Lastly, leaders may choose to do nothing and hope that none of these risks directly affects them. This is the highest-risk option, leaving no protection from potentially devastating financial and reputational consequences of an ineffective data sovereignty strategy.

Verification Debt: When Generative AI Speeds Change Faster Than Proof

Software delivery has always lived with an imbalance. It is easier to change a system than to demonstrate that the change is safe under real workloads, real dependencies, and real failure modes. ... The risk is not that teams become careless. The risk is that what looks correct on the surface becomes abundant while evidence remains scarce. ... A useful name for what accumulates in the mismatch is verification debt. It is the gap between what you released and what you have demonstrated, with evidence gathered under conditions that resemble production, to be safe and resilient. Technical debt is a bet about future cost of change. Verification debt is unknown risk you are running right now. Here, verification does not mean theorem proving. It means evidence from tests, staged rollouts, security checks, and live production signals that is strong enough to block a release or trigger a rollback. It is uncertainty about runtime behavior under realistic conditions, not code cleanliness, not maintainability, and not simply missing unit tests. If you want to spot verification debt without inventing new dashboards, look at proxies you may already track. ... AI can help with parts of verification. It can suggest tests, propose edge cases, and summarize logs. It can raise verification capacity. But it cannot conjure missing intent, and it cannot replace the need to exercise the system and treat the resulting evidence as strong enough to change the release decision. Review is helpful. Review is evidence of readability and intent.

Executive-level CISO titles surge amid rising scope strain

Executive-level CISOs were more likely to report outside IT than peers with VP or director titles, according to the findings. The report frames this as part of a broader shift in how organisations place accountability for cyber risk and oversight. The findings arrive as boards and senior executives assess cyber exposure alongside other enterprise risks. The report links these expectations to the need for security leaders to engage across legal, risk, operations and other functions. ... Smaller organisations and industries with leaner security teams showed the highest levels of strain, the report says. It adds that CISOs warn these imbalances can delay strategic initiatives and push teams towards reactive security operations. The report positions this issue as a management challenge as well as a governance question. It links scope creep with wider accountability and higher expectations on security leaders, even where budgets and staffing remain constrained. ... Recruiters and employers have watched turnover trends closely as demand for senior security leadership has remained high across many sectors. The report suggests that title, scope and reporting structure form part of how CISOs evaluate roles. ... "The demand for experienced CISOs remains strong as the role continues to become more complex and more 'executive'," said Martano. "Understanding how organizations define scope, reporting structure, and leadership access and visibility is critical for CISOs planning their next move and for companies looking to hire or retain security leaders."

What’s in, and what’s out: Data management in 2026 has a new attitude

Data governance is no longer a bolt-on exercise. Platforms like Unity Catalog, Snowflake Horizon and AWS Glue Catalog are building governance into the foundation itself. This shift is driven by the realization that external governance layers add friction and rarely deliver reliable end-to-end coverage. The new pattern is native automation. Data quality checks, anomaly alerts and usage monitoring run continuously in the background. ... Companies want pipelines that maintain themselves. They want fewer moving parts and fewer late-night failures caused by an overlooked script. Some organizations are even bypassing pipes altogether. Zero ETL patterns replicate data from operational systems to analytical environments instantly, eliminating the fragility that comes with nightly batch jobs. ... Traditional enterprise warehouses cannot handle unstructured data at scale and cannot deliver the real-time capabilities needed for AI. Yet the opposite extreme has failed too. The highly fragmented Modern Data Stack scattered responsibilities across too many small tools. It created governance chaos and slowed down AI readiness. Even the rigid interpretation of Data Mesh has faded. ... The idea of humans reviewing data manually is no longer realistic. Reactive cleanup costs too much and delivers too little. Passive catalogs that serve as wikis are declining. Active metadata systems that monitor data continuously are now essential.

How Algorithmic Systems Automate Inequality

The deployment of predictive analytics in public administration is usually justified by the twin pillars of austerity and accuracy. Governments and private entities argue that automated decision-making systems reduce administrative bloat while eliminating the subjectivity of human caseworkers. ... This dynamic is clearest in the digitization of the welfare state. When agencies turn to machine learning to detect fraud, they rarely begin with a blank slate, training their models on historical enforcement data. Because low-income and minority populations have historically been subject to higher rates of surveillance and policing, these datasets are saturated with selection bias. The algorithm, lacking sociopolitical context, interprets this over-representation as an objective indicator of risk, identifying correlation and deploying it as causality. ... Algorithmic discrimination, however, is diffuse and difficult to contest. A rejected job applicant or a flagged welfare recipient rarely has access to the proprietary score that disqualified them, let alone the training data or the weighting variable—they face a black box that offers a decision without a rationale. This opacity makes it nearly impossible for an individual to challenge the outcome, effectively insulating the deploying organisation from accountability. ... Algorithmic systems do not observe the world directly; they inherit their view of reality from datasets shaped by prior policy choices and enforcement practices. To assess such systems responsibly requires scrutiny of the provenance of the data on which decisions are built and the assumptions encoded in the variables selected.

DevSecOps for MLOps: Securing the Full Machine Learning Lifecycle

The term "MLSecOps" sounds like consultant-speak. I was skeptical too. But after auditing ML pipelines at eleven companies over the past eighteen months, I've concluded we need the term because we need the concept — extending DevSecOps practices across the full machine learning lifecycle in ways that account for ML-specific threats. The Cloud Security Alliance's framework is useful here. Securing ML systems means protecting "the confidentiality, integrity, availability, and traceability of data, software, and models." That last word — traceability — is where most teams fail catastrophically. In traditional software, you can trace a deployed binary back to source code, commit hash, build pipeline, and even the engineer who approved the merge. ... Securing ML data pipelines requires adopting practices that feel tedious until the day they save you. I'm talking about data validation frameworks, dataset versioning, anomaly detection at ingestion, and schema enforcement like your business depends on it — because it does. Last September, I worked with an e-commerce company deploying a recommendation model. Their data pipeline pulled from fifteen different sources — user behavior logs, inventory databases, third-party demographic data. Zero validation beyond basic type checking. We implemented Great Expectations — an open-source data validation framework — as a mandatory CI check.

Autonomous Supply Chains: Catalyst for Building Cyber-Resilience

Autonomous supply chains are becoming essential for building resilience amid rising global disruptions. Enabled by a strong digital core, agentic architecture, AI and advanced data-driven intelligence, together with IoT and robotics, they facilitate operations that continuously learn, adapt and optimize across the value chain. ... Conventional thinking suggests that greater autonomy widens the attack surface and diminishes human oversight turning it into a security liability. However, if designed with cyber resilience at its core, autonomous supply chain can act like a “digital immune system,” becoming one of the most powerful enablers of security. ... As AI operations and autonomous supply chains scale, traditional perimeter simply won’t work. Organizations must adopt a Zero Trust security model to eliminate implicit trust at every access point. A Zero Trust model, centered on AI-driven identity and access management, ensures continuous authentication, network micro-segmentation and controlled access across users, devices and partners. By enforcing “never trust, always verify,” organizations can minimize breach impact and contain attackers from freely moving across systems, maintaining control even in highly automated environments. ... Autonomy in the supply chain thrives on data sharing and connectivity across suppliers, carriers, manufacturers, warehouses and retailers, making end-to-end visibility and governance vital for both efficiency and security.

When enterprise edge cases become core architecture

What matters most is not the presence of any single technology, but the requirements that come with it. Data that once lived in separate systems now must be consistent and trusted. Mobile devices are no longer occasional access points but everyday gateways. Hiring workflows introduce identity and access considerations sooner than many teams planned for. As those realities stack up, decisions that once arrived late in projects are moving closer to the start. Architecture and governance stop being cleanup work and start becoming prerequisites. ... AI is no longer layered onto finished systems. Mobile is no longer treated as an edge. Hiring is no longer insulated from broader governance and security models. Each of these shifts forces organizations to think earlier about data, access, ownership and interoperability than they are used to doing. What has changed is not just ambition, but feasibility. AI can now work across dozens of disparate systems in ways that were previously unrealistic. Long-standing integration challenges are no longer theoretical problems. They are increasingly actionable -- and increasingly unavoidable. ... As a result, integration, identity and governance can no longer sit quietly in the background. These decisions shape whether AI initiatives move beyond experimentation, whether access paths remain defensible and whether risk stays contained or spreads. Organizations that already have a clear view of their data, workflows and access models will find it easier to adapt.

Why New Enterprise Architecture Must Be Built From Steel, Not Straw

Architecture must reflect future ambition. Ideally, architects build systems with a clear view of where the product and business are heading. When a system architecture is built for the present situation, it’s likely lacking in flexibility and scalability. That said, sound strategic decisions should be informed by well-attested or well-reasoned trends, not just present needs and aspirations. ... Tech leaders should avoid overcommitting to unproven ideas—i.e., not get "caught up" in the hype. Safe experimentation frameworks (from hypothesis to conclusion) reduce risk by carefully applying best practices to testing out approaches. In a business context with something as important as the technology foundation the organization runs in, do not let anyone mischaracterize this as timidity. Critical failure is a career-limiting move, and potentially an organizational catastrophe. ... The art lies in designing systems that can absorb future shifts without constant rework. That comes from aligning technical decisions not only with what the company is today, but also what it intends to become. Future-ready architecture isn’t the comparatively steady and predictable discipline it was before AI-enabled software features. As a consequence, there’s wisdom in staying directional, rather than architecting for the next five years. Align technical decisions with long-term vision but built with optionality wherever possible.

Why Engineering Culture Is Everything: Building Teams That Actually Work

The culture is something that is a fact and it's also something intrinsic with human beings. We're people, we have a background. We were raised in one part of the world versus another. We have the way that we talk and things that we care about. All those things influence your team indirectly and directly. It's really important, you as a leader, to be aware that as an engineer, I use a lot of metaphors from monitoring and observability. We always talk about known knowns, known unknowns, and unknown unknowns. Those are really important to understand on a systems level, period, because your social technical system is also a system. The people that you work with, the way you work, your organization, it's a system. And if you're not aware of what are the metrics you need to track, what are the things that are threats to it, the good old strengths, weaknesses, opportunities, and threats. ... What we can learn from other industries is their lessons. Again, we are now on yet another industrial revolution. This time it's more of a knowledge revolution. We can learn from civil engineering like, okay, when the brick was invented, that was a revolution. When the brick was invented, what did people do in order to make sure that bricks matter? That's a fascinating and very curious story about the Freemasons. People forget the Freemasons were a culture about making sure that these constructions techniques, even more than the technologies, the techniques, were up to standards.

Daily Tech Digest - October 29, 2025

Quote for the day:

“If you don’t have a competitive advantage, don’t compete.” -- Jack Welch

Intuit learned to build AI agents for finance the hard way: Trust lost in buckets, earned back in spoonfuls

Intuit's technical strategy centers on a fundamental design decision. For financial queries and business intelligence, the system queries actual data, rather than generating responses through large language models (LLMs). Also critically important: That data isn't all in one place. Intuit's technical implementation allows to ingest data from multiple distinct sources: native Intuit data, -connected third-party systems like for payments and user-uploaded files such as spreadsheets containing vendor pricing lists or marketing campaign data. This creates a unified data layer that AI agents can query reliably. ... Beyond the technical architecture, Intuit has made a core user experience across its AI agents. This goes beyond simply providing correct answers: It means showing users the reasoning behind automated decisions. When Intuit's accounting agent categorizes a transaction, it doesn't just display the result; it shows the reasoning. This isn't marketing copy about , it's actual UI displaying data points and logic. ... In domains where accuracy is critical, consider whether you need content generation or data query translation. Intuit's decision to treat AI as an orchestration and natural language interface layer dramatically reduces hallucination risk and avoids using AI as a generative system.

Step aside, SOC. It’s time to ROC

The typical SOC playbook is designed to contain or remediate issues after the fact by applying a patch or restoring a backup, but they don’t anticipate or prevent the next hit. That structure leaves executives without the proper context or language they need to make financially sound decisions about their risk exposure. ... At its core, the Resilience Risk Operations Center (ROC) is a proactive intelligence hub. Think of it as a fusion center in which cyber, business and financial risk come together to form one clear picture. While the idea of a ROC isn’t entirely new — versions of it have existed across government and private sectors — the latest iterations emphasize collaboration between technical and financial teams to anticipate, rather than react to, threats. ... Of course, building the ROC wasn’t all smooth sailing. Just like military adversaries, cyber criminals are constantly evolving and improving. Scarier yet, just a single keystroke by a criminal actor can set off a chain reaction of significant disruptions. That makes trying to anticipate their next move feel like playing chess against an opponent who is changing the rules mid-game. There was also the challenge of breaking down the existing silos between cyber, risk and financial teams. ... The ROC concept represents the first real step in that journey towards . It’s not as a single product or platform, but as a strategic shift toward integrated, financially informed cyber defense.

Data Migration in Software Modernization: Balancing Automation and Developers’ Expertise

The process of is often far more labor-intensive than expected. We've only described a few basic features, and even implementing this little set requires splitting a single legacy table into three normalized tables. In real-world scenarios, the number of such transformations is often significantly higher. Additionally, consider the volume of data handled by applications that have been on the market for decades. Migrating such data structures is a major task. The amount of custom logic a developer must implement to ensure data integrity and correct representation can be substantial. ... Automated data migration tools can help developers migrate to a different database management system or to a new version of the DBMS in use, applying the required data manipulations to ensure accurate representation. Also, they can copy the id, email, and nickname fields with little trouble. Possibly, there will be no issues with replicating the old users table into a staging environment. Automated data migration tools can’t successfully perform the tasks required for the use case we described earlier. For instance, infer gender from names (e.g., determine "Sarah" is female, "John" is male), or populate the interests table dynamically from user-provided values. Also, there could be issues with deduplicating shared interests across users (e.g., don’t insert "kitchen gadgets" twice) or creating the correct many-to-many relationships in user_interests.

The Quiet Rise of AI’s Real Enablers

“Models need so much more data and in multiple formats,” shared George Westerman, Senior Lecturer and Principal Research Scientist, MIT Sloan School of Management. “Where it used to be making sense of structured data, which was relatively straightforward, now it’s: ‘What do we do with all this unstructured data? How do we tag it? How do we organize it? How do we store it?’ That’s a bigger challenge.” ... As engineers get pulled deeper into AI work, their visibility is rising. So is their influence on critical decisions. The report reveals that data engineers are now helping shape tooling choices, infrastructure plans, and even high-level business strategy. Two-thirds of the leaders say their engineers are involved in selecting vendors and tools. More than half say they help evaluate AI use cases and guide how different business units apply AI models. That represents a shift from execution to influence. These engineers are no longer just implementing someone else’s ideas. They are helping define the roadmap. It also signals something bigger. AI success is not just about algorithms. It is about coordination. ... So the role and visibility of data engineers are clearly changing. But are we seeing real gains in productivity? The report suggests yes. More than 70 percent of tech leaders said AI tools are already making their teams more productive. The workload might be heavier, but it’s also more focused. Engineers are spending less time fixing brittle pipelines and more time shaping long-term infrastructure.

The silent killer of CPG digital transformation: Data & knowledge decay

Data without standards is chaos. R&D might record sugar levels as “Brix,” QA uses “Bx,” and marketing reduces it to “sweetness score.” When departments speak different data languages, integration becomes impossible. ... When each function hoards its own version of the truth, leadership decisions are built on fragments. At one CPG I observed, R&D reported a product as cost-neutral to reformulate, while supply chain flagged a 12% increase. Both were “right” based on their datasets — but the company had no harmonized golden record. ... Senior formulators and engineers often retire or are poached, taking decades of know-how with them. warns that unmanaged knowledge loss directly threatens innovation capacity and recommends systematic capture methods. I’ve seen this play out: a CPG lost its lead emulsification expert to a competitor. Within six months, their innovation pipeline slowed dramatically, while their competitor accelerated. The knowledge wasn’t just valuable — it was strategic. ... Intuition still drives most big CPG decisions. While human judgment is critical, relying on gut feel alone is dangerous in the age of AI-powered formulation and predictive analytics. ... Define enterprise-wide data standards: Create master schemas for formulations, processes and claims. Mandate structured inputs. Henkel’s success demonstrates that without shared standards, even the best tools underperform.

From Chef to CISO: An Empathy-First Approach to Cybersecurity Leadership

Rather than focusing solely on technical credentials or a formal cybersecurity education, prioritizes curiosity and hunger for learning as the most critical qualities in potential hires. His approach emphasizes empathy as a cornerstone of security culture, encouraging his team to view security incidents not as failures to be punished, but as opportunities to coach and educate colleagues. ... We're very technically savvy and it's you have a weak moment or you get distracted because you're a busy person. Just coming at it and approaching it with a very thoughtful culture-oriented response is very important for me. Probably the top characteristic of my team. I'm super fortunate. And that I have people from ages, from end to end, backgrounds from end to end that are all part of the team. But one of those core principles that they all follow with is empathy and trying to grow culture because culture scales. ... anyone who's looking at adopting new technologies in the cybersecurity world is firstly understand that the attackers have access to just about everything that you have. So, they're going to come fast and they're going to come hard at you and its they can make a lot more mistakes than you have. So, you have to focus and ensure that you're getting right every day what they can have the opportunity to get wrong.

It takes an AWS outage to prioritize diversification

, caused by a data center malfunction in Northern Virginia, didn’t just disrupt its direct customers; it served as a stark reminder of how deeply our digital world relies on a select few cloud giants. A single system hiccup in one region reverberated worldwide, stopping critical services for millions of users. ... The AWS outage is part of a broader pattern of instability common to centralized systems. ... The AWS outage has reignited a longstanding argument for organizational diversification in the cloud sector. Diversification enhances resilience. It decentralizes an enterprise’s exposure to risks, ensuring that a single provider’s outage doesn’t completely paralyze operations. However, taking this step will require initiative—and courage—from IT leaders who’ve grown comfortable with the reliability and scale offered by dominant providers. This effort toward diversification isn’t just about using a (although a combined approach with multiple is an important aspect). Companies should also consider alternative platforms and solutions that add unique value to their IT portfolios. Sovereign clouds, specialized services from companies like , managed service providers, and offer viable options. Here’s why they’re worth exploring. ... The biggest challenge might be psychological rather than technical. Many companies have internalized the idea that the hyperscalers are the only real options for cloud infrastructure.

What brain privacy will look like in the age of neurotech

What has just introduced, what has now made native as part of its accessibility protocols, is to enable picking up your intentions through neural signals and sensors that AI decodes to allow you to navigate through all of that technology. So I think the first generation of most of these devices will be optional. That is, you can get the smart watch without the neural band, you can get the airpods without the in them. But just like you can't get an Apple watch now without getting an Apple watch with a heart rate sensor, second and third generation of these devices, I think your only option will be to get the devices that have the neural sensors in them. ... There's a couple of ways to think about hacking. One is getting access to what you're thinking and another one is changing what you're thinking. One of the now classic examples in the field is how researchers were able to, when somebody was using a neural headset to play a video game, embed prompts that the conscious mind wouldn't see to be able to figure out what the person's PIN code and address were for their bank account and mailing address. In much the same way that a person's mind could be probed for how they respond to Communist messaging, a person's mind could be probed to see recognition of a four digit code or some combination of numbers and letters to be able to try to get to a person's password without them even realizing that's what's happening.

Beyond Alerts and Algorithms: Redefining Cyber Resilience in the Age of AI-Driven Threats

In an average enterprise Security Operations Center (SOC), analysts face tens of thousands of alerts daily. Even the most advanced platforms struggle with false positives, forcing teams to spend the bulk of their time sifting through noise instead of investigating real threats. The result is a silent crisis: . Skilled analysts burn out, genuine threats slip through, and the mean time to respond (MTTR) increases dangerously. But the real issue isn’t just too many alerts — it’s the lack of context. Most tools operate in isolation. An endpoint alert means little without correlation to user behavior, network traffic, or threat intelligence. Without this contextual layer, detection lacks depth and intent remains invisible. ... Resilience, however, isn’t achieved once — it’s engineered continuously. Techniques like Continuous Automated Red Teaming (CART) and Breach & Attack Simulation (BAS) allow enterprises to test, validate, and evolve their defenses in real time. AI won’t replace human judgment — it enhances it. The SOC of the future will be machine-accelerated yet human-guided, capable of adapting dynamically to evolving threats. ... Today’s are more than security leaders — they’re business enablers. They sit at the intersection of risk, technology, and trust. Boards now expect them not just to protect data, but to safeguard reputation and ensure continuity.

Quantum Circuits brings dual-rail qubits to Nvidia’s CUDA-Q development platform

Quantum Circuits’ means that it combines two different quantum computing approaches — superconducting resonators with . The qubit itself is a photon, and there’s a superconducting circuit that controls the photon. “It matches the reliability benchmarks of ions and neutral atoms with the speed of the superconducting platform,” says Petrenko. There’s another bit of quantum magic built into the platform, he says — error awareness. “No other quantum computer tells you in real time if it encounters an error, but ours does,” he says. That means that there’s potential to correct errors before scaling up, rather than scaling up first and then trying to do error correction later. In the near-term, the high reliability and built-in error correction makes it an extremely powerful tool for developing new algorithms, says Petrenko. “You can start kind of opening up a new door and tackling new problems. We’ve leveraged that already for showing new things for machine learning.” It’s a different approach to what other quantum computer makers are taking, confirms . According to Sanders, this dual-rail method combines the best of both types of qubits, lengthening coherence time, plus integrating error correction. Right now, Seeker is only available via Quantum Circuits’ own cloud platform and only has eight qubits.