Daily Tech Digest by Kannan Subbiah: ITSM

Showing posts with label ITSM. Show all posts

Daily Tech Digest - September 29, 2026

Quote for the day:

"We don't grow when things are easy. We grow when we face challenges." -- Elizbeth McCormick

🎧 Listen to the audio debrief on YouTube

Duration: 18 mins • Perfect for listening on the go.

Nine unlikely trends shaping software development

The software development landscape is experiencing a surprising shift where older, foundational technologies are re-emerging to overtake modern trends. According to InfoWorld, nine unexpected reversals are currently shaping the industry. Plain JavaScript is moving to absorb TypeScript, transforming the latter into a simple linting tool rather than a mandatory compilation step. Similarly, SQL is seeing a strong resurgence over ORMs and NoSQL databases, valued for its rigorous structure and new capabilities like running in the browser via WebAssembly. Developers are also finding that local IDEs often outperform cloud development environments due to the sheer power of modern laptops. In system architecture, monolithic designs are beating out microservices, as teams realize the deep complexities and network latency of microservices are often unnecessary for their goals. Instead of complex API integrations, developers are embracing cohesive "batteries-included" frameworks that reduce brittle glue code. We are also seeing a shift back to on-premises hardware over default cloud deployments, a preference for specialized engineering roles over the myth of the true full-stack developer, WebAssembly challenging Docker with faster, lightweight portability, and Java reclaiming dominance on the server side thanks to highly scalable virtual threads.

Beyond redundancy: Why dynamic stability matters in AI data centers

As artificial intelligence transforms data centers, the traditional approach to facility resilience is no longer enough. The challenge has shifted from static redundancy to dynamic stability. In conventional computing setups, uninterruptible power supplies and backup generators act as insurance against hardware failure. However, massive clusters of AI accelerators can change their power demand in milliseconds during training cycles. These tightly synchronized shifts create massive, instant power transitions without any actual equipment failing. Because thousands of GPUs can jump from low to full power demand almost instantly, they stress the entire electrical system. Utilities, grid researchers, and infrastructure companies are now focusing on active control to keep generators, batteries, and the grid synchronized during these sudden load changes. Modern power systems are being reimagined as dynamic buffers rather than just emergency backups, utilizing advanced firmware to absorb rapid power spikes without constantly cycling and degrading batteries. Ultimately, it is not enough for an AI data center to merely survive a localized power loss event. Operators must actively manage how the entire electrical infrastructure behaves millisecond by millisecond, ensuring the facility remains fully stable and completely responsive to the extreme, repetitive power swings of heavy AI workloads.

The human-on-the-loop advantage for MSSPs

Artificial intelligence is quickly changing how Managed Security Service Providers (MSSPs) operate, offering the ability to analyze data, automate workflows, and accelerate investigations at speeds humans cannot match. MSSPs face growing pressures—including skills shortages, complex attack surfaces, and tight budgets—making AI a crucial tool for scaling operations. However, despite the rise of automated security, AI does not eliminate the need for skilled cybersecurity professionals. Instead, it shifts the focus to a "human-on-the-loop" model, where analysts no longer perform every task manually but set guardrails, review high-risk decisions, and step in during complex incidents. AI excels at finding patterns and reducing noise, but it lacks the contextual understanding and nuanced judgment required to navigate ambiguous, real-world security threats. Furthermore, as attackers increasingly use AI-enabled techniques like prompt injection and model exploitation, AI systems themselves have become part of the attack surface. This makes human oversight essential to validate findings and challenge automated decisions. Ultimately, the most successful MSSPs will be those that blend AI-driven efficiency with adaptable, highly trained professionals who know when to trust the technology and when to override it.

IT Service Operations Is Ready For Its AI Moment

IT service operations are stepping into a new era where artificial intelligence finally moves from theory to practical application. For years, service desks and IT operations teams have struggled with a growing volume of routine requests, endless alerts, and the constant pressure to resolve issues faster. Now, the integration of artificial intelligence is offering a reliable way to shift from a reactive approach to a more proactive model. By applying modern AI tools, organizations can automate the categorization and routing of support tickets, significantly reducing the manual effort required from IT staff. Furthermore, intelligent virtual agents and improved self-service portals provide employees with immediate answers to common problems, creating a smoother and more efficient experience for everyone involved. For more complex incidents, AI assists support teams by quickly summarizing historical data and suggesting potential fixes, which directly cuts down the time it takes to restore normal operations. However, achieving this transition requires more than just buying new software. Technology leaders must focus on organizing their underlying data and refining their existing service workflows. When executed thoughtfully, adopting AI in service operations frees up technical teams to focus on strategic projects rather than getting bogged down by repetitive troubleshooting.

7 reasons IT managers fail to exceed your expectations

Many IT managers fail to meet or exceed expectations despite having strong technical backgrounds, often because the role requires skills they haven't developed. According to industry experts, the transition from a top-performing individual contributor to a manager requires critical thinking, business understanding, and leadership—areas where technical training falls short. Seven core reasons outline why IT managers often struggle in their roles. First, many are promoted without formal management training, leaving them ill-equipped to guide teams. They may also lack the emotional intelligence and interpersonal skills necessary to handle complex situations. Additionally, an individual might simply be the wrong fit for a specific management position, or they may lack clear expectations and performance metrics from their own supervisors. Sometimes, professionals take management roles just to advance their careers, even if they prefer staying technical. When they do take the role, they often juggle too many responsibilities without clear prioritization from the CIO, making it hard to stay on track. Finally, struggling managers often focus purely on flawless technology execution rather than solving the actual business problems at hand. CIOs can fix these issues by offering mentorship, establishing technical career tracks, and setting clear, business-driven goals.

Background Check Fraud: What Screening Can Miss

It is a troubling reality for security and human resources leaders that every fraudulent employee discovered by experts had successfully passed a standard background check. This vulnerability is not a flaw in the background checks themselves, which simply answer a narrow question by confirming that records exist, documents are legitimate, and names match database entries. Instead, the issue lies in the widening identity gap that has become an enormous business risk, costing companies hundreds of millions of dollars. Bad actors can now easily steal real identities, build convincing personas, optimize resumes for automated screeners, and even use generative artificial intelligence to navigate video interviews. Because traditional screening systems are not designed to compare a person's claimed history against independent sources, they fail to reveal inconsistencies in a broader digital footprint. A fabricated persona often appears legitimate if the underlying documents check out. To combat this growing threat, organizations must adopt a strategy of ongoing identity corroboration throughout the entire employment lifecycle. This broader approach focuses on ensuring that an individual is consistent, traceable, and genuine across multiple independent sources, shifting the focus from merely asking if a document is real to verifying if the person actually is who they claim to be.

Stolen AI credentials feed growing LLM proxy economy

Threat actors are increasingly utilizing over 80,000 proxy servers, known as transfer stations, to cloak illicit traffic to frontier AI models. This growing underground economy relies on stolen AI subscription credentials and API keys, which are often harvested through information stealers, phishing campaigns, and supply chain attacks targeting privileged developer accounts. By hiding the geographic origin of their traffic, attackers bypass provider controls to conduct model distillation attacks. In these attacks, carefully designed prompts extract valuable knowledge from top tier models to train competing AI systems. Security researchers have traced a significant portion of this activity to IP addresses in China and Hong Kong, echoing recent warnings from federal agencies about industrial scale distillation efforts. Beyond distillation, these proxy networks fuel widespread AI token theft, leading to hundreds of thousands of dollars in financial losses for victimized organizations. The proxies are often powered by open source relay platforms like sub2api, supported by a surprisingly robust commercial ecosystem of resellers and proxy vendors. To combat this rising threat, security experts strongly advise organizations to treat AI credentials as critical production secrets. Enterprises should implement short lived tokens, enforce strict spending limits, monitor for unusual request volumes, and quickly revoke any compromised keys.

AI Resilience: As AI Gets Smarter, Are Humans Still Getting Better?

As organizations shift toward more autonomous AI systems that reason and act, a critical new risk is emerging: cognitive dependency. While traditional AI governance focuses on machine accuracy and safety, there is growing concern about what happens to human capability when critical thinking is heavily delegated to technology. Offloading complex tasks like analysis and decision-making creates an efficiency paradox where enormous productivity gains might lead to gradual cognitive atrophy in human workers. To counter this, meaningful oversight must go beyond merely having a "human in the loop" who passively clicks approval buttons. True oversight requires a "human at the helm" who retains the ability to understand context, challenge the AI's assumptions, and confidently override recommendations when necessary. This introduces the concept of "AI resilience"—the organizational imperative to ensure employees maintain their independent judgment and domain expertise alongside AI adoption. Building this resilience involves deliberate practices, such as requiring humans to formulate their own initial judgments before viewing AI outputs and conducting critical tasks independently of AI. Ultimately, the goal is not to limit artificial intelligence, but to ensure that as machines become smarter, human workers do not lose the essential critical thinking skills required to properly govern them.

Five Ways To Use AI Coding Agents to Improve Your Software Architecture

AI coding agents are becoming essential tools for improving software architecture, especially as systems grow more complex and often rely on poorly understood legacy services. Modern architectures frequently integrate older services for specific tasks, but these often lack accurate documentation, making their use risky. AI coding agents can bridge this knowledge gap by mapping system designs, documenting data flows, and identifying potential security or logic flaws within legacy code. If necessary, these agents can even refactor the code to improve maintainability and mitigate architectural risks. Beyond dealing with legacy systems, AI agents are highly effective at finding and fixing both generic and organization-specific architectural flaws, such as API design issues or Domain-Driven Design boundary violations. They are also adept at identifying and patching security vulnerabilities, which is particularly valuable when architectures incorporate open-source packages. Furthermore, while AI agents significantly speed up coding and free teams to experiment, they must be guided by specific, measurable architectural goals and trade-offs to ensure quality. By doing so, teams can rapidly generate Minimum Viable Architectures (MVAs) and evaluate the code through measurable tests, creating a solid foundation for robust, scalable, and secure systems.

Chrome Store Hosts 'Poper Blocker' Spyware Downloaded by Millions

Millions of users have unwittingly downloaded a malicious browser extension called Poper Blocker, believing it to be a legitimate ad blocker. Despite carrying Google’s "Featured" badge and "Established Publisher" status on the Chrome Web Store, researchers at Bay Area Labs identified the program as sophisticated spyware. Once installed, the extension quietly gathers extensive amounts of sensitive information. It records detailed browser histories, captures screenshots, and extracts highly specific data from AI chatbot interactions on platforms like ChatGPT and Gemini. To bypass security reviews, the software remains inactive for its first 24 hours and uses methods to avoid detection, such as hiding its code and recognizing test environments. It then communicates with an external server to execute harmful commands. The developer behind the app, an opaque company known as Big Star Labs, has previously been caught distributing similar spyware, yet several of its applications remain freely available to millions of users. Security experts warn that standard data protection tools struggle to detect this behavior because the stolen data is heavily disguised. The situation highlights a broader issue in the digital marketplace, where users have very limited ways to distinguish safe utilities from deceptive software designed to quietly monitor their private lives each day.

Daily Tech Digest - September 09, 2026

Quote for the day:

"The only way to know if we are creating value is to measure the impact of what we ship." -- Teresa Torres

🎧 Listen to the audio debrief on YouTube

▶ Play Audio Digest

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

Who owns the whole life of an enterprise IT asset?

The article discusses a common weakness in how businesses manage their enterprise hardware. While organizations are typically good at assigning responsibility for specific tasks—such as purchasing, deploying, or repairing a server—they often fail to clarify who is accountable for the asset over its entire useful life. This fragmented approach means that crucial information is frequently lost between different stages and teams. For instance, a deployment configuration change might severely complicate troubleshooting years later, or a missing repair history could lead to poor decisions about whether an upgrade is actually worthwhile. When the records fail to travel with the equipment, the next team inherits the hardware without understanding its complete background. To solve this problem, enterprises need a designated owner who holds authority to coordinate across various functions and ensure the asset’s history remains intact and accessible. Every transition should be treated as a formal deliverable, leaving behind a clear record of what was changed and why. By maintaining a continuous, well-documented history, companies can make much better decisions regarding whether to retain, repair, repurpose, or eventually retire their critical IT assets. Ultimately, the business itself must retain true ownership of the outcome.

The AI Fluency Crisis: Upgrading Passive Data Catalogs to Active Context Engines

Although modern companies have built strong data infrastructures and stable pipelines, they often struggle to successfully deploy advanced artificial intelligence. This problem arises because, while the technical setup is structurally sound, it lacks the essential business context needed for the system to interpret information accurately. In other words, the challenge has moved from simply storing data to actually understanding its meaning. An AI model might have access to massive amounts of perfectly organized information, but if it misunderstands fundamental business terms—like what defines an "active customer"—its practical value quickly falls apart. Historically, organizations relied on data catalogs and business glossaries to manage these definitions. While these traditional repositories are excellent tools for human analysts who can use their own intuition and experience to interpret the information, they do not work well for artificial intelligence. Humans can read a definition, trace where the data came from, and accurately apply it to their work. AI systems, however, lack this built-in enterprise intuition, making them prone to misinterpreting data when they rely solely on passive catalogs. To succeed, companies must find ways to actively provide these systems with the vital business context they need.

EU age assurance debate intensifies as Macron seeks bloc-wide social media law

French President Emmanuel Macron is urging the European Commission to adopt an EU-wide law that establishes a minimum age for social media platforms. France recently attempted to pass its own age assurance legislation, but it was blocked by the country's Constitutional Council over free speech concerns. By appealing directly to European Commission President Ursula von der Leyen, Macron hopes a unified, bloc-wide framework will bypass this national roadblock and effectively protect children across Europe. Instead of a strict prohibition, experts suggest the EU might propose a hybrid approach combining baseline age requirements with parental consent and strict rules against addictive platform designs. This push for regulation highlights growing concerns that a lack of coordinated action will lead to fragmented national policies. However, the debate remains highly contested. Privacy groups strongly oppose mandatory digital age checks, arguing they pave the way for mass surveillance and threaten internet freedom. Some advocates argue that if age gates are used, they must rely on privacy-preserving technologies like zero-knowledge proofs. Still, proponents of the regulation maintain that the ideal of a completely unrestricted internet is outdated, arguing that legal oversight is necessary to hold major tech platforms accountable.

Implementing Chaos Engineering in Financial Payment Systems: Lessons from Enterprise ECS Deployments

Chaos engineering is increasingly essential for financial payment systems, particularly those using Amazon Elastic Container Service (ECS). Traditional chaos playbooks, designed for stateless web applications, often fail in fintech environments due to strict compliance rules and complex transaction states. While typical web experiments can be stopped cleanly, payment transactions mid-flight may become stuck in ambiguous states requiring manual intervention. Furthermore, regulatory frameworks like PCI DSS and SOC 2 require formal approval for intentional production degradation. Teams must adapt by starting experiments on non-critical services before moving to primary transaction paths. ECS introduces specific vulnerabilities, such as a dangerous startup window where newly launched tasks accept traffic before they are fully initialized. Chaos experiments should target these blind spots proactively. Additionally, real-world failure behaviors often diverge from configured settings. For instance, a sixty-second DNS time-to-live might actually produce a ninety-three-second failover window due to intermediate caching. Similarly, ECS availability zone rebalancing can cause start-stop loops during partial degradation. By treating chaos experiments as formal change requests with defined steady states and rollback conditions, engineering teams can build resilient payment systems, satisfy strict audit requirements, and uncover hidden infrastructure flaws before they cause a critical, costly outage.

The EU AI Act just gave you a breach notification clock you didn’t know about

The European Union Artificial Intelligence Act has introduced a strict new deadline for incident reporting that many technology leaders might be overlooking. Under Article 73, which went into effect in August, companies providing high-risk AI systems must report serious incidents within 15 days, and in some severe cases, within just two to ten days. Unlike traditional data breaches that trigger immediate technical alerts from unauthorized access, AI incidents often surface much later and indirectly. For example, a flawed algorithm might silently deny benefits or loans, creating a harmful pattern that goes completely unnoticed by standard security monitoring tools until customers begin complaining weeks later. This fundamentally changes how organizations must handle incident response. Most companies lack a dedicated process for determining whether an AI output directly caused a downstream harm. To adapt, businesses must designate clear owners for these complex judgment calls rather than leaving them to chance during a crisis. Additionally, security teams need to lower the threshold for opening investigations, treating business unit complaints and customer escalations with the same urgency as technical alerts. Taking these proactive steps ensures organizations remain compliant and better equipped to manage the hidden risks of artificial intelligence.

Service Account Credential Rotation: The Blast-Radius Checklist

Rotating service account credentials can be risky, often causing production breakdowns because organizations lose track of how and where machine identities are used. Unlike human accounts, machine credentials—such as API keys, passwords, and tokens—frequently pile up across pipelines, vaults, and scripts without clear ownership. This creates fear around revocation, as an unmapped dependency could cause an entire application to fail. To safely rotate credentials and understand their "blast radius," security teams must answer eight essential questions. They must verify if the credential is still valid and whether it has been exposed, which escalates the risk. They also need to check its access scope to understand potential security impacts. Teams must map every consumer relying on the credential, locate its "source of truth" in a vault, and identify duplicate copies spread across systems. Finding the current owner is critical for coordinating the change, and establishing a rollback plan ensures quick recovery if rotation breaks a live system. By answering these questions and mapping dependencies before taking action, organizations can turn a high-risk gamble into a controlled production change, minimizing downtime while effectively securing long-lived secrets.

Why observability has become essential to the CIO's job

Observability has steadily evolved from a simple troubleshooting tool for developers into an essential management resource for modern Chief Information Officers. As technology infrastructures become more complex and interconnected, observability provides a very clear picture of how systems are performing and whether technology investments are delivering real value. It allows technology leaders to make practical decisions, such as identifying unused software licenses or safely extending the lifespan of company laptops based on actual usage data. The rapid adoption of artificial intelligence introduces both new challenges and new opportunities for observability. On one hand, autonomous AI agents and applications create additional layers of complexity that require careful monitoring to ensure they operate correctly and safely. On the other hand, artificial intelligence significantly improves observability tools by automatically sifting through massive amounts of data, reducing unhelpful alerts, and highlighting genuine issues faster than traditional methods. While the fundamental goal remains the same, identifying and fixing problems quickly, the future of observability is shifting toward a more proactive approach. Eventually, artificial intelligence could function as a helpful digital assistant that anticipates system failures and resolves them before they disrupt the business, ensuring smooth operations across increasingly complicated enterprise environments.

How European enterprises can meet sovereignty demands without giving up global reach

European enterprises are currently facing a complex and vital challenge: balancing strict data sovereignty regulations with the urgent need for global scale and connectivity. As digital operations expand, companies must strictly comply with evolving local privacy laws and maintain complete control over their sensitive information. However, they must accomplish this without isolating themselves from the broader international cloud ecosystem, which is essential for modern business. To successfully navigate this tension, organizations are increasingly adopting distributed and localized infrastructure models. This strategic shift allows them to securely store sensitive data in local environments that meet all regulatory standards, while still interacting with global partners and services. Instead of relying entirely on centralized public networks, businesses are utilizing private, direct interconnections. This method safely routes data across borders, effectively bypassing the vulnerabilities of the public internet and ensuring that information stays protected. Ultimately, this approach provides a reliable path forward, giving companies the ability to enforce strict geographic boundaries and guarantee ongoing compliance. By modernizing their digital infrastructure, European businesses can safeguard their critical assets without sacrificing their competitive edge, continuing to drive innovation and support sustainable international growth in a highly connected modern global economy.

50% of CISOs see Mythos as a sign to exit the profession

Chief Information Security Officers are facing unprecedented stress, leading half of them to consider quitting due to the rapid rise of advanced artificial intelligence models like Anthropic's Mythos. A recent survey shows that pressure from company leadership to quickly adopt these tools is far outpacing the ability of security teams to manage the associated risks. Security leaders are exhausted by a landscape where attackers weaponize vulnerabilities almost instantly. Adding to this heavy burden is the increasing personal liability placed on executives when data breaches inevitably occur. Many new job candidates are now demanding liability insurance before even asking about budgets or team sizes. However, industry experts point out that while advanced technology heightens existing problems, it also offers practical solutions. Security teams can leverage artificial intelligence to improve their own defenses, provided they start with low-risk applications and avoid untested models in production. Despite the grueling demands, where anything less than total perfection is often viewed as a failure, some security professionals still find the work deeply rewarding. For these resilient leaders, defending their organizations and customers against complex modern threats remains a highly engaging and meaningful challenge that keeps them dedicated to the field.

AI Agent Security Is Recreating the Password Problem

As artificial intelligence agents become increasingly common in business operations, they are inadvertently recreating the classic password problem. Historically, passwords posed a security risk because they could be separated from the user and reused until someone detected the breach. Today, when teams give AI agents reusable credentials or standing service accounts to perform tasks, they introduce a similar vulnerability. An AI agent might retain access to sensitive systems like customer databases or financial records long after its original assignment is complete. Because these agents can independently decide which tools to call, lingering access can be easily exploited if the agent encounters malicious instructions or deeply compromised workflows. To prevent this, organizations need to stop giving AI agents permanent static secrets. Instead, security teams should implement brokered access models. In this setup, an agent must securely request temporary permission for each specific action it takes. A policy enforcement layer evaluates the request based on the delegated authority and the potential risk. Once the specific task concludes, the granted access immediately expires. By controlling permissions dynamically and closely monitoring automated actions, companies can safely utilize artificial intelligence without allowing temporary access to become a permanent and dangerous vulnerability.

Daily Tech Digest - June 23, 2025

Quote for the day:

"Sheep are always looking for a new shepherd when the terrain gets rocky." -- Karen Marie Moning

The 10 biggest issues IT faces today

“The AI explosion and how quickly it has come upon us is the top issue for me,” says Mark Sherwood, executive vice president and CIO of Wolters Kluwer, a global professional services and software firm. “In my experience, AI has changed and progressed faster than anything I’ve ever seen.” To keep up with that rapid evolution, Sherwood says he is focused on making innovation part of everyday work for his engineering team. ... “Modern digital platforms generate staggering volumes of telemetry, logs, and metrics across an increasingly complex and distributed architecture. Without intelligent systems, IT teams drown in alert fatigue or miss critical signals amid the noise,” he explains. “What was once a manageable rules-based monitoring challenge has evolved into a big data and machine learning problem.” He continues, saying, “This shift requires IT organizations to rethink how they ingest, manage, and act upon operational data. It’s not just about observability; it’s about interpretability and actionability at scale. ... CIOs today are also paying closer attention to geopolitical news and determining what it means for them, their IT departments, and their organizations. “These are uncertain times geopolitically, and CIOs are asking how that will affect IT portfolios and budgets and initiatives,” Squeo says.

Clouded judgement: Resilience, risk and the rise of repatriation

While the findings reflect growing concern, they also highlight a strategic shift, with 78% of leaders now considering digital sovereignty when selecting tech partners, and 68% saying they will only adopt AI services where they have full certainty over data ownership. For some, the answer is to take back control. Cloud repatriation is gaining some traction, at least in terms of mindset, but as yet, this is not translating into a mass exodus from the hyperscalers. And yet, calls for digital sovereignty are getting louder. In Europe, the Euro-Stack open letter has reignited the debate, urging policymakers to champion a competitive, sovereign digital infrastructure. But while politics might be a trigger, the key question is not whether businesses are abandoning cloud (most aren’t) but whether the balance of cloud usage is changing, driven as much by cost as performance needs and rising regulatory risks. ... “Despite access to cloud cost-optimisation teams, there was limited room to reduce expenses,” says Jonny Huxtable, CEO of LinkPool. After assessing bare-metal and colocation options, LinkPool decided to move fully to Pulsant’s colocation service. The company claims the move achieved a 90% to 95% cost reduction alongside major performance improvements and enhanced disaster recovery capabilities.

Cookie management under the Digital Personal Data Protection Act, 2023

Effective cookie management under the DPDP Act, as detailed in the BRDCMS, requires real time updates to user preferences. Users must have access to a dedicated cookie preferences interface that allows them to modify or revoke their consent without undue complexity or delay. This interface should be easily accessible, typically through privacy settings or a dedicated cookie management dashboard. The real-time nature of these updates is crucial for maintaining compliance with the principles of consent as enshrined under the DPDP Act. When a user withdraws consent for specific cookie categories, the system must immediately cease the collection and processing of data through those cookies, ensuring that the user’s privacy preferences are respected without delay. Transparency is one of the fundamental pillars of the DPDP Act and extends to cookie usage disclosure. While the DPDP Act itself remains silent on specific cookie policies, the BRDCMS mandates the provision of a clear and accessible cookie policy. Organisations must provide clear and accessible cookie policies which outline the purposes of cookie usage, the data sharing practices and the implications of different consent choices. The cookie policy serves as a comprehensive resource enabling users to make informed decisions of their consent preferences.

AI agents win over professionals - but only to do their grunt work, Stanford study finds

According to the report, the majority of workers are ready to embrace agents for the automation of low-stakes and repetitive tasks, "even after reflecting on potential job loss concerns and work enjoyment." Respondents said they hoped to focus on more engaging and important tasks, mirroring what's become something of a marketing mantra among big tech companies pushing AI agents: that these systems will free workers and businesses from drudgery, so they can focus on more meaningful work. The authors also noted "critical mismatches" between the tasks that AI agents are being deployed to handle -- such as software development and business analysis -- and the tasks that workers are actually looking to automate. ... The study could have big implications for the future of human-AI collaboration in the workplace. Using a metric that they call the Human Agency Scale (HAS), the authors found "that workers generally prefer higher levels of human agency than what experts deem technologically necessary." ... The report further showed that the rise of AI automation is causing a shift in the human skills that are most valued in the workplace: information-processing and analysis skills, the authors said, are becoming less valuable as machines become increasingly competent in these domains, while interpersonal skills -- including "assisting and caring for others" -- is more important than ever.

New OLTP: Postgres With Separate Compute and Storage

The traditional methods for integrating databases are complex and not suited to AI, Xin said. The challenge lies in integrating analytics and AI with transactional workloads. Consider what developers would do when adding a feature to a code base, Xin said in his keynote address at the Data + AI Summit. They’d create a new branch of the codebase and make changes to the new branch. They’d use that branch to check bugs, perform testing and so on. Xin said creating a new branch is an instant operation. What’s the equivalent for databases? You only clone your production databases. It might take days. How do you set up secure networking? How do you create ETL pipelines and log data from one to another? ... Streaming is now a first-class citizen in the enterprise, Mohan told me. The separation of compute and storage makes a difference. We are approaching an era when applications will scale infinitely, both in terms of the number of instances and their scale-out capabilities. And that leads us to new questions about how we start to think about evaluation, observability and semantics. Accuracy matters. ... ADP may have the world’s best payroll data, Mohan said, but then that data has to be processed through ETL into an analytics solution like Databricks. Then comes the analytics and the data science work. The customer has to perform a significant amount of data engineering work and preparation.

Can AI Save Us from AI? The High-Stakes Race in Cybersecurity

Reluctant executives and budget hawks can shoulder some of the responsibility for slow AI adoption, but they’re hardly the only barriers. Increasingly, employees are voicing legitimate concerns about surveillance, privacy and the long-term impact of automation on job security. At the same time, enterprises may face structural issues when it comes to integration: fragmented systems, a lack of data inventory and access controls, and other legacy architectures can also hinder the secure integration and scalability of AI-driven security solutions. Meanwhile, bad actors face none of these considerations. They have immediate, unfettered access to open-source AI tools, which can enhance the speed and force of an attack. They operate without AI tool guardrails, governance, oversight or ethical constraints. ... Insider threat detection is also maturing. AI models can detect suspicious behavior, such as unusual access to data, privilege changes or timing inconsistencies, that may indicate a compromised account or insider threat. Early adopters, such as financial institutions, are using behavioral AI to flag synthetic identities by spotting subtle deviations that traditional tools often lack. They can also monitor behavioral intent signals, such as a worker researching resignation policies before initiating mass file downloads, providing early warnings of potential data exfiltration.

The complexities of satellite compute

“In cellular communications on the ground, this was solved a few decades ago. But doing it in space, you have to have the computing horsepower to do those handoffs as well as the throughput capability.” This additional compute needs to be in "a radiation tolerant form, and in such a way that they don't consume too much power and generate too much heat to cause massive thermal problems on the satellites." In LEO, satellites face a barrage of radiation. "It's an environment that's very rich in protons," O'Neill says. "And protons can cause upsets in configuration registers, they can even cause latch-ups in certain integrated circuits." The need to be more radiation tolerant has also pushed the industry towards newer hardware as, the smaller the process node, the lower the operating voltage. "Reducing operating voltage makes you less susceptible to destructive effects," O'Neill explains. One issue, a single event latch up, sees the satellite conduct a lot of current from power to ground through the integrated circuit, potentially frying it. ... Modern integrated circuits are a lot less susceptible to these single-event latch-ups, but are not completely immune. "While the core of the circuit may be operating at a very low voltage, 0.7 or 0.8 volts, you still have I/O circuits in the integrated circuit that may be required to interoperate with other ICs at 3.3 volts or 2.5 volts," O'Neill adds.

How CISOs can justify security investments in financial terms

A common challenge we see is the absence of a formal ERM program, or the fragmentation of risk functions, where enterprise, cybersecurity, and third-party risks are evaluated using different impact criteria. This lack of alignment makes it difficult for CISOs to communicate effectively with the C-suite and board. Standardizing risk programs and using consistent impact criteria enables clearer risk comparisons, shared understanding, and more strategic decision-making. This challenge is further exacerbated by the rise of AI-specific regulations and frameworks, including the NIST AI Risk Management Framework, the EU AI Act, the NYC Bias Audit Law, and the Colorado Artificial Intelligence Act. ... Communicating security investments in clear, business-aligned risk terms—such as High, Medium, or Low—using agreed-upon impact criteria like financial exposure, operational disruption, reputational harm, and customer impact makes it significantly easier to justify spending and align with enterprise priorities. ... In our Virtual CISO engagements, we’ve found that a risk-based, outcome-driven approach is highly effective with executive leadership. We frame cyber risk tolerance in financial and operational terms, quantify the business value of proposed investments, and tie security initiatives directly to strategic objectives.

From fear to fluency: Why empathy is the missing ingredient in AI rollouts

In the past, teams had time to adapt to new technologies. Operating systems or enterprise resource planning (ERP) tools evolved over years, giving users more room to learn these platforms and acquire the skills to use them. Unlike previous tech shifts, this one with AI doesn’t come with a long runway. Change arrives overnight, and expectations follow just as fast. Many employees feel like they’re being asked to keep pace with systems they haven’t had time to learn, let alone trust. A recent example would be ChatGPT reaching 100 million monthly active users just two months after launch. ... This underlines the emotional and behavioral complexity of adoption. Some people are naturally curious and quick to experiment with new technology while others are skeptical, risk-averse or anxious about job security. ... Adopting AI is not just a technical initiative, it’s a cultural reset, one that challenges leaders to show up with more empathy and not just expertise. Success depends on how well leaders can inspire trust and empathy across their organizations. The 4 E’s of adoption offer more than a framework. They reflect a leadership mindset rooted in inclusion, clarity and care. By embedding empathy into structure and using metrics to illuminate progress rather than pressure outcomes, teams become more adaptable and resilient.

Why networks need AIOps and predictive analytics

Predictive Analytics – a key capability of AIOps – forecasts future network performance and problems, enabling early intervention and proactive maintenance. Further, early prediction of bottlenecks or additional requirements helps to optimise the management of network resources. For example, when organisations have advance warning about traffic surges, they can allocate capacity to prevent congestion and outages, and enhance overall network performance. A range of mundane tasks, from incident response to work order generation to network configuration to proactive IT health checks and maintenance scheduling, can be automated with AIOps to reduce the load on IT staff and free them up to concentrate on more strategic activities. ... When traditional monitoring tools were unable to identify bottlenecks in a healthcare provider’s network that was seeing a slowdown in its electronic health records (EHR) system during busy hours, a switch to AIOps resolved the problem. By enabling observability across domains, the system highlighted that performance dipped when users logged in during shift changes. It also predicted slowdowns half an hour in advance and automatically provisioned additional resources to handle the surge in activity. The result was a 70 percent reduction in the most important EHR slowdowns, improvement in system responsiveness, and freeing up of IT human resources.

Daily Tech Digest - April 14, 2025

Quote for the day:

"Power should be reserved for weightlifting and boats, and leadership really involves responsibility." -- Herb Kelleher

The quiet data breach hiding in AI workflows

Prompt leaks happen when sensitive data, such as proprietary information, personal records, or internal communications, is unintentionally exposed through interactions with LLMs. These leaks can occur through both user inputs and model outputs. On the input side, the most common risk comes from employees. A developer might paste proprietary code into an AI tool to get debugging help. A salesperson might upload a contract to rewrite it in plain language. These prompts can contain names, internal systems info, financials, or even credentials. Once entered into a public LLM, that data is often logged, cached, or retained without the organization’s control. Even when companies adopt enterprise-grade LLMs, the risk doesn’t go away. Researchers found that many inputs posed some level of data leakage risk, including personal identifiers, financial data, and business-sensitive information. Output-based prompt leaks are even harder to detect. If an LLM is fine-tuned on confidential documents such as HR records or customer service transcripts, it might reproduce specific phrases, names, or private information when queried. This is known as data cross-contamination, and it can occur even in well-designed systems if access controls are loose or the training data was not properly scrubbed.

The Rise of Security Debt: Your Security IOUs Are Due

Despite measurable improvements, security debt — defined as flaws that remain unfixed for more than a year after discovery — continues to put enterprises at risk. Security debt impacts almost three-quarters (74.2%) of organizations, up from 71% in previous measurements. More frighteningly, half of all organizations suffer from critical security debt: a dangerous combination of high-severity, long-unresolved flaws. There's a reason it is described as critical debt: the longer a security flaw survives within an enterprise, the less likely it will be resolved. Today, more than a quarter (28%) of flaws remain open two years after discovery, and even after five years, 9% of flaws still linger in applications. ... Applications are only as secure as the code used to write them, and security flaws are a fact of life in every code base in the world. That being said, the origin of the code that is being used matters. Leveraging third-party code has become standard practice across the industry, which introduces added risks. ... organizations need the ability to correlate and contextualize findings in a single view to prioritize their backlog based on context. This allows companies to reduce the most risk with the least effort. Since the average time to fix flaws has increased dramatically, programs seeking to improve their security posture must focus on the findings that matter most in their specific context.

How to Cut the Hidden Costs of IT Downtime

"Workers struggling with these problems waste productive time waiting for fixes," said Ryan MacDonald, CTO at Liquid Web. Businesses can reduce these costs by investing in proactive IT support, automating troubleshooting processes, and training workers on best practices to prevent repeat problems, he said. MacDonald explained that while tech failures are inevitable, companies often take a reactive rather than proactive approach to IT. Instead of addressing persistent issues at their root, organizations frequently apply short-term fixes, resulting in continuous inefficiencies and mounting expenses. ... Companies that fail to modernize their systems will continue to experience recurring IT problems that hinder productivity and increase operational costs. In addition to upgrading infrastructure, organizations must conduct regular IT audits to proactively identify inefficiencies before they escalate into major disruptions. MacDonald stressed the importance of continuous evaluation. "Regularly scheduled IT audits allow companies to find recurring inefficiencies and invest money into fixing them before they become costly disruption points," he said. Rather than waiting for issues to break, businesses should implement proactive IT strategies, which can save time, reduce financial losses, and improve overall system reliability.

A multicloud experiment in agentic AI: Lessons learned

At its core, an agentic AI system is a self-governing decision-making system. It uses AI to assign and execute tasks autonomously, responding to changing conditions while balancing cost, performance, resource availability, and other factors. I wanted to leverage multiple public cloud platforms harmoniously. The architecture would have to be flexible enough to balance cloud-specific features while achieving platform-agnostic consistency. ... challenges with interoperability, platform-specific nuances, and cost optimization remain. More work is needed to improve the viability of multicloud architectures. The big gotcha is that the cost was surprisingly high. The price of resource usage on public cloud providers, egress fees, and other expenses seemed to spring up unannounced. Using public clouds for agentic AI deployments may be too expensive for many organizations and push them to cheaper on-prem alternatives, including private clouds, managed services providers, and colocation providers. I can tell you firsthand that those platforms are more affordable in today’s market and provide many of the same services and tools. This experiment was a small but meaningful step toward realizing a future where cloud environments serve as dynamic, self-managing ecosystems.

What boards want and don’t want to hear from cybersecurity leaders

A lack of clarity can lead to either oversharing technical details or not providing enough strategic context. Paul Connelly, former CISO turned board advisor, independent director and mentor, finds many CISOs focus too heavily on metrics while the board is looking for more strategic insights. The board doesn’t need to know the results of your phishing test, says Connelly. Boards are focused on risks the organization faces, strategies to address these risks, progress updates, obstacles to success, and whether they’re tackling the right things. “I coach CISOs to study their board — read their bios, understand their background, and understand the fiduciary responsibility of a board,” he says. The goal is to understand the make-up of the board and their priorities and channel their metrics into risk and threat analysis for the business. Using this information, CISOs can develop a story about their program aligned with the business. “That high-level story — supported by measurements — is what boards want to hear, not a bunch of metrics on malicious emails and critical patches or scary Chicken Little-type of threats,” Connelly tells CSO. However, it’s not a one-way interaction, yet many CISOs are engaging with boards that lack the appropriate skills and understanding to foster meaningful discussions on cyber threats. “Very few boards have any directors with true expertise in technology or cyber,” says Connelly.

The future of insurance is digital, intelligent, and customer-first

The Indian insurance sector is undergoing transformative changes, driven by insurtech innovations, personalised policies, and efficient claim settlements. Reliance General Insurance leads this evolution by integrating AI, data science, and automation to enhance customer experiences. According to Deloitte, 70% of Central European insurers have recently partnered with insurtech, with 74% expressing satisfaction, highlighting the global trend of technological collaboration. Emphasising innovation, speed, and customer-centric measures, the industry aims to demystify insurance, boost its adoption, and eliminate service hindrances, steering towards a technology-oriented future. ... Protecting our customer’s data is essential at Reliance General Insurance. To avoid the misuse of the customer information, the company employs a strong multi-layered security framework involving encryption, threat intelligence services, and real-time monitoring. To help mitigate these risks, we also offer cyber insurance products. ... As much as self-regulatory innovation evokes progressive strides, risk management becomes paramount in the adoption of insurtech solutions. Seamlessly integrating new technologies is the objective, and Reliance General employs constant feedback monitoring to ensure new technologies meet security and regulatory standards.

Examining the business case for multi-million token LLMs

As enterprises weigh the costs of scaling infrastructure against potential gains in productivity and accuracy, the question remains: Are we unlocking new frontiers in AI reasoning, or simply stretching the limits of token memory without meaningful improvements? This article examines the technical and economic trade-offs, benchmarking challenges and evolving enterprise workflows shaping the future of large-context LLMs. ... Increasing the context window also helps the model better reference relevant details and reduces the likelihood of generating incorrect or fabricated information. A 2024 Stanford study found that 128K-token models reduced hallucination rates by 18% compared to RAG systems when analyzing merger agreements. However, early adopters have reported some challenges: JPMorgan Chase’s research demonstrates how models perform poorly on approximately 75% of their context, with performance on complex financial tasks collapsing to near-zero beyond 32K tokens. Models still broadly struggle with long-range recall, often prioritizing recent data over deeper insights. This raises questions: Does a 4-million-token window truly enhance reasoning, or is it just a costly expansion of memory? How much of this vast input does the model actually use? And do the benefits outweigh the rising computational costs?

IT compensation satisfaction at an all-time low

“We’re going through a leveling of the economy right now,” Sutton said, adding that during difficult business periods employees crave consistency and reliability. “There is a little bit of satisfaction and contentment with what is seen as a stable role.” Industry observers also said that although money is a critical factor in how appreciated employees feel, unhappiness with one’s IT role is often a result of other factors, such as changing job descriptions and a general lack of job security. “Compensation is not the only tool enterprises have to improve employee experience and satisfaction. Enterprises can make sure that their employees are focused on work that excites them and they can see the value of,” Forrester’s Mark said. “Provide ample opportunities for upskilling in line not just with the technology strategy, but also with employees’ career aspirations. Ensure that employees feel empowered and have autonomy over decisions which impact them, and of course manage work-life balance, demonstrating that organizations do not simply value the work outputs, but the employees themselves as unique individuals.” Matt Kimball, VP and principal analyst for Moor Insights and Strategy, agreed that employee sentiment goes well beyond salary and bonuses.

Amazon Gift Card Email Hooks Microsoft Credentials

The Cofense Phishing Defense Center (PDC) has recently identified a new credential phishing campaign that uses an email disguised as an Amazon e-gift card from the recipient’s employer. While the email appears to offer a substantial reward, its true purpose is to harvest Microsoft credentials from unsuspecting recipients. The combination of the large monetary value and the appearance of an email seemingly from their employer lures the recipient into a false sense of security that leaves them unaware of the dangers ahead. ... Once the recipient submits their email address, they will be redirected to a phishing page, as shown in Figure 3. The phishing page is well-disguised as a legitimate Microsoft login site, once again prompting the victim to input their credentials. Legitimate Microsoft Outlook login pages should be hosted on domains belonging to Microsoft (such as live.com or outlook.com), but as you can see in Figure 3, the domain for this site is officefilecenter[.]com, which was created less than a month before the time of analysis. Credential phishing emails such as these are a perfect example of the various ways that threat actors can exploit the emotions of the recipient. Whether it is the theme of phish, the content within, or the time of the year, threat actors will utilize anything they can to make sure you do not catch on until it’s too late.

Driving Sustainability Forward with IIoT: Smarter Processes for a Greener Future

AI-driven IIoT systems are transforming how industries manage raw materials, inventory, and human resources. In smart factories, AI forecasts demand, streamlines production schedules, and optimizes supply chains to reduce waste and emissions. For instance, AI calculates the exact quantity of materials needed for production, preventing overstocking and minimizing excess. It also enhances SIOP and logistics by consolidating shipments and selecting eco-friendly transportation routes, reducing the carbon footprint of global supply chains. Predictive maintenance, powered by AI, contributes by detecting equipment issues early, preventing breakdowns, extending lifespan and uptime while reducing defective outputs. ... IIoT is a key enabler of the circular economy, which focuses on recycling, reusing, and reducing waste. Automated systems allow manufacturers to recycle heat, water, and materials within their facilities, creating closed-loop processes. For example, excess heat from industrial ovens can be captured and repurposed for heating water or other facility needs. While sensors monitor production processes to optimize material usage and reduce scrap, product take-back programs are another cornerstone of the circular economy.

Daily Tech Digest - August 06, 2024

Why the Network Matters to Generative AI

Applications, today, are distributed. Our core research tells us more than half (60%) of organizations operate hybrid applications; that is, with components deployed in core, cloud, and edge locations. That makes the Internet their network, and the lifeline upon which they depend for speed and, ultimately, security. Furthermore, our focused research tells us that organizations are already multi-model, on average deploying 2.9 models. And where are those models going? Just over one-third (35%) are deploying in both public cloud and on-premises. Applications that use those models, of course, are being distributed in both environments. According to Red Hat, some of those models are being used to facilitate the modernization of legacy applications. ... One is likely tempted to ask why we need such a thing. The problem is we can’t affect the Internet. Not really. For all our attempts to use QoS to prioritize traffic and carefully select the right provider, who has all the right peering points, we can’t really do much about it. For one thing, over-the-Internet connectivity doesn’t typically reach into another environment, in which there are all kinds of network challenges like overlapping IP addresses, not to mention the difficulty in standardizing security policies and monitoring network activity.

Aware of what tech debt costs them, CIOs still can’t make it an IT priority

The trick for CIOs who have significant tech debt is to sell it to organization leadership, he says. One way to frame the need to address tech debt is to tie it to IT modernization. “You can’t modernize without addressing tech debt,” Saroff says. “Talk about digital transformation.” ... “You don’t just say, ‘We’ve got an old ERP system that is out of vendor support,’ because they’ll argue, ‘It still works; it’s worked fine for years,’” he says. “Instead, you have to say, ‘We need a new ERP system because you have this new customer intimacy program, and we’ll either have to spend millions of dollars doing weird integrations between multiple databases, or we could upgrade the ERP.’” ... “A lot of it gets into even modernization as you’re building new applications and new software,” he says. “Oftentimes, if you’re interfacing with older platforms that have sources of data that aren’t modernized, it can make those projects delayed or more complicated.” As organizational leaders push CIOs to launch AI projects, an overlooked area of tech debt is data management, adds Ricardo Madan, senior vice president for global technology services at IT consulting firm TEKsystems.

Is efficiency on your cloud architect’s radar?

Remember that we can certainly measure the efficiency of each of the architecture’s components, but that only tells you half of the story. A system may have anywhere from 10 to 1,000 components. Together, they create a converged architecture, which provides several advantages in measuring and ensuring efficiency. Converged architectures facilitate centralized management by combining computing, storage, and networking resources. ... With an integrated approach, converged architectures can dynamically distribute resources based on real-time demand. This reduces idle resources and enhances utilization, leading to better efficiency. Automation tools embedded within converged architectures help automate routine tasks such as scaling, provisioning, and load balancing. These tools can adjust resource allocation in real time, ensuring optimal performance without manual intervention. Advanced monitoring tools and analytics platforms built into converged architectures provide detailed insights into resource usage, cost patterns, and performance metrics. This enables continuous optimization and proactive management of cloud resources.

ITSM concerns when integrating new AI services

The key to establishing stringent access controls lies in feeding each LLM only the information that its users should consume. This approach eliminates the concept of a generalist LLM fed with all the company’s information, thereby ensuring that access to data is properly restricted and aligned with user roles and responsibilities. ... To maintain strict control over sensitive data while leveraging the benefits of AI, organizations should adopt a hybrid approach that combines AI-as-a-Service (AIaaS) with self-hosted models. For tasks involving confidential information, such as financial analysis and risk assessment, deploying self-hosted AI models ensures data security and control. Meanwhile, utilizing AIaaS providers like AWS for less sensitive tasks, such as predictive maintenance and routine IT support, allows organizations to benefit from the scalability and advanced features offered by cloud-based AI services. This hybrid strategy ensures that sensitive data remains secure within the organization’s infrastructure while taking advantage of the innovation and efficiency provided by AIaaS for other operations.

Fighting Back Against Multi-Staged Ransomware Attacks Crippling Businesses

Ransomware has evolved from lone wolf hackers operating from basements to complex organized crime syndicates that operate just like any other professional organization. Modern ransomware gangs employ engineers that develop the malware and platform; employ help desk staff to answer technical queries; employ analysts that identify target organizations; and ironically, employ PR pros for crisis management. The ransomware ecosystem also comprises multiple groups with specific roles. For example, one group (operators) builds and maintains the malware and rents out their infrastructure and expertise (a.k.a. ransomware-as-a-service). Initial access brokers specialize in breaking into organizations and selling the acquired access, data, and credentials. Ransomware affiliates execute the attack, compromise the victim, manage negotiations, and share a portion of their profits with the operators. Even state-sponsored attackers have joined the ransomware game due to its potential to cause wide-scale disruption and because it is very lucrative.

Optimizing Software Quality: Unit Testing and Automation

Any long-term project without proper test coverage is destined to be rewritten from scratch sooner or later. Unit testing is a must-have for the majority of projects, yet there are cases when one might omit this step. For example, you are creating a project for demonstrational purposes. The timeline is very tough. Your system is a combination of hardware and software, and at the beginning of the project, it's not entirely clear what the final product will look like. ... in automation testing the test cases are executed automatically. It happens much faster than manual testing and can be carried out even during nighttime as the whole process requires minimum human interference. This approach is an absolute game changer when you need to get quick feedback. However, as with any automation, it may need substantial time and financial resources during the initial setup stage. Even so, it is totally worth using it, as it will make the whole process more efficient and the code more reliable. The first step here is to understand if the project incorporates test automation. You need to ensure that the project has a robust test automation framework in place.

In the age of gen AI upskilling, learn and let learn

Gen AI upskilling is neither a one-off endeavor nor a quick fix. The technology’s sophistication and ongoing evolution requires dedicated educational pathways powered by continuous learning opportunities and financial support. So, as leaders, we need to provide resources for employees to participate in learning opportunities (that is, workshops), attend third-party courses offered by groups like LinkedIn, or receive tuition reimbursements for upskilling opportunities found independently. We must also ensure that these resources are accessible to our entire employee base, regardless of the nature of an employee’s day-to-day role. From there, you can institutionalize mechanisms for documenting and sharing learnings. This includes building and popularizing communication avenues that motivate employees to share feedback, learn together and surface potential roadblocks. Encouraging a healthy dialogue around learning, and contributing to these conversations yourself, often leads to greater innovation across your organization. At my company, we tend to blend the learning and sharing together.

Embracing Technology: Lessons Business Leaders Can Learn from Sports Organizations

To maintain their competitive edge, sports organizations are undertaking comprehensive digital transformations. Digital technologies are integrated across all facets of operations, transforming people, processes, and technology. Data analytics guide decisions in areas such as player recruitment, game strategies, and marketing efforts. ... The convergence of sports and technology reveals new business opportunities. Sponsorships from technology companies showcase their capabilities to targeted audiences and open up new markets. Innovations in sports technology, such as advanced training equipment and analytical tools, are driving unprecedented possibilities. By embracing these insights, business leaders can unlock new avenues for growth and innovation in their own industries. Partnering with technology firms can lead to the development of new products, services, and market opportunities, ensuring sustained success and relevance in an ever-evolving business landscape.

Containerization Can Render Apps More Agile Painlessly

Application development and deployment methods will change because the app developer no longer has to think about the integration of an app with an underlying operating system and associated infrastructure. This is because the container already has the correct configuration of all these elements. If an app developer wants to immediately install his app in both Linux and Windows environments, he can do it. ... Most IT staff have found that they need specialized tools for container management, and that they can’t use the tools that they are accustomed to. Companies like Kubernetes, Dynatrace, and Docker all provide container management tools, but mastering these tools requires IT staff to be trained on the tools. Security and governance also present challenges in the container environment because each container uses its own operating system kernel. If an OS security vulnerability is discovered, the OS kernel images across all containers must be synchronously fixed with a security patch to resolve the vulnerability. In cases like this, it’s ideal to have a means of automating the fix process, but it might be necessary to do it manually at first.

Can AI even be open source? It's complicated

Clearly, we need to devise an open-source definition that fits AI programs to stop these faux-source efforts in their tracks. Unfortunately, that's easier said than done. While people constantly fuss over the finer details of what's open-source code and what isn't, the Open Source Initiative (OSI) has nailed down the definition, the Open Source Definition (OSD), for almost twenty years. The convergence of open source and AI is much more complicated. In fact, Joseph Jacks, founder of the Venture Capitalist (VC) business FOSS Capital, argued there is "no such thing as open-source AI" since "open source was invented explicitly for software source code." It's true. In addition, open-source's legal foundation is copyright law. As Jacks observed, "Neural Net Weights (NNWs) [which are essential in AI] are not software source code -- they are unreadable by humans, nor are they debuggable." As Stefano Maffulli, OSI executive director, has told me, software and data are mixed in AI, and existing open-source licenses are breaking down. Specifically, trouble emerges when all that data and code are merged in AI/ML artifacts -- such as datasets, models, and weights.

Quote for the day:

"Leadership does not depend on being right." -- Ivan Illich