Daily Tech Digest by Kannan Subbiah: management

Showing posts with label management. Show all posts

Daily Tech Digest - June 18, 2026

Quote for the day:

“The most important thing in communication is hearing what isn’t said.” -- Peter F. Drucker

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

Why Account Takeovers Are Rising and How to Stop Them

Account takeovers are increasing because organizations now manage thousands of identities across complex hybrid, cloud, and remote work environments. Instead of attacking infrastructure, cybercriminals are targeting the authentication process itself, finding it much faster and quieter. While multifactor authentication remains important, attackers have adapted by using prompt bombing to exhaust users into approving access, or by stealing session tokens to bypass logins entirely. Additionally, phishing campaigns have become harder to spot, often using legitimate hosting services to trick even cautious employees into giving up their credentials. Another major vulnerability stems from employees using unmanaged personal devices to access corporate networks. Malware on these devices can easily harvest passwords and session cookies. Because traditional security tools often treat a successful login as complete proof of trust, these compromised devices easily slip through the cracks. To stop modern account takeovers, organizations must move beyond simply checking usernames and passwords at the door. They need continuous verification systems that assess device health and monitor session risks throughout the entire access lifecycle. By verifying that a device is genuinely safe and updated before and during a session, companies can effectively block unauthorized access.

Securing digital keys when your phone unlocks the car

Alysia Johnson, President of the Car Connectivity Consortium (CCC), outlines the evolution of the CCC Digital Key from a brand-specific convenience to a standardized, multi-vendor credential. This transition shifts the security model from implicit trust within a single company's hardware to a system demanding verifiable trust across a diverse ecosystem. To address this, the CCC relies on standardized certification, secure elements, and interoperable protocols. Version 4 of the standard focuses on improving interoperability, validation, and consistent behavior across various devices and vehicles, rather than addressing a new specific threat, building upon the high security baseline established in Version 3. NFC, often a fallback when batteries die, is not a weak link. It requires close proximity and explicit user action, maintaining the same security principles as the broader architecture. The system supports swift credential revocation if a device is lost or compromised, synchronizing across the ecosystem and utilizing cryptographic challenge-response mechanisms to prevent replay attacks. Recognizing the long lifespan of vehicles, the CCC designed the standard with crypto-agility, allowing algorithms to evolve as needed. Post-quantum migration is also an active topic within the consortium to ensure long-term security.

5 things CIOs must do as sovereignty becomes a design constraint

As global tensions rise and regulations increase, businesses can no longer assume that location does not matter. Geography has become a strict requirement, forcing technology leaders to rethink where they place their data and systems. First, companies must treat physical location as a fundamental technical decision, moving away from relying entirely on a single global provider. Instead, they should adopt a more practical approach. Second, businesses need to design their systems for deep resilience rather than pure efficiency, reducing the risk of relying too heavily on any single vendor by actively diversifying their technology setup. Third, it is essential to sort applications and data based on their specific risk levels. While most data can safely remain in public platforms, highly sensitive information requires secure, localized storage. Fourth, companies must build their systems with the ongoing flexibility to move applications easily if global or regulatory conditions change, avoiding rigid vendor contracts. Finally, the concept of secure access must extend beyond the data center to remote workers, focusing on identity verification rather than just basic device security. Ultimately, managing technology is now about balancing long-term risks instead of simply hunting for the absolute lowest costs.

Security Community Slams US Ban on Exporting Mythos, Fable

The cybersecurity community is strongly criticizing the United States government’s decision to ban the export of Anthropic’s new artificial intelligence models, Claude Fable 5 and Mythos 5, to foreign nationals. The government enacted this ban over national security concerns, citing the models' potential ability to find and exploit software vulnerabilities. This action was allegedly prompted by a reported method to bypass the software's safety limits. In response, dozens of prominent security experts have signed an open letter urging the government to reverse the restriction. They argue that blocking access to these advanced tools actively harms the nation's digital defenses by preventing security teams from finding and fixing vulnerabilities before attackers do. Furthermore, industry leaders point out that the ban will do very little to actually stop foreign adversaries or cybercriminals. Adversary nations like China and various financially motivated attackers already possess equivalent technological capabilities, either through available public alternatives or their own undisclosed research. Ultimately, experts believe that restricting these tools based on fear or an incomplete understanding of the technology leaves network defenders at a significant disadvantage, while completely failing to meaningfully impede the malicious actors the ban intends to target.

20 principles of good management that most managers don't practice

Many managers fail not from a lack of knowledge, but from an inability to consistently apply foundational management principles under pressure. Organizations frequently promote individuals based on their technical skills rather than their leadership capabilities, leading to entirely predictable workplace dysfunction. Genuinely effective management relies on disciplined habits rather than innate talent. The core principles involve straightforward but consistently neglected daily practices. First, effective leaders provide prompt, relevant feedback rather than waiting for formal annual reviews, ensuring guidance feels like support rather than judgment. Second, they ask questions instead of merely issuing answers, training their teams to think critically and solve complex problems independently. Third, they distribute decision-making authority to those closest to the actual work, taking the time to explain their reasoning to cultivate better future judgment among the staff. Fourth, they set explicit expectations to eliminate confusion and establish shared accountability, allowing employees to operate with clear direction. Finally, they actively protect their team's time and attention by minimizing unnecessary meetings and establishing communication norms that allow for deep, focused work. Ultimately, management succeeds through steady commitment to these basic practices, fostering genuine trust and autonomy.

Observability Is the New Control Plane for Enterprise Transformation

As businesses adopt increasingly complex technologies like cloud environments and artificial intelligence, they face a critical challenge: understanding how these interconnected systems actually perform. Many leaders lack the clear data needed to make informed decisions about their technology investments, leading to a significant gap between what they build and what they can effectively manage. Traditional tracking methods were built for simpler setups and simply cannot handle today's scattered and unpredictable systems. Operating without clear visibility carries steep costs. When technology fails, companies lose money for every hour an outage lasts. Engineering teams waste valuable time trying to piece together information from disconnected tools instead of fixing the root problem. Beyond immediate downtime, this lack of shared information creates a hidden tax on the entire organization, slowing down operations and complicating incident reviews. However, companies that adopt a unified approach to monitoring their technology see reliable benefits. By bringing all their system data into a single cohesive view, organizations can steadily reduce the financial impact of outages and achieve clear returns on their investment, proving that true success lies in fully understanding their technology rather than just deploying more of it.

Before enabling embedded AI, Indian enterprises need vendor model disclosure

The article discusses the crucial need for transparency as Indian enterprises increasingly adopt software tools with embedded artificial intelligence. While these built-in AI features promise enhanced productivity, they also introduce significant challenges regarding data privacy, security, and ethical governance. To manage these risks effectively, companies must demand comprehensive disclosure from their technology vendors. This transparency should clearly outline how the underlying models are trained, what kinds of data they process, and how user privacy is maintained. Without this information, enterprises face the danger of intellectual property leaks, compliance violations, and unintended algorithmic biases. The piece highlights that true accountability cannot be achieved in a vacuum; instead, it requires collaborative standards between software developers and corporate users. By establishing clear model disclosures, Indian businesses can safely deploy automated systems while maintaining a strong ethical foundation and protecting proprietary information. Ultimately, the author advises decision-makers to move beyond the initial excitement of automation and instead focus on establishing rigorous verification protocols before fully integrating these tools into their core workflows.

AI's Catastrophic Risk Isn't Rogue Machines, It's Cognitive Surrender

The real danger of artificial intelligence may not be the science-fiction nightmare of rogue machines turning against us, but rather a subtle, internal shift toward "cognitive surrender." As AI tools increasingly handle our analysis, coding, and writing, they dismantle the traditional incentives for learning and mastery. When individuals can generate competent work in seconds, the long-term process of building skills—once a foundation for personal identity and professional pride—starts to feel unnecessary or even futile. This trend is worsened by a broader sense of economic insecurity among younger generations, who are already losing faith in the traditional "work hard to succeed" narrative. Because the future feels increasingly unstable and inaccessible, many are tempted to bypass the friction of deep thought, choosing instead to outsource their deliberation to AI. This constant reliance on artificial intelligence threatens to weaken our capacity for sustained, independent reasoning. Ultimately, the challenge is not just that we might be replaced by machines, but that we may voluntarily abandon the effort and struggle required to develop our own expertise. Even if AI can perform tasks, it cannot replicate the uniquely human satisfaction found in the process of creating something through genuine personal effort.

AI is eroding trust. Accounting and finance professionals can rebuild it

Accounting and finance professionals are currently facing a significant decline in industry confidence. While economic and global pressures play a part, the rapid adoption of artificial intelligence has emerged as a primary concern. Many professionals worry that new software is being implemented too quickly without the necessary plans or controls. There are also valid concerns about the quality of the technology's output, as minor automation errors can easily multiply, leading to major reporting mistakes and basic compliance issues. Ultimately, this creates a widespread loss of trust in financial data and related decisions. To rebuild this trust, finance professionals must step in to bridge the gap between software systems and human oversight. Rather than simply learning the technical details of the software, accountants need to focus on practical uses like forecasting and managing risk. It is essential for professionals to act as leaders in compliance, learning how to identify biases, correct mistakes, and oversee these new systems effectively. By combining the speed of the technology with dependable human analysis, teams can deliver accurate recommendations. Developing these skills through targeted training programs will ensure professionals remain effective and can responsibly guide their teams forward.

The Technology Trend Hiding in Plain Sight: Why Businesses Are Rediscovering the Power of Constraints

For decades, technological progress has been defined by abundance, offering companies an ever-expanding array of choices, data, and computing power. However, this limitless possibility has created new challenges. Many businesses now find themselves overwhelmed by options, making decision-making difficult and diluting their focus. In response, organizations are quietly rediscovering the strategic value of constraints. Rather than viewing limitations as obstacles, leaders are realizing that boundaries actually drive better outcomes. Constraints force companies to prioritize what truly matters, clarify their objectives, and distinguish between what is merely possible and what is genuinely essential. In a highly complex environment, the simple ability to focus is becoming a significant competitive advantage. Limits help organizations simplify their daily operations, manage data more effectively, and introduce new systems at a pace that employees can comfortably absorb. Trust itself relies on clear boundaries and solid governance. As companies mature in their technology use, they are shifting away from adopting every new advancement and instead optimizing the systems that deliver the most value. Ultimately, success no longer relies on having access to endless resources, but on having the discipline to know exactly what to leave out.

Daily Tech Digest - July 31, 2025

Quote for the day:

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

AppGen: A Software Development Revolution That Won't Happen

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

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

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

New AI model offers faster, greener way for vulnerability detection

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

Building Adaptive Data Centers: Breaking Free from IT Obsolescence

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

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

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

How to Build Your Analytics Stack to Enable Executive Data Storytelling

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

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

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

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

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

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

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

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

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

Daily Tech Digest - November 09, 2023

MIT Physicists Transform Pencil Lead Into Electronic “Gold”

MIT physicists have metaphorically turned graphite, or pencil lead, into gold by isolating five ultrathin flakes stacked in a specific order. The resulting material can then be tuned to exhibit three important properties never before seen in natural graphite. ... “We found that the material could be insulating, magnetic, or topological,” Ju says. The latter is somewhat related to both conductors and insulators. Essentially, Ju explains, a topological material allows the unimpeded movement of electrons around the edges of a material, but not through the middle. The electrons are traveling in one direction along a “highway” at the edge of the material separated by a median that makes up the center of the material. So the edge of a topological material is a perfect conductor, while the center is an insulator. “Our work establishes rhombohedral stacked multilayer graphene as a highly tunable platform to study these new possibilities of strongly correlated and topological physics,” Ju and his coauthors conclude in Nature Nanotechnology

Conscientious Computing – Facing into Big Tech Challenges

The tech industry has driven incredibly rapid innovation by taking advantage of increasingly cheap and more powerful computing – but at what unintended cost? What collateral damage has been created in our era of “move fast and break things”? Sadly, it’s now becoming apparent we have overlooked the broader impacts of our technological solutions. As software proliferates through every facet of life and the scale of it increases, we need to think more about where this leads us from people, planet and financial perspectives. ... The classic Scope, Cost, Time pyramid – but often it’s the **observable ** functional quality that is prioritised. For that I’ll use a somewhat surreal version of an iceberg – as so much of technical (and effectively sustainability debt – a topic for a future blog) is hidden below the water line. Every engineering decision (or indecision) has ethical and sustainability consequences, often invisible from within our isolated bubbles. Just as the industry has had to raise its game on topics such as security, privacy and compliance, we desperately need to raise our game holistically on sustainability.

The CIO’s fatal flaw: Too much leadership, not enough management

So why does leadership get all the buzz? A cynic might suggest that the more respect doing-the-work gets, the more the company might have to pay the people who do that work, which in turn would mean those who manage the work would get paid more than those who think and charismatically express deep and inspirational thoughts. And as there are more people who do work than those who manage it, respecting the work and those who do it would be expensive. Don’t misunderstand. Done properly, leading is a lot of work, and because leading is about people, not processes or tools and technology; it’s time consuming, too. And in fact, when I conduct leadership seminars, the biggest barrier to success for most participants is figuring out and committing to their time budget. Leadership, that is, involves setting direction, making or facilitating decisions, staffing, delegating, motivating, overseeing team dynamics, engineering the business culture, and communicating. Leaders who are committed to improving at their trade must figure out how much time they plan to devote to each of these eight tasks, which is hard enough.

The Next IT Challenge Is All about Speed and Self-Service

One of the most significant roadblocks to rapid cloud adoption is sheer complexity. Provisioning a cloud environment involves dozens of dependent services, intricate configurations, security policies and data governance issues. The cognitive load on IT teams is significant, and the situation is exacerbated by manual processes that are still in place. The vast majority of engineering teams still depend on legacy ticketing systems to request IT for cloud environments, which adds a significant load on IT and also slows engineering teams. This slows down the entire operation, making it difficult for IT and engineering to support business needs effectively. In fact, in one study conducted by Rafay Systems, application developers at enterprises revealed that 25% of organizations reportedly take three months or longer to deploy a modern application or service after its code is complete. The real goal for any IT department is to support the needs of the business. Today, they do that better, faster and more cost-effectively by leveraging cloud technologies to realize all the business benefits of the modern applications being deployed.

The DPDP Act: Bolstering data protection & privacy, making India future-ready

The DPDP Act has a direct impact across industries. Organisations not only need to reassess their existing compliance status and gear up to cope with the new norms but also create a phased action plan for various processes. Moreover, if labeled as SDF, organisations also need to appoint a Data Protection Officer (DPO). In addition, organisations need to devise appropriate data protection and privacy policy framework in alignment with the DPDP Act. Further, consent forms and mechanisms have to be developed to ensure standard procedures as laid out in the legislation. Companies have to additionally invest to adopt the necessary changes in compliance with the law. They need to list down their third-party data handlers, consent types and processes, privacy notices, contract clauses, categorise data, and develop breach management processes. Sharing his perspective on the DPDP Act, Amit Jaju, Senior Managing Director, Ankura Consulting Group (India) says, “The Digital Personal Data Protection Act 2023 has ushered in a new era of data privacy and protection, compelling solution providers to realign their business strategies with its mandates.

Will AI hurt or help workers? It's complicated

Here's what is certain: CIOs see AI as being useful, but not replacing higher-level workers. JetRockets recently surveyed US CIOs. In its report, How Generative AI is Impacting IT Leaders & Organizations, the custom-software firm found that CIOs are primarily using AI for cybersecurity and threat detection (81%), with predictive maintenance and equipment monitoring (69%) and software development / product development (68%) in second and third place, respectively. Security, you ask? Yes, security. CrowdStrike, a security company, sees a huge demand building for AI-based security virtual assistants. A Gartner study on virtual assistants predicted, "By 2024, 40% of advanced virtual assistants will be industry-domain-specific; by 2025, advanced virtual assistants will provide advisory and intervention roles for 30% of knowledge workers, up from 5% in 2021." By CrowdStrike's reckoning, AI will "help organizations scale their cybersecurity workforce by three times and reduce operating costs by close to half a million dollars." That's serious cash.

From Chaos to Confidence: The Indispensable Role of Security Architecture

Beyond mere firefighting, security architecture embraces the proactive art of strategic defense. It takes a risk-based approach to identifying potential threats, assessing weak points in an organization's IT stack, architecting forward-looking designs and prioritizing security initiatives. By aligning security investments with the organization's risk tolerance and business priorities, security architecture ensures that precious resources are optimally allocated for maximum security defense designed with in-depth zero trust security principles in mind. This reduces enterprise application deployment and operational security costs. It is similar to designing high-rise buildings in a standard manner, following all safety codes and by-laws while still allowing individual apartment owners to design and create their homes as they would prefer. Cyberattacks have become increasingly sophisticated and frequent. As a result, it is imperative for defense systems to have comprehensive, purpose-built architectures and designs in place to protect against such threats. Security architecture provides a complete defense framework by integrating various security components

Top 5 IT disaster scenarios DR teams must test

Failed backups are some of the most frequent IT disasters. Businesses can replace hardware and software, but if the data and all backups are gone, bringing them back might be impossible or incredibly expensive. Sys admins must periodically test their ability to restore from backups to ensure backups are working correctly and the restore process does not have some unseen fatal flaw. At the same time, there should always be multiple generations of backups, with some of those backup sets off site. ... Hardware failure can take many forms, including a system not using RAID, a single disk loss taking down a whole system, faulty network switches and power supply failures. Most hardware-based IT disaster scenarios can be mitigated with relative ease, but at the cost of added complexity and a price tag. One example is a database server. Such a server can be turned into a database cluster with highly available storage and networking. The cost for doing this would easily double the cost of a single nonredundant server. Administrators would also have to undergo training to manage such an environment.

Mastering AI Quality: Strategies for CDOs and Tech Leaders

Most chief data officers (CDOs) work hard to make their data operations into “glass boxes” --transparent, explainable, explorable, trustworthy resources for their companies. Then comes artificial intelligence and machine learning (AI/ML), with their allure of using that data for ever-more impressive strategic leaps, efficiencies, and growth potential. However, there’s a problem. Nearly all AI/ML tools are “black boxes.” They are so inscrutable even their creators are concerned about how they produce their results. The speed and depth at which these tools can process data without human intervention or input presents a danger to technology leaders seeking control of their data and who want to ensure and verify the quality of analytics that use it. Combine this with a push to remove humans from the decision loop and you have a potent recipe for decisions to go off the rails. ... With a human collaborator or a human-designed algorithm, it is generally easy to elicit a meaningful response to the question, “Why is this result what it is?” With AI -- and generative AI in particular -- that may not be the case.

Revamping IT for AI System Support

“It’s important for everybody to understand how fast this [AI] is going to change,” said Eric Schmidt, former CEO and chairman of Google. “The negatives are quite profound.” Among the concerns is that AI firms still had “no solutions for issues around algorithmic bias or attribution, or for copyright disputes now in litigation over the use of writing, books, images, film, and artworks in AI model training. Many other as yet unforeseen legal, ethical, and cultural questions are expected to arise across all kinds of military, medical, educational, and manufacturing uses.” The challenge for companies and for IT is that the law always lags technology. There will be few hard and fast rules for AI as it advances relentlessly. So, AI runs the risk of running off ethical and legal guardrails. In this environment, legal cases are likely to arise that define case law and how AI issues will be addressed. The danger for IT and companies is that they don’t want to be become the defining cases for the law by getting sued. CIOs can take action by raising awareness of AI as a corporate risk management concern to their boards and CEOs.

Quote for the day:

"Holding on to the unchangeable past is a waste of energy and serves no purpose in creating a better future." -- Unknown

Daily Tech Digest - February 16, 2022

Metaverse: Making it a universe for all

With a lot of speculation and little clarity about how it will work, technology companies and governments are only starting to invest in the concept. However, these investments and innovations continue to be riddled with the same concerns that various social scientists and philosophers have been asking of the promises made by the internet and social media. Set off to “democratise the good and disrupt the bad”, the internet has actively helped in the creation of international monopolies holding powers more than governments of nations. Although it has brought immense information to our fingertips, gatekeepers of knowledge still continue to profit by encouraging exclusion, our social relationships have taken a back-seat as we become increasingly affected with our identities online, and many vulnerable groups are left behind due to infrastructural inaccessibility to phones, laptops, computers, and the internet. As the world speculates to step in the metaverse in the next decade, these same questions come to the fore.

How to manage software developers without micromanaging

If software developers detest micromanaging, many have a stronger contempt for yearly performance reviews. Developers target real-time performance objectives and aim to improve velocity, code deployment frequency, cycle times, and other key performance indicators. Scrum teams discuss their performance at the end of every sprint, so the feedback from yearly and quarterly performance reviews can seem superfluous or irrelevant. But there’s also the practical reality that organizations require methods to recognize whether agile teams and software developers meet or exceed performance, development, and business objectives. How can managers get what they need without making developers miserable? What follows are seven recommended practices that align with principles in agile, scrum, devops, and the software development lifecycle and that could be applied to reviewing software developers. I don’t write them as SMART goals, but leaders should adopt the relevant ones as such based on the organization’s agile ways of working and business objectives.

Neuralink: Elon Musk's brain implant firm refutes animal abuse claims

The complaint centers on the care provided to test monkeys during and after implant and removal procedures at UC Davis. PCRM alleges that Neuralink and UC Davis staff failed to provide monkeys with adequate veterinary care, used an unapproved substance called BioGlue that killed monkeys in the experiments, and euthanized several monkeys. Details of the monkies' conditions were revealed in documents released by the university after PCRM filed a public records lawsuit in 2021. Neuralink says that during the 2.5 years at UC Davis, its tests were only conducted on cadavers or "terminal procedures", which involved the "humane euthanasia of an anesthetized animal at the completion of the surgery." "The initial work from these procedures allowed us to develop our novel surgical and robot procedures, establishing safer protocols for subsequent survival surgeries," the company says. During survival studies, Neuralink says two animals were euthanized at planned dates and six animals were euthanized at the medical advice of UC Davis veterinary staff.

Creating a more sustainable IT department

Technical debt requires more infrastructure, which results in more and more carbon emissions. In addition, technical debt requires more manual processes. For example, if we have three different ERP systems that are not integrated, it takes more manual effort just to extract the reports for financial reporting, which results in more paperwork. Technical debt accumulation results in much more latency in processes, much more manual effort, much more infrastructure -- and all that adds to our carbon footprint. Because of that we in the technology group also have an eye toward not accumulating net new technical debt. We do that, in part, by looking at how we manage our vendors so we don't end up buying similar products [for different areas of the business] as well as how we introduce new technologies into our ecosystem to avoid duplication and to ensure they can scale across the organization. ... Continued hybrid and flexible working options for our employees also helps us support reduced emissions because employees don't have to commute. We have also implemented our own business systems management platform that facilitates hot-desking in the workplace.

How Dutch hackers are working to make the internet safe

However happy the foundation was with the donation, it did lead to a slight panic. “It was just before the turn of the year, the term ‘wealth tax’ came up, so we hastily set up a fund and found an administration office to handle the annual accounts,” says Van ’t Hof. This accelerated the professionalisation of the foundation. A new structure was set up, with DIVD as the fundamental institute. “Victor Gevers was the chairman of that before, but because we wanted a different structure, that stopped and we had to look for a director. Surprisingly, everyone pointed in my direction. I took on that task,” says Van ’t Hof. Under the flag of the DIVD Institute is the fund that is meant to bundle all subsidies, donations and other money flows. “From that fund, we can finance projects that contribute to a safer internet,” explains the DIVD director. To give shape to the global ambition, a separate foundation was also set up, CSIRT.global, of which Eward Driehuis is in charge. “That foundation will set up departments in other countries so that volunteer hackers there can also help to scan and report,” says Van ’t Hof.

How to Run a Cassandra Operation in Docker

Containers thrive in a world of modern applications that demand faster delivery, better portability and seamless scalability. Gartner predicts that by 2022, more than 75% of global organizations will be running containerized applications in production. The steam behind this growing trend is none other than Docker. Docker is an open source containerization platform that lets developers package applications into containers that include everything they need to run in different environments. For enterprises, however, it can be tricky to manage individual Docker containers at scale, giving way to the popular container orchestration platform: Kubernetes (K8s). In short, Kubernetes makes it easy to deploy, manage and scale containers — and is the dominant orchestration platform used in enterprises today. This makes learning Kubernetes a must for every budding application developer; but first, you need to understand containers and Docker. In this Cassandra Operations in Docker workshop, you’ll become familiar with Docker and learn how to deploy a cloud native application in containers.

MIT Develops New Programming Language for High-Performance Computers

The ATL project combines two of the main research interests of Ragan-Kelley and Chlipala. Ragan-Kelley has long been concerned with the optimization of algorithms in the context of high-performance computing. Chlipala, meanwhile, has focused more on the formal (as in mathematically-based) verification of algorithmic optimizations. This represents their first collaboration. Bernstein and Liu were brought into the enterprise last year, and ATL is the result. It now stands as the first, and so far the only, tensor language with formally verified optimizations. Liu cautions, however, that ATL is still just a prototype — albeit a promising one — that’s been tested on a number of small programs. “One of our main goals, looking ahead, is to improve the scalability of ATL, so that it can be used for the larger programs we see in the real world,” she says. In the past, optimizations of these programs have typically been done by hand, on a much more ad hoc basis, which often involves trial and error, and sometimes a good deal of error.

Goal-Driven Kanban

Adopting goal-driven Kanban was done in one team. Initially, the team used Scrum. Due to the nature of the business, the team had a significant percentage of tasks that were dependent on other teams and various stakeholders and thus the team continuously was not able to complete them in time. Naturally, this caused frustration, and the team decided to switch to Kanban. This cured the issue but over time, the team members started feeling that they work as a “feature factory”. They were missing challenges. Thus Goal-Driven Kanban was born. After receiving management support and agreement with Product Management, the team chose their first goal. It immediately revealed the need to re-plan other features and tasks since the team had to re-focus on the agreed goal. It required rough estimation of the goal, understanding of the team capacity and further agreements with stakeholders. While working on the goal the team had to tackle various challenges, because the bar was high and the team had to all work together doing overall design and development.

A product manager’s guide to web3

Many PMs develop skills like “communication” and “influence” at larger organizations, or even startups where they need to work closely with founders and rally overworked teams. This makes sense because persuasion and coordination have been core to the web2 PM job. Those skills don’t matter as much here. Web3 PM is more focused on execution and community—like signing a big new protocol partner or getting tons of anon users via Twitter. In web2 I was afraid to tweet much, for professional consequences. Now I’d be untrustworthy if I didn’t tweet a lot. Making a viral meme is more important than writing a good email. That is because getting positive attention in the frenetic world of web3 is more valuable than “alignment.” ... Web3 moves too quickly for pontification; new protocols launch daily and DeFi concepts like bonding curves and OHM forks are being tested in real time, so visions and strategies quickly become outdated. This may change over time as the space matures and product vision becomes more of a competitive advantage.

NIST releases software, IoT, and consumer cybersecurity labeling guidance

The order asked NIST to produce guidance for federal agency staff who have software procurement-related responsibilities and is intended to help federal agency staff know what information to request from software producers regarding their secure software development practices. The new NIST document spells out minimum recommendations for federal agencies to follow as they acquire software or a product containing software. The order also directed NIST to define actions or outcomes for software producers, such as commercial-off-the-shelf (COTS) product vendors, government-off-the-shelf software developers, contractors, and other custom software developers. ... NIST notes that its guidance is limited to federal agency procurement of software, which includes firmware, operating systems, applications, and application services, as well as products containing software. Software developed by federal agencies is out of scope, as is open-source software freely and directly obtained by federal agencies.

Quote for the day:

"Leadership is liberating people to do what is required of them in the most effective and humane way possible." -- Max DePree

Daily Tech Digest - January 03, 2022

Get the most value from your data with data lakehouse architecture

A data lakehouse is essentially the next breed of cloud data lake and warehousing architecture that combines the best of both worlds. It is an architectural approach for managing all data formats (structured, semi-structured, or unstructured) as well as supporting multiple data workloads (data warehouse, BI, AI/ML, and streaming). Data lakehouses are underpinned by a new open system architecture that allows data teams to implement data structures through smart data management features similar to data warehouses over a low-cost storage platform that is similar to the ones used in data lakes. ... A data lakehouse architecture allows data teams to glean insights faster as they have the opportunity to harness data without accessing multiple systems. A data lakehouse architecture can also help companies ensure that data teams have the most accurate and updated data at their disposal for mission-critical machine learning, enterprise analytics initiatives, and reporting purposes. There are several reasons to look at modern data lakehouse architecture in order to drive sustainable data management practices.

A CISO’s guide to discussing cybersecurity with the board

When you get a chance to speak with executives, you typically don’t have much time to discuss details. And frankly, that’s not what executives are looking for, anyway. It’s important to phrase cybersecurity conversations in a way that resonates with the leaders. Messaging starts with understanding the C-suite and boards’ priorities. Usually, they are interested in big picture initiatives, so explain why cyber investment is critical to the success of these initiatives. For example, if the CEO wants to increase total revenue by 5% in the next year, explain how they can prevent major unnecessary losses from a cyber attack with an investment in cybersecurity. Once you know the executive team and board’s goals, look to specific members, and identify a potential ally. Has one team recently had a workplace security breach? Does one leader have a difficult time getting his or her team to understand the makings of a phishing scheme? These interests and experiences can help guide the explanation of the security solution. If you’re a CISO, you’re well-versed in cybersecurity, but remember that not everyone is as involved in the subject as you are, and business leaders probably will not understand technical jargon.

Best of 2021 – Containers vs. Bare Metal, VMs and Serverless for DevOps

A bare metal machine is a dedicated server using dedicated hardware. Data centers have many bare metal servers that are racked and stacked in clusters, all interconnected through switches and routers. Human and automated users of a data center access the machines through access servers, high security firewalls and load balancers. The virtual machine introduced an operating system simulation layer between the bare metal server’s operating system and the application, so one bare metal server can support more than one application stack with a variety of operating systems. This provides a layer of abstraction that allows the servers in a data center to be software-configured and repurposed on demand. In this way, a virtual machine can be scaled horizontally, by configuring multiple parallel machines, or vertically, by configuring machines to allocate more power to a virtual machine. One of the problems with virtual machines is that the virtual operating system simulation layer is quite “thick,” and the time required to load and configure each VM typically takes some time. In a DevOps environment, changes occur frequently.

Desktop High-Performance Computing

Many engineering teams rely on desktop products that only run on Microsoft Windows. Desktop engineering tools that perform tasks such as optical ray tracing, genome sequencing, or computational fluid dynamics often couple graphical user interfaces with complex algorithms that can take many hours to run on traditional workstations, even when powerful CPUs and large amounts of RAM are available. Until recently, there has been no convenient way to scale complex desktop computational engineering workloads seamlessly to the cloud. Fortunately, the advent of AWS Cloud Development Kit (CDK), AWS Elastic Container Service (ECS), and Docker finally make it easy to scale desktop engineering workloads written in C# and other languages to the cloud. ... The desktop component first builds and packages a Docker image that can perform the engineering workload (factor an integer). AWS CDK, executing on the desktop, deploys the Docker image to AWS and stands up cloud infrastructure consisting of input/output worker queues and a serverless ECS Fargate cluster.

Micromanagement is not the answer

Neuroscience also reveals why micromanaging is counterproductive. Donna Volpitta, an expert in “brain-based mental health literacy,” explained to me that the two most fundamental needs of the human brain are security and autonomy, both of which are built on trust. Leaders who instill a sense of trust in their employees foster that sense of security and autonomy and, in turn, loyalty. When leaders micromanage their employees, they undermine that sense of trust, which tends to breed evasion behaviors in employees. It’s a natural brain response. “Our brains have two basic operating modes—short-term and long-term,” Volpitta says. “Short-term is about survival. It’s the freeze-flight-fight response or, as I call it, the ‘grasshopper’ brain that is jumping all over. Long-term thinking considers consequences [and] relationships, and is necessary for complex problem solving. It’s the ‘ant’ brain, slower and steadier.” She says micromanagement constantly triggers short-term, survival thinking detrimental to both social interactions and task completion.

Unblocking the bottlenecks: 2022 predictions for AI and computer vision

One of the key challenges of deep learning is the need for huge amounts of annotated data to train large neural networks. While this is the conventional way to train computer vision models, the latest generation of technology providers are taking an innovative approach that enables machine learning with comparatively less training data. This includes moving away from supervised learning to self-supervised and weakly supervised learnings where data availability is less of an issue. This approach, also known as few shot learning, detects objects as well as new concepts with considerably less input data. In many cases the algorithm can be trained with as little as 20 images. ... Privacy remains a major concern in the AI sector. In most cases, a business must share its data assets with the AI provider via third party servers or platforms when training computer vision models. Under such arrangements there is always the risk that the third party could be hacked or even exploit valuable metadata for its own projects. As a result, we’re seeing the rise of Privacy Enhancing Computation, which enables data to be shared between different ecosystems in order to create value, while maintaining data confidentiality.

How Automation Can Solve the Security Talent Shortage Puzzle

Supporting remote and hybrid work requires organizations to invest in and implement new technologies that facilitate needs such as remote access, secure file-sharing, real-time collaboration and videoconferencing. Businesses must also hire professionals to configure, implement and maintain these tools with an eye towards security – a primary concern here, as businesses of all sizes now live or die by the availability and integrity of their data. The increasing complexity of IT environments – many of which are now pressured to support bring-your-own-device (BYOD) policies – has only intensified the need for competent cybersecurity talent. It’s not surprising that the ongoing shortage of trained professionals makes it difficult for organizations to expand their business and adopt new technologies. Almost half a million cybersecurity jobs remain open in the U.S. alone, forcing businesses to compete aggressively to fill these roles. Yet, economic pressures make it particularly difficult for small-to mid-sized businesses (SBMs) to play this game. Most cannot hope to match the high salaries that large enterprises offer.

HIPAA Privacy and Security: At a Crossroads in 2022?

The likely expansion of OCR’s mission into protecting the confidentiality of SUD data comes as actions to enforce the HITECH Breach Notification and HIPAA Security Rule appear to be at a standstill. According to the data compiled by OCR, in 2021 there were more than 660 breaches of the unauthorized disclosure of unsecured PHI reported by HIPAA-covered entities and their business associates that compromised the health information of over 40 million people. A significant number of the breaches reported to OCR appear to show violations of the HIPAA standards due to late reporting and failure to adequately secure information systems or train workforce members on safeguarding PHI. In 2021, OCR announced settlements in two enforcement actions involving compliance with the HIPAA Security Rule standards. OCR has been mum on its approach to enforcement of the HIPAA breach and security rules. One explanation could be the impact being felt by the 5th Circuit Court of Appeals decision overturning an enforcement action against the University of Texas MD Anderson Cancer Center.

Will we see GPT-3 moment for computer vision?

It is truly the age of large models. Each of these models is bigger and more advanced than the previous one. Take, for example, GPT-3 – when it was introduced in 2020, it was the largest language model trained on 175 billion parameters. Fast forward one year, and we already have the GLaM model, which is a trillion weight model. Transformer models like GPT-3 and GLaM are transforming natural language processing. We are having active conversations around these models making job roles like writers and even programmers obsolete. While these can be dismissed as speculations, for now, one cannot deny that these large language models have truly transformed the field of NLP. Could this innovation be extended to other fields – like computer vision? Can we have a GPT-3 moment for computer vision? OpenAI recently released GLIDE, a text-to-image generator, where the researchers applied guided diffusion to the problem of text conditional image synthesis. For GLIDE, the researchers trained a 3.5 billion parameter diffusion model that uses a text encoder. Next, they compared CLIP (Contrastive Language-Image Pre-training) guidance and classifier free guidance.

What is Legacy Modernization

To achieve a good level of agility the systems supporting the organization also have to quickly react and change to the surrounding environment. Legacy systems place a constraint on agility since they are often difficult to change or provide inefficient support to business activities. This is not unusual, at the time of the system design there were perhaps technology constraints that no longer exist, or the system was designed for a particular way of working that is no longer relevant. Legacy Modernization changes or replaces legacy systems, making the organization more efficient and cost-effective. Not only can this optimize existing business processes, but it can open new business opportunities. Security is an important driver for Legacy Modernization. Cyber-attacks on organizations are common and become more sophisticated over time. The security of a system degrades over time, and legacy systems may no longer have the support or the technologies required to deter modern attack methods, this makes them an easy target for hackers. This represents a significant business risk to an organization.

Quote for the day:

"Leadership is particularly necessary to ensure ready acceptance of the unfamiliar and that which is contrary to tradition." -- Cyril Falls

Daily Tech Digest - September 04, 2021

AMD files teleportation patent to supercharge quantum computing

AMD has proposed a patent for 'teleportation,' meaning things could be about to get much more efficient around here. With the incredible technological feats humanity achieves on a daily basis, and Nvidia's Jensen going off on one last year about GeForce holodecks and time machines, it's easy for us to slip into a headspace that lets us believe genuine human teleportation is just around the corner. "Finally," you sigh, mouthing the headline to yourself. "Goodbye work commute, hello popping to Japan for an authentic Ramen on my lunch break." ... Essentially, the 'out-of-order' execution method AMD is looking to lay claim to ensures some Qubits that would be left idle—waiting for their calculation step to come around—are able to execute independent of a prior result. Where usually they would need to wait for previous Qubits to provide instructions, they can calculate simultaneously, no need to wait in line. So, no, we're not going to be zipping through wormholes just yet. But if AMD's designs come through, we could be looking at much more efficient, scalable and stable quantum computing architecture than we have now.

The Internet of Things Requires a Connected Data Infrastructure

Not long ago, a terabyte of information was an enormous amount and might be the foundation for solid decision-making. These days, it won’t cut it. For example, looking at a terabyte of data might yield a decision that’s 70% accurate. But leaving 30% to chance is unacceptable when it comes to real-time vehicle safety. On the other hand, having the ability to ingest and process 40 terabytes — from all sources, edge to core — can result in an accuracy rate well exceeding 90% accuracy. Something jumps in front of your car — is it a person, a dog, a trash bag, a child’s ball? Real-time systems need to determine the level of risk and react in micro milliseconds. Real-time processing has to be done closer to where the decisions are being made. In terms of IoT, a lot of questions can be answered by using a digital twin. These create additional layers of insights and provide a better understanding of what’s happening in any given situation and decide on the most appropriate course of immediate action. Digital twins take insight not just from the raw sensors — the edge compute nodes — but a combination of real-time data at the edge and historical data at the core.

Can Your Organization Benefit from Edge Data Centers?

Organizations considering a move to edge computing should begin their journey by inventorying their applications and infrastructure. It's also a good idea to assess current and future user requirements, focusing on where data is created and what actions need to be performed on that data. "Generally speaking, the more susceptible data is to latency, bandwidth, or security issues, the more likely the business is to benefit from edge capabilities," said Vipin Jain, CTO of edge computing startup Pensando. “Focus on a small number of pilot projects and partner with integrators/ISVs with experience in similar deployments." Fugate recommended examining business functions and processes and linking them to the application and infrastructure services they depend on. "This will ensure that there isn’t one key centralized service that could stop critical business functions," he said. "The idea is to determine what functions must survive regardless of an infrastructure or connectivity failure." Fugate also advised determining how to effectively manage and secure distributed edge platforms.

How to Speed Up Your Digital Transformation

The complexity-in-use is often overlooked in digitalization projects because those in charge think that accounting for task and system complexity independent of one another is enough. In our case, at the beginning of the transformation, tasks and processes were considered relatively stable and independent from the new system. As a result, the loan-editing clerks were unable to complete business-critical tasks for weeks, and management needed to completely reinvent their change management approach to turn the project around and overcome operational problems in the high complexity-in-use area. They brought in more people to reduce the backlog, developed new training materials, and even changed the newly implemented system — a problem-solving technique organizations with smaller budgets wouldn’t find easy to deploy. In the end, our study partner managed this herculean task, but it took them months to get the struggling departments back on track.

Ecosystems at The Edge: Where the Data Center Becomes a Marketplace

Rapidly evolving edge computing architectures are often seen as a way for businesses to enable new applications that require low latency and place computing close to the origin of data. While those are important use cases, what is less often discussed is the opportunity for businesses to leverage the edge to spawn ecosystems that generate new revenue. To realize this value, companies must think of the edge as more than just a collection point for data from intelligent devices. They should broaden their vision to see the edge as a new business hub. These small data centers can evolve into full-fledged service providers that attract local businesses, generate e-commerce transactions and enable interconnections that never touch the central cloud. Edge computing is an expansion of cloud infrastructure that moves data collection, processing and services closer to the point at which data is created or used. It is the fastest-growing segment of the cloud category with the total market expected to expand 37% annually through 2027, according to Grand View Research.

NSA: We 'don't know when or even if' a quantum computer will ever be able to break today's public-key encryption

In the NSA's summary, a CRQC – should one ever exist – "would be capable of undermining the widely deployed public key algorithms used for asymmetric key exchanges and digital signatures" – and what a relief it is that no one has one of these machines yet. The post-quantum encryption industry has long sought to portray itself as an immediate threat to today's encryption, as El Reg detailed in 2019. "The current widely used cryptography and hashing algorithms are based on certain mathematical calculations taking an impractical amount of time to solve," explained Martin Lee, a technical lead at Cisco's Talos infosec arm. "With the advent of quantum computers, we risk that these calculations will become easy to perform, and that our cryptographic software will no longer protect systems." Given that nations and labs are working toward building crypto-busting quantum computers, the NSA said it was working on "quantum-resistant public key" algorithms for private suppliers to the US government to use, having had its Post-Quantum Standardization Effort running since 2016.

14 Ways AI Could Become A Detriment To Society

There are multiple ways that AI could become a detriment to society. Machine learning, a subfield of AI, learns from vast quantities of data and hence carries the risk of perpetuating data bias. AI use cases including facial recognition and predictive analytics could adversely impact protected classes in areas such as loan rejection, criminal justice and racial bias, leading to unfair outcomes for certain people. ... AI is only as good as the data that is used to train it. From an industry perspective, this is problematic given there is often a lack of training data for true failures in critical systems. This becomes dangerous when a wrong prediction leads to potentially life-threatening events such as manufacturing accidents or oil spills. This is why a focus on hybrid AI and “explainable AI” is necessary. ... Unfortunately, cybercriminals have historically been better and faster adopters of technology than the rest of us. AI can become a detriment to society when deepfakes and deep learning models are used as vehicles for social engineering by scammers to steal money, sensitive data and confidential intellectual property by pretending to be people and entities we trust.

Reviewing the Eight Fallacies of Distributed Computing

The challenges of distributed systems, and the broad science around the techniques and mechanisms used to build them, are now well researched. The thing you learn when addressing these challenges in the real world, however, is that academic understanding only gets you so far. Building distributed systems involves engineering pragmatism and trade-offs, and the best solutions are the ones you discover by experience and experiment. ... However, the engineering reality is that multiple kinds of failures can, and will, occur at the same time. The ideal solution now depends on the statistical distribution of failures; or on analysis of error budgets, and the specific service impact of certain errors. The recovery mechanisms can themselves fail due to system unreliability, and the probability of those failures might impact the solution. And of course, you have the dangers of complexity: solutions that are theoretically sound, but complex, might be far more complicated to manage or understand whenever an incident takes place than simpler mechanisms that are theoretically not as complete.

Machine Learning Algorithm Sidesteps the Scientific Method

We might be most familiar with machine learning algorithms as they are used in recommendation engines, and facial recognition and natural language processing applications. In the field of physics, however, machine learning algorithms are typically used to model complex processes like plasma disruptions in magnetic fusion devices, or modeling the dynamic motions of fluids. In the case of this work by the Princeton team, the algorithm skips the interim steps of needing to be explicitly programmed with the conventions of physics. “The algorithms developed are robust against variations of the governing laws of physics because the method does not require any knowledge of the laws of physics other than the fundamental assumption that the governing laws are field theories,” said the team. “When the effects of special relativity or general relativity are important, the algorithms are expected to be valid as well.” The researchers’ approach was inspired in part by Oxford philosopher Nick Bostrom’s philosophical thought experiment that the universe is actually a computer simulation.

What's the Real Difference Between Leadership and Management?

Leaders, like entrepreneurs, are constantly looking for ways to add to their world of expertise. They tend to enjoy reading, researching and connecting with like-minded individuals; they constantly aim to grow. They are usually open-minded and seek opportunities that challenge them to expand their level of thinking, which in turn leads to developing more solutions to problems that may arise. Managers, many times, rely on existing knowledge and skills by repeating proven strategies or behaviors that may have worked in the past to help maintain a steady track record within their field of success with clients. ... Leaders create trust and bonds between their mentees that go beyond expression or definition. Their mentees become raving fanatics willing to go above and beyond the usual scope of supporting their leader in achieving his or her mission. In the long run, the overwhelming support from his or her fanatics helps increase the value and credibility of the leader. On the other hand, managers direct, delegate, enforce and advise either an individual or group that typically represents a brand or organization looking for direction. Followers do as they are told and rarely ask questions.

Quote for the day:

"Most people don't know how AWESOME they are, until you tell them. Be sure to tell them." -- Kelvin Ringold