Daily Tech Digest by Kannan Subbiah: data analytics

Showing posts with label data analytics. Show all posts

Daily Tech Digest - September 19, 2025

Quote for the day:

"The whole secret of a successful life is to find out what is one's destiny to do, and then do it." -- Henry Ford

How CISOs Can Drive Effective AI Governance

For CISOs, finding that balance between security and speed is critical in the age of AI. This technology simultaneously represents the greatest opportunity and greatest risk enterprises have faced since the dawn of the internet. Move too fast without guardrails, and sensitive data leaks into prompts, shadow AI proliferates, or regulatory gaps become liabilities. Move too slow, and competitors pull ahead with transformative efficiencies that are too powerful to compete with. Either path comes with ramifications that can cost CISOs their job. In turn, they cannot lead a "department of no" where AI adoption initiatives are stymied by the organization's security function. It is crucial to instead find a path to yes, mapping governance to organizational risk tolerance and business priorities so that the security function serves as a true revenue enabler. ... Even with strong policies and roadmaps in place, employees will continue to use AI in ways that aren't formally approved. The goal for security leaders shouldn't be to ban AI, but to make responsible use the easiest and most attractive option. That means equipping employees with enterprise-grade AI tools, whether purchased or homegrown, so they do not need to reach for insecure alternatives. In addition, it means highlighting and reinforcing positive behaviors so that employees see value in following the guardrails rather than bypassing them.

AI developer certifications tech companies want

Certifications help ensure developers understand AI governance, security, and responsible use, Hinchcliffe says. Certifications from vendors such as Microsoft and Google, along with OpenAI partner programs, are driving uptake, he says. “Strategic CIOs see certifications less as long-term guarantees of expertise and more as a short-term control and competency mechanism during rapid change,” he says. ... While certifications aren’t the sole deciding factor in landing a job, they often help candidates stand out in competitive roles where AI literacy is becoming a crucial factor, Taplin says. “This is especially true for new software engineers, who can gain a leg up by focusing on certifications early to enhance their career prospects,” he says. ... “The real demand is for AI skills, and certifications are simply one way to build those skills in a structured manner,” says Kyle Elliott, technology career coach and hiring expert. “Hiring managers are not necessarily looking for candidates with AI certifications,” Elliott says. “However, an AI certification, especially if completed in the last year or currently in progress, can signal to a hiring manager that you are well-versed in the latest AI trends. In other words, it’s a quick way to show that you speak the language of AI.” Software developers should not expect AI certifications to be a “silver bullet for landing a job or earning a promotion,” Elliott says.

How important is data analytics in cycling?

Beyond recovery and nutrition, data analytics plays a pivotal role in shaping race-day decisions. The team combines structured data like power outputs, route elevation, and weather forecasts with unstructured data gathered from online posts by cycling enthusiasts. These data streams are fed into predictive models that anticipate race dynamics and help fine-tune equipment selection, down to tire pressure and aerodynamic adjustments. Metrics like Training Stress Score (TSS) and Heart Rate Variability (HRV) help monitor each rider’s fatigue and readiness, ensuring that training plans are both challenging and sustainable. “We analyze how environmental conditions affect each rider’s output and recovery,” Ryder says. ... The team’s data-driven strategy even extends to post-race analysis. At their hub, they evaluate power output, rider positioning, and performance variances. ... Looking ahead, Ryder sees artificial intelligence playing a greater role. The team is exploring machine learning models that predict tactical behavior from opponents and identify when riders are close to burnout. Through conversational analytics in Qlik, they envision proactive alerts such as, “This rider may not be fit to race tomorrow,” based on cumulative stress and recovery data. The team’s ethos is clear. Success doesn’t only come from racing harder. It comes from racing smarter.

Balancing Growth and Sustainability: How Data Centers Can Navigate the Energy Demands of the AI Era

Given the systemic limitations on reliable power sources, practical solutions are needed. We must address power sustainability, upstream power infrastructure, new data center equipment and trained labor to deliver it all. By being proactive, we can “bend” the energy growth curve by decoupling data center growth from AI computing’s energy consumption. ... Before the AI boom, large data centers could grin and bear longer lead times for utilities; however, the immediate and skyrocketing demand for data centers to power AI applications calls for creative solutions. Data center developers and designers planning to build in energy-constrained regions need to consider deploying alternative prime power sources and/or energy storage systems to launch new data centers. This includes natural gas turbines, HVO-fueled generators, wind, solar, fuel cells, battery energy storage systems (BESS), and to a limited degree, small modular reactors. ... The utility company and grid operator’s intimate knowledge of the grid and local regulatory, governmental and political landscape makes them critical partners in the site selection, design, permitting, and construction of new data centers. Utilities provide critical insights on power capacity, costs, carbon intensity, power quality, grid stability and load management to ensure sustainable and reliable operations.

LLMs can boost cybersecurity decisions, but not for everyone

Resilience played a major role in the results. High-resilience individuals performed well with or without LLM support, and they were better at using AI guidance without becoming over-reliant on it. Low-resilience participants did not gain as much from LLMs. In some cases, their performance did not improve or even declined. This creates a risk of uneven outcomes. Teams could see gaps widen between those who can critically evaluate AI suggestions and those who cannot. Over time, this may lead to over-reliance on models, reduced independent thinking, and a loss of diversity in how problems are approached. According to Lanyado, security leaders need to plan for these differences when building teams and training programs. “Not every organization and/or employee interacts with automation in the same way, and differences in team readiness can widen security risks,” he said. ... The findings suggest that organizations cannot assume adding an LLM will raise everyone’s performance equally. Without design, these tools could make some team members more effective while leaving others behind. The researchers recommend designing AI systems that adapt to the user. High-resilience individuals may benefit from open-ended suggestions. Lower-resilience users might need guidance, confidence indicators, or prompts that encourage them to consider alternative viewpoints.

Augment or Automate? Two Competing Visions for AI’s Economic Future

Looked at more critically, ChatGPT has become a supercharged Google search that leaps from finding information to synthesizing and judging it, a clear homogenization of human capacity that might lead to a world of grey-zone AI slop. ... While ChatGPT follows the people, Claude is following the money, hoping to capitalize on business needs to improve efficiency and productivity. By focusing on complex, high-value work, the company is signaling it believes the future of AI lies not in making everyone more productive, but in automating knowledge work that once required specialized human expertise. ... These divergent strategies result in different financial trajectories. OpenAI enjoys massive scale, with hundreds of millions of users providing a broad funnel for subscriptions. It generates an overwhelming amount of traffic that is of relatively lower value. OpenAI is betting the real money will flow through licensing its tools to Microsoft, where it can be embedded in Copilot and Office products to generate recurring revenue streams to offset its infrastructure and operating costs. Anthropic has fewer users but stronger unit economics. Its focus on enterprise use means customers are better positioned to purchase more expensive premium services that can demonstrate strong return-on-investment.

4 four ways to overcome the skills crisis and prepare your workforce for the age of AI

Orla Daly, CIO at Skillsoft, told ZDNET that the research shows business leaders must keep pace with the changing requirements for capabilities in different operational areas. "Significant percentages of skills are no longer relevant. The skills that we'll need in 2030 are only just evolving now," she said. "If you're not making upskilling and learning part of your core business strategy, then you're going to ultimately become uncompetitive in terms of retaining talent and delivering on your organizational outcomes." ... Daly said companies must pay more attention to the skills of their employees, including measuring and testing those proficiencies. "That's about using a combination of benchmarks, which we use at Skillsoft, that allow you, through testing, to understand the skills that you have," she said. "It's also about how you understand that capability in terms of real-world applications and measuring those skills in the context of the jobs that are being done." ... "You need to make measurement central to the business strategy, and have a program around learning, so it's part of the everyday culture of the business," she said. "From the executive level down, you need to say learning is a core part of the organization. Learning then turns up in all of your business operating frameworks in terms of how you track and measure the outcomes of programs, similar to other investments that you would make."

Sovereign AI meets Stockholm’s data center future

Sovereign AI refers to the ability of a nation to develop and operate AI platforms within its own borders, under its own laws and energy systems. ... By ensuring that sensitive data and critical compute resources remain local, sovereign AI reduces exposure to geopolitical risk, supports regulatory compliance and builds trust among both public and private stakeholders. Recent initiatives in Stockholm highlight how sovereign AI can be embedded into existing data center ecosystems. Purpose-built AI compute clusters, equipped with the latest GPU architectures, are being deployed on renewable power and integrated into local district heating networks, where excess server heat is recycled back into the city grid. These facilities are designed not only for high-performance workloads but also for long-term sustainability, aligning with Sweden’s climate and digital sovereignty goals. The strategy is clear: pair advanced AI infrastructure with domestic control and clean energy. By doing so, Stockholm can position itself as a European leader in sovereign AI, where innovation, security and sustainability converge in a way that few other markets can match. ... Stockholm’s ecosystem radiates gravitational pull. With more green, efficient and sovereign-capable data centers emerging, they attract additional clients and investments and reinforce the region’s dominance.

Agentic AI poised to pioneer the future of cybersecurity in the BFSI sector

Enter agentic AI systems that represent a network of intelligent agents having the capability for independent decision-making and adaptive learning. This extends the capabilities of traditional AI systems by incorporating autonomous decision-making and execution, while adopting proactive security measures. It is poised to revolutionise cybersecurity in the banking and financial services sector while bridging the gap between the speed of cyber-attacks and the slow, human-driven incident response. ... Agentic AI will proactively and autonomously hunt for threats across the IT systems within the financial institution by actively looking for vulnerabilities and possible threat vectors before they are exploited by threat actors. Agentic AI systems leverage their capabilities in simulation, where potential attack scenarios are modeled to identify vulnerabilities in the security posture. Data from logs, network traffic, and activities from endpoints are correlated to spot attack vectors as a part of the threat hunting process. ... AI agents have to be deployed into both customer-facing for better customer experience as well as internal systems. By establishing an agentic AI ecosystem, agents can collaborate across functions. Risk management, compliance monitoring, operational efficiency, and fraud detection functions can be streamlined, too.

Shai-Hulud Attacks Shake Software Supply Chain Security Confidence

This isn’t the first time NPM’s reputation has been put to the test. The JavaScript community has seen a trio of supply chain attacks in rapid succession. Just recently, we saw the “manifest confusion” exploit, which tricked dependency trackers, and prior to that, a series of typosquatting and account-takeover incidents—remember the infamous “coa” and “rc” package hijacks? Now comes the latest beast from the sand: the Shai-Hulud supply chain attack. This is, depending on how you count, the third major NPM incident in recent memory—and arguably the most insidious. ... According to the detailed analysis by JFrog, attackers compromised multiple popular packages, including several that mimicked or targeted legitimate CrowdStrike modules. Before you panic: this wasn’t a direct attack on CrowdStrike itself, but the attackers were clever—by using names like “crowdstrike” and latching onto a trusted security vendor’s brand, they hoped to worm their payloads into unsuspecting production environments. ... What makes these attacks so damaging is less about the technical sophistication (though, don’t get me wrong, this one is clever) and more about how they shake our trust in the very idea of open collaboration. Every dev who’s ever typed `npm install` had to trust not just the original author, but every maintainer, every transitive dependency, and the opaque process of package publishing itself.

Daily Tech Digest - June 06, 2025

Quote for the day:

"Next generation leaders are those who would rather challenge what needs to change and pay the price than remain silent and die on the inside." -- Andy Stanley

The intersection of identity security and data privacy laws for a safe digital space

The integration of identity security with data privacy has become essential for corporations, governing bodies, and policymakers. Compliance regulations are set by frameworks such as the Digital Personal Data Protection (DPDP) Bill and the CERT-In directives – but encryption and access control alone are no longer enough. AI-driven identity security tools flag access combinations before they become gateways to fraud, monitor behavior anomalies in real-time, and offer deep, contextual visibility into both human and machine identities. All these factors combined bring about compliance-free, trust-building resilient security: proactive security that is self-adjusting, overcoming various challenges encountered today. By aligning intelligent identity security tools with privacy regulations, organisations gain more than just protection—they earn credibility. ... The DPDP Act tracks closely to global benchmarks such as GDPR and data protection regulations in Singapore and Australia which mandate organisations to implement appropriate security measures to protect personal data and amp up response to data breaches. They also assert that organisations that embrace and prioritise data privacy and identity security stand to gain the optimum level of reduced risk and enhanced trust from customers, partners and regulators.

Who needs real things when everything can be a hologram?

Meta founder and CEO Mark Zuckerberg said recently on Theo Von’s “This Past Weekend” podcast that everything is shifting to holograms. A hologram is a three-dimensional image that represents an object in a way that allows it to be viewed from different angles, creating the illusion of depth. Zuckerberg predicts that most of our physical objects will become obsolete and replaced by holographic versions seen through augmented reality (AR) glasses. The conversation floated the idea that books, board games, ping-pong tables, and even smartphones could all be virtualized, replacing the physical, real-world versions. Zuckerberg also expects that somewhere between one and two billion people could replace their smartphones with AR glasses within four years. One potential problem with that prediction: the public has to want to replace physical objects with holographic versions. So far, Apple’s experience with Apple Vision Pro does not imply that the public is clamoring for holographic replacements. ... I have no doubt that holograms will increasingly become ubiquitous in our lives. But I doubt that a majority will ever prefer a holographic virtual book over a physical book or even a physical e-book reader. The same goes for other objects in our lives. I also suspect both Zuckerberg’s motives and his predictive powers.

How AI Is Rewriting the CIO’s Workforce Strategy

With the mystique fading, enterprises are replacing large prompt-engineering teams with AI platform engineers, MLOps architects, and cross-trained analysts. A prompt engineer in 2023 often becomes a context architect by 2025; data scientists evolve into AI integrators; business-intelligence analysts transition into AI interaction designers; and DevOps engineers step up as MLOps platform leads. The cultural shift matters as much as the job titles. AI work is no longer about one-off magic, it is about building reliable infrastructure. CIOs generally face three choices. One is to spend on systems that make prompts reproducible and maintainable, such as RAG pipelines or proprietary context platforms. Another is to cut excessive spending on niche roles now being absorbed by automation. The third is to reskill internal talent, transforming today’s prompt writers into tomorrow’s systems thinkers who understand context flows, memory management, and AI security. A skilled prompt engineer today can become an exceptional context architect tomorrow, provided the organization invests in training. ... Prompt engineering isn’t dead, but its peak as a standalone role may already be behind us. The smartest organizations are shifting to systems that abstract prompt complexity and scale their AI capability without becoming dependent on a single human’s creativity.

Biometric privacy on trial: The constitutional stakes in United States v. Brown

The divergence between the two federal circuit courts has created a classic “circuit split,” a situation that almost inevitably calls for resolution by the U.S. Supreme Court. Legal scholars point out that this split could not be more consequential, as it directly affects how courts across the country treat compelled access to devices that contain vast troves of personal, private, and potentially incriminating information. What’s at stake in the Brown decision goes far beyond criminal law. In the digital age, smartphones are extensions of the self, containing everything from personal messages and photos to financial records, location data, and even health information. Unlocking one’s device may reveal more than a house search could have in the 18th century, and the very kind of search the Bill of Rights was designed to restrict. If the D.C. Circuit’s reasoning prevails, biometric security methods like Apple’s Face ID, Samsung’s iris scans, and various fingerprint unlock systems could receive constitutional protection when used to lock private data. That, in turn, could significantly limit law enforcement’s ability to compel access to devices without a warrant or consent. Moreover, such a ruling would align biometric authentication with established protections for passcodes.

GenAI controls and ZTNA architecture set SSE vendors apart

“[SSE] provides a range of security capabilities, including adaptive access based on identity and context, malware protection, data security, and threat prevention, as well as the associated analytics and visibility,” Gartner writes. “It enables more direct connectivity for hybrid users by reducing latency and providing the potential for improved user experience.” Must-haves include advanced data protection capabilities – such as unified data leak protection (DLP), content-aware encryption, and label-based controls – that enable enterprises to enforce consistent data security policies across web, cloud, and private applications. Securing Software-as-a-Service (SaaS) applications is another important area, according to Gartner. SaaS security posture management (SSPM) and deep API integrations provide real-time visibility into SaaS app usage, configurations, and user behaviors, which Gartner says can help security teams remediate risks before they become incidents. Gartner defines SSPM as a category of tools that continuously assess and manage the security posture of SaaS apps. ... Other necessary capabilities for a complete SSE solution include digital experience monitoring (DEM) and AI-driven automation and coaching, according to Gartner.

5 Risk Management Lessons OT Cybersecurity Leaders Can’t Afford to Ignore

A weak or shared passwords, outdated software, and misconfigured networks are consistently leveraged by malicious actors. Seemingly minor oversights can create significant gaps in an organization’s defenses, allowing attackers to gain unauthorized access and cause havoc. When the basics break down, particularly in converged IT/OT environments where attackers only need one foothold, consequences escalate fast. ... One common misconception in critical infrastructure is that OT systems are safe unless directly targeted. However, the reality is far more nuanced. Many incidents impacting OT environments originate as seemingly innocuous IT intrusions. Attackers enter through an overlooked endpoint or compromised credential in the enterprise network and then move laterally into the OT environment through weak segmentation or misconfigured gateways. This pattern has repeatedly emerged in the pipeline sector. ... Time and again, post-mortems reveal the same pattern: organizations lacking in tested procedures, clear roles, or real-world readiness. A proactive posture begins with rigorous risk assessments, threat modeling, and vulnerability scanning—not once, but as a cycle that evolves with the threat landscape. This plan should outline clear procedures for detecting, containing, and recovering from cyber incidents.

You Can Build Authentication In-House, But Should You?

Auth isn’t a static feature. It evolves — layer by layer — as your product grows, your user base diversifies, and enterprise customers introduce new requirements. Over time, the simple system you started with is forced to stretch well beyond its original architecture. Every engineering team that builds auth internally will encounter key inflection points — moments when the complexity, security risk, and maintenance burden begin to outweigh the benefits of control. ... Once you’re selling into larger businesses, SSO becomes a hard requirement for enterprises. Customers want to integrate with their own identity providers like Okta, Microsoft Entra, or Google Workspace using protocols like SAML or OIDC. Implementing these protocols is non-trivial, especially when each customer has their own quirks and expectations around onboarding, metadata exchange, and user mapping. ... Once SSO is in place, the following enterprise requirement is often SCIM (System for Cross-domain Identity Management). SCIM, also known as Directory Sync, enables organizations to provision automatically and deprovision user accounts through their identity provider. Supporting it properly means syncing state between your system and theirs and handling partial failures gracefully. ... The newest wave of complexity in modern authentication comes from AI agents and LLM-powered applications.

Developer Joy: A Better Way to Boost Developer Productivity

Play isn’t just fluff; it’s a tool. Whether it’s trying something new in a codebase, hacking together a prototype, or taking a break to let the brain wander, joy helps developers learn faster, solve problems more creatively, and stay engaged. ... Aim to reduce friction and toil, the little frustrations that break momentum and make work feel like a slog. Long build and test times are common culprits. At Gradle, the team is particularly interested in improving the reliability of tests by giving developers the right tools to understand intermittent failures. ... When we’re stuck on a problem, we’ll often bang our head against the code until midnight, without getting anywhere. Then in the morning, suddenly it takes five minutes for the solution to click into place. A good night’s sleep is the best debugging tool, but why? What happens? This is the default mode network at work. The default mode network is a set of connections in your brain that activates when you’re truly idle. This network is responsible for many vital brain functions, including creativity and complex problem-solving. Instead of filling every spare moment with busywork, take proper breaks. Go for a walk. Knit. Garden. "Dead time" in these examples isn't slacking, it’s deep problem-solving in disguise.

Get out of the audit committee: Why CISOs need dedicated board time

The problem is the limited time allocated to CISOs in audit committee meetings is not sufficient for comprehensive cybersecurity discussions. Increasingly, more time is needed for conversations around managing the complex risk landscape. In previous CISO roles, Gerchow had a similar cadence, with quarterly time with the security committee and quarterly time with the board. He also had closed door sessions with only board members. “Anyone who’s an employee of the company, even the CEO, has to drop off the call or leave the room, so it’s just you with the board or the director of the board,” he tells CSO. He found these particularly important for enabling frank conversations, which might centre on budget, roadblocks to new security implementations or whether he and his team are getting enough time to implement security programs. “They may ask: ‘How are things really going? Are you getting the support you need?’ It’s a transparent conversation without the other executives of the company being present.” ... In previous CISO roles, Gerchow had a similar cadence, with quarterly time with the security committee and quarterly time with the board. He also had closed door sessions with only board members. “Anyone who’s an employee of the company, even the CEO, has to drop off the call or leave the room, so it’s just you with the board or the director of the board,” he tells CSO.

Mind the Gap: AI-Driven Data and Analytics Disruption

The Holy Grail of metadata collection is extracting meaning from program code: data structures and entities, data elements, functionality, and lineage. For me, this is one of the most potentially interesting and impactful applications of AI to information management. I’ve tried it, and it works. I loaded an old C program that had no comments but reasonably descriptive variable names into ChatGPT, and it figured out what the program was doing, the purpose of each function, and gave a description for each variable. Eventually this capability will be used like other code analysis tools currently used by development teams as part of the CI/CD pipeline. Run one set of tools to look for code defects. Run another to extract and curate metadata. Someone will still have to review the results, but this gets us a long way there. ... Large language models can be applied in analytics a couple different ways. The first is to generate the answer solely from the LLM. Start by ingesting your corporate information into the LLM as context. Then, ask it a question directly and it will generate an answer. Hopefully the correct answer. But would you trust the answer? Associative memories are not the most reliable for database-style lookups. Imagine ingesting all of the company’s transactions then asking for the total net revenue for a particular customer. Why would you do that? Just use a database.

Daily Tech Digest - December 04, 2024

Will AI help doctors decide whether you live or die?

One of the things GPT-4 “was terrible at” compared to human doctors is causally linked diagnoses, Rodman said. “There was a case where you had to recognize that a patient had dermatomyositis, an autoimmune condition responding to cancer, because of colon cancer. The physicians mostly recognized that the patient had colon cancer, and it was causing dermatomyositis. GPT got really stuck,” he said. IDC’s Shegewi points out that if AI models are not tuned rigorously and with “proper guardrails” or safety mechanisms, the technology can provide “plausible but incorrect information, leading to misinformation. “Clinicians may also become de-skilled as over-reliance on the outputs of AI diminishes critical thinking,” Shegewi said. “Large-scale deployments will likely raise issues concerning patient data privacy and regulatory compliance. The risk for bias, inherent in any AI model, is also huge and might harm underrepresented populations.” Additionally, AI’s increasing use by healthcare insurance companies doesn’t typically translate into what’s best for a patient. Doctors who face an onslaught of AI-generated patient care denials from insurance companies are fighting back — and they’re using the same technology to automate their appeals.

The Rise Of ‘Quiet Hiring’: 5 Ways To Use Trend For A Career Advantage

Adaptability is key in quiet hiring. When I interviewed Ross Thornley, Co-founder of AQai, an organization that provides adaptability training, he said, "We’re entering a period of volatility where expanding adaptability skills is essential." Whether it’s learning to manage budgets, mastering new software, or brushing up on leadership skills, the more versatile you are, the more indispensable you become. ... You might feel uncomfortable tooting your own horn, but staying silent about your successes can hurt you in the long run. Keep track of your achievements as you take on extra responsibilities. Highlight the skills you’re building and the results you’re delivering. Then, share them in conversations with your manager or during performance reviews. By showcasing your value, you ensure your work doesn’t go unnoticed. ... When holding onto status-quo ways, employees limit themselves from reaching heights that might improve engagement. Without exploration, there’s a greater potential to be misaligned with a job or responsibility that isn’t motivating. Every new role—whether formal or not—is an opportunity to grow and explore. Use this time to test out roles you might not have considered. See if you enjoy the work or if it’s a stepping stone to something even better.

Creating a unified data, AI and infrastructure strategy to scale innovation ambitions

To effectively leverage data and AI, organisations must first shift their mindset from merely collecting data to actively connecting the dots. This involves identifying the core problem that needs to be addressed and focusing on use cases that will yield maximum business impact, rather than isolating data collection and AI model development. ... To enhance AI implementation, organisations should shift from a use-case-driven approach to a capability-driven strategy, focusing on building reusable AI capabilities such as conversational AI and voice analytics for both internal and external service desks. A company exploring numerous use cases can then group them into distinct capabilities for greater efficiency. Establishing a centralised team dedicated to data, AI and infrastructure is essential to create a robust foundation and platform while allowing business units to develop their own AI-powered applications on top, ensuring consistency across the organisation. ... To succeed in scaling innovation and AI, organisations must move from merely collecting data to actively connecting data, AI and infrastructure. Today’s advancements in cloud and data management technologies enable this integration, fostering collaboration and driving innovation at scale.

AWS introduces S3 Tables, a new bucket type for data analytics

The new bucket type is S3 Table, for storing data in Apache Iceberg format. Iceberg is an open table format (OTF) used for storing data for analytics, and with richer features than Parquet alone. Parquet is the format used by Hadoop and by many data processing frameworks. Parquet and Iceberg are already widely used on S3, so why a new bucket type? Warfield said the popularity of Parquet in S3 was the rationale for S3 Tables. "We actually serve about 15 million requests per second to Parquet tables," he told us, but there is a maintenance burden. Internally, he said, "the structure of them is a lot like git, a ledger of changes, and the mutations get added as snapshots. Even with a relatively low rate of updates into your OTF you can quickly end up with hundreds of thousands of objects under your table." The consequence is poor performance. "In the OTF world it was anticipated that this would happen, but it was left to the customer to do the table maintenance tasks," Warfield said. The Iceberg project includes code to expire snapshots and clean up metadata, but it is still necessary "to go and schedule and run those Spark jobs." Apache Spark is a SQL engine for large scale data. Parquet on S3 was "a storage system on top of a storage system," said Warfield, making it sub-optimal.

Innovation Is Fun, but Infrastructure Pays the Bills

Innovation and platform infrastructure are intertwined — each move affects the other. Yet, many companies are stumbling because they’re too focused on innovation. They’re churning out apps, features, and updates at breakneck speed, all while standing on a wobbly foundation. It’s a classic case of putting the cart before the horse, and it affects the intended impact of some really great ideas. A strong platform infrastructure is your ticket to scalability and flexibility. It lets you pivot quickly to meet new market demands, integrate cutting-edge technologies, and expand your services without tearing everything down and starting from scratch. Plus, it trims the fat off your development and deployment times, letting you bring innovative ideas to market faster. Sidestepping platform infrastructure is a recipe for disaster. It can make your application sluggish, prone to crashes, and a sitting duck for cyberattacks. This isn’t just a headache for users — it’s a surefire way to tarnish your product’s reputation and negatively affect its success. Think of it like building a mansion on a shaky foundation; it doesn’t matter how grand it looks if it’s doomed to collapse.

Open-washing and the illusion of AI openness

Open-washing in AI refers to companies overstating their commitment to openness while keeping critical components proprietary. This approach isn’t new. We’ve seen cloud-washing, AI-washing, and now open-washing, all called out here. Marketing firms want the concept of being “open” to put them in a virtuous category of companies that save baby seals from oil spills. I don’t knock them, but let’s not get too far over our skis, billion-dollar tech companies. ... At the heart of open-washing is a distortion of the principles of openness, transparency, and reusability. Transparency in AI would entail publicly documenting how models are developed, trained, fine-tuned, and deployed. This would include full access to the data sets, weights, architectures, and decision-making processes involved in the models’ construction. Most AI companies fall short of this level of transparency. By selectively releasing parts of their models—often stripped of key details—they craft an illusion of openness. Reusability, another pillar of openness, is much the same. Companies allow access to their models via APIs or lightweight downloadable versions but prevent meaningful adaptation by tying usage to proprietary ecosystems.

Microsoft hit with more litigation accusing it of predatory pricing

“All UK businesses and organizations that bought licenses for Windows Server via Amazon’s AWS, Google Cloud Platform, and Alibaba Cloud may have been overcharged and will be represented in this new ‘opt-out’ collective action,” the law firm statement said. The accusations make sense when viewed from a compliance/regulatory perspective. Although companies are allowed to give volume discounts and to offer other pricing differences for different customers, compliance issues kick in when the company controls an especially high percentage of the market. ... “Put simply, Microsoft is punishing UK businesses and organizations for using Google, Amazon, and Alibaba for cloud computing by forcing them to pay more money for Windows Server. By doing so, Microsoft is trying to force customers into using its cloud computing service, Azure, and restricting competition in the sector,” Stasi said. “This lawsuit aims to challenge Microsoft’s anti-competitive behavior, push them to reveal exactly how much businesses in the UK have been illegally penalized, and return the money to organizations that have been unfairly overcharged.”

Balancing tradition and innovation in the digital age

It’s easy to get carried away by the hype of cutting-edge technology. For me, it’s about making sure that you always ask yourself if you’re solving an actual business problem. That has to be front of mind, as opposed to being solution- or tech-first. You also have to ask yourself if the business problem requires nascent or proven tech? Once you figure that out, the tech side answer is relatively straightforward. So, even with leveraging emerging tech, you need to think congruently about your business model. ... Security is the first thing I looked at. Even in my interview, I said it would be the first thing I looked at, and it has been. Security and privacy are the basic foundations of trust, and customer and community trust is what our business is built on. So, my approach is to spend money to bring in deep expertise, which I have, and empower them to go deep into our current state and be honest about any gaps we might have. And to think about where we implement both tactical and strategic ways to bridge those gaps. It’s also important to be clear about the risk we hold and how long we want to hold it for and focus on building a response plan. So, if and when an incident occurs, we can recover and respond gracefully and have solid comms plans and playbooks in place.

Threat intelligence and why it matters for cybersecurity

Cyber threat intelligence – who needs it? The short answer is everyone. Cyber threat intelligence is for anyone with a vested interest in the cybersecurity infrastructure of an organization. Although CTI can be tailored to suit any audience, in most cases, threat intelligence teams work closely with the Security Operation Centre (SOC) that monitors and protects a business on a daily basis. Research shows that CTI has proved beneficial to people at all levels of government (national, regional or local), from security officers, police chiefs and policymakers, to information technology specialists and law enforcement officers. It also provides value to many other professionals, such as IT managers, accountants and criminal analysts. ... The creation of cyber threat intelligence is a circular process known as an “intelligence cycle”. In this cycle, which consists of five stages, data collection is planned, implemented and evaluated; the results are then analysed to produce intelligence, which is later disseminated and re-evaluated against new information and consumer feedback. The circularity of the process means that gaps are identified in the intelligence delivered, initiating new collection requirements and launching the intelligence cycle all over again.

Securing AI’s new frontier: Visibility, governance, and mitigating compliance risks

Securing and governing the use of data for AI/ML model training is perhaps the most challenging and pressing issue in AI security. Using confidential or protected information during the training or fine-tuning process comes with the risk that data could be recoverable through model extraction techniques or using common adversarial techniques (i.e., prompt injection, jailbreak). Following data security and least-privilege access best practices is essential for protecting data during development, but bespoke AI runtime threat detection is response is required to avoid exfiltration of data via model responses. ... Securing AI applications in production is equally important as securing the underlying infrastructure and is a key component of maintaining a secure data and AI lifecycle. This requires real-time monitoring of both prompts and responses to identify, notify, and block security and safety threats. A robust AI security solution prevents adversarial attacks like prompt injection, masks sensitive data to prevent exfiltration via a model response, and also addresses safety concerns such as bias, fairness, and harmful content.

Quote for the day:

"Leading people is like cooking. Don_t stir too much; It annoys the ingredients_and spoils the food" -- Rick Julian

Daily Tech Digest - October 28, 2024

Generative AI isn’t coming for you — your reluctance to adopt it is

Faced with a growing to-do list and the new balancing act of returning from maternity leave to an expanded role leading public relations for a publicly-traded tech company, I opened Jasper AI. I admittedly smirked at some of the functionality. Changing the tone? Is this AI emotionally intelligent? Maybe more so than some former colleagues. I began on a blank screen. I started writing a few lines and asked the AI to complete the piece for me. I reveled in the schadenfreude of its failure. It summarized what I had written at the top of the document and just spit it out below. Ha! I had proven my superiority. I went back into my cave, denying myself and my organization the benefits of this transformative technology. The next time I used gen AI, something in me changed. I realized how much prompting matters. You can’t just type a few initial sentences and expect the AI to understand what you want. It still can’t read our minds (I think). But there are dozens of templates that the AI understands. For PR professionals, there are templates for press releases, media pitches, crisis communications statements, press kits and more.

What's Preventing CIOs From Achieving Their AI Goals?

"While no CIO wants to be left behind, they are also prudent about their AI adoption journeys and how they implement the technology for business in a responsible manner," said Dr. Jai Ganesh, chief product officer, HARMAN International. "While there are many business use cases, enterprises are prioritizing these on a must-have immediately to implement basis." ... He also oversees AI implementation across his company. Technology leaders say it will take at least two to three years before AI becomes mainstream across the enterprise. Rakesh Jayaprakash, chief analytics evangelist at ManageEngine, told ISMG that we would start to see "very tangible results" at a larger scale in another one or two years. "Tangible results" refer to commoditization of AI, which accelerates the ROI, he said. "While there is a lot of hype around AI now, the true value comes when the organizations are able to see the outcomes," Jayaprakash said. "Right now, many organizations jump in with very high expectations of what is possible through AI, because we've started to use tools such as ChatGPT to accomplish very simple tasks. But when it comes to organization-level use cases, those are a little more complex."

Bridging the Data Gap: The Role of Industrial DataOps in Digital Transformation

One of the main issues faced by organizations is the lack of context in industrial data. Unlike IT systems, where data is typically well-defined and structured, data from industrial environments often lacks the necessary context to be useful. For example, a temperature reading from a manufacturing machine might be labeled simply as “temperature sensor 1,” leaving operators to guess its relevance without proper identification. This lack of contextualization—when applied to thousands of data points across multiple facilities— Is a major barrier to advanced analytics and digitalization programs. ... By implementing Industrial DataOps, organizations can address this gap by contextualizing data as close to the source as possible—ideally at the edge of the network. This approach empowers operators who have tribal knowledge of the data and its sources to deliver ready-to-use data to IT and line of business users in their organization. Decisions become faster and more informed. The ultimate goal is to transform raw data into valuable insights that drive operational improvements. ... As organizations adopt Industrial DataOps, they unlock the potential for rapid innovation. With a solid data management framework in place, OT teams can easily explore new use cases and validate hypotheses.

Ensuring AI-readiness of Data Is a Long-term Commitment

Data becomes an intellectual property when one enters the world of GenAI, and it is the way with which one can customize algorithms to reflect the brand voice and deliver great client services. Keeping the scenario in mind, Birkhead states that modernizing data and ensuring its AI-readiness is a long-term commitment. While organizations can make incremental progress year after year, building an analytic factory to produce AI models that support the business takes strategy, investment, and an enabling leadership team. Highlighting JPMC’s data strategy, Birkhead states that the components include data design principles, operating models, principles around platforms, tooling, and capabilities. Additionally, talent, governance, data, and AI ethics also come into play, but the ultimate goal is to have incredibly high-quality data that is self-describing and understandable by both humans and machines. From Birkhead’s standpoint, to be AI-ready with data, organizations have to get data to a state where a data scientist, user, or AI researcher can go into a marketplace and understand everything about the data.

Business Etiquette Classes Boom as People Relearn How to Act at Work

Workers who had substantial professional experience before the pandemic, including managers and executives, still need help adapting to hybrid and remote work, Senning said. He has been coaching leaders on best practices for such things as communicating through your calendar and deciding whether to call, text or use Slack to reach an employee. stablishing etiquette for video meetings has also been a challenge for many firms, he notes. Bad behavior in virtual meetings has occasionally made headlines in recent years, such as the backlash against Vishal Garg, CEO of the mortgage lending firm Better.com, for announcing mass layoffs over Zoom ahead of the holidays in 2021. "If I had a magic button that I could push that could get people to treat video meetings with 50 percent of the same level of professionalism they treat an in-person meeting, I would make a lot of HR, personnel managers, and executives very, very happy," Senning said. Tech companies also are paying for etiquette and professionalism training for their workers, especially if they're bringing in employees who have never worked in person before, according to Crystal Bailey, director of the Etiquette Institute of Washington, who counts Amazon among her clients.

Exploring the Power of AI in Software Development - Part 1: Processes

AI holds the power to significantly enhance the requirement analysis and planning processes at the early stages of the software development life cycle (SDLC). It can analyze massive amounts of data in order to identify user needs and preferences, allowing developers to make informed decisions about features and functionality. ... AI can also look at coding rates per user story within an app architecture context and allow Product Managers to better determine project timelines and resource needs. In doing so, they can more accurately predict the risk-reward of time-to-market versus high quality for every release, knowing that no software will be 100% defect-free. ... With AI, you have a pair programmer who has infinite patience. Someone who will not judge you for seemingly "stupid" questions. Having this kind of support can increase an engineer's capabilities and productivity. So often as a junior engineer, I was afraid to ask the senior engineers on my team questions because I thought I should know the answer. Engineers can use AI without the worry of judgment, so no question is stupid, no answer should be known.

How AI is Shaping the Future of Product Development

Product testing and iteration processes are also being revolutionized by AI, which results in shorter development cycles and better product outcomes as well. While tried and true testing methods can work well, they often have long cycles or may miss problems. Quiet contrary to traditional testing, AI-driven automation suggests a new degree of efficiency and accuracy. AI tools for early-stage testing makes it possible to discover issues quickly and try out potential applications, which lowers the demand on manual resources spent in validating components or debugging. Not just that, AI's ability to analyze code bases comprehensively provides targeted insights for ongoing improvements. By integrating AI into testing processes, businesses can accelerate development cycles, reduce costs, and deliver products that better align with user expectations. ... By embedding AI into their growth strategies, companies can benefit in numerous ways. It allows for more targeted and personalized experience to be delivered, subsequently personalizing the products or services provided by companies. Such a custom-built solution not only enhances user experience but also helps create brand loyalty. Additionally, AI allows companies to have data-driven decision making that facilitates strategic planning and execution.

From Safety to Innovation: How AI Safety Institutes Inform AI Governance

According to the report, this “first wave” of AISIs has three common characteristics:Safety-focus: The first wave of AISIs was informed by the Bletchley AI Safety Summit, which declared that “AI should be designed, developed, deployed, and used in a manner that is safe, in such a way as to be human-centric, trustworthy, and responsible.” These institutes are particularly concerned with mitigating abuse and safeguarding frontier AI models. Government-led: These AISIs are governmental institutions, providing them with the “authority, legitimacy, and resources” needed to address AI safety issues. Their governmental status helps them access leading AI models to run evaluations, and importantly, it gives them greater leverage in negotiating with companies unwilling to comply. Technical: AISIs are focused on attracting technical experts to ensure an evidence-based approach to AI safety. The report also points out some key ways AISIs are unique. For one, AISIs are not a “catch-all” entity to tackle the complex and ever-evolving AI governance landscape. They are also relatively free of the bureaucracy commonly associated with governmental agencies. This may be due to the fact that these institutes have very little regulatory authority and focus more on establishing best practices and conducting safety evaluations to inform responsible AI development.

Current Top Trends in Data Analytics

One of the most impactful data analytics trends right now is the integration of AI and machine learning (ML) into analytics frameworks, observes Anil Inamdar, global head of data services at data monitoring and management firm Instaclustr by NetApp, an online interview. "We are seeing the emergence of a new data 4.0 era, which builds on previous shifts that focused on automation, competitive analytics, and digital transformation," Inamdar states. "This distinct new phase leverages AI/ML and generative AI to significantly enhance data analytics capabilities," he says. While the transformative potential is now here for the taking, enterprises must carefully strategize across several key areas. ... Data governance should be a top concern for all enterprises. "If it isn't yours, you’re heading for a world of hurt," warns Kris Moniz, national data and analytics practice lead for business and technology advisory firm Centric Consulting, via email. Data governance dictates the rules under which data should be managed, Moniz says. "It doesn’t just do this by determining who gets access to what," he notes. "It also does it by defining what your data is, setting processes that can guarantee its quality, building frameworks that align disparate systems across common domains, and setting standards for common data that all systems should consume."

Effective Data Mesh Begins Wtih Robust Data Governance

When implemented correctly, removing the dependency on centralised systems and IT teams can truly transform the way organisations operate. However, introducing a data mesh can also raise fears and concerns relating to storage, duplication, management, and compliance, all of which must be addressed if it is to succeed. With decentralised data management, it’s also critical that everyone follows the same stringent set of rules, particularly regarding the creation, storage, and protection of data. If not, issues will quickly arise. Additionally, if any team leaders or department heads put their own tools or processes in place, the results may cause far more problems than they solve. Trusting individuals to stick to data guidelines is too risky. Instead, adherence should be enforced in a way that ensures standards are followed, without impacting agility or frustrating users. This may sound impractical, but a computational governance approach can impose the necessary restrictions, while at the same time accelerating project delivery. Naturally, not everyone will be quick (or keen) to adjust, but with additional support and training even the most reluctant individuals can learn how to adopt a more entrepreneurial mindset.

Quote for the day:

"Trust is the lubrication that makes it possible for organizations to work." -- Warren G. Bennis

Daily Tech Digest - January 21, 2024

What is RAG? More accurate and reliable LLMs

Retrieval-Augmented Generation (RAG) is an AI framework that significantly impacts the field of Natural Language Processing (NLP). It is designed to improve the accuracy and richness of content produced by language models. Here’s a synthesis of the key points regarding RAG from various sources: RAG is a system that retrieves facts from an external knowledge base to provide grounding for large language models (LLMs). This grounding ensures that the information generated by the LLMs is based on accurate and current data, which is particularly important given that LLMs can sometimes produce inconsistent outputs; The framework operates as a hybrid model, integrating both retrieval and generative models. This integration allows RAG to produce text that is not only contextually accurate but also rich in information. The capability of RAG to draw from extensive databases of information enables it to contribute contextually relevant and detailed content to the generative process; RAG addresses a limitation of foundational language models, which are generally trained offline on broad domain corpora and are not updated with new information post-training.

Redefining Quantum Bits: The Graphene Valley Breakthrough

Because quantum information is much more prone to being corrupted – and therefore become unsuitable for computational tasks – by the surrounding environment than its classical counterpart, researchers who study different qubit candidates must characterize their coherence properties: these tell them how well and for how long quantum information can survive in their qubit system. In most traditional quantum dots, electron spin decoherence can be caused by the spin-orbit interaction, which introduces an unwanted coupling between the electron spin and the vibrations of the host lattice, and the hyperfine interaction between the electron spin and the surrounding nuclear spins. In graphene as well as in other carbon-based materials, spin-orbit coupling and hyperfine interaction are both weak: this makes graphene quantum dots especially appealing for spin qubits. The results reported by Garreis, Tong, and co-authors add one more promising facet to the picture. ... The hexagonal symmetry observed in this so-called real space is also present in momentum space, where the vertices of the lattice don’t correspond to the spatial locations of carbon atoms but to values of momentum associated with the free electrons on the lattice.

5 Ways AI Can Make Your Human-To-Human Relationships More Effective

Understanding your audience is a major challenge for many business leaders. After all, if you knew what did or didn’t appeal to your audience, it would be much easier to speak to them in a meaningful, engaging way that sparks lasting connections. And AI can help here, too. This was illustrated to me during a recent conversation with James Webb, co-founder and CTO of Comb Insights, whose app uses proprietary AI to provide sentiment scores on comments on social media posts. "Using AI to quickly evaluate the overall sentiment of the comments on a post can give business leaders an immediate understanding of whether their content resonated with their audience,” he told me in an interview. “Seeing the ratio of positive to neutral or negative comments, and seeing the most common words that show up in the comments, can provide quick insights into why a post succeeded or failed. With this instant understanding of their audience, businesses can pivot in the type of social media content they produce so they can strengthen these important digital relationships.”

The missing link of the AI safety conversation

From a practical standpoint, the high cost of AI development means that companies are more likely to rely on a single model to build their product — but product outages or governance failures can then cause a ripple effect of impact. What happens if the model you’ve built your company on no longer exists or has been degraded? Thankfully, OpenAI continues to exist today, but consider how many companies would be out of luck if OpenAI lost its employees and could no longer maintain its stack. Another risk is relying heavily on systems that are randomly probabilistic. We are not used to this and the world we live in so far has been engineered and designed to function with a definitive answer. Even if OpenAI continues to thrive, their models are fluid in terms of output, and they constantly tweak them, which means the code you have written to support these and the results your customers are relying on can change without your knowledge or control. Centralization also creates safety issues. These companies are working in the best interest of themselves. If there is a safety or risk concern with a model, you have much less control over fixing that issue or less access to alternatives.

Intro to Digital Fingerprints

Digital fingerprinting is a technique used to identify users across different websites based on their unique device and browser characteristics. These characteristics - fingerprint parameters, can include various software, hardware (CPU, RAM, GPU, media devices - cameras, mics, speakers), location, time zone, IP, screen size/resolution, browser/OS languages, network, internet provider-related and other attributes. The combination of these parameters creates a unique identifier - fingerprint, that can be used to track a user's online activity. Fingerprints play a crucial role in online security, enabling services to identify and authenticate unique users. They also make it possible for users to trick such systems to stay anonymous online. However, if you can manipulate your fingerprints, you can run tens or hundreds or more different accounts to pretend that they are unique, authentic users. While this may sound cool, it has serious implications as it can make it possible to create an army of bots that can spread spam and fakes all over the internet, potentially resulting in fraudulent actions.

Looking at a data-driven financial future for India

In the intricate landscape of financial services, managing vast data, complex silos, and strict compliance demands a strategic solution. A hybrid data mesh is an innovative approach to financial operations that brings flexibility and coherence. This method combines a distributed architecture with an SSOT, ensuring accurate, secure, and compliant data handling. Data distribution across systems and functions facilitates quick insights while adhering to quality and privacy standards. The hybrid data mesh concept integrates the advantages of a distributed architecture tailored to domain-specific data with the SSOT, providing enhanced flexibility and scalability. This fusion ensures data coherence and accuracy while allowing domain independence, reinforcing security, and streamlining traceable and auditable compliance. Predictive models can be tailored to specific products or customer segments by harnessing AI and ML tools, enhancing decision-making in a dynamic market. This streamlined approach identifies growth opportunities and nurtures a culture of adaptability and innovation.

L&D trends that will define 2024

AI-assisted coding/software development employs AI to help write and review code. The potential of the technology to assist new developers in improving their code and saving time is valuable. The edtech sector, in particular, will employ AI to create customised learning experiences besides using tools that offer instant feedback on code. We could be looking at automating assessments for unbiased, error-free evaluations. Manually identifying personalised learning journeys for numerous individuals is time-consuming and extremely difficult. AI-assisted coding can help solve this operational challenge. Soon, we’ll give users quick, accurate responses and allow them to accelerate their learning journeys. ... Organisations will focus on data-driven, business-aligned learning initiatives for specific job-role competencies. This is to qualify L&D impact by easily tracking employee metrics such as job performance, efficiency, engagement, and employee satisfaction in new ways. When properly implemented, the accumulated data can raise confidence levels among higher-ups and lead to sustained investment in training practices. Organisations also analyse the information to identify areas of positive impact and focus on L&D in those regions for frequently better outcomes.

New Guidance Urges US Water Sector to Boost Cyber Resilience

"Cyber threat actors are aware of - and deliberately target - single points of failure," the guidance states. "A compromise or failure of a water and wastewater sector organization could cause cascading impacts throughout the sector and other critical infrastructure sectors." The incident response guide aims to provide organizations with best practices for all four stages of the incident response life cycle - from preparation through detection, recovery and post-incident activities. The guidance says "the cyber incident reporting landscape is constantly evolving" and encourages water sector officials to review their reporting obligations and "consider engaging in additional voluntary reporting and/or information sharing" measures. Eric Goldstein, CISA's executive director for cybersecurity, said in a statement announcing the joint guidance that the U.S. water and wastewater sector "is under constant threat from malicious cyber actors." "In the new year, CISA will continue to focus on taking every action possible to support 'target-rich, cyber-poor' entities like WWS utilities by providing actionable resources and encouraging all organizations to report cyber incidents," he said.

Banking at the Precipice: Navigating the Fifth Industrial Revolution

As retail banking stands amid the Fourth Industrial Revolution’s digital transformation, leaders now must prepare for an imminent Fifth Industrial Revolution poised to profoundly reshape markets and experiences. Defined by extreme personalization, mass customization and precision augmentation, the emerging revolution’s exact disruptions remain somewhat undefined. Yet advancements in generative artificial intelligence, ambient interfaces and hyper-connectivity hint at consumer-in-command days ahead. ... Most of these Fifth Industrial Revolution financial applications seem unimaginable today. Imagine augmented live views layering physical surfaces like a retail store, billboard or car dealership with tailored offers based on persona identification and real-time transactional and behavioral data. Moving further, imagine a ‘digital twin agent’ seamlessly negotiating a personalized deal or pre-approved financing instantly. In this world, augmented and mixed reality interfaces, bridging physical and virtual worlds, will be able to move money experiences from transactions to value-based propositions based on where your eyes focus and engagements you have had in the past.

How generative AI is changing entrepreneurship

Entrepreneurs are expected to do a wide range of time-consuming tasks, from writing emails and answering phone calls to orchestrating product demonstrations and coding a website. “AI does all of those things well,” Mollick said. “It lets you focus more on what your top skill is, and it kind of handles everything else.” Generative AI can also serve as a guide. “A third of Americans have a business idea that they haven’t acted on because they don’t know what to do next,” Mollick said. “The AI can tell you what to do next, help you write the emails, [and] help you build the product.” Mollick noted that users should be aware of the benefits and limitations of the technology. “It’s kind of like an intern who wants to make you happy and therefore lies a lot and is kind of naive [and] never admits that they made a mistake,” he said. “Once you think about [AI] that way, you end up in much better shape.” Generative AI is a new general-purpose technology — one that comes around once in a generation and touches just about everything humans do, Mollick said, like electricity, computers, and the internet have. For entrepreneurs, generative AI can assist with researching ideas, coming up with logos and names, creating a website, and more, Mollick said.

Quote for the day:

"Leadership is not about titles, positions, or flow charts. It is about one life influencing another." -- John C. Maxwell

Daily Tech Digest - January 25, 2023

How Quantum Metric is using data analytics to optimize digital teams

Quantum Metric was Ciabarra’s attempt to solve problems he personally faced while running his online app store, Intelliborn. As the company grew to over one million active users per day, he uncovered how difficult it was to see and understand all of his customers at scale, and in real time. “I had used Google Analytics, which was great to see how traffic was growing, but it couldn’t tell me where my customers were struggling, and why. I would fix something that someone on Twitter was ‘yelling’ at me about, but it sometimes would impact my business, and sometimes it wouldn’t,” Ciabarra told VentureBeat. “I thought — why is this so hard? Maybe addressing the squeaky wheel didn’t make sense from a business perspective.” That sparked the idea for Quantum Metric. So, with his cofounding engineer, David Wang, alongside his cat, Indy, Ciabarra went on to develop the first version of the Quantum Metric platform. It focused on surfacing customer frustrations and helping organizations see their customer experience through session replays.

Creating a competitive edge with a cloud maturity strategy

Companies cannot become cloud mature overnight. Cloud maturity involves a strategic effort from all levels of the businesses to look carefully at cloud spend, mitigate cloud-related risks, and upskill workers in cloud technologies. Those that manage to achieve a high level of cloud maturity remain much more competitive than firms that stop at merely adopting cloud technologies. According to McKinsey, Fortune 500 companies could earn more than $1 trillion dollars by 2030 as a result of cloud adoption and optimisation. Deutsche Bank recognised that in order to keep up with the future of banking and remain competitive, it needed to become more cloud mature. ... Cloud maturity is essential to a company’s success – but first leaders need to make sure their employees are equipped with the skills required to solve security issues. Only then will businesses be ready to implement the right strategies to maximise their return on investment and realise the full potential of cloud computing.

CNET Is Testing an AI Engine. Here's What We've Learned, Mistakes and All

Over the past 25 years, CNET built its expertise in testing and assessing new technology to separate the hype from reality and help drive conversations about how those advancements can solve real-world problems. That same approach applies to how we do our work, which is guided by two key principles: We stand by the integrity and quality of the information we provide our readers, and we believe you can create a better future when you embrace new ideas. The case for AI-drafted stories and next-generation storytelling tools is compelling, especially as the tech evolves with new tools like ChatGPT. These tools can help media companies like ours create useful stories that offer readers the expert advice they need, deliver more personalized content and give writers and editors more time to test, evaluate, research and report in their areas of expertise. In November, one of our editorial teams, CNET Money, launched a test using an internally designed AI engine – not ChatGPT – to help editors create a set of basic explainers around financial services topics.

Common Misconceptions About Modern Ransomware

Not too long ago, if someone decided to pay a ransom, they might not receive the decryption keys after doing so. However, today, ransom payers usually do receive the keys. This was a quiet shift that took place over several years. Before this shift took place, the unsophisticated encryption process could be considered hit or miss. Today, ransomware and threat actors hit more than they miss. Often, they can encrypt most of the data—and do so quickly. Just several years ago, a threat group would take many months to move around in a network, find data sources, monitor traffic and begin an encryption process. Fast forward to today, and the average attack-to-encryption time is 4.5 days. During the early days of ransomware attacks, threat groups would occasionally move to a domain controller and gain access to an active directory. This granted them the keys to the kingdom and had a detrimental effect on the victim organization. Today, because of poor active directory security and configurations, threat groups can often elevate their credentials and their own active directory rapidly.

Can AI replace cloud architects?

The most likely path is that tactical AI tools will continue to appear. These tools will focus on specific architectural areas, such as network design, database design, platform selection, cloud-native design, security, governance, use of containers, etc. The output should be as good as, if not better than what we see today because these tools will leverage almost perfect data and won’t have those pesky human frailties that drive some architecture designs—emotions and feelings. Of course, some of these AI tools exist today (don’t tell me about your tool) and are progressing toward this ideal. However, their usefulness varies depending on the task. Tactical AI tools must still be operated by knowledgeable people who understand how to ask the right questions and validate the designs and recommendations the tool produces. Although it may take fewer people to pull off the tactical component design of a large cloud architecture, the process will not likely eliminate all humans. Remember, many of these mistakes occur because enterprises have difficulty finding skilled cloud pros.

Chinese threat actor DragonSpark targets East Asian businesses

SparkRAT uses WebSocket protocol to communicate with the C2 server and features an upgrade system. This allows the RAT to automatically upgrade itself to the latest version available on the C2 server upon start-up by issuing an upgrade request. “This is an HTTP POST request, with the commit query parameter storing the current version of the tool,” researchers noted. In the attacks analyzed by the researchers, the SparkRAT version used was built on November 1, 2022, and deployed 26 commands. “Since SparkRAT is a multi-platform and feature-rich tool, and is regularly updated with new features, we estimate that the RAT will remain attractive to cybercriminals and other threat actors in the future,” researchers said. DragonSpark also uses Golang-based m6699.exe, to interpret runtime encoded source code and launch a shellcode loader. This initial shellcode loader contacts the C2 server and executes the next-stage shellcode loader.

Microsoft to Block Excel Add-ins to Stop Office Exploits

Excel add-in files are designated with the XLL file extension. They provide a way to use third-party tools and functions in Microsoft Excel that aren't natively part of the software; they're similar to dynamic link libraries (DLLs) but with specific features for Excel spreadsheets. For cyberattackers, they offer a way to read and write data within spreadsheets, add custom functions, and interact with Excel objects across platforms, Vanja Svajcer, a researcher with Cisco's Talos group, said in a December analysis. And indeed, attackers started experimenting with XLLs in 2017, with more widespread usage coming after the technique became part of common malware frameworks, such as Dridex. ... One of the reasons for that is because Microsoft Office does not block the feature but raises a dialogue box instead, a common approach that Microsoft has taken in the past, Svajcer wrote: "Before an XLL file is loaded, Excel displays a warning about the possibility of malicious code being included. Unfortunately, this protection technique is often ineffective as a protection against the malicious code, as many users tend to disregard the warning."

The Intersection of Trust and Employee Productivity

Unfortunately, many companies adopt a "block first, ask questions later" approach to security, which can erode employee trust and undermine the benefits of empowering employees to choose their own applications. In our previous research at Cerby, we found that 19% of employees ignore application blocks and continue to use the apps they prefer, despite such restrictions. This suggests that organizations should seek to balance high levels of trust in employees with zero trust principles for data, applications, assets and services (DAAS). A more effective approach may be to adopt an enrollment-based approach to security that balances trust-positive initiatives like employee choice of applications with cybersecurity and compliance requirements. By adopting this approach, organizations can build digital trust with employees by giving them more control over the tools and technologies they use while still ensuring the security and reliability of their systems and processes for consumers. But the benefits of building high levels of employee trust go beyond improved job performance and satisfaction.

Examining the CIO time management dilemma

The skill profile and expectations of the CIO have, therefore, shifted to balance both business management with technology, so, where necessary, CIOs need to bolster those skills accordingly to deliver the right solutions for the business. “What makes a strong CIO is being able to recognize where the blind spots in their skill sets are and bring supplemental skills in with other leaders in the organization,” she adds. So the CIO role has evolved into this business manager position to understand how technology delivers value to the business. “And because technology is becoming the way we do business, it becomes imperative for the CIO to have that business acumen in addition to the technology,” she says, adding having that acumen is necessary to articulate justifying investment in it to enable organizational growth. In addition, as CEOs have increased their investment into digital advances in security, AI, and data analytics, their demand for results has grown, according to Gartner VP analyst Daniel Sanchez-Reina.

Cloud egress costs: What they are and how to dodge them

Egress charges work the other way, by discouraging firms from transferring data out, either to other cloud providers, or to on-premise systems. “They’ve made the commercial decision that ingress should be effectively absorbed within the consolidated cost of service represented in the unit prices of cloud components, but egress charges are separated out,” says Adrian Bradley, head of cloud transformation at consulting firm KPMG. “At the heart of that, it is a real cost. The more a client consumes of it, the more it costs the cloud providers.” Firms have seen egress charges rise as they look to do more with their data, such as mining archives for business intelligence purposes, or to train artificial intelligence (AI) engines. Data transfers can also increase where organisations have a formalised hybrid or multi-cloud strategy. “Either there’s a need to do a lot more data egress, or perhaps there’s just simply the positive use of cloud to develop new products and services that intrinsically use more data,” says Bradley. The result is that firms are moving more data from cloud storage, and are being hit by increasing costs.

Quote for the day:

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