Daily Tech Digest by Kannan Subbiah: government IT

Showing posts with label government IT. Show all posts

Daily Tech Digest - September 05, 2026

Quote for the day:

"Success... seems to be connected with action. Successful people keep moving. They make mistakes, but they don't quit." -- Conrad Hilton

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

Why is the Cloud Changing Again?

The rise of artificial intelligence is fundamentally changing how companies store and manage their data, moving the industry away from a one-size-fits-all public cloud model. Traditional cloud setups were excellent for standard web traffic and everyday software, acting like an efficient public transit system. However, artificial intelligence requires processing massive amounts of data at high speeds, which can cause severe delays and soaring costs on shared networks. To handle these heavy workloads, businesses are shifting toward a more specialized, decentralized approach. Additionally, because artificial intelligence learns from the information it processes, companies are increasingly concerned about the security and privacy of their sensitive data. This has driven a strong movement toward bringing data back home to private, local servers. Governments are also introducing stricter privacy laws, requiring companies to keep citizen data within their own national borders rather than storing it in global facilities. As a result, organizations are adopting a flexible strategy where they use public servers for everyday tasks, regional servers to comply with local regulations, and highly secure private servers for their most valuable information. This balanced method allows businesses to use advanced systems while maintaining strict control over their security, legal compliance, and digital assets.

Keeping OT security up to date is more than patching systems

Securing operational technology (OT) in industrial environments involves much more than applying simple software updates. As cyber threats against critical infrastructure like manufacturing and energy continue to rise, protecting these systems requires a fundamentally different approach than traditional IT security. While IT focuses primarily on protecting data, OT security must balance digital defense with real-world safety and continuous physical operations. Because large industrial systems often remain in active use for several decades, they cannot always be patched or upgraded as easily as typical office computers. Rather than relying solely on specialized technical controls, organizations must deeply understand their operational dependencies and gain completely clear visibility into their connected assets and third-party vendor access. Major disruptions frequently stem from basic weaknesses, such as poor network segmentation or compromised IT environments that spill over into industrial operations, rather than highly complex, sophisticated attacks. To build truly effective defenses, companies need strong internal governance that clearly defines responsibilities across engineering, operations, and security teams. Ultimately, organizations should view OT security not just as a narrow technical issue, but as a critical element of overall business resilience. By combining standard cybersecurity practices with deep industrial expertise, companies can protect their vital operations while successfully adapting to ever-evolving security risks.

Your R&D doesn’t need to be flashy

Software development teams often feel pressure to build flashy, highly marketable features to impress users. However, the most valuable research and development work usually happens entirely behind the scenes. While a brand-new interface button might make for a great product demonstration, real long-term user satisfaction depends on foundational elements like speed, reliability, and security. When software performs exactly as expected without delays or glitches, users can focus entirely on their work rather than fighting with the tool itself. Modern professionals, such as architects or engineers, rely on software to handle increasingly complex and automated tasks. If an application fails to execute a command accurately or compromises sensitive project data, the user's trust is instantly broken, and the financial consequences can be severe. This is why development teams must prioritize secure, reliable environments over cosmetic upgrades. By analyzing how people actually use the product, developers can identify the invisible improvements that truly matter, such as open standards that allow seamless collaboration across different platforms. Ultimately, the best software acts as a quiet partner, anticipating a user's needs and handling repetitive work so they can stay immersed in their creative flow.

Querying and Performing Transactions Across Multiple Database Schemas in a Modular Monolith

In a modular monolith, assigning a dedicated database schema to each module establishes strong boundaries but introduces significant challenges for querying data and managing transactions. Because direct database access between modules violates these boundaries, traditional approaches like joining tables across different schemas or relying on single database transactions are no longer viable. To solve querying issues, developers can use several strategies. The simplest method involves direct API calls, where modules communicate through public interfaces, ensuring strict boundaries despite potential performance compromises. For scenarios requiring faster reads, teams can rely on domain events to duplicate and denormalize data across modules, though this requires managing eventual consistency. Alternatively, database views allow developers to join tables across schemas at the database level, which is particularly effective for reporting purposes. Another strong option is the Backend for Frontend pattern, where a dedicated service aggregates data from multiple modules before sending it to the user. Handling transactions across multiple schemas requires a shift away from traditional methods. Instead of relying on a single commit, systems must utilize event driven architectures and patterns like sagas. While this approach ensures loose coupling, scalability, and resilience, it also introduces complexity by requiring compensating transactions and careful error handling to maintain data consistency.

Gmail labels: Your secret weapon against inbox chaos

Gmail labels provide a powerful and flexible alternative to traditional email folders, acting more like customizable tags that allow multiple categories to be applied to a single message. By mastering these tools, users can significantly reduce inbox chaos and streamline their daily communication. A great starting point is creating and color-coding various labels, then grouping them into parent and sublabel hierarchies to maintain a consistently neat sidebar. To save time during everyday tasks, you can proactively apply these labels while composing a new email or assign them simultaneously while archiving a read message. Labels also dramatically improve your ability to find old information; typing specific label operators directly into the search bar instantly narrows down vast results. Furthermore, users can fully automate their workflow by setting up custom Gmail filters. These filters automatically apply specific labels to incoming messages based on criteria like the sender's address or specific subject line keywords. This intelligent automation allows urgent emails to stand out immediately while quietly routing less critical messages away from your main inbox view. Finally, labels can be connected to custom notification settings, ensuring you only receive alerts for the messages that truly matter. By adopting these simple strategies, anyone can transform an overwhelming inbox into a highly organized system.

When cyber capability becomes abundant: Rethinking government cyber resilience

As artificial intelligence rapidly evolves, it is fundamentally changing the economics of cybersecurity for government agencies. Historically, sophisticated cyber operations required scarce, expensive human expertise. Today, AI has significantly reduced these costs, making powerful cyber capabilities widely available to both attackers and defenders. This shift creates unprecedented challenges for government agencies, which protect critical infrastructure and systems essential to national security, public health, and emergency response. Because attackers can now discover and exploit vulnerabilities faster than organizations can fix them, government security leaders are losing confidence in traditional defensive strategies. To adapt to this new reality, governments must rethink their approach to cyber resilience across operational and institutional levels. Operationally, agencies need to move away from trying to fix every single technical flaw. Instead, they must prioritize risks based on their potential impact on public missions. A moderate vulnerability in an emergency response system matters far more than a severe flaw in a low impact network. By translating technical data into real world operational context, governments can better focus their limited resources on protecting what truly matters. Ultimately, success requires agencies to rapidly reduce their exposure, contain breaches driven by artificial intelligence, and actively shape a safer overall cyber ecosystem.

Cyber resilience in the age of AI will be decided in the boardroom

As modern business innovation speeds up due to artificial intelligence, it also provides attackers with powerful new ways to disrupt operations. Companies have spent heavily on defensive software, but having more tools often creates confusing complexity rather than clear protection. Because automated threats move faster than ever, the true test of an organization is not whether it can prevent every single incident, but how well it handles a crisis when it happens. Cybersecurity is no longer just a technical issue meant for the information technology department; it is a fundamental business challenge that belongs in the boardroom. Company leaders must understand their critical digital dependencies and how a failure would impact revenue, reputation, and daily functioning. Security should be woven into every major business decision from the start, prioritizing clear processes over having the most complicated software. True resilience relies heavily on human behavior. An organization must build a culture where employees feel safe reporting mistakes, questioning unusual requests, and practicing response plans before an actual emergency occurs. Ultimately, survival during a digital attack depends on clear communication, decisive leadership, and the ability to keep essential services running smoothly and effectively, ensuring that trust and stability are maintained alongside technological growth.

How Differential Privacy Will Transform Enterprise Data Strategy

Differential privacy is quickly moving from a theoretical concept to a critical component of enterprise data strategy. While previous methods like encryption and de-identification have struggled to protect against re-identification as data volumes grow, differential privacy offers a mathematically proven way to guarantee that an individual's data cannot be reverse-engineered from broader analytical outputs. This technique is already being used successfully by major organizations, including the U.S. Census Bureau, Apple, Google, and Microsoft, and the market is projected to expand significantly by 2030. However, many business leaders mistakenly view this technology merely as a compliance tool. Its true value lies in unlocking data utility, allowing companies to safely share information across internal departments and with partners without exposing sensitive details. To succeed, organizations must understand that differential privacy is not a simple plug-and-play product, nor can it be retrofitted easily into existing pipelines. It requires a fundamental shift in how data is processed and governed. Experts advise companies to start with a single high-value use case, such as customer analytics, and prioritize building strong central governance before focusing on the underlying tooling. Adopting this approach now gives enterprises a significant competitive advantage in responsible data strategy.

What the AI Warning Letter Completely Missed

A recent warning from major technology companies highlights that artificial intelligence will soon make cyberattacks cheaper and more common, urging immediate action to strengthen defenses. While this threat is very real, the proposed solutions overlook the most critical component: the human beings required to do the work. The industry often focuses heavily on advanced tools and theoretical scenarios while ignoring the practical reality that defense depends entirely on skilled people. Every recommendation to improve security, whether it involves fixing weaknesses, reviewing code, or deploying new software, requires a trained operator. The gap in our current readiness is not a lack of software products, but a severe shortage of equipped personnel, especially within smaller organizations and local utilities. To truly prepare for emerging threats, companies must invest directly in the workers already managing these systems, teaching them how to secure their specific environments. Furthermore, technology providers should offer concrete, direct support rather than just access to software models. Defensive tools must be judged by how effectively a small, overworked team can actually use them during an emergency. Ultimately, technology alone will not secure our infrastructure against intelligent threats. True resilience requires betting on motivated, well trained people who are ready to handle the daily work of defense.

Why digital transformations still fail

Digital transformations continue to fail largely because companies let technology, heavily promoted by consulting firms, dictate their strategy rather than focusing on actual business needs. Consultants have consistently sold identical, prepackaged systems to maximize their own profits, completely ignoring the unique requirements of each organization. This approach has resulted in massive budget overruns, delayed timelines, and overly complex systems that fail to perform as promised. Instead of redesigning their processes, companies simply moved their existing problems onto expensive cloud platforms, increasing their costs without gaining any real benefits. Now, as the industry shifts its focus toward artificial intelligence, businesses are repeating these exact same mistakes. Organizations are rushing to add artificial intelligence to everything without a clear reason, while placing unqualified staff into critical design roles. To succeed moving forward, businesses must adopt a much simpler approach. They need to stop overspending on unnecessary computing power and invest heavily in proper foundational training for their internal teams. Ultimately, technology exists solely to serve the business. Any successful change must begin by identifying clear business requirements and working backward to find the most practical, cost-effective solution, rather than blindly purchasing the most complicated or trendy new software option available today.

Daily Tech Digest - July 28, 2026

Quote for the day:

“People rarely succeed unless they have fun in what they are doing.” -- Dale Carnegie

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Duration: 20 mins • Perfect for listening on the go.

Tokens Are the New Headcount: Is There a New Labor Model?

Businesses are starting to measure their productive capacity not just by how many people they employ, but by how many computational units, or tokens, their artificial intelligence systems process. Traditionally, scaling a company meant hiring more staff, which brought predictable increases in human resources costs, management layers, and physical workspace needs. Now, organizations are supplementing or completely replacing certain repetitive tasks with automated systems that run on large language models. In this shifting landscape, the basic unit of work is gradually changing. A token represents a piece of text or data processed by an algorithm. As companies integrate these tools into their daily operations, they plan their future budgets around computing power and software usage rather than relying only on salaries and benefits. This transition allows for a more flexible approach to getting things done, as computational resources can be scaled up or down based on immediate demand without the complexities of hiring or layoffs. Ultimately, this represents a fundamental shift in how organizations think about labor, moving from a purely human workforce to a blended model where machine processing capability is measured, planned, and valued as a core component of a company's overall productive output and business strategy.

How CISOs can rise to the business resilience challenge

As business resilience overtakes traditional threat prevention, Chief Information Security Officers are increasingly stepping into the role of internal resilience leaders. Rather than focusing solely on keeping systems online, modern security executives must balance system uptime with strict data protection. The acceptable balance depends entirely on the industry. For instance, banks may tolerate extended downtime to prevent data loss, whereas retail organizations often prioritize rapid recovery to maintain revenue streams. The rapid growth of artificial intelligence and scattered internal data further complicates this effort, as organizations struggle to secure undocumented information across their networks. To effectively rise to this challenge, security leaders must define the absolute minimum operations their companies need to function. They must also regularly practice recovery procedures, treating them as live, real-world exercises rather than passive documentation. Experts suggest adopting a dedicated operations approach, applying the same continuous testing to recovery protocols as organizations apply to development. Crucially, security leaders do not need to shoulder this burden alone. By forming strategic partnerships with governance, risk, compliance, and core operations executives, they can frame cybersecurity risks directly in terms of business impact. This collaborative approach secures necessary funding and ensures overall business continuity remains a shared organizational responsibility.

The What, Why, and How of Mixture of Experts (MoE)

Mixture of Experts is rapidly becoming the standard architecture for large language models because it solves a significant scaling problem. In a traditional model, every single parameter is activated for every word processed. As models grow larger to become more capable, this approach becomes incredibly slow and expensive to operate. The Mixture of Experts approach fixes this by dividing parts of the neural network into smaller sub-networks, known as experts. When the model processes a piece of text, a routing mechanism evaluates each token and sends it only to the most relevant one or two experts. This allows the overall model to have a massive total capacity while keeping the actual computation per token relatively low and manageable. A common misconception is that these experts specialize in broad, human-defined subjects like mathematics, complex coding, or historical facts. In reality, they focus on low-level statistical and syntactic patterns, such as punctuation or specific word types. When training these models, a major challenge is preventing a few experts from doing all the work. Developers typically use a load-balancing technique to ensure traffic is distributed evenly across all experts, preventing wasted capacity and maintaining efficient performance throughout the overall computing system.

6 strategic trade-offs CIOs can’t afford to get wrong

As artificial intelligence and cybersecurity demands reshape the modern business landscape, chief information officers face six critical choices. The first challenge is balancing spending on foundational operations with investments in new growth. Underfunding daily IT needs risks system stability, while neglecting growth initiatives threatens overall competitiveness. Second, technology leaders must weigh rapid innovation against operational resilience. Pushing new systems too fast can easily disrupt daily operations, but moving too slowly leads to outdated technology. Third, the push for innovation must be balanced against risk management. Businesses want quick results, but leaders must always ensure proper oversight, privacy, and accountability. Fourth, companies must closely match the speed of technological change with their own organizational readiness, often requiring controlled rollouts and staff training to prevent teams from becoming overwhelmed. Fifth, leaders need to firmly balance data accessibility with data protection. Vast amounts of sensitive information must be available for new projects without compromising security or privacy protocols. Finally, organizations face a stark choice between the desired use of artificial intelligence and its rapidly mounting financial costs. Many are currently favoring innovation by accepting higher bills in the short term, though a major shift toward stricter cost optimization is widely anticipated as actual expenses frequently exceed initial estimates.

AI Demands More Engineering Discipline, Not Less

The shift toward building systems with artificial intelligence often leads teams to believe they can bypass traditional software engineering practices. However, integrating models into production environments actually requires a stricter adherence to foundational engineering principles, rather than abandoning them. When developers rely on language models or machine learning algorithms to drive core features, they introduce a significant layer of unpredictability. Unlike traditional code, which follows explicit logic, these systems deal with probabilities and vast datasets, meaning unexpected behaviors are inevitable. To handle this challenge, teams must focus heavily on rigorous testing, version control, and continuous monitoring. You cannot just deploy a model and assume it will continue working correctly as data changes over time. Real world applications demand robust pipelines to manage updates safely and fallbacks to catch errors when the model inevitably makes a mistake. Furthermore, security and privacy practices become even more critical when handling the large amounts of data required to make these systems function. Ultimately, the successful deployment of these tools does not come from the models themselves, but from the reliable, solid architecture built around them. Treating artificial intelligence as an excuse to ignore established engineering methods will only lead to fragile applications and operational failures in the long run.

Measuring ROI from cybersecurity investments: Looking beyond prevention to business value

Cybersecurity has shifted from a basic technology requirement to a primary business priority that directly impacts long-term growth and operational resilience. However, measuring the return on investment for these initiatives remains challenging because success is typically defined by the absence of disruptions rather than direct revenue generation. Instead of relying solely on technical indicators or the number of threats blocked, organizations should evaluate security through the lens of business value. This means focusing on practical metrics like how quickly an issue is detected, the ability to maintain critical operations during an attack, and overall risk reduction. While preventing attacks is important, minimizing the impact of any incident through quick recovery and reduced downtime often delivers greater practical value. Furthermore, automating routine security tasks improves overall efficiency and lowers administrative costs, allowing teams to handle more complex issues. Rather than viewing security as a barrier or a short-term expense, businesses should see it as a foundation that enables confident expansion into new technologies. By integrating security into their daily operations and maintaining clear visibility across all systems, organizations can build lasting trust with their customers. Ultimately, effective security investments provide the stability necessary to innovate and operate safely in a connected environment.

Clean Architecture for Serverless: Business Logic You Can Take Anywhere

The presentation explores the practical realities of using the Kotlin programming language within serverless environments, focusing on the compromises and performance benefits it offers to developers. It begins by addressing a common challenge in serverless computing: the initial delay when a function runs for the first time, often called a cold start. Because the Java Virtual Machine traditionally takes time to load, using it in a serverless context can cause noticeable lag. The talk explains how Kotlin, when combined with advanced compilation tools, helps solve this problem by converting the code into a native executable that loads almost instantly. This approach significantly reduces memory usage and startup times, making it a viable option for short lived functions. The speaker also walks through typical project setups and demonstrates how the clear and concise syntax of the language allows developers to write less code while maintaining readability. While acknowledging that moving away from traditional server setups requires adjustments in how applications are designed and monitored, the presentation concludes that Kotlin provides a solid, reliable foundation for building modern functions. The combination of strong type safety and modern language features makes it a sensible choice for teams looking to simplify their infrastructure and daily operations.

Local Governments Face Increasing Cyberattacks

Local governments are increasingly targeted by cyberattacks because they hold valuable personal data but often lack the budget and staffing required to maintain robust security. Cybercriminals recognize this vulnerability, treating ransomware attacks on small municipalities as a high-volume business and carefully adjusting their ransom demands to amounts these towns can actually afford. With local IT teams frequently reduced to just one or two people juggling multiple responsibilities, staying ahead of sophisticated security threats becomes a constant struggle. To address this widening disparity, Alabama has introduced a centralized statewide approach that offers a very promising solution. Through a partnership with Auburn University and federal grant funding, the state provides essential cybersecurity services, such as continuous monitoring, penetration testing, and multi-factor authentication, at no cost to participating communities. This shared-services model allows small towns to reach a strong security baseline that would otherwise be financially out of reach. While cybersecurity experts openly praise this collective defense strategy and actively encourage other states to adopt similar frameworks, they also caution that centralized security hubs require sustained financial support. Furthermore, because these central hubs access multiple municipal networks, they must maintain exceptional defenses themselves to prevent becoming prime targets for attackers seeking access to multiple local agencies.

Martin Fowler's Tech Debt Quadrant

Martin Fowler’s Technical Debt Quadrant is a practical framework that categorizes software debt to help teams manage it effectively. Rather than treating all technical debt as equal, the model evaluates it along two axes: whether the debt was taken on intentionally and whether the decision was made carefully or carelessly. This creates four distinct categories. Reckless and deliberate debt occurs when a team knowingly takes bad shortcuts without a plan to fix them, usually requiring a shift in team culture. Prudent and deliberate debt involves calculated tradeoffs made to meet business goals, much like a strategic loan that the team plans to repay. Reckless and inadvertent debt happens when developers lack the experience to realize they are making mistakes, which highlights a need for training and mentorship. Finally, prudent and inadvertent debt is the natural result of a team learning better ways to build a system over time, requiring steady, ongoing improvements. The guide also highlights a modern challenge: code generated by artificial intelligence. Because these tools produce code so rapidly and lack human intent, they can introduce massive amounts of complex debt if left unchecked. By identifying which category their debt falls into, teams can apply the right strategy instead of wasting time on the wrong fixes.

India’s DPI export strategy evolves beyond identity and payments to AI

India is expanding its digital public infrastructure strategy beyond its foundational identity and payment systems to focus on artificial intelligence, multilingual services, and specific sectors like healthcare and pensions. While the country is already testing its identity and payment frameworks in 25 nations, recent discussions highlight a shift toward integrating AI to improve public service delivery. A key element of this evolution is the development of voice-guided, multilingual interfaces. Tools like Bhashini aim to bridge language and literacy gaps by allowing users to interact with government services through spoken language. Furthermore, the massive amount of data generated by these digital systems is being used to improve financial inclusion, such as providing better credit access for small businesses based on their transaction histories. Indian officials emphasize the importance of digital sovereignty, advocating for localized AI models that understand regional languages and adhere to strict privacy controls. As the infrastructure moves into specialized areas, leaders are calling for the formal integration of these systems into government operations. This means shifting from standalone technology projects to a permanent, secure architecture built on user consent. Ultimately, India intends to share this broader digital framework globally, offering it as a tested model for digital democracy and inclusive growth.

Daily Tech Digest - July 15, 2025

Quote for the day:

“Rarely have I seen a situation where doing less than the other guy is a good strategy.” -- Jimmy Spithill

CyberArk: Rise in Machine Identities Poses New Risks

The CyberArk report outlines the substantial business consequences of failing to protect machine identities, leaving organizations vulnerable to costly outages and breaches. Seventy-two percent of organizations experienced at least one certificate-related outage over the past year - a sharp increase compared to prior years. Additionally, 50% reported security incidents or breaches stemming from compromised machine identities. Companies that have experienced non-human identity security breaches include xAI, Uber, Schneider Electric, Cloudflare and BeyondTrust, among others. "Machine identities of all kinds will continue to skyrocket over the next year, bringing not only greater complexity but also increased risks," said Kurt Sand, general manager of machine identity security at CyberArk. "Cybercriminals are increasingly targeting machine identities - from API keys to code-signing certificates - to exploit vulnerabilities, compromise systems and disrupt critical infrastructure, leaving even the most advanced businesses dangerously exposed." ... Fifty percent of security leaders reported security incidents or breaches linked to compromised machine identities in the previous year. These incidents led to delays in application launches for 51% companies, customer-impacting outages for 44% and unauthorized access to sensitive systems for 43%.

What Can Businesses Do About Ethical Dilemmas Posed by AI?

Digital discrimination is a product of bias incorporated into the AI algorithms and deployed at various levels of development and deployment. The biases mainly result from the data used to train the large language models (LLMs). If the data reflects previous iniquities or underrepresents certain social groups, the algorithm has the potential to learn and perpetuate those iniquities. Biases may occasionally culminate in contextual abuse when an algorithm is used beyond the environment or audience for which it was intended or trained. Such a mismatch may result in poor predictions, misclassifications, or unfair treatment of particular groups. Lack of monitoring and transparency merely adds to the problem. In the absence of oversight, biased results are not discovered. ... Human-in-the-loop systems allow intervention in real time whenever AI acts unjustly or unexpectedly, thus minimizing potential harm and reinforcing trust. Human judgment makes choices more inclusive and socially sensitive by including cultural, emotional, or situational elements, which AI lacks. When humans remain in the loop of decision-making, accountability is shared and traceable. This removes ethical blind spots and holds users accountable for consequences.

Beyond the hype: AI disruption in India’s legal practice

The competitive dynamics are stark. When AI can complete a ten-hour task in two hours, firms face a pricing paradox: how to maintain profitability while passing efficiency gains to the clients? Traditional hourly billing models become unsustainable when the underlying time economics change dramatically. ... Effective AI integration hinges on a strong technological foundation, encompassing secure data architecture, advanced cybersecurity measures and a seamless and hassle-free interoperability between systems and already existing platforms. SAM’s centralised Harvey AI approach and CAM’s multi-tool strategy both imply significant investment in these backend capabilities. ... Merely automating existing workflows fails to leverage AI’s transformative potential. To unlock AI’s full transformative value, firms must rethink their legal processes – streamlining tasks, reallocating human resources to higher order functions and embedding AI at the core of decision-making processes and document production cycles. ... AI enables alternative service models that go beyond the billable hour. Firms that rethink on how they can price say, by offering subscription-based or outcome-driven services, and position themselves as strategic partners rather than task executors, will be best positioned to capture long-term client value in an AI-first legal economy.

‘Chronodebt’: The lose/lose situation few CIOs can escape

One needn’t be an expert in the field of technical architecture to know that basing a capability as essential as air traffic control on such obviously obsolete technology is a bad idea. Someone should lose their job over this. And yet, nobody has lost their job over this, nor should they have. That’s because the root cause of the FAA’s woes — poor chronodebt management, in case you haven’t been paying attention — is a discipline that’s rarely tracked by reliable metrics and almost-as-rarely budgeted for. Metrics first: While the discipline of IT project estimation is far from reliable, it’s good enough to be useful in estimating chronodebt’s remediation costs — in the FAA’s case what it would have to spend to fix or replace its integrations and the integration platforms on which those integrations rely. That’s good enough, with no need for precision. Those running the FAA for all these years could, that is, estimate the cost of replacing the programs used to export and update its repositories, and replacing the 3 ½” diskettes and paper strips on which they rely. But, telling you what you already know, good business decisions are based not just on estimated costs, but on benefits netted against those costs. The problem with chronodebt is that there are no clear and obvious ways to quantify the benefits to be had by reducing it.

Can System Initiative fix devops?

System Initiative turns traditional devops on its head. It translates what would normally be infrastructure configuration code into data, creating digital twins that model the infrastructure. Actions like restarting servers or running complex deployments are expressed as functions, then chained together in a dynamic, graphical UI. A living diagram of your infrastructure refreshes with your changes. Digital twins allow the system to automatically infer workflows and changes of state. “We’re modeling the world as it is,” says Jacob. For example, when you connect a Docker container to a new Amazon Elastic Container Service instance, System Initiative recognizes the relationship and updates the model accordingly. Developers can turn workflows — like deploying a container on AWS — into reusable models with just a few clicks, improving speed. The GUI-driven platform auto-generates API calls to cloud infrastructure under the hood. ... An abstraction like System Initiative could embrace this flexibility while bringing uniformity to how infrastructure is modeled and operated across clouds. The multicloud implications are especially intriguing, given the rise in adoption of multiple clouds and the scarcity of strong cross-cloud management tools. A visual model of the environment makes it easier for devops teams to collaborate based on a shared understanding, says Jacob — removing bottlenecks, speeding feedback loops, and accelerating time to value.

An exodus evolves: The new digital infrastructure market

Regulatory pressures have crystallised around concerns over reliance on a small number of US-based cloud providers. With some hyperscalers openly admitting that they cannot guarantee data stays within a jurisdiction during transfer, other types of infrastructure make it easier to maintain compliance with UK and EU regulations. This is a clear strategy to avoid future financial and reputational damage. ... 2025 is a pivotal year for digital infrastructure. Public cloud will remain an essential part of the IT landscape. But the future of data strategy lies in making informed, strategic decisions, leveraging the right mix of infrastructure solutions for specific workloads and business needs. As part of our research, we assessed the shape of this hybrid market. ... With one eye to the future, UK-based cloud providers must be positioned as a strategic advantage, offering benefits such as data sovereignty, regulatory compliance, and reduced latency. Businesses will need to situate themselves ever more precisely on the spectrum of digital infrastructure. Their location will reflect how they embrace a hybrid model that balances public cloud, private cloud, colocation and on-premise options. This approach will not only optimise performance and costs but also provide long-term resilience in an evolving digital economy.

How Trump's Cyber Cuts Dismantle Federal Information Sharing

"The budget cuts, personnel reductions and other policy changes have decreased the volume and frequency of CISA's information sharing activities in both formal and informal channels," Daniel told ISMG. While sector-specific ISACs still share information, threat sharing efforts tied to federal funding - such as the Multi-State ISAC, which supports state and local governments - "have been negatively affected," he said . One former CISA staffer who recently accepted the administration's deferred resignation offer told ISMG the agency's information-sharing efforts "were among the first to take a hit" from the administration's cuts, with many feeling pressured into silence. ... Analysts have also warned that cuts to cyber staff across federal agencies and risks to initiatives including the National Vulnerability Database and Common Vulnerabilities and Exposures program could harm cybersecurity far beyond U.S. borders. The CVE program is dealing with backlogs and a recent threat to shut down funding over a federal contracting issue. Failure of the CVE Program "would have wide impacts on vulnerability management efficiency and effectiveness globally," said John Banghart, senior director for cybersecurity services at Venable and a key architect of the Obama administration's cybersecurity policy as a former director for federal cybersecurity for the National Security Council.

Securing vehicles as they become platforms for code and data

Recently security researchers have demonstrated real-world attacks against connected cars, such as wireless brake manipulation on heavy trucks by spoofing J-bus diagnostic packets. Another very recent example is successful attacks against autonomous car LIDAR systems. While the distribution of EV and advanced cars becomes more pervasive across our society, we expect these types of attacks and methods to continue to grow in complexity. Which makes a continuous, real-time approach to securing the entire ecosystem (from charger, to car, to driver) even more so important. ... Over-the-air (OTA) update hijacking is very real and often enabled by poor security design, such as lack of encryption, improper authentication between the car and backend, and lack of integrity or checksum validation. Attack vectors that the traditional computer industry has dealt with for years are now becoming a harsh reality in the automotive sector. Luckily, many of the same approaches used to mitigate these risks in IT can also apply here ... When we look at just the automobile, we have a variety of connected systems which typically all come from different manufacturers (Android Automotive, or QNX as examples) which increases the potential for supply chain abuse. We also have devices which the driver introduces which interacts with the car’s APIs creating new entry points for attackers.

Strategizing with AI: How leaders can upgrade strategic planning with multi-agent platforms

Building resiliency and optionality into a strategic plan challenges humans’ cognitive (and financial) bandwidth. The seemingly endless array of future scenarios, coupled with our own human biases, conspires to anchor our understanding of the future in what we’ve seen in the past. Generative AI (GenAI) can help overcome this common organizational tendency for entrenched thinking, and mitigate the challenges of being human, while exploiting LLMs’ creativity as well as their ability to mirror human behavioral patterns. ... In fact, our argument reflects our own experience using a multi-agent LLM simulation platform built by the BCG Henderson Institute. We’ve used this platform to mirror actual war games and scenario planning sessions we’ve led with clients in the past. As we’ve seen firsthand, what makes an LLM multi-agent simulation so powerful is the possibility of exploiting two unique features of GenAI—its anthropomorphism, or ability to mimic human behavior, and its stochasticity, or creativity. LLMs can role-play in remarkably human-like fashion: Research by Stanford and Google published earlier this year suggests that LLMs are able to simulate individual personalities closely enough to respond to certain types of surveys with 85% accuracy as the individuals themselves.

The Network Challenges of IoT Integration

IoT interoperability and compatible security protocols are a particular challenge. Although NIST and ISO, among other organizations, have issued IoT standards, smaller IoT manufacturers don't always have the resources to follow their guidance. This becomes a network problem because companies have to retool these IoT devices before they can be used on their enterprise networks. Moreover, because many IoT gadgets are delivered with default security settings that are easy to undo, each device has to be hand-configured to ensure it meets company security standards. To avoid potential interoperability pitfalls, network staff should evaluate prospective technology before anything is purchased. ... First, to achieve high QoS, every data pipeline on the network must be analyzed -- as well as every single system, application and network device. Once assessed, each component must be hand-calibrated to run at the highest performance levels possible. This is a detailed and specialized job. Most network staff don't have trained QoS technicians on board, so they must go externally for help. Second, which areas of the business get maximum QoS, and which don't? A medical clinic, for example, requires high QoS to support a telehealth application where doctors and patients communicate.

Daily Tech Digest - June 24, 2025

Quote for the day:

"When you stop chasing the wrong things you give the right things a chance to catch you." -- Lolly Daskal

Why Agentic AI Is a Developer's New Ally, Not Adversary

Because agentic AI can complete complex workflows rather than simply generating content, it opens the door to a variety of AI-assisted use cases in software development that extend far beyond writing code — which, to date, has been the main way that software developers have leveraged AI. ... But agentic AI eliminates the need to spell out instructions or carry out manual actions entirely. With just a sentence or two, developers can prompt AI to perform complex, multi-step tasks. It's important to note that, for the most part, agentic AI use cases like those described above remain theoretical. Agentic AI remains a fairly new and quickly evolving field. The technology to do the sorts of things mentioned here theoretically exists, but existing tool sets for enabling specific agentic AI use cases are limited. ... It's also important to note that agentic AI poses new challenges for software developers. One is the risk that AI will make the wrong decisions. Like any LLM-based technology, AI agents can hallucinate, causing them to perform in undesirable ways. For this reason, it's tough to imagine entrusting high-stakes tasks to AI agents without requiring a human to supervise and validate them. Agentic AI also poses security risks. If agentic AI systems are compromised by threat actors, any tools or data that AI agents can access (such as source code) could also be exposed.

Modernizing Identity Security Beyond MFA

The next phase of identity security must focus on phishing-resistant authentication, seamless access, and decentralized identity management. The key principle guiding this transformation is a principle of phishing resistance by design. The adoption of FIDO2 and WebAuthn standards enables passwordless authentication using cryptographic key pairs. Because the private key never leaves the user’s device, attackers cannot intercept it. These methods eliminate the weakest link — human error — by ensuring that authentication remains secure even if users unknowingly interact with malicious links or phishing campaigns. ... By leveraging blockchain-based verified credentials — digitally signed, tamper-evident credentials issued by a trusted entity — wallets enable users to securely authenticate to multiple resources without exposing their personal data to third parties. These credentials can include identity proofs, such as government-issued IDs, employment verification, or certifications, which enable strong authentication. Using them for authentication reduces the risk of identity theft while improving privacy. Modern authentication must allow users to register once and reuse their credentials seamlessly across services. This concept reduces redundant onboarding processes and minimizes the need for multiple authentication methods.

The Pros and Cons of Becoming a Government CIO

Seeking a job as a government CIO offers a chance to make a real impact on the lives of citizens, says Aparna Achanta, security architect and leader at IBM Consulting -- Federal. CIOs typically lead a wide range of projects, such as upgrading systems in education, public safety, healthcare, and other areas that provide critical public services. "They [government CIOs] work on large-scale projects that benefit communities beyond profits, which can be very rewarding and impactful," Achanta observed in an online interview. "The job also gives you an opportunity for leadership growth and the chance to work with a wide range of departments and people." ... "Being a government CIO might mean dealing with slow processes and bureaucracy," Achanta says. "Most of the time, decisions take longer because they have to go through several layers of approval, which can delay projects.” Government CIOs face unique challenges, including budget constraints, a constantly evolving mission, and increased scrutiny from government leaders and the public. "Public servants must be adept at change management in order to be able to pivot and implement the priorities of their administration to the best of their ability," Tamburrino says. Government CIOs are often frustrated by a hierarchy that runs at a far slower pace than their enterprise counterparts.

Why work-life balance in cybersecurity must start with executive support

Watching your mental and physical health is critical. Setting boundaries is something that helps the entire team, not just as a cyber leader. One rule we have in my team is that we do not use work chat after business hours unless there are critical events. Everyone needs a break and sometimes hearing a text or chat notification can create undue stress. Another critical aspect of being a cybersecurity professional is to hold to your integrity. People often do not like the fact that we have to monitor, report, and investigate systems and human behavior. When we get pushback for this with unprofessional behavior or defensiveness, it can often cause great personal stress. ... Executive leadership plays one of the most critical roles in supporting the CISO. Without executive level support, we would be crushed by the demands and the frequent conflicts of interest we experience. For example, project managers, CIOs, and other IT leadership roles might prioritize budget, cost, timelines, or other needs above security. A security professional prioritizes people (safety) and security above cost or timelines. The nature of our roles requires executive leadership support to balance the security and privacy risk (and what is acceptable to an executive). I think in several instances the executive board and CEOs understand this, but we are still a growing profession and there needs to be more education in this area.

Building Trust in Synthetic Media Through Responsible AI Governance

Relying solely on labeling tools faces multiple operational challenges. First, labeling tools often lack accuracy. This creates a paradox: inaccurate labels may legitimize harmful media, while unlabelled content may appear trustworthy. Moreover, users may not view basic AI edits, such as color correction, as manipulation, while opinions differ on changes like facial adjustments or filters. It remains unclear whether simple colour changes require a label, or if labeling should only occur when media is substantively altered or generated using AI. Similarly, many synthetic media artifacts may not fit the standard definition of pornography, such as images showing white substances on a person’s face; however, they can often be humiliating. ... Second, synthetic media use cases exist on a spectrum, and the presence of mixed AI- and human-generated content adds complexity and uncertainty in moderation strategies. For example, when moderating human-generated media, social media platforms only need to identify and remove harmful material. In the case of synthetic media, it is often necessary to first determine whether the content is AI-generated and then assess its potential harm. This added complexity may lead platforms to adopt overly cautious approaches to avoid liability. These challenges can undermine the effectiveness of labeling.

How future-ready leadership can power business value

Leadership in 2025 requires more than expertise; it demands adaptability, compassion, and tech fluency. “Leadership today isn’t about having all the answers; it’s about creating an environment where teams can sense, interpret, and act with speed, autonomy, and purpose,” said Govind. As the learning journey of Conduent pivots from stabilization to growth, he shared that the leaders need to do two key things in the current scenario: be human-centric and be digitally fluent. Similarly, Srilatha highlighted a fundamental shift happening among the leaders: “Leaders today must lead with both compassion and courage while taking tough decisions with kindness.” She also underlined the rising importance of the three Rs in modern leadership: Reskilling, resilience, and rethinking. ... Govind pointed to something deceptively simple: acting on feedback. “We didn’t just collect feedback, we analyzed sentiment, made changes, and closed the loop. That made stakeholders feel heard.” This approach led Conduent to experiment with program duration, where they went from 12 to 8 to 6 months.’ “Learning is a continuum, not a one-off event,” Govind added. ... Leadership development is no longer optional or one-size-fits-all. It’s a business imperative—designed around human needs and powered by digital fluency.

The CISO’s 5-step guide to securing AI operations

As AI applications extend to third parties, CISOs will need tailored audits of third-party data, AI security controls, supply chain security, and so on. Security leaders must also pay attention to emerging and often changing AI regulations. The EU AI Act is the most comprehensive to date, emphasizing safety, transparency, non-discrimination, and environmental friendliness. Others, such as the Colorado Artificial Intelligence Act (CAIA), may change rapidly as consumer reaction, enterprise experience, and legal case law evolves. CISOs should anticipate other state, federal, regional, and industry regulations. ... Established secure software development lifecycles should be amended to cover things such as AI threat modeling, data handling, API security, etc. ... End user training should include acceptable use, data handling, misinformation, and deepfake training. Human risk management (HRM) solutions from vendors such as Mimecast may be necessary to keep up with AI threats and customize training to different individuals and roles. ... Simultaneously, security leaders should schedule roadmap meetings with leading security technology partners. Come to these meetings prepared to discuss specific needs rather than sit through pie-in-the-sky PowerPoint presentations. CISOs should also ask vendors directly about how AI will be used for existing technology tuning and optimization.

State of Open Source Report Reveals Low Confidence in Big Data Management

"Many organizations know what data they are looking for and how they want to process it but lack the in-house expertise to manage the platform itself," said Matthew Weier O'Phinney, Principal Product Manager at Perforce OpenLogic. "This leads to some moving to commercial Big Data solutions, but those that can't afford that option may be forced to rely on less-experienced engineers. In which case, issues with data privacy, inability to scale, and cost overruns could materialize." ... EOL operating system, CentOS Linux, showed surprisingly high usage, with 40% of large enterprises still using it in production. While CentOS usage declined in Europe and North America in the past year, it is still the third most used Linux distribution overall (behind Ubuntu and Debian), and the top distribution in Asia. For teams deploying EOL CentOS, 83% cited security and compliance as their biggest concern around their deployments. ... "Open source is the engine driving innovation in Big Data, AI, and beyond—but adoption alone isn't enough," said Gael Blondelle, Chief Membership Officer of the Eclipse Foundation. "To unlock its full potential, organizations need to invest in their people, establish the right processes, and actively contribute to the long-term sustainability and growth of the technologies they depend on."

Cybercrime goes corporate: A trillion-dollar industry undermining global security

The CaaS market is a booming economy in the shadows, driving annual revenues into billions. While precise figures are elusive due to its illicit nature, reports suggest it's a substantial and growing market. CaaS contributes significantly, and the broader cybersecurity services market is projected to reach hundreds of billions of dollars in the coming years. If measured as a country, cybercrime would already be the world's third-largest economy, with projected annual damages reaching USD 10.5 trillion by 2025, as per some cybersecurity ventures. This growth is fueled by the same principles that drive legitimate businesses: specialisation, efficiency, and accessibility. CaaS platforms function much like dark online marketplaces. They offer pre-made hacking kits, phishing templates, and even access to already compromised computer networks. These services significantly lower the entry barrier for aspiring criminals. ... Enterprises must recognise that attackers often hit multiple systems simultaneously—computers, user identities, and cloud environments. This creates significant "noise" if security tools operate in isolation. Relying on many disparate security products makes it difficult to gain a holistic view and understand that seemingly separate incidents are often part of a single, coordinated attack.

Modern apps broke observability. Here’s how we fix it.

For developers, figuring out where things went wrong is difficult. In a survey looking at the biggest challenges to observability, 58% of developers said that identifying blind spots is a top concern. Stack traces may help, but they rarely provide enough context to diagnose issues quickly; developers chase down screenshots, reproduce problems, and piece together clues manually using the metric and log data from APM tools; a bug that could take 30 minutes to fix ends up consuming days or weeks. Meanwhile, telemetry data accumulates in massive volumes—expensive to store and hard to interpret. Without tools to turn data into insight, you’re left with three problems: high bills, burnout, and time wasted fixing bugs—bugs that don’t have a major impact on core business functions or drive revenue when increasing developer efficiency is a top strategic goal at organizations. ... More than anything, we need a cultural change. Observability must be built into products from the start. That means thinking early about how we’ll track adoption, usage, and outcomes—not just deliver features. Too often, teams ship functionality only to find no one is using it. Observability should show whether users ever saw the feature, where they dropped off, or what got in the way. That kind of visibility doesn’t come from backend logs alone.

Daily Tech Digest - October 22, 2024

GenAI surges in law firms: Will it spell the end of the billable hour?

All areas of law will use genAI, according to Joshua Lenon, Clio’s Lawyer in Residence. That’s because AI content generation and task automation tools can help the business side and practice efforts of law firms. However, areas that have repetitive workflows and large document volumes – like civil litigation – will adopt genAI e-discovery tools more quickly. Practice areas that charge exclusively flat fees – like traffic offenses and immigration – are already the largest adopters of genAi. ... Nearly three-quarters of a law firm’s hourly billable tasks are exposed to AI automation, with 81% of legal secretaries’ and administrative assistants’ tasks being automatable, compared to 57% of lawyers’ tasks, according a survey of both legal professionals (1,028) and another adults (1,003) in the U.S. general population, by Clio. Hourly billing has long been the preference of many professionals, from lawyers to consultants, but AI adoption is upending this model where clients are charged for the time spent on services. ... People have been talking about the demise of the billable hour for about 30 years “and nothing’s killed it yet,” said Ryan O’Leary, research director for privacy and legal technology at IDC. “But if anything will, it’ll be this.”

IT security and government services: Balancing transparency and security

For cyber defenses, government IT leaders should invest in website hosting services with Secure Sockets Layer (SSL) encryption, and further enhancing security with HTTP Strict Transport Security (HSTS). These measures ensure that all data exchanged via government sites is encrypted, protecting resident self-service features such as online voter registration, permit submissions, utility bill payments, and more. By enforcing HSTS, websites are also protected from protocol downgrade attacks and cookie hijacking, ensuring that all connections remain secure, and reducing the risk of data interception. Other marks of a reliable website hosting solution provider include DDoS mitigation coverage and reliability around regular software patching and updates. For all digital partners, it’s essential to consider third-party risk. Some of the most valuable information residents should be able to access – meeting minutes, agendas, and other documents pertaining to local governing decisions – are hosted by document management vendors. To ensure this access is secure, each vendor must be vetted on its security capabilities, so that critical data is always protected, and hackers are not able to prevent access for residents or laterally move further into government networks.

Software buying trends are changing: From SaaS to outcome as a service

The last decade saw the rise of Software-as-a-Service (SaaS), transforming how businesses approached software deployment. This decade belongs to Outcomes-as-a-Service. CIOs are no longer interested in building large internal developer teams or experimenting with different platforms. They seek business impacting solutions with tangible outcomes that drive business success. Business teams need solutions that deliver results today, not tomorrow. ... AI-powered hyperautomation combines generative AI, BPM, RPA, integrations, analytics, and app-building to drive end-to-end outcomes. In today’s dynamic business environment, an integrated approach is essential. Siloed automation with narrowly focused platforms is no longer sufficient. ... AI-platforms excel in delivering outcomes at speed and scale. Leveraging automation expertise, they ensure outcomes linked to growth, efficiency, and compliance. The platform implements continuous cycles of process mining, implementation, adoption, and solution refinement until desired objectives are met.They also offer a comprehensive solution, managing everything from process definition and refinement to platform implementation, support, application development, and adoption.

How Retailers Are Using Tech for Competitive Advantage

“While technology can streamline operations, an overreliance on automation without human touch can sometimes backfire,” Peters says. “Consumers still value human interaction, especially in complex support scenarios. It’s crucial for retailers to balance automation with human agents, particularly in areas that require empathy and nuanced decision-making.” ... Companies of all sizes benefit from greater organizational efficiency, and tech has been the fuel powering digital transformation. For example, Lowes uses AR for home improvement shopping while Sephora uses it for virtual make up try-ons. Walmart is stepping up automation in its battle against Amazon. But smaller retailers are benefiting, too. ... “One of our customer’s last large-scale automation took them five years from the time they started the concept to deployment,” Naslund says. “For context, the pandemic, was four and a half years, and the amount of volatility that the supply chain saw over the four years was insane. We saw inventory gluts, inventory shortages, and panic buying. Then you saw a warehouse shortage capacity, everybody's panicking to get warehouses. Then, they suddenly have too much space.”

Why and How IT Leaders Can Embrace the AI Revolution

AI software certainly has some consequences for IT departments. There may be some new types of workflows to manage, new user requests to support, and new application deployments to track. But unless your business is actually building complex AI solutions from scratch — which it probably isn't or shouldn't because sophisticated, mature AI tools and services are available from external vendors, complete with support plans and SLAs — implementing AI is not actually that challenging. That's because most third-party AI solutions boil down to SaaS apps that work just like any other SaaS: The vendor builds, manages, and supports them, with few resources and little effort necessary on the part of customers' IT departments. From the perspective of IT, implementing AI isn't all that different from implementing any other type of software. ... For IT, there are really not any novel data privacy or security risks at stake here. The app ingests financial data, but so do plenty of non-AI applications. IT's responsibility when it comes to managing data security for this type of app boils down to vetting the vendor by reviewing its data management and compliance practices. The fact that the app uses AI doesn't change this process.

Has the time come for integrated network and security platforms?

Interest in platformization is growing among enterprises, asserts Extreme Networks, which recently surveyed 200 CIOs and senior IT leaders for its research, CIO Insights Report: Priorities and Investment Plans in the Era of Platformization. ... A platform that helps organizations transition their network to the cloud to streamline IT efficiency and lower total cost of ownership is important, respondents said. In addition, 55% of respondents emphasized the need to integrate from a broad ecosystem of networking and security offerings, indicating a clear demand for unified platforms, Extreme concluded. ... “The message I got from the survey was that customers are operating in a world where there’s a massive proliferation of products, or applications, and that’s really translating into complexity. Complexity is equal to risk, and that complexity is happening in multiple places,” said Extreme Networks CTO Nabil Bukhari. Complexity is an interesting topic because it changes, Bukhari said. The first Ford cars were basically just an engine with brakes, but they were complicated to start and drive. “Now, if you look at a car, they are like data centers on wheels. But driving and owning them is exponentially easier,” Bukhari said.

How legacy IT systems can hold your business back

While legacy IT systems may still be functional, they can hold a business back from reaching its full potential – especially if market competitors are busy upgrading their own systems. Companies need to carefully evaluate the costs and benefits of keeping legacy systems in place and develop a plan to modernize their IT infrastructure. Investing in a modern data center solution can, over time, improve business agility, security, and your organization’s bottom line. ... This is especially true when it comes to next-generation applications using LLMs and machine learning (ML) for AI-dependent applications. Enterprise servers, storage and networking hardware, and software manufactured before about 2016 were not designed with scaled-up data workloads in mind – especially workloads for genAI, which just started to take off in 2021. This can hinder growth and force companies to invest in additional hardware or software just to maintain their current operations. Legacy systems are also more prone to failures and outages due to aging hardware and software. This downtime disrupts operations and leads to lost revenue, especially for critical business functions. Additionally, data loss from system crashes can be costly to recover from.

Architecture Inversion: Scale by Moving Computation, Not Data

Now why should the rest of us care, blessed as we are with a lack of most of the billions of users TikTok, Google and the likes are burdened with? A number of factors are becoming relevant:ML algorithms are improving and so is local compute capacity, meaning fully scoring items gives a larger boost in quality and ultimately profit than used to be the case. With the advent of vector embeddings, the signals consumed by such algorithms have grown by one to two orders of magnitude, making the network bottleneck more severe. Applying ever more data to solve problems is increasingly cost effective, which means more data needs to be rescored to maintain a constant quality loss. As the consumers of data from such systems move from being mostly humans to mostly LLMs in RAG solutions, it becomes beneficial to deliver larger amounts of scored data faster in more applications than before. ... For these reasons, the scaling tricks of the very biggest players are becoming increasingly relevant for the rest of us, which has led to the current proliferation of architecture inversion, going from traditional two-tier systems where data is looked up from a search engine or database and sent to a stateless compute tier to inserting that compute into the data itself.

The secret to successful digital initiatives is pretty simple, according to Gartner

As with all technologies, seeing results from AI comes down to focusing like a laser beam on the problem at hand: "In my experience, the businesses that start with a real use case and problem are seeing an ROI," Julian LaNeve, chief technology officer at Astronomer, a data platform company, told ZDNET. "They define a well-scoped, impactful problem and use gen AI to solve [it], and it's easy to measure success and ROI. The most successful business cases identify how to solve a problem that the business already cares deeply about and [will] deliver additional value to customers." Technology maturity also makes a difference in success rates. "Previous generations of AI were narrower in scope but have been successful," said Dominic Sartorio, vice president at Denodo, a data management provider. "AI is helping with predictive maintenance of manufactured goods, predicting demand spikes in [the] markets, and finding the optimal routes for logistics, and [has] been successful for many years." Furthermore, according to Gartner, companies that treat their digital initiatives in a collaborative fashion -- between business and IT leaders -- rather than leaving all things digital up to their IT departments are successful with technology.

Showing AI users diversity in training data can boost perceived fairness and trust

The work investigated whether displaying racial diversity cues—the visual signals on AI interfaces that communicate the racial composition of the training data and the backgrounds of the typically crowd-sourced workers who labeled it—can enhance users' expectations of algorithmic fairness and trust. Their findings were recently published in the journal Human-Computer Interaction. AI training data is often systematically biased in terms of race, gender and other characteristics, according to S. Shyam Sundar, Evan Pugh University Professor and director of the Center for Socially Responsible Artificial Intelligence at Penn State. "Users may not realize that they could be perpetuating biased human decision-making by using certain AI systems," he said. Lead author Cheng "Chris" Chen, assistant professor of communication design at Elon University, who earned her doctorate in mass communications from Penn State, explained that users are often unable to evaluate biases embedded in the AI systems because they don't have information about the training data or the trainers. "This bias presents itself after the user has completed their task, meaning the harm has already been inflicted, so users don't have enough information to decide if they trust the AI before they use it," Chen said

Quote for the day:

"It takes courage and maturity to know the difference between a hoping and a wishing." -- Rashida Jourdain