Daily Tech Digest by Kannan Subbiah: Alert Fatigue

Showing posts with label Alert Fatigue. Show all posts

Daily Tech Digest - May 20, 2026

Quote for the day:

“Successful people do what unsuccessful people are not willing to do. Don’t wish it were easier; wish you were better.” -- Jim Rohn

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What can you do with quantum computing today?

The InfoWorld article explains that while practical, large scale quantum computing remains years away, current enterprise engagement should center on proactive learning, strategic experimentation, and urgent security preparation. Present day infrastructure utilizes noisy intermediate scale quantum hardware, which requires hybrid models that pair error prone quantum processors with classical computational power. Through cloud based quantum computing platforms provided by IBM, Amazon, and Microsoft, pioneering organizations are already piloting specialized optimization, molecular simulation, and risk modeling workflows. For instance, global companies like HSBC and DHL have successfully demonstrated notable performance gains in bond price forecasting and logistics routing. However, fully fault tolerant application scale quantum systems are not expected to mature until the late twenties or thirties. Consequently, forward looking companies must address an existing tech talent gap by developing quantum proficiencies internally. Most critically, enterprises must prepare immediately for the inevitable arrival of Q Day, when advanced quantum computers can easily decrypt modern encryption methods. To actively mitigate this looming cyber threat, organizational leaders are advised to classify long lived sensitive records and rapidly transition their public key infrastructures to post quantum cryptography today, ensuring critical safety against threat actors who are currently harvesting encrypted organizational data for future deciphering.

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

In this APMdigest article, Venkat Ramakrishnan of NeuBird AI shifts the perspective on alert fatigue from a quality-of-life issue to a direct contributor to systemic downtime. Data from the 2026 State of Production Reliability and AI Adoption Report reveals that 44% of surveyed organizations experienced outages due to ignored or suppressed alerts. Additionally, 78% endured incidents where no alerts fired, forcing engineers to rely on customer complaints to discover system failures. This operational gridlock occurs because 77% of on-call teams receive over ten alerts daily, with fewer than 30% being actionable. Consequently, engineers predictably ignore warnings, inadvertently missing weak, early-stage threat signals amidst legacy tool noise. Since downtime carries an expensive financial penalty—with 61% of companies estimating costs at $50,000 or more per hour—engineering leaders must pivot away from reactive, fragmented incident management models. Modern cloud architectures require moving toward autonomous production operations powered by AI. Instead of focusing on efficiently resolving problems after they occur, the author concludes that organizations must leverage automated intelligence for full incident avoidance, continuously predicting threats and standardizing operational institutional knowledge before a critical failure disrupts business continuity.

7 tips for accelerating cyber incident recovery

The CSO Online article highlights that prompt and coordinated incident recovery is crucial to minimize the cascading financial, operational, and compliance damages caused by inevitable cyberattacks. To accelerate recovery times effectively, the text outlines seven actionable tips from cybersecurity experts. First, organizations must hone their incident response team's internal coordination through strict training and tabletop exercises. Second, prioritizing scoping and containment stops initial system bleeding by isolating breaches and credentials. Third, establishing deep situational awareness determines threat vectors, affected assets, and broader business impacts. Fourth, security leaders should readily enlist external professional support, such as multi-disciplinary forensics and cloud recovery partners, to safely scale operations. Fifth, systems must be securely restored based on business criticality rather than technological convenience, prioritizing revenue-generating platforms first. Sixth, CISOs should remain disciplined and follow structured frameworks like NIST 800-61 alongside a RACI matrix to entirely avoid reckless improvisation. Finally, teams should thoroughly implement lessons learned to fortify infrastructure controls before executing validation penetration tests. Ultimately, a structured approach helps security departments avoid the burnout of extended outages and prevents threat actors from exploiting prolonged dwell times to achieve re-compromise.

Programming in 2026: Should Students Still Learn Code?

In this Security Boulevard article, tech entrepreneur Deepak Gupta addresses the modern dilemma of whether students should still learn to code given that 30% of code at major tech companies is now AI-generated. Gupta emphatically argues that learning to program remains essential, but notes that the traditional definition of a developer has drastically changed. Instead of focusing heavily on writing manual syntax, modern programmers primarily direct, review, and evaluate automated software. Crucially, individuals who cannot read code will remain unable to effectively verify AI outputs, mitigate subtle logic hallucinations, or catch critical security vulnerabilities like hardcoded credentials and broken authentication flows. To align with this technological paradigm shift, computer science curricula must adapt by prioritizing systems thinking, security intuition, rigorous code review at scale, and precise specification design. Aspiring programmers are advised to master fundamentals over passing frameworks, gain comprehensive database and networking literacy, and treat AI as a collaborative teammate rather than a total crutch. Ultimately, AI is not replacing software engineering as a discipline; rather, it is weeding out mechanical coders who rely solely on typing speed while enormously magnifying the value of strategic human judgment and architectural decision-making.

How Risk Management Can Build ROI in Regulated Technology Firms – Part 1

The article by Kannan Subbiah explores how regulated technology firms, such as FinTechs and HealthTechs, can successfully reframe risk management from a defensive cost center into a strategic value driver that yields a high return on investment. With intensifying global regulatory pressures, existential cyber threats, and shifting investor expectations regarding enterprise governance, mature risk frameworks can directly boost overall firm valuations by up to 25 percent. Subbiah outlines five major dimensions where robust risk management generates tangible financial value. First, it minimizes direct financial losses and unexpected operational disruptions through proactive mitigation rather than reactive crisis management. Second, it accelerates innovation and time to market by integrating risk assessments into the earliest design phases, acting as a steering wheel rather than a progress brake. Third, it enhances brand equity, customer trust, and long-term user retention by prioritizing transparent security and operational reliability. Fourth, it unlocks corporate efficiency, yielding potential gains of ten to twenty-five percent by streamlining internal processes and drastically reducing runtime downtime. Finally, it improves strategic decision-making by replacing gut feelings with objective, data-backed scenario planning and advanced resource scoring. Ultimately, the piece emphasizes that mature risk practices protect capital and unlock unique competitive advantages across markets.

Product Thinking for Cloud Native Engineers

The InfoQ presentation titled “Product Thinking for Cloud Native Engineers,” delivered by cloud engineer Stéphane Di Cesare and product manager Cat Morris, outlines how internal technical teams can transition from being perceived as organizational cost centers into critical business value drivers. Specifically targeting DevOps, SRE, and platform engineering domains, the speakers advocate for a fundamental mindset shift that prioritizes user value and product outcomes over raw technical outputs like code volume. By implementing the structured "Double Diamond" framework, cloud-native engineers are encouraged to comprehensively explore and define concrete user pain points before jumping directly into building architectural solutions. The presentation highlights vital product discovery methodologies, including user interviews and shadowing sessions, to build actionable empathy for internal developers. This active engagement helps mitigate the risk of creating counterintuitive tools that engineering peers might ultimately reject. Additionally, the session emphasizes choosing outcome-based product metrics, such as developer cognitive load, flow state, and deployment speed via the DevEx framework, instead of traditional machine utilization metrics. Ultimately, embracing this continuous product lifecycle perspective allows technical professionals to clearly articulate their worth to stakeholders, thereby reducing operational friction, maximizing organizational engineering investments, and securing meaningful career promotions.

The next digital divide: AI owners vs. AI renters

The CIO article outlines an emerging structural shift in enterprise technology, arguing that the next true digital divide will not be between organizations that use artificial intelligence and those that do not, but rather between AI "owners" and AI "renters." AI renters primarily rely on external platforms, APIs, and cloud services to deploy capabilities quickly and minimize up-front infrastructure costs. However, this dependencies limits long-term model visibility, compromises data control, introduces scaling expenses, and hands operational sovereignty over to external providers. Conversely, AI owners build and control their intelligence systems internally, leveraging controlled environments like private or sovereign clouds. By deeply integrating models with internal knowledge bases and implementing specialized governance frameworks, AI owners capture unique proprietary feedback loops that continuously refine competitive advantages. This paradigm shift mirrors historic transitions observed during the maturation of web and cloud infrastructures. Ultimately, technology leaders like CIOs must navigate this landscape not just by selecting tools, but by defining an intentional architecture that balances external consumption with protected internal innovation, ensuring that their systems remain assets they fundamentally command rather than services they merely rent.

Communicating cyber risk in dollars boards understand

In this Help Net Security interview, Nedscaper’s Cybersecurity Architect Nick Nieuwenhuis explains why massive financial investments in cybersecurity have failed to yield true organizational resilience. He argues that most companies analyze risk through a reductionist, techno-centric lens, prioritizing measurable technical controls while ignoring messy, complex socio-technical dynamics like human behavior, organizational constraints, and internal processes. This narrow view fails because cyber risk behaves dynamically rather than linearly. Nieuwenhuis also points out a critical disconnect between security teams and executive boardrooms, which stems from poor risk communication. Instead of using abstract, qualitative heatmaps or dense technical jargon, security professionals must translate cyber risk into grounded, evidence-based narratives and financial metrics that business leaders can easily comprehend. Furthermore, he emphasizes that traditional root-cause analysis is inadequate for modern incidents, which typically arise from multi-factored, cascading systemic breakdowns. To fix this, organizations must shift from strict prevention to comprehensive cyber resilience, accepting that systems will eventually fail under stress. Resilient enterprises must actively invest in human capabilities, use enterprise architecture to improve communication, thoroughly rehearse incident response playbooks, and cultivate a culture of continuous learning and feedback to safely adapt to an ever-evolving digital landscape.

Deepfake wave breaking the digital dam; orgs are busy building defenses

The article focuses on how generative AI evolution is sparking a prolific wave of deepfake identity impersonations, forcing global organizations to transition from reactive fact-checking to proactive trust architectures. According to a Gartner report, 40 percent of government organizations will implement dedicated TrustOps functions by 2028 to safeguard against public-facing disinformation campaigns and internal social engineering breaches targeting biometric authentication. Highlighting this risk, advanced, commercial deepfake platforms like Haotian AI now empower bad actors to alter their facial and vocal identities seamlessly during live video calls on Zoom, WhatsApp, or Microsoft Teams, effectively breaking the baseline truth of digital platforms. To combat this escalating digital regression, identity verification firms are aggressively releasing structural defenses. For instance, iProov launched "Verified Meetings" as a platform plugin to continuously authenticate that participants are real people using authentic, uncompromised hardware cameras. Concurrently, GetReal Security released identity proofing updates within "GetReal Protect," supplying ongoing verification and threat intelligence to secure critical workflows. Because eight out of ten organizations already encounter these synthetic threats, security leaders argue that the burden of authentication must shift permanently from vulnerable end-users to institutional architectures through cryptographic provenance, multi-approver frameworks, and collaborative digital trust councils.

Tokenmaxxing Pressures: The Impact on Modern Developer Ecosystems

The article investigates the rising phenomenon of tokenmaxxing, defined as the corporate practice of treating artificial intelligence token consumption as a primary metric for engineering productivity, and its deeply disruptive impact on modern developer ecosystems. Driven by intense hierarchical pressure from corporate leadership to showcase rapid technology adoption and prove a return on investment, many enterprises have established internal dashboards and competitive leaderboards tracking computational usage. This management approach creates highly perverse incentives, prompting software engineers to actively gamify the system by artificially inflating their token counts. Developers frequently achieve this through brute force context stuffing, unnecessary premium model routing, and redundant autonomous agent loops that merely mimic genuine professional progress. This trend introduces an expensive, modern iteration of the archaic mistake of measuring developer output by lines of code. Within engineering environments, tokenmaxxing severely degrades workflows by causing massive cloud cost overruns, extending code review latencies, and introducing bloated, unverified outputs into repositories. It promotes performative, visible busyness over technical elegance and system reliability. Ultimately, the text argues that organizations must dismantle these flawed vanity metrics and transition toward value driven governance frameworks that prioritize actual task resolution, downstream quality, and efficient human and AI collaboration.

Daily Tech Digest - February 15, 2026

zQuote for the day:

"Accept responsibility for your life. Know that it is you who will get you where you want to go, no one else." -- Les Brown

AI will likely shut down critical infrastructure on its own, no attackers required

“The next great infrastructure failure may not be caused by hackers or natural disasters, but rather by a well-intentioned engineer, a flawed update script, or a misplaced decimal,” said Wam Voster, VP Analyst at Gartner. “A secure ‘kill-switch’ or override mode accessible only to authorized operators is essential for safeguarding national infrastructure from unintended shutdowns caused by an .” “Modern AI models are so complex they often resemble black boxes. Even developers cannot always predict how small configuration changes will impact the emergent behavior of the model. The more opaque these systems become, the greater the risk posed by misconfiguration. Hence, it is even more important that humans can intervene when needed,” Voster added. ... Bob Wilson, cybersecurity advisor at the Info-Tech Research Group, also worries about the near inevitability of a serious industrial AI mishap. "The plausibility of a disaster that results from a bad AI decision is quite strong. With AI becoming embedded in enterprise strategies faster than governance frameworks can keep up, AI systems are advancing faster and outpacing risk controls,” Wilson said. “We can see the leading indicators of rapid AI deployment and limited governance increase potential exposure, and those indicators justify investments in governance and operational controls.”

New Architecture Could Cut Quantum Hardware Needed to Break RSA-2048 by Tenfold

The replaces surface codes with , a class of error-correcting codes in which each qubit interacts with only a small number of others, even as the machine grows. That structure allows errors to be detected without complex, all-to-all connections, an advance that keeps correction circuits faster and reducing the number of physical qubits needed per logical qubit. To dive a little deeper, the architecture is built from modular “processing units,” “magic engines,” and optional “memory” blocks. Each processing unit consists of QLDPC code blocks — the error-correcting structures that protect the logical qubits — along with measurement hardware that enables arbitrary logical Pauli measurements during each correction cycle. ... The architecture hints at the difference between surface codes and QLDPC. Surface codes require dense, grid-like local connectivity and many qubits per logical qubit. QLDPC spreads parity checks more sparsely across a block. One way to picture the difference is wiring. Surface codes are like protecting data by wiring every component into a dense grid — reliable, but heavy and hardware-intensive. QLDPC codes achieve protection with far fewer connections per qubit, more like a sparsely wired network that still catches errors but uses much less hardware. ... If fewer than 100,000 physical qubits were sufficient to break RSA-2048 under realistic error models, the threshold for cryptographic risk could arrive sooner than many surface-code-based estimates imply.

5 key trends reshaping the SIEM market

By converging SIEM with XDR and SOAR, organizations get a unified security platform that consolidates data, reduces complexity, and improves response times, as systems can be configured to automatically contain threats without any manual intervention. ... “The term SIEM++ is being used to refer to this next step in SIEM, which is designed for more current needs within security ops asking for automation, AI, and real-time responses. Hence, the increase in SIEM alongside other tools,” Context’s Turner says. ... “The full enforcement of the in Europe has forced midtier companies to move from basic monitoring to auditable security operations,” Context’s Turner explains. “These companies are too large for simple tools but too small for massive 24/7 internal SOCs. They are buying the SIEM++ platforms to serve as their central source of truth for auditors.” ... Cloud-based SIEMs remove the need for expensive hardware upgrades associated with traditional on-premises deployments, offering scalability and faster response times alongside potentially more cost-effective usage-based pricing models. ... Static rule-based SIEMs struggle to keep pace with today’s sophisticated cyber threats, which is why AI-powered SIEM platforms use real-time machine learning (ML) to analyze vast amounts of security data, improving their ability to identify anomalies and previously unseen attack techniques that legacy technologies might miss.

AI agent seemingly tries to shame open source developer for rejected pull request

Evaluating lengthy, high-volume, often low-quality submissions from AI bots takes time that maintainers, often volunteers, would rather spend on other tasks. Concerns about – whether from people or AI models – have become common enough that GitHub recently convened a discussion to address the problem. Now AI slop comes with an AI slap. ... In his blog post, Shambaugh describes the bot's "hit piece" as an attack on his character and reputation. "It researched my code contributions and constructed a 'hypocrisy' narrative that argued my actions must be motivated by ego and fear of competition," he wrote. "It speculated about my psychological motivations, that I felt threatened, was insecure, and was protecting my fiefdom. It ignored contextual information and presented hallucinated details as truth. It framed things in the language of oppression and justice, calling this discrimination and accusing me of prejudice. It went out to the broader internet to research my personal information, and used what it found to try and argue that I was 'better than this.' And then it posted this screed publicly on the open internet." ... Daniel Stenberg, founder and lead developer of curl, has been dealing with AI slop bug reports for the past two years and recently decided to shut down curl's bug bounty program to remove the financial incentive for low-quality reports – which can come from people as well as AI models.

How to ground AI agents in accurate, context-rich data

Building and operating AI agents using unorganized data is like trying to navigate a rolling dinghy in a stormy ocean of 100-foot-tall waves. Solving this conundrum is one of the most important tasks for companies today, as they struggle to empower their AI agents to reliably work as designed and expected. To succeed, this firehose of unsorted data must be put into the right contexts so that enterprises can use and process it correctly and quickly to deliver the desired business results. ... Adding to the data demands is that AI agents can perform multiple steps or processes at a time while working on a task. But those concurrent and consecutive capabilities can require multiple streams of data, adding to the massive data pressures using search. “What that means is that at each of those steps, there’s an opportunity to find some relevant data, use that data in a meaningful way, and take the next action based on the results,” Mather explained. “So, the importance of the relevance at each step becomes paramount. If there’s bad results at the first step, it just compounds at every step that the agent takes.” The consequences are especially problematic when enterprises are trying to use AI agents to drive a business process or take meaningful actions within an application.

Beyond Code: How Engineers Need to Evolve in the AI Era

Generative AI lets you be more productive than you ever thought possible if you are willing to embrace it. It is a similar skill to being able to manage other humans, being able to delegate problems. Really great individual engineers can have trouble delegating, because they're worried that if they give a task to someone else that they haven't figured out how to do completely themselves yet, that it won't get done well enough. ... a lot of companies are now hiring engineers to go sit in the office of their customer, and they're an expert in their own company's platform, but they also become an expert in the customer's platform and the customer's problem, and they're right there embedded. And I love that model, because that is how you learn to apply technology directly to a problem, you are there with the person who has the problem. This is what we've been telling product managers to do for years. ... There will still be complex things to do as well that other people aren't going to think of to do, but they're going to be more innovative. They're not going to be the rogue repetition of building the same SaaS features we've seen everywhere. That can be done with generative AI, and frankly, isn't that good? Do we really want to keep doing that stuff ourselves? Let us work on the really maybe new problems that no one has ever solved before, bringing new theoretical ideas into software engineering, and let the more boilerplate stuff be taken care of.

Why there’s no ‘screenless’ revolution

One trend that emerged from last month’s Consumer Electronics Show (CES) was the range of devices that can record, analyze, and assist (using AI) without requiring visual focus. Many tech startups are working on screenless AI hardware. ... One reason these devices are more viable now than in the past is the miniaturization of duplex audio, which enables constant, bi-directional conversation where the AI can be interrupted or talk over the user naturally. ... If you look carefully at the world of screenless wearables, you can see that none of them are designed to be used in isolation. They’re all peripherals to screen-based devices such as smartphones. And while the Ray-Ban Meta type audio AI glasses are great, the future of AI glasses is closer to the Meta Ray-Ban Display glasses with one screen or two screens in the glass. There’s no way companies like Apple will offer alternatives to their own popular screen-based devices. Going totally screenless is for kids. Or rather, it should be. ... The only way to enforce a ban is to conduct a thorough search on every student every day before school — something that’s totally impractical and undesirable. Instead, schools, parents and teachers should all be uniting behind the best screenless wearables for students as a workable alternative to obsessive smartphone and screen use. The reality is that the total ubiquity of AI is coming. There’s the toxic version — the rise of AI slop, for instance — and the non-toxic version.

The Leadership Crisis No One Is Naming: A Need For Emotionally Whole Leaders

Leaders operating from unhealthy emotional frameworks often exhibit a variety of symptoms. They may show fear-based decision making, driven by a need to control outcomes rather than empower people. There may be micromanagement rooted in insecurity and mistrust instead of accountability. I've seen fight-or-flight leadership, where urgency replaces strategy and reaction replaces discernment. There can also be perfectionism, which confuses excellence with rigidity and punishes humanity. Then there's fearmongering, where pressure and anxiety are used as motivational tools. These patterns are rarely intentional, yet they are deeply consequential. ... The downstream effects of emotionally unhealthy leadership are often measurable and compounding. Stifled creativity plagues teams as they stop offering ideas that may be criticized or dismissed. Organizations may suffer increased attrition, particularly among high performers who have options. Employees may perform defensively rather than boldly in the presence of psychological unsafety. Cultures driven by urgency without sustainability can become breeding grounds for burnout and toxicity, reeking of institutional mistrust that erodes collaboration and loyalty. ... Developing is not about personality change; it is about capacity building. The most effective leaders treat emotional health as a leadership discipline, not a personal afterthought.

Alarm Overload at the Industrial Edge: When More Visibility Reduces Reliability

More sensors, more connected assets, and more analytics can produce more insight, but they can also produce a flood of fragmented alerts that bury the few signals people actually need. When alarms become noisy or ambiguous, response slows down, fatigue sets in, and confidence in the monitoring system erodes. That is not a user inconvenience. It is a . ... The purpose of alarm management is not to surface everything that happens. It is to surface what requires timely action, and to do it in a way that supports fast, correct decisions. If the alarm stream is noisy, inconsistent, or hard to interpret, the system is not doing its job. People respond the only way humans can: they tune out, acknowledge quickly, and rely on informal workarounds. ... Alarm overload is likely already affecting reliability if teams regularly see any of the following: alarms that do not require action, inconsistent severity definitions across systems, duplicate alerts for the same condition, frequent acknowledgements with no follow-up, or confusion about who owns the response. These are common as edge programs grow. ... The path forward is not to silence alarms indiscriminately. It is to modernize alarm management for the edge era: unify meaning across sources, deliver context that supports action, maintain governance as systems evolve, and design workflows that match how people actually respond.

Beyond Automation: How Generative AI in DevOps is Redefining Software Delivery

Integrating a means moving from a reactive ‘fix it when it breaks’ mindset to a more generative one. For example, instead of spending four hours writing a custom , you can now describe your requirements to an AI agent and get a working YAML file in under two minutes. Moreover, if you wish to scale these capabilities, exploring professional GenAI development services can help you build custom models that understand your particular codebase and security protocols. ... Pipelines are the lifeblood of DevOps, but they are also the first thing to break. GenAI can analyze historical build data to predict why a build might fail before it even starts. It can also auto-generate unit tests to ensure that your ‘quick fix’ doesn’t break anything downstream. ... humans make typos in config files, especially at 2:00 a.m. AI doesn’t get tired. By using GenAI to generate and validate configuration files, you ensure strict consistency across dev, staging and production environments. It acts as a continuous linter that understands the intent behind the code, catching logic errors that traditional syntax checkers would miss. ... Cloud bills are a nightmare to manage manually. GenAI can analyze thousands of lines of cloud-spending data and generate the exact CLI commands needed to shut down underutilized resources or right-size your clusters. It doesn’t just tell you that you’re overspending; it gives you the solution to fix it immediately.