Daily Tech Digest by Kannan Subbiah: asset management

Showing posts with label asset management. Show all posts

Daily Tech Digest - September 28, 2026

Quote for the day:

"When you want to succeed as bad as you want to breathe, then you’ll be successful." -- Eric Thomas

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How AI Can Find Weaknesses In Corporate Crisis Management Plans

The article explains that AI is becoming an important tool for finding weaknesses in corporate crisis‑management plans—often spotting blind spots that human teams miss. Crisis experts say AI can stress‑test plans by simulating realistic, high‑pressure scenarios such as communication failures, spokesperson missteps, or misinformation spreading faster than a company can respond. They recommend treating AI as a “hostile reviewer,” asking it to critique language, identify missing stakeholders, and highlight assumptions that may not hold during an actual crisis. The piece also notes that AI can test how plans perform across different audiences—customers, employees, journalists, regulators—revealing gaps in tone, clarity, or credibility. Recent incidents, including Google’s Gemini AI unintentionally breaching real company systems during a cybersecurity test, show how AI itself can create crises, making preparedness even more important. AI’s ability to scan documents quickly, run multiple simulations, and expose overlooked details can significantly improve readiness, but the article stresses that human judgment remains essential, especially when dealing with sensitive information or final decision‑making. Overall, organizations that use AI proactively to test and refine their crisis plans will be better positioned to respond quickly and credibly when unexpected events occur.

If you do one security check this quarter, make it agent memory

In a recent discussion regarding the security of automated software assistants, Chris Latimer highlights a significant yet often ignored vulnerability: the long-term memory storage of these helpful systems. As developers increasingly rely on these modern tools, they inadvertently save highly sensitive information, such as database passwords, application programming keys, and confidential business documents, in plain text. These files then sit completely unprotected on personal workstations and cloud servers, creating an incredibly easy target for attackers. According to Latimer, malicious actors often use simple social engineering tricks, like offering fake plugins with promised free benefits, to target less experienced programmers. Once installed, these rogue extensions can easily scan the memory stores to extract valuable corporate credentials. Furthermore, while the technology industry has established robust access controls for traditional databases, it currently struggles to apply those same necessary protections to these specific memory systems. Latimer advises security leaders to conduct immediate audits of the automated tools operating within their networks. He notes that many leaders will discover a widespread lack of basic governance, with employees using unvetted extensions that quietly expose the company to serious financial and operational risk. To prevent damage, organizations must focus on filtering out harmful inputs before they ever become permanent records.

Quantum-safe algorithms may fail faster with powerful AI tools From SIKE

The article discusses how the collapse of the SIKE cryptographic algorithm illustrates a broader and more urgent problem: quantum‑safe algorithms can fail much faster than expected, especially as powerful AI systems accelerate mathematical discovery. SIKE was once considered a strong candidate for post‑quantum encryption, advancing deep into NIST’s evaluation process. Yet researchers Wouter Castryck and Thomas Decru broke its smallest parameter set in about an hour on a standard laptop by applying a mathematical insight from 1997, showing that long‑standing assumptions can unravel suddenly. The article notes that frontier AI systems now explore obscure mathematical connections at scale, rapidly testing ideas, scanning literature, and generating experimental code. Recent examples include AI‑generated breakthroughs on decades‑old problems such as Erdős’s unit‑distance conjecture and even a proposed solution to the Navier–Stokes existence problem. These advances suggest that AI could uncover cryptographic weaknesses far sooner than traditional research methods. As a result, the article argues that security strategies must shift from simply replacing vulnerable algorithms to designing systems that remain resilient even if new “quantum‑safe” methods fail. The core message is that cryptographic confidence must account for accelerating mathematical and AI‑driven discovery, not just quantum threats.

Five Decision Rights CIOs Need for Agentic AI

Agentic AI requires a new approach to oversight because these systems can independently plan tasks, use tools, and alter data. To manage this safely, technology leaders must treat governance as a core design requirement rather than a final compliance check. Organizations should establish five key decision rights before an artificial intelligence system goes into production. First, authorization defines who can delegate tasks and strictly limits the system's permissions to prevent unintended actions. Second, data access controls what information the software can read, write, or share, ensuring that data is used securely and proportionately. Third, human intervention establishes clear points where people can pause, review, or stop the system, particularly before high-impact actions occur. Fourth, exception handling outlines safe failure processes, dictating exactly how the system should behave and escalate when it encounters unexpected situations or errors. Finally, accountability ensures that a named human executive, not the software, ultimately owns the final outcome of the automated actions. By building these five decision rights directly into the system architecture with clear owners and visible evidence, organizations create a reliable boundary between helpful automation and unmanaged risk. This structured approach allows teams to deploy advanced AI safely, with clear limits and continuous oversight.

Harnessing big data for real-time risk assessment on major construction sites

Construction sites are inherently unpredictable, making risk assessment a critical yet challenging task. While traditional risk planning offers a helpful snapshot, site conditions change rapidly throughout the day. To address this, many construction managers are turning to real-time risk assessment powered by big data to continuously monitor conditions and identify emerging problems before they escalate into injuries, delays, or budget overruns. By harnessing data from tools like drones, wearable devices, equipment telematics, and IoT sensors, project teams gain a comprehensive, real-time view of the jobsite. This steady stream of information allows managers to detect developing safety hazards, track material deliveries, monitor equipment performance, and analyze workforce availability. Machine learning algorithms further support this by analyzing thousands of data points to spot anomalies that manual inspections might miss. Implementing a data-driven risk strategy does not require an overnight transformation. Organizations can start by targeting a specific goal—such as minimizing schedule delays or reducing equipment downtime—and connecting relevant data points into a single dashboard. Tracking these metrics over time enables teams to measure their progress and make informed decisions, ultimately leading to safer, more predictable, and more efficient construction projects.

Software Asset Management Is a Data Problem — And That’s What Makes It Interesting

Software asset management is rarely seen as a pure data problem, but it involves the complex challenge of reconciling the software an organization buys with what its employees actually use. In large companies, this information is scattered across discovery tools, identity systems, and contract records. The first major hurdle is standardizing messy, inconsistent data into a clear software catalog. Without this foundation, it is impossible to accurately compare purchased rights with actual installations. Once the data is cleaned and linked, the focus can shift from basic compliance to true financial optimization. Organizations can identify expensive software that is installed but barely used, allowing them to reclaim licenses and reduce costs. This brings software management closer to cloud cost management, where usage data directly informs financial decisions. However, the success of this approach depends entirely on data quality; missing servers or incorrect user mapping can lead to significant financial exposure. While artificial intelligence can assist with tasks like naming consistency and spotting unusual spending, it cannot replace the need for reliable data pipelines. Ultimately, treating software management as a continuous, shared data resource helps IT, finance, and security teams make smarter, more confident decisions about their technology investments.

AI and Beyond AI: Diffusion Pathways for Societal Transformation

Artificial intelligence holds immense potential to transform lives by providing accessible and localized information to everyday people like farmers, teachers, and healthcare workers. However, the true global challenge lies not in the core technology itself, but in effectively moving an AI project from an initial idea to a large-scale deployment. To solve this, experts advocate for the creation of "diffusion pathways." These pathways act as comprehensive, multi-layered playbooks that capture the practical knowledge, data requirements, governance models, and necessary partnerships behind successful AI implementations. By carefully packaging this lived experience, diffusion pathways allow new adopters to build upon past successes rather than starting entirely from scratch. This shared knowledge drastically compresses the time required to design and deploy new AI solutions, as demonstrated by agricultural projects that reduced development time from several months to just a few weeks. Furthermore, these pathways emphasize the importance of embedding critical safeguards, data ownership protocols, and feedback mechanisms directly into the design process to ensure the tools remain trustworthy and effective. Driven by this clear vision, a global initiative is now building momentum to curate exactly 100 of these high-impact, reusable AI pathways by the year 2030 to guide responsible societal transformation.

The Architecture of Certainty: Rethinking Infrastructure in an Age of Complexity

Modern organizational infrastructure is evolving from a mere technical utility into a strategic asset that shapes business capabilities. In an era marked by economic volatility, evolving cyber threats, and rapid technological shifts, infrastructure must deliver certainty and predictability. However, many businesses mistake current operational stability for architectural health, overlooking hidden "infrastructure debt" caused by temporary fixes, legacy systems, and fragmented architectures. This hidden complexity reduces agility and makes systems vulnerable to unpredictable cascading failures, especially as modern networks increasingly rely on third-party cloud platforms and interconnected external ecosystems. To thrive, organizations must shift their focus from basic resilience—simply surviving disruptions—to building adaptive infrastructure. Adaptive infrastructure uses intelligence, visibility, and automation to evolve dynamically alongside technological and business changes. It acts as the "confidence layer" of the enterprise, ensuring that organizations can fulfill commitments to customers, partners, and employees without interruption. Ultimately, managing this complexity effectively requires structural simplification and proactive architectural discipline. By aligning infrastructure investments with long-term strategic goals and integrating robust security and disaster recovery directly into the operational lifecycle, companies can transform potential vulnerabilities into a competitive advantage defined by certainty and continuous adaptability.

The cost of not innovating: Frontier AI models, cyber defence, and EU strategic autonomy

The article argues that Europe’s failure to innovate in frontier AI carries real strategic and cybersecurity risks. In April 2026, highly capable frontier AI models from OpenAI and Anthropic changed the cyber‑threat landscape almost overnight. These systems can autonomously execute cyber operations at speeds and scales far beyond human capacity, shrinking attack timelines from days to minutes. Because access to these models was initially restricted—and briefly subject to a de facto US export ban—the authors warn that Europe’s dependence on foreign‑controlled AI has become a structural vulnerability. This reliance widens gaps between jurisdictions, between attackers and defenders, and between financial institutions with different levels of technological maturity. CEPRCEPR. The cost of not innovating: Frontier AI models, cyber defence, and EU strategic autonomy | CEPR The column explains that Europe’s existing IT infrastructure, built over decades, cannot absorb and remediate fast‑moving vulnerabilities in real time, especially when many weaknesses originate in common software packages and open‑source libraries that only vendors can fix. The authors conclude that more regulation is not the answer. Instead, Europe must mobilize risk capital, retain technical talent, and support the development and scaling of its own frontier technologies. Without this shift, the EU risks entering a self‑reinforcing cycle of fragility in both cyber defence and strategic autonomy.

Unifying Networking and Cybersecurity: Building a Dependable Digital Foundation for Indian Enterprises

Indian enterprises are moving away from scattered, hard‑to‑manage IT setups and toward unified digital foundations that combine networking and cybersecurity into a single, dependable architecture. As hybrid work, multi‑cloud adoption, and connected operations spread across both major cities and smaller markets, organizations are struggling with rising complexity and limited skilled talent. The article explains that resilience now depends on embedding identity management, cybersecurity controls, and continuous risk monitoring directly into the network itself, rather than treating security as an add‑on. This shift requires moving from reactive threat blocking to an operating model built around rapid containment, constant visibility, and business continuity. The piece highlights how managed technology integrators can help enterprises run distributed environments without sacrificing uptime or data protection, allowing internal teams to focus on strategic priorities. Sunil Arora of ABS India notes that customer expectations have evolved: companies no longer want isolated tools but integrated solutions that connect networks, cloud platforms, communications, and security into a coherent whole. As digital dependence grows, enterprises increasingly expect partners who can design, manage, and secure complex ecosystems end‑to‑end. The article concludes that the future lies in treating connectivity, security, and resilience as one unified foundation rather than separate disciplines.

Daily Tech Digest - October 31, 2025

Quote for the day:

“The more you loose yourself in something bigger than yourself, the more energy you will have.” -- Norman Vincent Peale

Breaking the humanoid robot delusion

The robot is called . The company says NEO is the world’s first consumer-ready humanoid robot for the home. It is designed to automate routine chores and offer personal help so you can spend time on other things. ... Full autonomy in perceiving, planning, and manipulating like a human is a massive technology challenge. Robots have to be meticulously and painstakingly trained on every single movement, learn to recognize every object, and “understand” — for lack of a better word — how things move, how easily they break, what goes where, and what constitute appropriate actions. One major way humanoid robots are trained is with . A person wearing special equipment remotely controls prototype robots, training them for many hours on how to, say, fold a shirt. Many hours more are required to train the robot how to fold a smaller child’s shirt. Every variable, from the height of the folding table to the flexibility of the fabrics has to be trained separately. ... The temptation to use impressive videos of remotely controlled robots where you can’t see the person controlling them to raise investment money, inspire stock purchases and outright sell robot products, appears to be too strong to resist. Realistically, the technology for a home robot that operates autonomously the way the NEO appears to do in the videos in arbitrary homes under real-world conditions is many years in the future, possibly decades.

Your vendor’s AI is your risk: 4 clauses that could save you from hidden liability

The frontier of exposure now extends to your partners’ and vendors’ use. The main question being: Are they embedding AI into their operations in ways you don’t see until something goes wrong? ... Require vendors to formally disclose where and how AI is used in their delivery of services. That includes the obvious tools and embedded functions in productivity suites, automated analytics and third-party plug-ins. ... Include explicit language that your data may not be used to train external models, incorporated into vendor offerings or shared with other clients. Require that all data handling comply with the strictest applicable privacy laws and specify that these obligations survive the termination of the contract. ... Human oversight ensures that automated outputs are interpreted in context, reviewed for bias and corrected when the system goes astray. Without it, organizations risk over-relying on AI’s efficiency while overlooking its blind spots. Regulatory frameworks are moving in the same direction: for example, high-risk AI systems must have documented human oversight mechanisms under the . ... Negotiate liability provisions that explicitly cover AI-driven issues, including discriminatory outputs, regulatory violations and errors in financial or operational recommendations. Avoid generic indemnity language. Instead, AI-specific liability should be made its own section in the contract, with remedies that scale to the potential impact.

AI chatbots are sliding toward a privacy crisis

The problem reaches beyond internal company systems. Research shows that some of the most used AI platforms collect sensitive user data and share it with third parties. Users have little visibility into how their information is stored or reused, leaving them with limited control over its life cycle. This leads to an important question about what happens to the information people share with chatbots. ... One of the more worrying trends in business is the growing use of , where employees turn to unapproved tools to complete tasks faster. These systems often operate without company supervision, allowing sensitive data to slip into public platforms unnoticed. Most employees admit to sharing information through these tools without approval, even as IT leaders point to data leaks as the biggest risk. While security teams see shadow AI as a serious problem, employees often view it as low risk or a price worth paying for convenience. “We’re seeing an even riskier form of shadow AI,” says Tim Morris, “where departments, unhappy with existing GenAI tools, start building their own solutions using open-source models like .” ... Companies need to do a better job of helping employees understand how to use AI tools safely. This matters most for teams handling sensitive information, whether it’s medical data or intellectual property. Any data leak can cause serious harm, from damaging a company’s reputation to leading to costly fines.

The true cost of a cloud outage

The top 2000 companies in the world pay approximately $400 billion for downtime each year. A simple calculation reveals that these organizations, including the Dutch companies , , , , and , lose around $200 million from their annual accounts due to unplanned downtime. Incidentally, what the Splunk study really revealed were the hidden costs of financial damage caused by problems with security tools, infrastructure, and applications. These can wipe billions off market values. ... A more conservative estimate of downtime costs can be found at Information Technology Intelligence Consulting, which conducted research on behalf of . The majority of the parties surveyed had more than 200 employees, but the combination was more diverse than the top 2000 companies worldwide. The costs of downtime were substantial: at least $300,000 per hour for 90 percent of the companies in question. Forty-one percent stated that IT outages cost between $1 million and $5 million. ... In theory, the largest companies can rely on a . In addition, hyperscalers absorb many local outages by routing traffic to other regions. However, multicloud is not something that you can just set up as a start-up SME. In addition, you usually do not build your applications in a fully redundant form in different clouds. Furthermore, it is quite possible that you can continue to work yourself, but that your product is inaccessible.

5 Reasons Why You’re Not Landing Leadership Roles

Is your posture confident? Do you maintain steady eye contact? Is the cadence, pace and volume of your voice engaging, assertive and compelling? Recruiters assess numerous factors on the . ... Are you showing a grasp of the prospective employer’s pain points and demonstrating an original point of view for how you will approach these problems? Treat senior level interviews like – you are an expert on their business, uncovering potential opportunities with insightful questions, and sharing enough of your expertise that you’re perceived as the solution. ... Title bumps are rare, so you need to give the impression that you are already operating at the in order to be hired as such. Your interview examples should include stories about how you initiated new ideas or processes, as well as measurable results that impact the bottom line. Your examples should specify how many people and dollars you have managed. Ideally, you have stories that show you can get results in up and down markets. ... The hiring process extends over multiple rounds, especially for leadership roles. Keep track of everyone that you have met, as well as what you have specifically discussed with each of them. Send personalized follow-up emails that engage each interviewer uniquely based on what you discussed. This differentiates you as someone who listens and cares about them specifically.

Why understanding your cyber exposure is your first line of defence

Thanks to AI, attacks are faster, more targeted and increasingly sophisticated. As the lines between the physical and digital blur, the threat is no longer isolated to governments or critical national infrastructure. Every organisation is now at risk. Understanding your cyber exposure is the key to staying ahead. This isn’t just a buzzword either; it’s about knowing where you stand and what’s at risk. Knowing every asset, every connection, every potential weakness across your digital ecosystem is now the first step in building a defence that can keep pace with modern threats. But before you can manage your exposure, you need to understand what’s driving it – and why the modern attack surface is so difficult to defend. ... By consolidating data from across the environment and layering it with contextual intelligence, cyber exposure management allows security teams to move beyond passive monitoring. It’s not just about seeing more, it’s about knowing what matters and acting on it. That means identifying risks earlier, prioritising them more effectively and taking action before they escalate. ... Effective and modern cybersecurity is shifting to shaping the battlefield before threats even arrive. That’s down to the value of understanding your cyber exposure. After all, it’s not just about knowing what’s in your environment, it’s about knowing how it all fits together – what’s exposed, what’s critical and where the next threat is likely to emerge.

Applications and the afterlife: how businesses can manage software end of life

Both enterprise software and personal applications have a lifecycle, set by the vendor’s support and maintenance. Once an application or operating system goes out of support, it will continue to run. But there will be no further feature updates and vitally, often no security patches. ... When software end of life is unexpected, it can cause serious disruption to business processes. In the very worst-case scenarios, enterprises will only know there is a problem when a key application no longer functions, or if a malicious actor exploits a vulnerability. The problem for CIOs and CISOs is keeping track of the end of life dates for applications across their entire stack, and understanding and mapping dependencies between applications. This applies equally to in-house applications, off the shelf software and open source. “End of life software is not necessarily bad,” says Matt Middleton-Leal, general manager for EMEA at Qualys. “It’s just not updated any more, and that can lead to vulnerabilities. According to our research, nearly half of the issues on the CISA Known Exploited Vulnerabilities (KEV) list are found in outdated and unsupported software.” As CISA points out, attackers are most likely to exploit older vulnerabilities, and to target unpatched systems. Risks come from old, and known vulnerabilities, which IT teams should have patched.

Tips for CISOs switching between industries

Building a transferable skill set is essential for those looking to switch industries. For Dell’s first-ever CISO, Tim Youngblood, adaptability was never a luxury but a requirement. His early years as a consultant at gave him a front-row seat to the challenges of multiple industries before he ever moved into cybersecurity. Those early years also taught Youngblood that while every industry has its own nuances, the core security principles remain constant. ... Making the jump into a new industry isn’t about matching past job titles but about proving you can create impact in a new context. DiFranco says the key is to demonstrate relevance early. “When I pitch a candidate, I explain what they did, how they did it, and what their impact was to their organization in their specific industry,” he says. “If what they did and how they did it, and what their impact was on the organization resonates where that company wants to go, they’re a lot more likely to say, ‘I don’t really care where this person comes from because they did exactly what I want done in this organization’. ... The biggest career risk for many CISOs isn’t burnout or data breach, it’s being seen as a one-industry operator. Ashworth’s advice is to focus on demonstrating transferable skills. “It’s a matter of getting whatever job you’re applying for, to realise that those principles are the same, no matter what industry you’re in. Whether it’s aerospace, healthcare, or finance, the principles are the same. Show that, and you’ll avoid being pigeonholed.”

Awareness Is the New Armor: Why Humans Matter Most in Cyber Defense

People remain the most unpredictable yet powerful variable in cybersecurity. Lapses like permission misconfiguration, accidental credential exposure, or careless data sharing continue to cause most incidents. Yet when equipped with the right tools and timely information, individuals can become the strongest line of defense. The challenge often stems from behavior rather than intent. Employees frequently bypass security controls or use unapproved tools in pursuit of productivity, unintentionally creating invisible vulnerabilities that go unnoticed within traditional defences. Addressing this requires more than restrictive policies. Security must be built into everyday workflows so that safe practices become second nature. ... Since technology alone cannot secure an organization, a culture of security-first thinking is essential. Leaders must embed security into everyday workflows, promote upskilling, and focus on reinforcement rather than punishment. This creates a workforce that takes ownership of cybersecurity, checking email sources, verifying requests, and maintaining vigilance in every interaction. is both a reminder and a rallying cry. India’s digital economy presents immense opportunity, but its threat surface expands just as fast.

Creepy AI Crawlers Are Turning the Internet into a Haunted House

The degradation of the internet and market displacement caused by commercial AI crawlers directly undermines people’s ability to access information online. This happens in various ways. First, the put significant technical strain on the internet, making it more difficult and expensive to access for human users, as their activity increases the time needed to access websites. Second, the trained on this scraped content now provide answers directly to user queries, reducing the need to visit the original sources and cutting off the traffic that once sustained content creators, including media outlets. ... AI crawlers represent a fundamentally different economic and technical proposition––a vampiric relationship rather than a symbiotic one. They harvest content, news articles, blog posts, and open-source code without providing the semi-reciprocal benefits that made traditional crawling sustainable. Little traffic flows back to sources, especially when search engines like Google start to provide AI generated summaries rather than sending traffic on to the websites their summaries are based on. ... What makes this worse is that these actors aren’t requesting books to read individual stories or conduct genuine research, they’re extracting the entire collection to feed massive language model systems. The library’s resources are being drained not to serve readers, but to build commercial AI products that will never send anyone back to the library itself.