Showing posts with label DNSSEC. Show all posts
Showing posts with label DNSSEC. Show all posts

Daily Tech Digest - March 23, 2026


Quote for the day:

"Successful leaders see the opportunities in every difficulty rather than the difficulty in every opportunity" -- Reed Markham


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Testing autonomous agents (Or: how I learned to stop worrying and embrace chaos)

The VentureBeat article "Testing autonomous agents (Or: how I learned to stop worrying and embrace chaos)" explores the critical shift from simple chatbots to autonomous AI agents that function more like independent employees. As agents gain the power to execute actions without human confirmation, the authors argue that "plausible" reasoning is no longer sufficient; systems must instead be engineered for graceful failure and absolute reliability. To achieve this, a four-layered architecture is proposed: high-quality model selection, deterministic guardrails using traditional validation logic, confidence quantification to identify ambiguity, and comprehensive observability for auditing reasoning chains. Reliability is further reinforced by defining clear permission, semantic, and operational boundaries to limit the "blast radius" of potential errors. The article emphasizes that traditional software testing is inadequate for probabilistic systems, advocating instead for simulation environments, red teaming, and "shadow mode" deployments where agents’ decisions are compared against human actions. Ultimately, building enterprise-grade autonomy requires a risk-based investment in safeguards and a rethink of organizational accountability, ensuring that human-in-the-loop patterns remain a central safety mechanism as these systems navigate the complex, often unpredictable reality of production environments.


NIST updates its DNS security guidance for the first time in over a decade

NIST has released Special Publication 800-81r3, the Secure Domain Name System Deployment Guide, marking its first significant update to DNS security standards in over twelve years. This comprehensive revision addresses the modern threat landscape by focusing on three critical pillars: utilizing DNS as an active security control, securing protocols, and hardening infrastructure. A central theme is the implementation of protective DNS (PDNS), which empowers organizations to analyze queries and block access to malicious domains proactively. The guide provides technical advice on deploying encrypted DNS protocols like DNS over TLS, HTTPS, and QUIC to ensure data privacy and integrity. Furthermore, it modernizes DNSSEC recommendations by favoring efficient cryptographic algorithms like ECDSA and Edwards-curve over legacy RSA methods. Organizational hygiene is also prioritized, with strategies to mitigate risks like dangling CNAME records and lame delegations that lead to domain hijacking. By advocating for the separation of authoritative and recursive functions and geographic dispersal, NIST aims to bolster the resilience of network connections. This updated framework serves as an essential roadmap for cybersecurity leaders and technical teams tasked with maintaining secure, future-proof DNS environments in an increasingly complex digital ecosystem.


The insider threat rises again

The article "The Insider Threat Rises Again" examines the escalating risks posed by internal actors in modern organizations. Driven by evolving technologies and shifting work dynamics, insider incidents have become increasingly frequent and costly, with 42% of organizations reporting a rise in both malicious and negligent cases over the past year. The financial impact is staggering, averaging $13.1 million per incident. Today's threat landscape is multifaceted, encompassing deliberate sabotage, inadvertent errors, and the emergence of "coerced insiders" targeted via social media or the dark web. Remote work has exacerbated these risks by lowering psychological barriers to data exfiltration, while AI enables data theft at an unprecedented scale. Furthermore, the article highlights sophisticated tactics like North Korean operatives posing as fake IT workers to gain persistent network access. To combat these threats, experts argue that traditional perimeter security is no longer sufficient. Organizations must instead adopt adaptive controls that monitor high-risk actions in real-time and create friction at the point of data access. Moving beyond managing human behavior, effective security now requires meeting users at the point of risk to identify and block suspicious activity regardless of the actor's credentials.


25 Years of the Agile Manifesto, and the End of the Road for AppSec?

In the article "25 Years of the Agile Manifesto and the End of the Road for AppSec," the author reflects on how the evolution of software development has rendered traditional Application Security (AppSec) models obsolete. Since the inception of the Agile Manifesto, the industry has shifted from slow, monolithic release cycles to rapid, continuous delivery. The core argument is that conventional AppSec—often characterized by "gatekeeping," manual reviews, and siloed security teams—cannot keep pace with the velocity of modern DevOps. This friction creates a bottleneck that developers frequently bypass to meet deadlines, ultimately compromising security. The piece suggests that we have reached the "end of the road" for security as a separate, reactionary phase. Instead, the future lies in "shifting left" and "shifting everywhere," where security is fully integrated into the CI/CD pipeline through automation and developer-centric tools. By empowering developers to take ownership of security within their existing workflows, organizations can achieve the speed promised by Agile without sacrificing safety. Ultimately, the article calls for a cultural and technical transformation where AppSec evolves from a final checkpoint into an invisible, continuous component of the software development lifecycle, ensuring resilience in an increasingly fast-paced digital landscape.


The era of cheap technology could be over

The article suggests that the long-standing era of affordable consumer and enterprise technology is drawing to a close, primarily driven by an unprecedented global shortage of critical hardware components. This shift is largely attributed to the explosive growth of artificial intelligence, which has created an insatiable demand for high-performance processors, memory, and solid-state storage. Manufacturers are increasingly prioritizing high-margin AI-specific hardware over commodity components used in PCs, smartphones, and servers, leading to significant price hikes. Market analysts predict a dramatic surge in DRAM and SSD prices, with some estimates suggesting a 130% increase by the end of the year. Consequently, shipments for personal computers and mobile devices are expected to decline as manufacturing costs become prohibitive. Beyond the AI boom, the crisis is exacerbated by post-pandemic market cycles and geopolitical tensions that continue to destabilize global supply chains. To navigate this new landscape, IT leaders are being forced to rethink procurement strategies, opting for data cleansing, tiered storage solutions, and extending the lifecycle of existing hardware. Ultimately, while these shortages strain budgets, they may encourage more disciplined data management practices as businesses adapt to a more expensive technological environment.


The AI era of incident response: What autonomous operations mean for enterprise IT

The article explores the transformative shift in enterprise IT as it moves toward an era of autonomous operations driven by artificial intelligence. Traditionally, incident response has been a reactive, manual process, leaving IT teams overwhelmed by a constant deluge of alerts and complex troubleshooting tasks. However, as modern environments grow increasingly intricate across cloud and hybrid infrastructures, manual intervention is no longer sustainable. The author argues that AI and machine learning are revolutionizing this landscape by enabling proactive monitoring and automated remediation. These AIOps tools can analyze massive datasets in real-time to identify patterns, pinpoint root causes, and resolve issues before they escalate into significant outages. This transition significantly reduces the Mean Time to Repair (MTTR) and shifts the focus of IT staff from constant firefighting to higher-value strategic initiatives. While human oversight remains essential, the role of IT professionals is evolving into one of managing intelligent systems rather than performing repetitive manual labor. Ultimately, embracing autonomous operations allows organizations to achieve greater system reliability, operational efficiency, and a superior developer experience, marking a definitive end to the limitations of legacy incident management frameworks.


Securing Automation: Why the Specification Stage Is the Right Time to Embed OT Cybersecurity

Manufacturers today are rapidly adopting automation to meet rising demand, yet a significant gap remains in cybersecurity investment, often leaving operational technology (OT) vulnerable. This article argues that the most effective remedy is to embed security requirements directly into the initial specification phase of projects. By integrating specific, testable criteria into Requests for Proposals (RFPs), security becomes a contractually enforceable deliverable rather than a costly afterthought. Effective requirements must adhere to six key attributes: they should be achievable, unambiguous, concise, complete, singular, and verifiable. This structured approach allows for rigorous validation during Factory Acceptance Testing (FAT) and Site Acceptance Testing (SAT), ensuring systems are hardened before they go live. Beyond technical specifications, the author emphasizes a holistic strategy encompassing people and processes, such as developing OT-specific security policies and conducting regular incident-response drills. Resilience is also highlighted through the implementation of immutable backups and "safe-state" logic to maintain production during disruptions. Ultimately, establishing an OT governance board ensures that security remains a continuous, executive-level priority, safeguarding automation investments while maintaining the speed and efficiency essential for modern industrial competitiveness.


The Illusion of Managed Data Products

In "The Illusion of Managed Data Products," Dr. Jarkko Moilanen explores the critical gap between perceiving data as a managed asset and the operational reality of true control. He argues that many organizations mistake visibility—achieved through data catalogs and dashboards—for actual management. While these tools identify existing products and track performance, they often fail to trigger meaningful action when issues arise. This creates an illusion of order where structure and metadata exist, but ownership remains static and metrics lack consequences. Moilanen identifies "diffusion of responsibility" and "latency" as key barriers, where signals are observed but not systematically tied to accountability or execution. To overcome this, the author advocates for a shift from mere observation to an active operating model. This involves creating a closed loop where every signal leads to a defined owner, a triggered action, and subsequent verification. By integrating business outcomes with governance and leveraging AI to bridge the gap between detection and response, organizations can move beyond descriptive catalogs toward a system of coordinated execution. Ultimately, managing data products requires more than just better visualization; it demands a structural transformation that prioritizes responsiveness and ensures that every data insight results in tangible business momentum.


Resilience by Design: How Axis Bank is redefining cybersecurity for the AI-driven banking era

The article titled "Resilience by Design: How Axis Bank is redefining cybersecurity for the AI-driven banking era" features Vinay Tiwari, CISO of Axis Bank, and his vision for securing modern financial services. As banking transitions into an AI-driven landscape, Tiwari emphasizes "resilience by design," a strategy that integrates security into the core of every digital initiative rather than treating it as an afterthought. The bank’s approach is anchored by three critical domains: robust cyber risk governance, secured data architecture, and continuous threat analysis. A central pillar of this transformation is the implementation of Zero Trust Architecture, which replaces implicit trust with continuous verification across all network interactions. Furthermore, Axis Bank leverages advanced AI/ML-powered threat intelligence and automated security operations to detect anomalies and mitigate risks proactively. Beyond technology, Tiwari stresses that true resilience stems from a human-centered culture. By launching comprehensive awareness programs, the bank empowers employees to recognize social engineering and phishing threats. Ultimately, this multifaceted strategy—combining hybrid-cloud protection, preemptive defense, and unified compliance—aims to build digital trust. This ensures that as Axis Bank scales, its security posture remains robust enough to counter the evolving complexities of the modern cyber threat landscape.


Why Data Governance Keeps Falling Short and 6 Actions to Fix It

In this article, Malcolm Hawker explores why data governance initiatives often fail to deliver their promised value, attributing the shortfall to a combination of human, cultural, and organizational barriers. A primary issue is the conceptual misunderstanding where leadership views data governance as a technical IT responsibility rather than a fundamental enterprise capability. This results in an overreliance on technology and a lack of genuine executive engagement beyond mere "buy-in." Furthermore, many organizations struggle to quantify the business benefits of governance, leading it to be perceived as a cost center rather than a value generator. To overcome these obstacles, Hawker proposes six strategic actions aimed at realigning governance with business goals. These include educating leadership to foster a data-driven culture, documenting clear business value, and acknowledging that governance is a cross-functional business issue rather than an IT problem. Additionally, he emphasizes the need to define the true value of data, cover the entire data supply chain, and integrate governance more closely with core business operations. By shifting focus from technological tools to people, leadership, and value quantification, organizations can transform data governance from a stagnant administrative burden into a dynamic driver of competitive advantage and regulatory compliance.

Daily Tech Digest - December 19, 2025


Quote for the day:

"A leader's dynamic does not come from special powers. It comes from a strong belief in a purpose and a willingness to express that conviction." -- Kouzes & Posner



AI tops CEO earnings calls as bubble fears intensify

Research by Hamburg-based IoT Analytics examined around 10,000 earnings calls from about 5,000 global companies listed in the US. The firm's latest quarterly study found that AI rose to the top of CEO agendas for the first time in the period, while concerns about a possible AI-related asset bubble also increased sharply. Mentions of an "AI bubble" climbed 64% compared with the previous quarter. IoT Analytics said executives often paired announcements of new AI investments with comments that questioned the sustainability of current market valuations and the pace of capital inflows into the sector. ... While the number of AI-related references reached a new high, comments that explicitly mentioned a "bubble" in connection with technology or financial markets grew even faster in percentage terms. The study recorded the strongest quarter-on-quarter jump in bubble-related language since it began tracking the metric. Executives used the term "bubble" in several contexts. Some discussed venture funding and valuations for private AI companies. Others raised questions about the level of spending on compute infrastructure and the potential for overcapacity. A smaller group linked bubble concerns to individual asset classes such as AI-related equities. The increase in bubble-related discussion came alongside continued announcements of long-term AI spending plans. 


AI governance becomes a board mandate as operational reality lags

Executives have clearly moved fast to formalize oversight. But the foundations needed to operationalize those frameworks—processes, controls, tooling, and skills embedded in day-to-day work—have not kept pace, according to the report. ... Many organizations still lack a comprehensive view of where AI is being used across their business, Singh explained. Shadow AI and unsanctioned tools proliferate, while sanctioned projects are not always cataloged in a central inventory. Without this map of AI systems and use cases, governance bodies are effectively trying to manage risk they cannot fully see. The second gap is conceptual. “There’s a myth that governance is the same as regulation,” Singh said. “Unfortunately, it’s not.” Governance, she argued, is much broader: It includes understanding and mitigating risk, but also proving out product quality, reliability, and alignment with organizational values. Treating governance as a compliance checkbox leaves major gaps in how AI actually behaves in production. The final one is AI literacy. “You can’t govern something you don’t use or understand,” Singh said. If only a small AI team truly grasps the technology while the rest of the organization is buying or deploying AI-enabled tools, governance frameworks will not translate into responsible decisions on the ground. ... What good governance looks like, Singh argued, is highly contextual. Organizations need to anchor governance in what they care about most. 


Legal Issues for Data Professionals: Data Centers in Space

If data is processed, copied, or stored on satellites, courts may be forced to decide whether space-based computing falls outside the scope of a “worldwide” license. A licensor could argue that the licensee exceeded the grant by moving data “off-planet,” creating an unintended new use. Moreover, even defining the equivalent of “territory” as “throughout the universe” raises questions as well as addressing them. The legal issues and regulatory rules involving data governance and legal rights in data centers in orbit have antecedents. ... Satellite-based data centers raise new questions: Where is an unauthorized copy of copyrighted material made for legal purposes, and which jurisdiction’s laws apply? A location in space complicates these legal issues and has implications for data governance. ... On Earth, IP enforcement against infringement relies on tools like forensic imaging, seizure of hard drives, discovery of server logs, and on-site inspections. Space breaks these tools. A court cannot easily order the seizure of a satellite. Inspecting hardware in orbit is not possible without specialized spacecraft. From a user’s perspective, retrieving logs may depend entirely on a vendor’s operation. ... Most cloud contracts and cyber insurance policies assume all processing happens on Earth. They do not address such things as satellite collisions, radiation damage, solar storms, loss of access due to orbital debris, or the failure of a satellite-to-Earth data link.


DNS as a Threat Vector: Detection and Mitigation Strategies

DNS is a critical control plane for modern digital infrastructure — resolving billions of queries per second, enabling content delivery, SaaS access, and virtually every online transaction. Its ubiquity and trust assumptions make it a high‑value target for attackers and a frequent root cause of outages. Unfortunately, this essential service can be exploited as a DoS vector. Attackers can harness misconfigured authoritative DNS servers, open DNS resolvers, or the networks that support such activities to initiate a flood of traffic to a target, impacting the service availability and causing disruptions in a large scale. This misuse of DNS capabilities makes it a potent tool in the hands of cybercriminals. ... DNS detection strategies focus on analyzing traffic patterns and query content for anomalies (like long/random subdomains, high volume, rare record types) to spot threats like tunneling, Domain Generation Algorithms, or malware, using AI/ML, threat intel, and SIEMs for real-time monitoring, payload analysis, and traffic analysis, complemented by DNSSEC and rate limiting for prevention. Legacy security tools often miss DNS threats. ... DNS mitigation strategies involve securing servers, controlling access (MFA, strong passwords), monitoring traffic for anomalies, rate-limiting queries, hardening configurations, and using specialized DDoS protection services to prevent amplification, hijacking, and spoofing attacks, ensuring domain integrity and availability.


The ‘chassis strategy’: How to build an innovation system that compounds value

The chassis strategy starts with a simple principle: centralize what must be common and decentralize what should evolve. You don’t need a monolithic innovation platform. You need a spine — a shared foundation of data, models and governance — that everything else plugs into. That spine ensures no matter who builds the next great idea — your team, a startup or a strategic partner — the learning, data and IP stay inside your system. ... You don’t need five years or an enterprise overhaul. A minimal but functional chassis can be built in nine months. The first three months are about framing and simplification. Pick three or four innovation domains — formulation, packaging, pricing or supply chain. Define the shared spine: your data schema, APIs and key metrics. Draw a bright line between what you’ll own (core) and what you’ll source (modules). The next three months are about building the core. Set up a unified data layer, model registry, API gateway and an experimentation sandbox. Keep it lightweight. No monoliths, no “innovation cloud.” Just the essentials that make reuse possible. The final three months are about plugging and proving. Integrate a few external modules — a supplier-insight engine, a generative packaging designer, a formulation optimizer. Track time to activation and reuse rate. The goal isn’t more features; it’s showing that vendors can connect fast, share data safely and strengthen the system.


AI is creating more software flaws – and they're getting worse

The CodeRabbit study found 10.83 issues with AI pull requests versus 6.45 for human-only ones, adding that AI pull requests were far more likely to have critical or major issues. "Even more striking: high-issue outliers were much more common in AI PRs, creating heavy review workloads," Loker said. Logic and correctness was the worst area for AI code, followed by code quality and maintainability and security. Because of that, CodeRabbit advised reviewers to watch out for those types of errors in AI code. ... "These include business logic mistakes, incorrect dependencies, flawed control flow, and misconfigurations," Loker wrote. "Logic errors are among the most expensive to fix and most likely to cause downstream incidents." AI code was also spotted omitting null checks, guardrails, and other error checking, which Loker noted are issues that can lead to outages in the real world. When it came to security, the most common mistake by AI was improper password handling and insecure object references, Loker noted, with security issues 2.74 times more common in AI code than that written by humans. Another major difference between AI code and human written-code was readability. "AI-produced code often looks consistent but violates local patterns around naming, clarity, and structure," Loker added.


Identity risk is changing faster than most security teams expect

Two forces are expected to influence trust systems in 2026. The first is the rise of autonomous AI agents. These agents run onboarding attempts, learn from rejection, and retry with improved tactics. Their speed compresses the window for detecting weaknesses and demands faster defensive responses. The second force comes from the long tail of quantum disruption. Growing quantum capability is putting pressure on classical cryptographic methods, which lose strength once computation reaches certain thresholds. Data encrypted today can be harvested and unlocked in the future. In response, some organizations are adopting quantum resilient hashing and beginning the transition toward post quantum cryptography that can withstand newer forms of computational power. ... A three part structure is emerging as a practical response. Hashing establishes integrity that cannot be altered. Encryption protects data while standards evolve. Predictive analysis identifies early drift and synthetic behavior before it scales. Together these elements support a continuous trust posture that strengthens as it absorbs more identity events. This model also addresses rising threats such as presentation spoofing, identity drift, and credential replay. All three are expected to increase in 2026 based on observed anomaly patterns. Since these vectors rely on repeated behaviors, long term monitoring is essential.


D&O liability protection rising for security leaders — unless you’re a midtier CISO

CISOs have the potential for more than one safety net, the first of which is a company’s indemnification provisions — rules typically embedded in the company’s articles of incorporation and bylaws. “The language of a company’s indemnification provisions must be properly worded — typically achieved by the general counsel and a board vote — to provide indemnification for a CISO equal to every other director or officer of a company,” explains John Peterson of World Insurance Associates, a provider of employment practice liability insurance. The second safety net for a CISO is the D&O liability insurance policy procured by the CISO’s company through an insurance broker. Even when a company has D&O insurance in place, Peterson advises CISOs to review those policies to make sure they are covered as an “insured person.” ... While enterprise CISOs often have access to legal teams and crisis PR advisors to help shield them, a midrange firm often has one or two people — possibly more — wearing multiple hats, like compliance, IT, and security all rolled into one. This can become an issue because “regulators, customers, and even the courts won’t lower the expectations just because the company is smaller,” Bagnall says. “Without legal protection, CISOs face significant personal and professional risk,” Bagnall said. 


The CIO Conundrum: Balancing Security and Innovation in the Age of AI SaaS

AI tools are now accessible, inexpensive, and often solve workflow friction that teams have lived with for years. The business is moving fast because the barrier to entry is low. This pace raises important questions for CIOs:Are we creating unnecessary friction where teams expect velocity? Have we made the “right path” faster than the workaround? Do our processes match how people work today? Shadow IT grows when official paths feel slow or unclear. Not because teams want to hide things, but because they feel innovation can’t wait. Governance must evolve to match that reality. ... Security should accelerate productivity, not constrain it. With strong identity controls, clear data boundaries, and automated configuration standards, we can introduce new tools without adding friction. These guardrails reduce the workload on security teams and create a predictable environment for employees. The business moves faster. IT gains visibility. The organization avoids the drift that creates risk and inefficiency. ... The question isn’t whether teams will continue exploring new tools, it’s whether we provide a responsible, scalable path forward. When intake is transparent, vetting is calibrated, and guardrails are embedded, the organization can innovate with confidence. The CIO’s job is to design frameworks that keep pace with the business, not frameworks the business waits on.


From hype to reality: The three forces defining security in 2026

Organisations should stop asking “what might agentic AI do” and start identifying the repeatable security workflows they want automated; for example: incident triage, patrol optimisation, evidence packaging; then measure agent performance against those KPIs. The winners in 2026 will be platforms that expose safe, auditable agent APIs and vendors who integrate them into end-to-end operational playbooks. ... Looking ahead, the widespread adoption of digital twins is poised to reshape the security industry’s approach to risk management and operational planning. With a unified, real-time view of complex environments, digital twins enable proactive decision-making, allowing security teams to anticipate threats, optimise resource allocation and continuously refine standard operating procedures. Over time, this capability will shift the industry from reactive incident response to predictive and preventative security strategies, where investment in training, infrastructure and technology is guided through simulated outcomes rather than historical events. ... AR and wearables have had turbulent history, but their resurgence in 2026 will be different — and AI is the reason. AI transforms wearables from simple capture devices into intelligent companions. It elevates AR from a visual overlay to a real-time, context-aware guidance layer. 

Daily Tech Digest - February 18, 2024

Remote Leadership Strategies for Sustained Engagement

The leaders foresee a future where AI and collaboration technologies continue to reduce the friction of remote working and increase collaboration in the virtual world. “With the release of solutions such as Apple Vision, this will be the start of truly immersive remote leadership and collaboration that is both inclusive and focussed on employee wellbeing,” Boast says. “All this said, I hope we continue to make an effort to meet in person periodically to refresh and renew connections.” For Ratnavira, leaders have a critical role in fostering trust, continuous communication, and feedback, which is key to unlocking the full potential of a remote workforce and building high-performance teams. “A culture-first organization intuitively figures remote work because there is a lot of trust placed in individuals and investment made in their overall growth,” says Sambandam. Remote work models have proven that success can thrive in this transformative approach. “What was once the ‘new normal’ is now etched into the fabric of our operations,” he adds. “This isn’t a temporary shift; it’s a paradigm shift with no point of return.”


The Rise of Small Language Models

Small language models are essentially more streamlined versions of LLMs, in regards to the size of their neural networks, and simpler architectures. Compared to LLMs, SLMs have fewer parameters and don’t need as much data and time to be trained — think minutes or a few hours of training time, versus many hours to even days to train a LLM. Because of their smaller size, SLMs are therefore generally more efficient and more straightforward to implement on-site, or on smaller devices. Moreover, because SLMs can be tailored to more narrow and specific applications, that makes them more practical for companies that require a language model that is trained on more limited datasets, and can be fine-tuned for a particular domain. Additionally, SLMs can be customized to meet an organization’s specific requirements for security and privacy. Thanks to their smaller codebases, the relative simplicity of SLMs also reduces their vulnerability to malicious attacks by minimizing potential surfaces for security breaches. On the flip side, the increased efficiency and agility of SLMs may translate to slightly reduced language processing abilities, depending on the benchmarks the model is being measured against.


Why software 'security debt' is becoming a serious problem for developers

Larger tech enterprises appear to be the most likely to have critical levels of security debt, according to the report, with over three times as many large tech firms found to have critical security debt compared to government organizations. The flaws that make up this debt were found in both the first-party code and third party application code taken from open source libraries, for example. The study found nearly two-thirds (63%) of the applications scanned had flaws in the first-party code, compared to 70% that had flaws in their third-party code. ... Eng’s advice for reducing security debt caused by flaws in first party code is to better integrate security testing into the entire software development lifecycle (SDLC) to ensure devs catch issues earlier in the process. If developers are forced to carry out security testing before they can merge new code into the main repository, this would go a long way in reducing flaws in first party code, Eng argued. But, Eng noted, this is not how the majority of businesses operate their development teams. “The problem is not every company is doing security testing at that level of granularity. 


Mythbust Your Way to Modern Data Management

Enterprises often believe there is one path for data compression. They may think that data compression is done exclusively in software on the host CPU. Because the CPU does the processing, there is the risk of a performance penalty under load, making it a non-starter for critical performance workloads. In the same way, the data pipeline within your organization is unique and tailored to your requirements, and architecting how data flows offers plenty of options. Data compression can be done in many ways, and the outcomes of choosing how and where compression should be processed can lead to benefits that cascade throughout the architecture. ... How can you improve the overall cost of ownership of your infrastructure? How can you increase storage and performance while decreasing power consumption? How can you make the data center more sustainable? When organizations try to solve these sorts of problems, data compression may not immediately leap to mind as the answer. Data compression doesn’t get more attention because organizations simply aren’t thinking about it as a problem-solving tool. This becomes clear when you look at search trends related to data and see that “enterprise data compression” is orders of magnitude lower down the results than something like “data management.”


Want to be a data scientist? Do these 4 things, according to business leaders

"You have to try new tech continuously," he says. "Don't hesitate to use generative AI to help you complete your job. Now, you can write code by saying to a model, 'Okay, write me something that does this.' So, be open -- embrace the tech. I think that's important." Martin says that he's not your typical chief data officer (CDO). Rather than just focusing on leadership concerns, he still gets his hands dirty with code -- and he advises up-and-coming data talent to do the same. "It's important if you want to get ahead that you understand what you're doing and that you're playing with tech," he says. "It gives me an edge, especially in mathematics and data science. I know about statistics, and I can build models myself." ... "While we can talk about math expertise, which is important because you need some level of academic capability, I think more important than that, certainly when I'm recruiting, is that I'm looking for the rounded individual," he says. "The straight A-grade student is great, but that person might not always be the best fit, because they've got to manage their time, they need to interact with the business, and they need to go and talk with stakeholders from across the business."


The best part of working in data and AI is the constant change

AI and analytics is such a vast field today that it gives people the freedom to chart their own course. You can choose to deep dive into an area of data – such as data governance, data management, data privacy, or become a data scientist working with ML models. You can take on the more technical roles of data engineering, data architecture, or take a more holistic advisory role in consulting the client on their end-to-end data and AI strategy. You can choose to work for a consulting firm like Accenture and help solve problems for clients across industries or be part of an organisation’s internal data teams. The field of AI and analytics offers many career paths and is only going to grow as we head towards a future underpinned by data and AI. ... While technical skills underpin many roles in the space and should be developed consistently, logical reasoning, strategic thinking, industry knowledge etc, play an important part as well. My advice is to build a network of mentors and peers who can be your guides in your career journey. The support and wisdom of those who have walked this path before can be invaluable. But, equally, trust your unique perspective and voice. Your diversity of thought is a strength that will set you apart.


A quantum-safe cryptography DNSSEC testbed

In the context of the DNS, DNSSEC may no longer guarantee authentication and integrity when powerful quantum computers become available. For the end user, this means that they can no longer be sure that when they browse to example.nl they will end up at the correct website (spoofing). They may also receive more spam and phishing emails since modern email security protocols rely on DNSSEC as well. Fortunately, cryptographers are working on creating cryptographic algorithms resistant to quantum computer attacks — so-called quantum-safe cryptographic algorithms. However, those quantum-safe algorithms often have very different characteristics than their non-quantum-safe counterparts, such as signature sizes, computation time requirements, memory requirements and, in some cases, key management requirements. As a consequence, those quantum-safe algorithms are not drop-in replacements for today’s algorithms. For DNSSEC, it is already known that there are stringent requirements when it comes to, for example, signature sizes and validation speed. But other factors, such as the size of the zone file, also have implications for the suitability of algorithms.


Someone had to say it: Scientists propose AI apocalypse kill switches

In theory, this could allow watchdogs to respond faster to abuses of sensitive technologies by cutting off access to chips remotely, but the authors warn that doing so isn't without risk. The implication being, if implemented incorrectly, that such a kill switch could become a target for cybercriminals to exploit. Another proposal would require multiple parties to sign off on potentially risky AI training tasks before they can be deployed at scale. "Nuclear weapons use similar mechanisms called permissive action links," they wrote. For nuclear weapons, these security locks are designed to prevent one person from going rogue and launching a first strike. For AI however, the idea is that if an individual or company wanted to train a model over a certain threshold in the cloud, they'd first need to get authorization to do so. Though a potent tool, the researchers observe that this could backfire by preventing the development of desirable AI. The argument seems to be that while the use of nuclear weapons has a pretty clear-cut outcome, AI isn't always so black and white. But if this feels a little too dystopian for your tastes, the paper dedicates an entire section to reallocating AI resources for the betterment of society as a whole.


Cloud mastery is a journey

A secure foundation is required for developing an enterprise’s strong digital immunity. This entails various aspects like safeguarding against hackers, disaster recovery strategies, and designing robust systems. Enterprises employ the defense-in-depth approach for protection against hackers. It means that every element of an IT environment should be built robustly and securely. For this, a few practical strategies include employing AI-powered firewalls, System Information Event Management, strong identity authentication, antivirus tools, vulnerability management, and teams of ethical hackers for simulated attacks. The cloud can be a powerful asset for building backup systems and disaster recovery plans. These are critical to combat potential data center failures caused by an event like a storm, fire or electrical outage. Focusing on resilience is equally important and extends beyond robust software. Resiliency means addressing every possible failure and threat in securing and maintaining the availability of systems, data and networks. For example, failures in services like firewalls and content distribution networks might be rare but are plausible. 


It’s Time to End the Myth of Untouchable Mainframe Security.

It is critical for mainframe security to re-enter the cybersecurity conversation, and that starts with doing away with commonly held misconceptions. First is the mistaken belief that due to their mature or streamlined architecture with fewer vulnerabilities, mainframes are virtually impervious to hackers. There is the misconception that they exist in isolation within the enterprise IT framework, disconnected from the external world where genuine threats lurk. And then there’s the age factor. People newer to the profession have relatively little experience with mainframe systems when compared to their more experienced counterparts and will tend to not question their viewpoints or approaches of their leaders or senior team members. This state of affairs can’t continue. In the contemporary landscape, modern mainframes are routinely accessed by employees and are intricately linked to applications that encompass a wide array of functions, ranging from processing e-commerce transactions to facilitating personal banking services. The implications of a breach can’t be overstated. 



Quote for the day:

"When you do what you fear most, then you can do anything." -- Stephen Richards