Daily Tech Digest by Kannan Subbiah

Daily Tech Digest - November 05, 2025

Quote for the day:

"Effective leaders know that resources are never the problem; it's always a matter of resourfulness." -- Tony Robbins

AI web browsers are cool, helpful, and utterly untrustworthy

AI browsers can and do interact with everything on a web page: summarizing content, reading emails, composing posts, looking at images, etc., etc. Every element on the page, whether you can see it or not, can hide an attack. A hacker can embed clipboard manipulations or other hacks that traditional browsers would never, not ever, execute automatically. ... AI browser agents can be tricked by hidden instructions embedded in websites via invisible text, images, scripts, or, believe it or not, bad grammar. Your eyes might glaze over at a long run-on sentence, but your AI web browser will read it all, including instructions for an attack hidden in plain sight within it. Such malicious commands are read and executed by the AI. This can lead to exposure of sensitive data, such as emails, authentication tokens, and login details, or triggering unwanted actions, including sending emails, posting to social media, or giving your computer a bad case of malware. ... Privacy is pretty much lost these days anyway, but with AI web browsers, we’ll have all the privacy of a goldfish in a bowl. Since AI browsers monitor our every last move, they process much more granular personal information than conventional browsers. Worrying about cookies and privacy is so 1990s. AI browsers track everything. This is then used to create highly detailed behavioral profiles. What? You didn’t know that AI browsers have built-in memory functions that retain your interactions, browser history, and content from other apps? How do you think they do what they do? Intuition? ESP?

AI can flag the risk, but only humans can close the loop

Companies embedding AI into vendor risk processes need governance structures that ensure transparency, accountability, and compliance. This includes maintaining an approved sources catalogue and requiring either the system or an analyst to validate findings and document the rationale behind them. Data minimization should be built into the design by defining what information is always in scope, such as sanctions or embargo lists, and what is contextually relevant, while excluding protected or sensitive attributes under GDPR and configuring AI to ignore them. Risk assessments should be tiered, calibrating the depth of checks to supplier criticality and geography to avoid unnecessary data collection for low-risk relationships while expanding scope for high-risk scenarios. Human accountability remains essential, with a named individual owning due diligence decisions while AI provides recommendations without replacing human judgment ... Regulators are likely to allow AI use if firms establish strong controls and demonstrate effective oversight, as required by frameworks like the EU AI Act. Responsibility remains with individuals or organizations; liability does not transfer to AI itself. While regulators may struggle to specify detailed technical rules, one clear shift is that “the data volume was too large to review” will no longer be an acceptable defense.

10 top devops practices no one is talking about

“A key, yet overlooked, devops practice is building true shared ownership, which means more than just putting teams in the same chat room,” says Chris Hendrich, associate CTO of AppMod at SADA. “It requires making production reliability and performance a primary success indicator for development, not solely an operational concern. This shared accountability is what builds the organizational competency of creating better, more resilient products.” ... “Baking an integrated code quality and code security approach into your devops workflow isn’t just good practice, it’s essential and a game-changer,” says Donald Fischer, VP at Sonar. “Tackling security alongside quality from day one isn’t merely about early bug detection; it’s about building fundamentally stronger, more trustworthy, and resilient software that is secure by design.” ... “Open source is a no-brainer for developers, but as the ecosystem grows, so do the risks of malware, unsafe AI models, license issues, outdated packages, poor performance, and missing features,” says Mitchell Johnson, CPDO of Sonatype. “Modern devops teams need visibility into what’s getting pulled in, not just to stay secure and compliant, but to make sure they’re building with high-quality components.” ... “Version-controlling database schemas and configurations across development, QA, and production is a quietly powerful devops practice,” says McMillan.

Cloud Identity Exposure Is 'a Critical Point of Failure'

Attackers keep targeting cloud-based identities to help them bypass endpoint and network defenses, says an August report from cybersecurity firm CrowdStrike. That report counts a 136% increase in cloud intrusions over the preceding 12 months, plus a 40% year-on-year increase in cloud intrusions tied to threat actors likely working for the Chinese government. "The cloud is a priority target for both criminals and nation-state threat actors," said Adam Meyers, head of counter adversary operations at CrowdStrike ... One challenge is that enough cloud identities justify elevated permissions, putting organizations at elevated risk when their credentials are exposed. Take security operations centers and incident response teams. In general, while "the principle of least privilege and minimal manual access" is a best practice, first responders often need immediate and "necessary access," says an August report from Darktrace. "Security teams need access to logs, snapshots and configuration data to understand how an attack unfolded, but giving blanket access opens the door to insider threats, misconfigurations and lateral movement." Rather than always allowing such access, experts recommend using tools that only provide it when needed, for example, through Amazon Web Services' Security Token Service. "Leveraging temporary credentials, such as AWS STS tokens, allows for just-in-time access during an investigation" that can be automatically revoked after, which "reduces the window of opportunity for potential attackers to exploit elevated permissions," Darktrace said.

How Software Development Teams Can Securely and Ethically Deploy AI Tools

Clearly, there is a danger that teams will trust AI too much, as these tools lack a command of the often nuanced context to recognize complex vulnerabilities. They may not fully grasp an application’s authentication or authorization framework, potentially leading to the omission of critical checks. If developers reach a state of complacency in their vigilance, the potential for such risks will only increase. ... Beyond security, team leaders and members must focus more on ethical and even legal considerations: Nearly one-half of software engineers are facing legal, compliance and ethical challenges in deploying AI, according the The AI Impact Report 2025 from LeadDev. The ethical/legal scenarios can take on a highly perplexing nature: A human engineer can read, learn from and write original code from an open-source library. But if an LLM does the same thing, it can be accused of engaging in derivative practices. What’s more, the current legal picture is a murky work in progress. Given the still-evolving judicial conclusions and guidelines, those using third-party AI tools need to ensure they are properly indemnified from potential copyright infringement liability, according to Ropes & Gray, a global law firm that advises clients on intellectual property and data matters. “Risk allocation in contracts concerning or contemplating AI models should be approached very carefully,” according to the firm.

How AI is Revolutionising RegTech and Compliance

Traditional approaches are failing, overwhelmed by increasing regulatory complexity and cross-border requirements. Enter RegTech: a technological revolution transforming how institutions manage regulatory obligations. Advanced artificial intelligence systems now predict compliance breaches weeks before they occur, while blockchain platforms create tamper-proof audit trails that streamline regulatory examinations. ... Natural language processing interprets complex regulatory documents automatically, updating compliance procedures within minutes of regulatory changes. Smart contracts execute compliance actions without human intervention, ensuring consistent adherence to evolving requirements. Leading institutions are achieving remarkable results. Barclays reduced regulatory document processing time from days to minutes using AI-powered analysis. JPMorgan's blockchain settlement system maintains compliance across multiple jurisdictions simultaneously. ... Regulatory-as-a-Service models are democratising access to sophisticated compliance capabilities. Smaller institutions can now access enterprise-grade RegTech through subscription services, reducing compliance costs by up to 50% whilst improving regulatory coverage. Challenges remain significant. Data privacy concerns intensify as compliance systems process vast quantities of sensitive information. Regulatory fragmentation across jurisdictions complicates platform development.

CEOs Go All-In on AI, But Talent Isn't Ready

Despite the enthusiasm for AI, workforce readiness is still a critical concern. Approximately 74% of Indian CEOs see AI talent readiness as a determinant of their company's future success, yet 34% admit to a widening skills gap. This talent gap is multifaceted; it's not only technical proficiency that's in short supply, but also expertise in blending data science with ethics, regulatory understanding and business acumen. About 26% struggle to find candidates who balance technical skill with collaboration capabilities. ... Regulatory uncertainty still weighs heavily on CEOs' minds, with nearly half of Indian CEOs awaiting clearer regulatory guidance before pushing bold innovation initiatives, compared to only 39% globally. This cautious stance underlines a pragmatic approach to integrating AI amid evolving governance landscapes. About 76% of Indian CEOs worry that slow AI regulation progress could hinder organizational success. Ethical concerns also loom large: 62% of Indian CEOs cite them as significant barriers, slightly higher than the 59% global average, underscoring the importance of embedding trust and governance frameworks alongside technological investments. "This is why culture and leadership are very important. The board of directors must have a degree of AI literacy. There must be psychological safety in the organization. Employees must feel safe and if there's clear governance, it means there is a proactive suggestion to use sanctioned AI that meets security requirements," John Barker

Powering financial services innovation: The critical role of colocation

As AI continues to evolve, its impact on financial services is becoming both broader and deeper – moving beyond high-level innovation into the operational core of the enterprise. Today’s financial institutions face a dual mandate: to accelerate AI adoption in pursuit of competitive advantage, and to do so within the constraints of an increasingly complex digital and regulatory environment. From risk modelling and fraud prevention to real-time analytics and customer personalization, AI is being embedded into mission-critical functions. Realising its full potential, however, isn't solely a matter of algorithms – it hinges on having a data-first strategy, with the right infrastructure and governance in place. ... With exponential data growth presenting challenges, customers gain access to a secure, compliant, resilient, and performant foundation. This foundation enables the implementation of new technologies and seamless orchestration of data flows. Our goal is to simplify data management complexity and serve as the single, trusted, global data center partner for our customers. As organizations optimize their AI strategies, many are exploring cloud repatriation – the process of moving certain workloads from the cloud back to on-premises or colocation environments. This strategic move can be crucial for AI success, as it allows for better control over sensitive data, reduced latency, and improved performance for demanding AI workloads.

Measuring, Reporting, and Improving: Making Resilience Tangible and Accountable

A continuity plan sitting on a shelf provides little assurance of resilience. What matters is whether organizations can demonstrate their strategies work, they are tested, and corrective actions are tracked. Measurement transforms resilience from an abstract concept into quantifiable performance. ... Metrics ensure resilience is not left to chance or anecdote. They provide boards and regulators with evidence of progress, reinforcing accountability at the executive and governance levels. A resilience strategy that cannot be measured cannot be trusted. ... The first step in strengthening measurement is to define resilience key performance indicators (KPIs) and key risk indicators (KRIs). These metrics should evaluate outcomes rather than simply tracking activities, ensuring performance reflects actual readiness. ... Measurement alone is not enough without transparency. Organizations must establish reporting practices that make resilience performance visible to boards, regulators, and, when appropriate, customers. Sharing outcomes openly not only demonstrates accountability but also builds trust and credibility. ... One challenge organizations often encounter when measuring resilience is metric overload. In the effort to capture every detail, leaders may track too many indicators, creating complexity that dilutes focus and makes it difficult to interpret results.

Bridging the Gap: Why DevOps Teams Are Quietly Becoming the Front Line of Security

For experienced DevOps practitioners, the idea of shifting security left isn't new. Static analysis in CI/CD pipelines, dependency scanning, and Infrastructure as Code (IaC) validation have become the norm. What's changed more recently is the pressure to respond to security events operationally, in addition to preventing them during builds. DevOps teams are adjusting in very real ways. Many are building security context into their logging practices, ensuring that logs are structured for debugging, and also for investigation and audit. Others are automating triage for security alerts using the same mindset they've applied to performance monitoring and deployment pipelines. Perhaps most importantly, DevOps teams are often the first to respond when something unusual shows up in system logs or access patterns. ... Security can be a shared responsibility across teams as long as boundaries and expectations are set. DevOps teams are defining their role in security more clearly by, for example, determining what gets logged, what counts as an anomaly, and who owns the investigation. They're also setting expectations around incident escalation, CVE response timeframes, and compliance requirements. When these lines are clear, security becomes an integrated part of the workflow instead of an extra burden. ... For many DevOps teams, security is part of the daily reality. It comes as a series of small, increasingly frequent interruptions.

Daily Tech Digest - November 04, 2025

Quote for the day:

"Listen with curiosity, speak with honesty act with integrity." -- Roy T Bennett

What does aligning security to the business really mean?

“Alignment to me means that information security supports the strategy of the organization,” says Sattler, who also serves as a board director with the governance association . ... “It’s not enough to say it; you actually have to do it,” she explains. “There is a contingent of cybersecurity that sees itself as an island, implementing defense in depth in every corner of the organization, adopting all these frameworks and standards, but there is diminishing returns in doing that. So instead of saying, ‘This is our cybersecurity discipline and we’re doing all these things because the benchmarks tell us to,’ have to align their efforts to their organization’s business model.” ... To align, she says, security leaders must “know the objectives the business has and use those to shape strategy, whether it’s cost containment, going into new markets, adopting cloud. The playbook starts from understanding the organizational priorities and then layering in what threat actors are doing in that industry and what could go wrong, what is the risk we can live with, and understanding and articulating the business impact of security incidents.” ... “When security is not aligned, security is reacting to changes rather than shaping changes,” says . “But when security isn’t chasing the business it’s because it’s at the table from the beginning and is saying, ‘Here’s how I can help the business grow and grow securely.’”

CISO Burnout – Epidemic, Endemic, or Simply Inevitable?

“Burnout and are different conditions, though they can coexist and share some symptoms,” says . “The constant hypervigilance required in our roles can mirror PTSD symptoms, and some cyber security professionals do experience what could be considered from constantly dealing with the aftermath of cyber-attacks.” Experiencing trauma can make you more susceptible to burnout, and burnout can exacerbate existing trauma responses. “Both conditions are serious and treatable, but they require different approaches,” she suggests. And both are further complicated by , a characteristic that is particularly prevalent in cybersecurity, and especially among CISOs. ... “From my experience working with senior cyber security leaders,” she continues, “burnout also affects their ability to lead their teams effectively. They become less empathetic, more prone to micromanaging, and, ironically, more likely to create the very conditions that lead to burnout in their staff. The strategic thinking that makes a great CISO (the ability to see the big picture, anticipate threats, and balance risk with business needs) gets clouded by exhaustion and cynicism. Perhaps most dangerously, burned-out CISOs often develop tunnel vision, focusing obsessively on certain threats while missing others entirely. When the person responsible for an organization’s entire security posture is running on empty, everyone is at risk.”

Uncovering the risks of unmanaged identities

often operate independently, making it difficult to track and monitor their activities without a centralized management system. These agents can adapt and change their behavior autonomously, which complicates efforts to predict and control their actions. While performing their duties, AI agents can even spin up other models and agents that have access to valuable data. ... Unmanaged identities significantly expand the attack surface, providing more entry points for attackers. They are prime targets for credential theft, which can lead to lateral movement within an organization’s network. Forgotten or over-permissioned accounts can facilitate privilege escalation, allowing attackers to gain unauthorized access to sensitive data. have been linked to unmanaged identities, underscoring the critical need for effective identity management. ... Inefficient access management due to unmanaged identities increases IT overhead and complexity. Unauthorized access or accidental deletions can disrupt business operations, leading to breaches, financial losses, and diminished customer trust. ... Unmanaged identities present a clear and present danger to organizations. They increase the risk of security breaches, compliance failures, and operational disruptions. It is imperative for organizations to prioritize identity discovery and management as a core security practice.

Empowering Teams: Decentralizing Architectural Decision-Making

Decisions form the core of software architecture, and practicing software architecture means working with decisions. Software development itself represents a constant stream of decisions. In a decentralized decision-making process, everyone contributes to architectural decisions, from developers to architects. For this approach, identifying whether a decision is architecturally significant and will impact the system now or in the future matters more than who made the decision or how long it took. Recording architectural decisions captures the why behind every what, creating valuable context for future learning and shared understanding. ... Timing for seeking feedback or advice depends on the nature of the decision. For impactful decisions affecting multiple system parts, or when lacking business or technical knowledge, seeking advice during the decision-making process yields better results. ADRs are immutable documents; once marked as adopted, they cannot be changed. If a decision needs revision, the previous ADR is superseded and a new one created. ... From the program leadership perspective, watching teams make independent decisions felt like being the first test driver in a Tesla using autopilot and hoping to avoid crashing. Staying out of decisions required conscious effort to avoid undermining the advice process and resorting back to make the decisions for the team.

The Fractured Cloud: How CIOs Can Navigate Geopolitical and Regulatory Complexity

Initially, cloud environments were largely interchangeable from a governance, compliance, and security perspective. It didn't really matter exactly which cloud data center hosted an organization's workloads, or which jurisdiction the data center was located in. IT leaders had the luxury of choosing cloud platforms and regions based primarily on factors such as pricing and latency, without having to consider geopolitics or the global regulatory environment. Fast forward to the present, however, and planning a cloud architecture -- let alone evolving an existing cloud strategy in response to changing needs -- has become much more complex. ... During the past decade or so, a host of regulations have emerged that apply to specific jurisdictions, including the and California Public Records Act (CPRA). Regulations dealing with AI, which are just now coming online, are likely to add even more diversity as different states or countries introduce varying laws. ... A related issue is the increasing pressure organizations face surrounding , which refers to the practice of keeping data within a certain country or jurisdiction. Regulations require this in some cases. Even if they don't, businesses may voluntarily choose to ensure data localization for the purposes of improving workload performance, or to assure customers that their data never leaves their home region.

Let's Get Physical: A New Convergence for Electrical Grid Security

Power plants and transmission/distribution system operators () have long focused on maintaining uptime and enhancing the resilience of their services; keeping the lights on is always the goal. That's especially true as the past few years have seen the rise of OT/OT convergence, wherein formerly siloed equipment that runs physical processes for critical infrastructure (operational technology, or OT) has been hooked up to the IT network and the Internet in some cases, exposing it to more cyberthreats. Now, another type of convergence been forcing a new conversation. ... In this new world, both industry regulators and analysts, like those at Black & Veatch, are arguing the same point: that where once keeping the lights on might have just meant maintaining equipment and avoiding fallen trees, today's grid operators need a robust, integrated physical and cybersecurity strategy to maintain continuous service. ... an IT operation might primarily concern itself with firewalls, or network monitoring; but "in many cases, cyberattacks can often involve physical access to sites, whether by malicious insiders or unwitting employees and contractors. Understanding who is present on-site, when and why, is critical to investigating and mitigating attacks on operations," Bramson explains.

Was data mesh just a fad?

Data mesh architecture promised to solve these problems. A polar opposite approach from a , a data mesh gives the source team ownership of the data and the responsibility to distribute the dataset. Other teams access the data from the source system directly, rather than from a centralized data lake. The data mesh was designed to be everything that the data lake system wasn’t. ... But the excitement around data mesh didn’t last. Many users became frustrated. Beneath the surface, almost every bottleneck between data providers and data consumers became an implementation challenge. The thing is, the data mesh approach isn’t a once-and-done change, but a long-term commitment to prepare a data schema in a certain way. Although every source team owns their dataset, they must maintain a schema that allows downstream systems to read the data, rather than replicating it. ... No, data mesh is not a fad, nor is it the next big thing that will solve all of your data challenges. But data mesh can dramatically reduce data management overhead, and at the same time improve data quality, for many companies. In essence, data mesh is a shift in mindset, one that completely changes the way you view data. Teams must envision data as a product, continuously showing commitment for the source team to own the data set and discouraging duplication.

8 ways to make responsible AI part of your company's DNA

" is a team sport," the report's authors explain. "Clear roles and tight hand-offs are now essential to scale safely and confidently as AI adoption accelerates." To leverage the advantages of responsible AI, recommends rolling out AI applications within an operating structure with three "lines of defense." First line: Builds and operates responsibly. Second line: Reviews and governs. Third line: Assures and audits. ... "For tech leaders and managers, making sure AI is responsible starts with how it's built," Rohan Sen, principal for cyber, data, and tech risk with PwC US. "To build trust and scale AI safely, focus on embedding responsible AI into every stage of the AI development lifecycle, and involve key functions like cyber, data governance, privacy, and regulatory compliance," said Sen. ... "Start with a value statement around ethical use," said Logan. "From here, prioritize periodic audits and consider a steering committee that spans privacy, security, legal, IT, and procurement. Ongoing transparency and open communication are paramount so users know what's approved, what's pending, and what's prohibited. Additionally, investing in training can help reinforce compliance and ethical usage." ... Make it a priority to "continually discuss how to responsibly use AI to increase value for clients while ensuring that both data security and IP concerns are addressed," said Tony Morgan, senior engineer at Priority Designs.

Context Engineering: The Next Frontier in AI-Driven DevOps

Context Engineering represents a significant evolution from the early days of , which focused on crafting the perfect, isolated instruction for an AI model. Context engineering, in contrast, is about orchestrating the entire information ecosystem around the AI. It’s the difference between giving someone a map (prompt engineering) and providing them with a real-time GPS that has traffic updates, road closures, and understands your personal driving preferences. ... The core components of context engineering in a DevOps environment include: Dynamic Information Assembly: Aggregating data from a multitude of DevOps tools, including monitoring platforms, , and infrastructure as code (IaC) repositories. Multi-Source Integration: Connecting to APIs, databases, and internal documentation to create a comprehensive view of the entire system. Temporal Awareness: Understanding the history of changes, incidents, and performance to identify patterns and predict future outcomes. ... In a traditional setup, the CI/CD pipeline would run a standard set of tests. But with context engineering, a context-aware AI agent analyzes the change. It recognizes the high-risk nature of the code, cross-references it with a recent security audit that flagged a related library, and automatically triggers an extended security testing suite. It also notifies the security team for a priority review. This is a far cry from the old days of one-size-fits-all pipelines.

Drowning in Data? Here’s Why You Need to Ditch the Rowboat for an Aircraft Carrier

In an effort to stay afloat, many enterprises are trying to patch their systems with incremental upgrades. They add more cloud instances. They layer on external tools. They spin up new teams to manage increasingly fragmented stacks. But scaling up a fragile system doesn’t make it strong. It just makes the cracks bigger. ... The deeper issue is this: the dominant architecture most enterprises still rely on was designed over a decade ago. It served a world where workloads operated in gigabytes or single-digit terabytes. Today, companies are navigating hundreds of petabytes, yet many are still using infrastructure built for a far smaller scale. It’s no wonder the systems are buckling under the weight. ... As organizations reevaluate their data architectures, several priorities are coming into sharper focus: Reducing fragmentation by moving toward more unified environments, where systems work in concert rather than in silos. Improving performance and cost-efficiency not just through hardware, but through smarter architecture and workload optimization. Lowering latency for high-demand workloads like , AI, and real-time analytics, where speed directly impacts decision-making. Managing the energy consumption bottleneck in ways that align with both financial and sustainability goals. Ultimately, this shift is about enabling teams to go from playing defense (maintaining systems and containing cost) to playing offense with faster, more actionable insights.

Daily Tech Digest - November 03, 2025

Quote for the day:

"With the new day comes new strength and new thoughts." -- Eleanor Roosevelt

Smaller, Smarter, Faster: AI Will Scale Differently in 2026

"Technology leaders face a pivotal year in 2026, where disruption, innovation and risk are expanding at unprecedented speed," said Gene Alvarez, distinguished vice president analyst at . "The top strategic technology trends identified for 2026 are tightly interwoven and reflect the realities of an AI-powered, hyperconnected world where organizations must drive responsible innovation, operational excellence and digital trust." The centerpiece of that thesis is the pivot from large, general-purpose to domain-specific language models, or , and , , designed to execute and audit business workflows. DSLMs promise higher accuracy, lower downstream compliance risk and cheaper inference costs; MAS promise orchestration and scale. ... The back half of is a sober reminder of the price of admission. First is . This is the C-suite-level trend of yanking critical data and apps out of global public clouds and moving them to local or "sovereign" clouds. Driven by regulations like and fears over the , this market is exploding. Second, the security model is flipping. Gartner's predicts a massive shift, forecasting that 50% of IT security spending will move from "detection and response" to "proactive protection" by 2030, up from less than 5% in 2024.

Today’s security leaders must adopt an asymmetric mindset

We’ve built an unbalanced view of threats. We pour resources into the risks we know how to manage — firewalls, access control, guard contracts — while neglecting the ones that move fastest and cut deepest: hybrid, cross-domain, and narrative-driven threats. Consider the . State-linked actors compromised multiple U.S. telecom networks for nearly a year, breaching routers, core systems, and even . What began as a cyber incident rippled across national security. Or, the hybrid criminal case in which a fake recruiter on lured a corporate employee into downloading malware while coordinating physical intimidation. Digital, physical, and psychological tactics in one operation. ... Asymmetric actors win by exploiting tempo, surprise, and blind spots. As the former U.S. Army Asymmetric Warfare Group explained, its mission was to “identify critical asymmetric threats… through global first-hand observations,” enabling rapid adaptation in a shifting threat environment. That’s the same level of insight security leaders should demand whether from small teams or entire corporations. They don’t respect our categories. They will hit us digitally, physically, and reputationally in whatever sequence maximizes confusion and slows our response. They’ll use low-cost tools to cause high-cost damage: small moves, outsized effects.

Employees keep finding new ways around company access controls

AI, SaaS, and personal devices are changing how people get work done, but the tools that protect company systems have not kept up, according to . Tools like , , and no longer align with how employees and AI agents access data. The result is what researchers call the “,” a growing distance between what organizations think they can control and how employees and AI systems access company data. The survey tracks four areas where this gap is widening: AI governance, SaaS and , credentials, and endpoint security. Each shows the same pattern of rapid adoption and limited oversight. ... Organizations now rely on hundreds of cloud apps, most outside IT’s visibility. Over half of employees admit they have downloaded work tools without permission, often because approved options are slower or lack needed features. This behavior drives . 70% of security professionals say SSO tools are not a complete solution for securing identities. On average, only about two-thirds of enterprise apps sit behind SSO, leaving a large portion unmanaged. Offboarding gaps make the problem worse. 38% of employees say they have accessed a former employer’s account or data after leaving the company. ... remains the default control for company hardware, but security leaders see its limits. MDM tools do not adequately safeguard managed devices or ensure compliance.

Securing APIs at Scale: Threats, Testing, and Governance

must be approached as a fundamental element of the design and development process, rather than an afterthought or add-on. Many organizations fall short in this regard, assuming that security measures can be patched onto an existing system by deploying security devices like () at the perimeter. In reality, secure APIs begin with the first line of code, integrating security controls throughout the design lifecycle. Even minor security gaps can result in significant economic losses, legal repercussions, and long-term brand damage. Designing APIs with inadequate security practices introduces risks that compound over time, often becoming a time bomb for organizations. ... APIs are attractive targets for attackers because they expose business logic, data flows, and authentication mechanisms. According to , 94% of organizations experienced an API-related security incident in the past year. The threats facing APIs are constantly evolving, becoming more sophisticated and targeted. ... Given the complexity and scale of API ecosystems, a proactive and comprehensive testing strategy is crucial. Relying solely on manual testing is no longer sufficient; automation is key. ... Technical controls are vital, but without a strong governance framework, API security efforts can quickly unravel. Without governance, APIs become a “wild west” of inconsistent standards, duplicated efforts, and accidental exposure.

The Agentic evolution, How Autonomous AI is Re-Architecting the Enterprise

The rise of is leading to a new kind of enterprise that functions more like a living system. In this model, AI agents and humans work together as collaborators. The agents handle ongoing operations and optimize outcomes, while humans provide strategy, creativity, and oversight. Organizations that can successfully combine human intelligence with machine autonomy will lead the next era of business transformation. They will move faster, adapt quicker, and make better use of their data and resources. The Agentic Leap is not only about new technology; it represents a deeper change in how enterprises think and operate. It marks the beginning of organizations that are not only supported by AI but are actively driven and shaped by it. This traditional hierarchy of command is gradually evolving into a network of intelligent collaboration, where humans and AI systems continuously exchange information, refine strategies, and act with shared intent. In this model, humans and AI agents function as true partners. Agents operate as intelligent executors and problem-solvers, constantly monitoring data flows, identifying opportunities, and adapting operations in real time. They can handle repetitive, data-intensive tasks, freeing humans to focus on higher-order functions such as strategic planning, creative innovation, and ethical oversight. Humans, in turn, provide contextual understanding, emotional intelligence, and long-term vision qualities that anchor AI-driven actions in purpose and responsibility.

6 essential rules for unleashing AI on your software development process - and the No. 1 risk

"AI is not something you can pull out of your toolbox and expect magical things to happen," cautioned Andrew Kum-Seun, research director at Info-Tech Research Group. "At least, not right now. IT managers must be prepared to address the human, workflow, and technical implications that naturally come with AI while being honest about what AI can do today for their organization." In other words, get your AI implementation in order before you attempt to apply it to getting your software development in order. ... As is meant to maintain humanity in software development, AI needs to support this vision. This must be a core component of AI-driven Agile development as well. "If leaders are unable to bridge their intent for AI with the team's concerns, they will likely see improper use of AI and, perhaps, deliberate sabotage in its implementation," said Kum-Seun. Another important step is to "keep all AI explainable by ensuring the use of AI tools that clearly cite where their suggestions come from -- no black-box code that cannot be simply verified," said Sopuch. "Human oversight is a required step. AI can write and refactor code, but humans absolutely must approve merges, product pushes, or any exceptions. Everything in the process must be logged, including prompts, outputs, and approvals so that an audit can easily take place on demand."

The AWS outage post-mortem is more revealing in what it doesn’t say

When AWS suffered a series of cascading failures that crashed its systems for hours in late October, the industry was once again reminded of its extreme dependence on major . The incident also shed an uncomfortable light on how fragile these massive environments have become. In ’s detailed post-mortem report, the cloud giant detailed a vast array of delicate systems that keeps global operations functioning — at least, most of the time. ... “The outage exposed how deeply interdependent and fragile our systems have become. It doesn’t provide any confidence that it won’t happen again. ‘Improved safeguards’ and ‘better change management’ sound like procedural fixes, but they’re not proof of architectural resilience. If AWS wants to win back enterprise confidence, it needs to show hard evidence that one regional incident can’t cascade across its global network again. Right now, customers still carry most of that risk themselves.” ... Ellis agreed with others that AWS didn’t detail why this cascading failure happened on that day, which makes it difficult for enterprise IT executives to have high confidence that something similar won’t happen in a month. “They talked about what things failed and not what caused the failure. Typically, failures like this are caused by a change in the environment. Someone wrote a script and it changed something or they hit a threshold. It could have been as simple as a disk failure in one of the nodes. I tend to think it’s a scaling problem.”

Five Real-World Ways AI Can Boost Your Bank’s Operations

Use of artificial intelligence decisioning has already had time to prove itself, and the results have been strong, according to Daryl Jones, senior director. The fit varies from one institution to another, "but the lift, overall, has been unquestionable," said Jones. He said institutions using AI in lending decisions have generally seen healthy increases in approvals, with solid results. One caveat is that as aspects of loan decisions transition to AI, institutions have to be careful how human lenders influence the software development process. ... Technology has long been a mainstay for antifraud, according to John Meyer, managing director. "We’ve had machine learning algorithms since the 1990s," said Meyer, but today’s antifraud applications of AI go a step beyond. He explained that the old technology could evaluate a few data points "on day two," once the damage was already done. By contrast, AI-based techniques can screen and surface instances truly needing human evaluation, according to Meyer. Such applications include verifying that paper checks are genuine. Meyer noted that check fraud remains a significant issue for the banking industry in spite of the rise of digital transactions. ... Even in a modern banking office, documents can be a rat’s nest. "We had a client on the West Coast that wanted to centralize all of its operational documents," said Clio Silman, managing director.

Context engineering: Improving AI by moving beyond the prompt

It isn’t a new practice for developers of AI models to ingest various sources of information to train their tools to provide the best outputs, notes Neeraj Abhyankar, vice president of data and AI at , a digital product engineering firm. He defines the recently coined term as a strategic capability that shapes how AI systems interact with the broader enterprise. ... Context engineering will be critical for autonomous agents trusted to perform complex tasks on an organization’s behalf without errors, he adds. ... Context engineering is an “architectural shift” in how AI systems are built, adds Louis Landry, CTO at data analytics firm . “Early generative AI was stateless, handling isolated interactions where prompt engineering was sufficient,” he says. “However, autonomous agents are fundamentally different. They persist across multiple interactions, make sequential decisions, and operate with varying levels of human oversight.” He suggests that AI users are moving away from the approach of, “How do I ask this AI a question?” to “How do I build systems that continuously supply agents with the right operational context?” “The shift is toward context-aware agent architectures, especially as we move from simple task-based agents to autonomous agentic systems that make decisions, chain together complex workflows, and operate independently,” Landry adds.

India’s Search for Digital Sovereignty

states are seeking to impose varying degrees of control over the internet. Often, these manifest as restrictions on information flows, which have consequences for civil liberties such as speech, expression, dissent, and the exchange of ideas in society. And, in a time when both geopolitical and domestic actors, state and non-state alike, cynically exploit open societies to exacerbate polarization and dehumanization, calls for greater control might seem appealing. However, it is vital that attempts to curb the concentration of power and resources of one set of actors do not merely transfer those same powers to another set. On the contrary, the goal should be to dissipate dominance, in general. ... It is not that alternative pathways to reduce concentration do not exist. Free and open source software, though not without its own challenges, is an approach that many can choose. Kailash Nadh, one of the founders of the United Foundation, has argued that for India to achieve technological self-determination, it needed to “publicly acknowledge” FOSS, and invest “time, effort and resources into” it. In late August, perhaps in a nod to the , positioned itself as a “Strategic Asset for Governments and Enterprises Focused on Digital Sovereignty and Privacy.” When it comes to information distribution and consumption, decentralized social networks and ideas such as “” have existed for several years, but have yet to gain traction in India’s policy discourse.

Daily Tech Digest - November 02, 2025

Quote for the day:

“Identify your problems but give your power and energy to solutions.” -- Tony Robbins

AI Agents: Elevating Cyber Threat Intelligence to Autonomous Response

Embedded across the security stack, AI agents can ingest vast volumes of threat data, triage alerts, correlate intelligence, and distribute insights in real time. For instance, agents can automate threat triage by filtering out false positives and flagging high-priority threats based on severity and relevance, thereby refining threat intelligence. They also enrich threat intelligence by cross-referencing multiple data sources to add meaningful context and track Indicators of Behavior (IoBs) that might otherwise go unnoticed. ... A major challenge for security teams is the inherent complexity they face. Often, the issue isn’t a lack of data or tools, but rather a lack of understanding the relevancy, coordination, collaboration and contextual actioning. Threat intelligence is frequently fragmented across systems, teams, and workflows, creating blind spots, unknowns and delays that attackers can exploit. ... As enterprises evolve, they can transform from leveraging one model to another. Both approaches have value, but striking the right balance between integrating smarter tools and securing cyber threat intelligence depends on clearly defining responsibilities. For most, a hybrid model will be the best fit, allowing AI agents to scale routine tasks while keeping humans in control of complex, high-stakes decisions within the framework of smarter cyber threat intelligence.

The Future Of Leadership Is Human: Why Empathy Outweighs Authority

When employees feel understood and valued, their brains operate in a state conducive to creativity and problem-solving. Conversely, when they perceive threat or indifference from leadership, their cognitive resources shift to self-preservation, limiting their capacity for innovation and collaboration. ... Developing empathetic leadership requires intentional systems and cultural changes. At our company, we've implemented several practices that have transformed our leadership culture, drawing inspiration from organizations that are leading this shift. ... Skeptics often question whether empathetic leadership can coexist with aggressive business goals and competitive markets, but evidence suggests the opposite. Empathetic leadership enables more aggressive goals because it unlocks human potential in ways that authority alone cannot. When people feel genuinely valued and understood, they contribute discretionary effort, share innovative ideas and advocate for the organization in ways that drive measurable business results. ... These results didn't happen overnight; they required genuine commitment to changing how we interact with our team members daily. I've personally shifted from viewing my role as "providing answers" to "asking better questions." Instead of dictating solutions in meetings, I now spend more time understanding the challenges my team faces and creating space for them to develop solutions.

Why password controls still matter in cybersecurity

Despite all the advanced authentication technologies, passwords continue to be the primary way attackers move through corporate networks. That makes it more important than ever to ensure your organization employs robust password controls. Today's IT environments are a tangled web of systems that defy simple security solutions. On-premises servers, cloud platforms, and remote work setups each add another layer of complexity to password management. ... Legacy accounts are like forgotten spare keys hidden under old doormats, just waiting for someone to find them. Windows Active Directory domains, standalone systems, and specialized application accounts have become the digital equivalent of unlocked side doors that nobody remembers to check. These forgotten entry points are a hacker's dream, offering easy access to networks that think they're buttoned up tight. ... Risk-based authentication takes this a step further, dynamically assessing each password change request based on context like device, location, and user behavior. It's like having a digital bouncer that knows exactly who should and shouldn't get past the velvet rope. ... Passwords aren't going anywhere. They remain the fallback for even the most advanced authentication methods. By implementing intelligent, dynamic password controls, your organization can turn them from a constant security challenge into a resilient defense mechanism.

What most companies get wrong about AI—and how to fix it, explains Ahead’s CPO

Despite the hype, Supancich is realistic about where most companies stand in their AI journey. Many, she says, know they need to "do something" with AI but lack clarity on what that should be. For Supancich, the priority is mapping processes, identifying the best use cases, and going deep in targeted areas to build real capability, rather than spreading efforts too thin. At Ahead, this means investing in both internal transformation and external consulting capabilities. The company has made AI training mandatory for all employees, equipping them with practical skills and demystifying the technology. The response, she reports, has been overwhelmingly positive, with employees discovering new ways to enhance their work and add value. Supancich is also alert to the data and privacy implications of AI, working closely with the CIO to ensure that the organisation’s approach is both innovative and secure. ... Throughout the conversation, one theme recurs: the centrality of leadership in navigating the future of work. Supancich sees the CPO as both guardian and architect of culture, a strategic partner who must be deeply involved in every aspect of the business. The future belongs to those who can blend technical fluency with emotional intelligence, strategic acumen with a passion for people.

Bake Ruthless Compliance Into CI/CD Without Slowing Releases

Compliance breaks when we glue it onto the end of a release, or when it’s someone’s “side job” to assemble evidence after the fact. The fix is to treat controls as non-functional requirements with acceptance criteria, put those criteria into policy-as-code, and make pipelines refuse to ship when the criteria aren’t met. A second source of breakage is ambiguity about shared responsibility. We push to managed services, assume the provider “has it,” and then discover that logging, encryption, or key rotation was our part of the dance. Map what belongs to us versus the platform, and turn that into explicit checks. The third killer is evidence debt. If we can’t answer “who approved what, when, with what config and tests” in under five minutes, the debt collectors will arrive during audit season. ... Compliance isn’t a meeting; it’s a pipeline step. Our CI/CD pipelines generate the evidence we need while doing the work we already do: building, testing, signing, scanning, and shipping. We don’t rely on optional post-build scanners or a “security stage” we can skip under pressure. Instead, we make the happy path compliant by default and fail fast when something’s off. That means SBOMs built with every image, vulnerability scanning with defined SLAs, provenance signed and attached to artifacts, and deployment gates that verify attestations.

Inside AstraZeneca’s AI Strategy: CDO Brian Dummann on Innovation, Governance and Speed

“One of our core values as a company is innovation. Our business is wired to be curious — to push the boundaries of science. And to be pioneers in science, we’ve got to be pioneers in technology.” That curiosity has created a healthy tension between demand and delivery. “I’ve got a company full of employees outside of the IT organization who are thirsty to get their hands on data and AI tools,” he says. “It’s a blessing and a challenge. They want new models, new platforms, and they want them now. It’s never fast enough.” ... Empowering employees to innovate is one thing; enabling them to do it safely and quickly is another. That’s where AstraZeneca’s AI Accelerator comes in — a cross-functional initiative designed to shorten the time between idea and implementation. “The ultimate goal is to accelerate how we can experiment with AI and use it to innovate across all areas of our business,” he says. “We’ve built an AI Accelerator whose sole purpose is to work through how to accelerate the introduction of new technologies or quickly review use cases.” Legacy processes, once measured in weeks or months, now need to operate in hours or days. The AI Accelerator brings together technology, legal, compliance, and governance teams to streamline assessments and approvals. ... “We’re now putting a lot more decision-making in the hands of our employees and empowering them,” he says. “With great power comes greater responsibility.”

8 ways to help your teams build lasting responsible AI

"For tech leaders and managers, making sure AI is responsible starts with how it's built," Rohan Sen, principal for cyber, data, and tech risk with PwC US and co-author of the survey report, told ZDNET. "To build trust and scale AI safely, focus on embedding responsible AI into every stage of the AI development lifecycle, and involve key functions like cyber, data governance, privacy, and regulatory compliance," said Sen. "Embed governance early and continuously. ... "Start with a value statement around ethical use," said Logan. "From here, prioritize periodic audits and consider a steering committee that spans privacy, security, legal, IT, and procurement. Ongoing transparency and open communication are paramount so users know what's approved, what's pending, and what's prohibited. Additionally, investing in training can help reinforce compliance and ethical usage." ... "A new AI capability will be so exciting that projects will charge ahead to use it in production. The result is often a spectacular demo. Then things break when real users start to rely on it. Maybe there's the wrong kind of transparency gap. Maybe it's not clear who's accountable if you return something illegal. Take extra time for a risk map or check model explainability. The business loss from missing the initial deadline is nothing compared to correcting a broken rollout."

Rising Identity Crime Losses Take a Growing Emotional Toll

What is changing now is how easily attackers can operationalize personal information data, observed Henrique Teixeira, a senior vice president for strategy at Saviynt, an identity governance and access management company in El Segundo, Calif. “In a recent attack I personally experienced, a criminal logged into one of my accounts using stolen credentials and then launched a subscription bombing campaign, flooding my inbox with hundreds of fake mailing list signups to bury legitimate fraud alerts,” he told TechNewsWorld. ... Kevin Lee, senior vice president for trust and safety at Sift, a fraud-prevention company for digital businesses, in San Francisco, called the suicide numbers “stark and concerning.” “Part of what’s driving this is probably the sheer magnitude of the losses,” he told TechNewsWorld. “When people are losing $100,000 or even $1 million due to identity theft, they’re losing years of savings they’ve built up. The financial devastation is compounded by feelings of shame and embarrassment, which keep people from seeking help.” There’s also the repeat victimization factor, he added. “When someone gets hit once and then targeted again, it creates this sense of helplessness,” he explained. “They feel like they can’t protect themselves, and that vulnerability is deeply traumatic.” “The report shows that victims who reach out to the ITRC have lower rates of suicidal thoughts, which tells us that having support and resources makes a real difference,” he said.

The Learning Gap in Generative AI Deployment

The learning gap is best understood as the space between what organisations experiment with and what they are able to deploy and scale effectively. It is an organisational phenomenon, as much about culture, governance, and leadership as about technology. ... Beyond training, the learning gap is perpetuated by structural and organisational barriers. One critical factor is the absence of effective feedback mechanisms. Generative AI tools are most valuable when they evolve in response to human inputs, errors, and changing contexts. Without monitoring systems and structured feedback loops, AI deployments remain static, brittle, and context-blind. Organisations that do not track performance, error rates, or user corrections fail to create a continuous learning cycle, leaving both humans and machines in a state of stagnation. ... Closing the learning gap requires a shift in focus from technology to organisation. Pilots must be anchored in real business problems, with measurable objectives that align with workflow needs. Incremental, context-sensitive deployment allows organisations to refine AI applications in situ, providing both employees and AI systems the feedback necessary to improve over time. Small-scale success builds confidence, generates data for iteration, and lays the groundwork for broader adoption. Equally important is the creation of structured learning opportunities within operational contexts.

How to Integrate Quantum-Safe Security into Your DevOps Workflow

To ensure that your DevOps workflow holds up against quantum threats, you must secure the information at rest and in transit. Consider implementing quantum-resistant encryption for your backups, credentials, pipeline secrets, and even internal communications, so that even your most sensitive data transfers remain safe. Some organizations are even experimenting with quantum key distribution solutions to safeguard the most critical communications, while others are taking a hybrid approach combining encryption with post-quantum algorithms. If you often exchange build outputs, orchestration signals, and credentials in your communication, you are going to need all the security you can get. ... For smoother integration of post-quantum security protocols, DevOps teams must opt for a phased and crypto-agile strategy that lets them leverage their legacy and quantum-safe algorithms. Doing so can also help DevOps maintain interoperability and reduce any operational disruption. ... Quantum security is not a one-time undertaking and is a recurring initiative that requires consistent efforts and time from your end. As the standards for cyberattacks and cyberdefense evolve, monitoring and improving our quantum security protocols should be an important part of your security strategy. You can also enhance your dashboards with quantum-specific metrics, such as cryptographic events and anomalies in encrypted traffic.

Daily Tech Digest - November 01, 2025

Quote for the day:

"Definiteness of purpose is the starting point of all achievement." -- W. Clement Stone

How to Fix Decades of Technical Debt

Technical debt drains companies of time, money and even customers. It arises whenever speed is prioritized over quality in software development, often driven by the pressure to accelerate time to market. In such cases, immediate delivery takes precedence, while long-term sustainability is compromised. The Twitter Fail Whale incident between 2007 and 2012 is testimony to the adage: "Haste makes waste." ... Gartner says companies that learn to manage technical debt will achieve at least 50% faster service delivery times to the business. But organizations that fail to do this properly can expect higher operating expenses, reduced performance and a longer time to market. ... Experts say the blame for technical debt should not be put squarely on the IT department. There are other reasons, and other forms of debt that hold back innovation. In his blog post, Masoud Bahrami, independent software consultant and architect, prefers to use terms such as "system debt" and "business debt," arguing that technical debt does not necessarily stem from outdated code, as many people assume. "Calling it technical makes it sound like only developers are responsible. So calling it purely technical is misleading. Some people prefer terms like design debt, organizational debt or software obligations. Each emphasizes a different aspect, but at its core, it's about unaddressed compromises that make future work more expensive and risky," he said.

Modernizing Collaboration Tools: The Digital Backbone of Resilience

Resilience is not only about planning and governance—it depends on the tools that enable real-time communication and decision-making. Disruptions test not only continuity strategies but also the technology that supports them. If incident management platforms are inaccessible, workforce scheduling collapses, or communication channels fail, even well-prepared organizations may falter. ... Crisis response depends on speed. When platforms are not integrated, departments must pass information manually or through multiple channels. Each delay multiplies risks. For example, IT may detect ransomware but cannot quickly communicate containment status to executives. Without updates, communications teams may delay customer notifications, and legal teams may miss regulatory deadlines. In crises, minutes matter. ... Integration across functions is another essential requirement. Incident management platforms should not operate in silos but instead bring together IT alerts, HR notifications, supply chain updates, and corporate communications. When these inputs are consolidated into a centralized dashboard, the resilience council and crisis management teams can view the same data in real time. This eliminates the risk of misaligned responses, where one department may act on incomplete information while another is waiting for updates. A truly integrated platform creates a single source of truth for decision-making under pressure.

AI-powered bug hunting shakes up bounty industry — for better or worse

Security researchers turning to AI is creating a “firehose of noise, false positives, and duplicates,” according to Ollmann. “The future of security testing isn’t about managing a crowd of bug hunters finding duplicate and low-quality bugs; it’s about accessing on demand the best experts to find and fix exploitable vulnerabilities — as part of a continuous, programmatic, offensive security program,” Ollmann says. Trevor Horwitz, CISO at UK-based investment research platform TrustNet, adds: “The best results still come from people who know how to guide the tools. AI brings speed and scale, but human judgment is what turns output into impact.” ... As common vulnerability types like cross-site scripting (XSS) and SQL injection become easier to mitigate, organizations are shifting their focus and rewards toward findings that expose deeper systemic risk, including identity, access, and business logic flaws, according to HackerOne. HackerOne’s latest annual benchmark report shows that improper access control and insecure direct object reference (IDOR) vulnerabilities increased between 18% and 29% year over year, highlighting where both attackers and defenders are now concentrating their efforts. “The challenge for organizations in 2025 will be balancing speed, transparency, and trust: measuring crowdsourced offensive testing while maintaining responsible disclosure, fair payouts, and AI-augmented vulnerability report validation,” HackerOne’s Hazen concludes.

Achieving critical key performance indicators (KPIs) in data center operations

KPIs like PUE, uptime, and utilization once sufficed. But in today’s interconnected data center environments, they are no longer enough. Legacy DCIM systems measure what they can see – but not what matters. Their metrics are static, siloed, and reactive, failing to reflect the complex interplay between IT, facilities, sustainability, and service delivery. ... Organizations embracing UIIM and AI tools are witnessing measurable improvements in operational maturity: Manual audits are replaced by automated compliance checks; Capacity planning evolves from static spreadsheets to predictive, data-driven modeling; Service disruptions are mitigated by foresight, not firefighting. These are not theoretical gains. For example, a major international bank operating over 50 global data centers successfully transitioned from fragmented legacy DCIM tools to Rit Tech’s XpedITe platform. By unifying management across three continents, the bank reduced implementation timelines by up to three times, lowered energy and operational costs, and significantly improved regulatory readiness – all through centralized, real-time oversight. ... Enduring digital infrastructure thinks ahead – it anticipates demand, automates risk mitigation, and scales with confidence. For organizations navigating complex regulatory landscapes, emerging energy mandates, and AI-scale workloads, the choice is stark: evolve to intelligent infrastructure management, or accept the escalating cost of reactive operations.

Accelerating Zero Trust With AI: A Strategic Imperative for IT Leaders

Zero trust requires stringent access controls and continuous verification of identities and devices. Manually managing these policies in a dynamic IT environment is not only cumbersome but also prone to error. AI can automate policy enforcement, ensuring that access controls are consistently applied across the organization. ... Effective identity and access management is at the core of zero trust. AI can enhance IAM by providing continuous authentication and adaptive access controls. “AI-driven access control systems can dynamically set each user's access level through risk assessment in real-time,” according to the CSA report. Traditional IAM solutions often rely on static credentials, such as passwords, which can be easily compromised. ... AI provides advanced analytics capabilities that can transform raw data into actionable insights. In a zero-trust framework, these insights are invaluable for making informed security decisions. AI can correlate data from various sources — such as network logs, endpoint data and threat intelligence feeds — to provide a holistic view of an organization’s security posture. ... One of the most significant advantages of AI in a zero-trust context is its predictive capabilities. The CSA report notes that by analyzing historical data and identifying patterns, AI can predict potential security incidents before they occur. This proactive approach enables organizations to address vulnerabilities and threats in their early stages, reducing the likelihood of successful attacks.

Zombie Projects Rise Again to Undermine Security

"Unlike a human being, software doesn’t give up in frustration, or try to modify its approach, when it repeatedly fails at the same task," she wrote. Automation "is great when those renewals succeed, but it also means that forgotten clients and devices can continue requesting renewals unsuccessfully for months, or even years." To solve the problem, the organization has adopted rate limiting and will pause account-hostname pairs, immediately rejecting any requests for a renewal. ... Automation is key to tackling the issue of zombie services, devices, and code. Scanning the package manifests in software, for example, is not enough, because nearly two-thirds of vulnerabilities are transitive — they occur in software package imported by another software package. Scanning manifests only catches about 77% of dependencies, says Black Duck's McGuire. "Focus on components that are both outdated and contain high [or] critical-risk vulnerabilities — de-prioritize everything else," he says. "Institute a strict and regular update cadence for open source components — you need to treat the maintenance of a third-party library with the same rigor you treat your own code." AI poses an even more complex set of problems, says Tenable's Avni. For one, AI services span across a variety of endpoints. Some are software-as-a-service (SaaS), some are integrated into applications, and others are AI agents running on endpoints.

Are room-temperature superconductors finally within reach?

Predicting superconductivity -- especially in materials that could operate at higher temperatures -- has remained an unsolved challenge. Existing theories have long been considered accurate only for low-temperature superconductors, explained Zi-Kui Liu, a professor of materials science and engineering at Penn State. ... For decades, scientists have relied on the Bardeen-Cooper-Schrieffer (BCS) theory to describe how conventional superconductors function at extremely low temperatures. According to this theory, electrons move without resistance because of interactions with vibrations in the atomic lattice, called phonons. These interactions allow electrons to pair up into what are known as Cooper pairs, which move in sync through the material, avoiding atomic collisions and preventing energy loss as heat. ... The breakthrough centers on a concept called zentropy theory. This approach merges principles from statistical mechanics, which studies the collective behavior of many particles, with quantum physics and modern computational modeling. Zentropy theory links a material's electronic structure to how its properties change with temperature, revealing when it transitions from a superconducting to a non-superconducting state. To apply the theory, scientists must understand how a material behaves at absolute zero (zero Kelvin), the coldest temperature possible, where all atomic motion ceases.

Beyond Accidental Quality: Finding Hidden Bugs with Generative Testing

Automated tests are the cornerstone of modern software development. They ensure that every time we build new functionalities, we do not break existing features our users rely on. Traditionally, we tackle this with example-based tests. We list specific scenarios (or test cases) that verify the expected behaviour. In a banking application, we might write a test to assert that transferring $100 to a friend’s bank account changes their balance from $180 to $280. However, example-based tests have a critical flaw. The quality of our software depends on the examples in our test suites. This leaves out a class of scenarios that the authors of the test did not envision – the "unknown unknowns". Generative testing is a more robust method of testing software. It shifts our focus from enumerating examples to verifying the fundamental invariant properties of our system. ... generative tests try to break the property with randomized inputs. The goal is to ensure that invariants of the system are not violated for a wide variety of inputs. Essentially, it is a three step process:Given a property (aka invariant); Generate varying inputs; To find the smallest input for which the property does not hold. As opposed to traditional test cases, inputs that trigger a bug are not written in the test – they are found by the test engine. That is crucial because finding counter examples to code written by us is not easy or an accurate process. Some bugs simply hide in plain sight – even in basic arithmetic operations like addition.

Learning from the AWS outage: Actions and resources

Drawing on lessons from this and previous incidents, here are three essential steps every organization should take. First, review your architecture and deploy real redundancy. Leverage multiple availability zones within your primary cloud provider and seriously consider multiregion and even multicloud resilience for your most critical workloads. If your business cannot tolerate extended downtime, these investments are no longer optional. Second, review and update your incident response and disaster recovery plans. Theoretical processes aren’t enough. Regularly test and simulate outages at the technical and business process levels. Ensure that playbooks are accurate, roles and responsibilities are clear, and every team knows how to execute under stress. Fast, coordinated responses can make the difference between a brief disruption and a full-scale catastrophe. Third, understand your cloud contracts and SLAs and negotiate better terms if possible. Speak with your providers about custom agreements if your scale can justify them. Document outages carefully and file claims promptly. More importantly, factor the actual risks—not just the “guaranteed” uptime—into your business and customer SLAs. Cloud outages are no longer rare. As enterprises deepen their reliance on the cloud, the risks rise. The most resilient businesses will treat each outage as a crucial learning opportunity to strengthen both technical defenses and contractual agreements before the next problem occurs.

When AI Is the Reason for Mass Layoffs, How Must CIOs Respond?

CIOs may be tempted to try and protect their teams from future layoffs -- and this is a noble goal -- but Dontha and others warn that this focus is the wrong approach to the biggest question of working in the AI age. "Protecting people from AI isn't the answer; preparing them for AI is," Dontha said. "The CIO's job is to redeploy human talent toward high-value work, not preserve yesterday's org chart." ... When a company describes its layoffs as part of a redistribution of resources into AI, it shines a spotlight on its future AI performance. CIOs were already feeling the pressure to find productivity gains and cost savings through AI tools, but the stakes are now higher -- and very public. ... It's not just CIOs at the companies affected that may be feeling this pressure. Several industry experts described these layoffs as signposts for other organizations: That AI strategy needs an overhaul, and that there is a new operational model to test, with fewer layers, faster cycles, and more automation in the middle. While they could be interpreted as warning signs, Turner-Williams stressed that this isn't a time to panic. Instead, CIOs should use this as an opportunity to get proactive. ... On the opposite side, Linthicum advised leaders to resist the push to find quick wins. He observed that, for all the expectations and excitement around AI's impact, ROI is still quite elusive when it comes to AI projects.