Daily Tech Digest by Kannan Subbiah

Daily Tech Digest - February 05, 2026

Quote for the day:

"We don't grow when things are easy. We grow when we face challenges." -- Elizabeth McCormick

AI Rapidly Rendering Cyber Defenses Obsolete

“Most organizations still don’t have a complete inventory of where AI is running or what data it touches,” he continued. “We’re talking millions of unmanaged AI interactions and untold terabytes of potentially sensitive data flowing into systems that no one is monitoring. You don’t have to be a CISO to recognize the inherent risk in that.” “You’re ending up with AI everywhere and controls nowhere,” added Ryan McCurdy ... “The risk is not theoretical,” he declared. “When you can’t inventory where AI is running and what it’s touching, you can’t enforce policy or investigate incidents with confidence.” ... While AI security discussions often focus on hypothetical future threats, the report noted, Zscaler’s red team testing revealed a more immediate reality: when enterprise AI systems are tested under real adversarial conditions, they break almost immediately. “AI systems are compromised quickly because they rely on multiple permissions working together, whether those permissions are granted via service accounts or inherited from user-level access,” explained Sunil Gottumukkala ... “We’re seeing exposed model endpoints without proper authentication, prompt injection vulnerabilities, and insecure API integrations with excessive permissions,” he said. “Default configurations are being shipped straight to production. Ultimately, it’s a fresh new field, and everyone’s rushing to stake a claim, get their revenue up, and get to market fastest.”

Offensive Security: A Strategic Imperative for the Modern CISO

Rather than remaining in a reactive stance focused solely on known threats, modern CISOs are required to adopt a proactive and strategic approach. This evolution necessitates the integration of offensive security as an essential element of a comprehensive cybersecurity strategy, rather than viewing it as a specialized technical activity. Boards now expect CISOs to anticipate emerging threats, assess and quantify risks, and clearly demonstrate how security investments contribute to safeguarding revenue, reputation, and organizational resilience. ... Offensive security takes a different approach. Rather than simply responding to threats, it actively replicates real-world attacks to uncover vulnerabilities before cybercriminals exploit them. ... Offensive security is crucial for today’s CISOs, helping them go beyond checking boxes for compliance to actively discover, confirm, and measure security risks—such as financial loss, damage to reputation, and disruptions to operations. By mimicking actual cyberattacks, CISOs can turn technical vulnerabilities into business risks, allowing for smarter resource use, clearer communication with the board, and greater overall resilience. ... Chief Information Security Officers (CISOs) are frequently required to substantiate their budget requests with clear, empirical data. Offensive security plays a critical role in demonstrating whether security investments effectively mitigate risk. CISOs must provide evidence that tools, processes, and teams contribute measurable value.

Cyber Insights 2026: Cyberwar and Rising Nation State Threats

While both cyberwar and cyberwarfare will increase through 2026, cyberwarfare is likely to increase more dramatically. The difference between the two should not be gauged by damage, but by primary intent. This difference is important because criminal activity can harm a business or industry, while nation state activity can damage whole countries. It is the primary intent or motivation that separates the two. Cyberwar is primarily motivated by financial gain. Cyberwarfare is primarily motivated by political gain, which means it could be a nation or an ideologically motivated group. ... The ultimate purpose of nation state cyberwarfare is to prepare the battlefield for kinetic war. We saw this with increased Russian activity against Ukraine immediately before the 2022 invasion. Other nations are not yet (at least we hope not) generally using cyber to prepare the battlefield. But they are increasingly pre-positioning themselves within critical industries to be able to do so. This geopolitical incentive together with the cyberattack and cyber stealth capabilities afforded by advanced AI, suggests that nation state pre-positioning attacks will increase dramatically over the next few years. Pre-positioning is not new, but it will increase. ... “Geopolitics aside, we can expect acts of cyberwar to increase over the coming years in large part thanks to AI,” says Art Gilliand, CEO at Delinea.

Cybersecurity planning keeps moving toward whole-of-society models

Private companies own and operate large portions of national digital infrastructure. Telecommunications networks, cloud services, energy grids, hospitals, and financial platforms all rely on private management. National strategies therefore emphasize sustained engagement with industry and civil society. Governments typically use consultations, working groups, and sector forums to incorporate operational input. These mechanisms support realistic policy design and encourage adoption across sectors. Incentives, guidance, and shared tooling frequently accompany regulatory requirements to support compliance. ... Interagency coordination remains a recurring focus. Ownership of objectives reduces duplication and supports faster response during incidents. National strategies frequently group objectives by responsible agency to support accountability and execution. International coordination also features prominently. Cyber threats cross borders with ease, leading governments to engage through bilateral agreements, regional partnerships, and multilateral forums. Shared standards, reporting practices, and norms of behavior support interoperability across jurisdictions. ... Security operations centers serve as focal points for detection and response. Metrics tied to detection and triage performance support accountability and operational maturity.

Should I stay or should I go?

In the big picture, CISO roles are hard, and so the majority of CISOs switch jobs every two to three years or less. Lack of support from senior leadership and lack of budget commensurate with the organization’s size and industry are top reasons for this CISO churn, according to The life and times of cybersecurity professionals report from the ISSA. More specifically, CISOs leave on account of limited board engagement, high accountability with insufficient authority, executive misalignment, and ongoing barriers to implementing risk management and resilience, according to an ISSA spokesperson. ... A common red flag and reason CISO’s leave their jobs is because leadership is paying “lip service” to auditors, customers and competitors, says FinTech CISO Marius Poskus, a popular blogger on security leadership who posted an essay about resigning from “security‑theater roles.” ... the biggest red flag is when leadership pushes against your professional and personal ethics. For example, when a CEO or board wants to conceal compliance gaps, cover up reportable breaches, and refuse to sign off on responsibility for gaps and reporting failures they’ve been made aware of. ... “A lot of red flags have to do with lack of security culture or mismatch in understanding the risk tolerance of the company and what the actual risks are. This red flag goes beyond: If they don’t want to be questioned about what they’ve done so far, that is a huge red flag that they’re covering something up,” Kabir explains.

Preparing for the Unpredictable and Reshaping Disaster Recovery

When desktops live on physical devices alone, recovery can be slow. IT teams must reimage machines, restore applications, recover files, and verify security before employees can resume work. In industries where every hour of downtime has financial, operational, or even safety implications, that delay is costly. DaaS changes the equation. With cloud-based desktops, organizations can provision clean, standardized environments in minutes. If a device is compromised, employees can simply log in from another device and get back to work immediately. This eliminates many of the bottlenecks associated with endpoint recovery and gives organizations a faster, more controlled way to respond to cyber incidents. ... However, beyond these technical benefits, the shift to DaaS encourages organizations to adopt a more proactive, strategic mindset toward resilience. It allows teams to operate more flexibly, adapt to hybrid work models, and maintain continuity through a wider range of disruptions. ... DaaS offers a practical, future-ready way to achieve that goal. By making desktops portable, recoverable, and consistently accessible, it empowers organizations to maintain operations even when the unexpected occurs. In a world defined by unpredictability, businesses that embrace cloud-based desktop recovery are better positioned not just to withstand crises, but to move through them with agility and confidence.

From Alert Fatigue to Agent-Assisted Intelligent Observability

The maintenance burden grows with the system. Teams spend significant time just keeping their observability infrastructure current. New services need instrumentation. Dashboards need updates. Alert thresholds need tuning as traffic patterns shift. Dependencies change and monitoring needs to adapt. It is routine, but necessary work, and it consumes hours that could be used building features or improving reliability. A typical microservices architecture generates enormous volumes of telemetry data. Logs from dozens of services. Metrics from hundreds of containers. Traces spanning multiple systems. When an incident happens, engineers face a correlation problem. ... The shift to intelligent observability changes how engineering work gets done. Instead of spending the first twenty minutes of every incident manually correlating logs and metrics across dashboards, engineers can review AI-generated summaries that link deployment timing, error patterns, and infrastructure changes. Incident tickets are automatically populated with context. Root cause analysis, which used to require extensive investigation, now starts with a clear hypothesis. Engineers still make the decisions, but they are working from a foundation of analyzed data rather than raw signals. ... Systems are getting more complex, data volumes are increasing, and downtime is getting more expensive. Human brains aren't getting bigger or faster.

AI is collapsing the career ladder - 5 ways to reach that leadership role now

Barry Panayi, group chief data officer at insurance firm Howden, said one of the first steps for would-be executives is to make a name for themselves. ... "Experiencing something completely different from the day-to-day job is about understanding the business. I think that exposure is what gives me confidence to have opinions on topics outside of my lane," he said. "It's those kinds of opinions and contributions that get you noticed, not being a great data person, because people will assume you're good at that area. After all, that's why the board hired you." ... "Show that you understand the organization's wider strategy and how your role and the team you lead fit within that approach," he said. "It's also about thinking commercially -- being able to demonstrate that you understand how the operational decisions you make, in whatever aspect you're leading, impact top and bottom-line business value. Think like a business shareholder, not just a manager of your team." ... "Paying it forward is really important for the next generation," she said. "And as a leader, if you're not creating the next generation and the generation after that, what are you doing?" McCarroll said Helios Towers has a strong culture of promoting and developing talent from within, including certifying people in Lean Six Sigma through a leadership program with Cranfield University, partnering closely with the internal HR department, and developing regular succession planning opportunities.

Leadership Is More Than Thinking—It's Doing

Leadership, at its core, isn't a point of view; it's a daily practice. Being an effective leader requires more than being a thinker. It's also about being a doer—someone willing to translate conviction into conduct, values into decisions and belief into behavior. ... It's often inconsistency, not substantial failure, that erodes workplace culture. Employees don't want to hear from leaders only after a decision has already been made. Being a true leader requires knowing what aspects of our environment we're willing to risk before making any decision at all. ... Every time leaders postpone necessary conversations, tolerate misaligned behavior or choose convenience over courage, they incur what I call leadership debt. Like financial debt, it compounds quietly, and it's always paid—but rarely by the leader who incurred it. ... thinking strategically has never been more important. But it's not enough to thrive. Organizations with exceptional strategic clarity can still falter because leaders underestimate the "doing" aspect of change. They may communicate the vision eloquently, then fail to stay close to employees' lived experience as they try to deliver that vision. Meanwhile, teams can rise to meet extraordinary challenges when leaders are present. Listening deeply, acknowledging uncertainty and acting with transparency foster confidence and reassurance in employees.

AI Governance in 2026: Is Your Organization Ready?

In 2026, regulators and courts will begin clarifying responsibility when these systems act with limited human oversight. For CIOs, this means governance must move closer to runtime. This includes things like real-time monitoring, automated guardrails, and defined escalation paths when systems deviate from expected behavior. ... The EU AI Act’s high-risk obligations become fully applicable in August 2026. In parallel, U.S. state attorneys general are increasingly using consumer protection and discrimination statutes to pursue AI-related claims. Importantly, regulators are signaling that documentation gaps themselves may constitute violations. ... Models that can’t clearly justify outputs or demonstrate how bias and safety risks are managed face growing resistance, regardless of accuracy claims. This trend is reinforced by guidance from the National Academy of Medicine and ongoing FDA oversight of software-based medical devices. In 2026, governance in healthcare will no longer differentiate vendors; it will determine whether systems can be deployed at all. Leaders in other regulated industries should expect similar dynamics to emerge over the next year. ... “Governance debt” will become visible at the executive level. Organizations without consistent, auditable oversight across AI systems will face higher costs, whether through fines, forced system withdrawals, reputational damage, or legal fees.

Daily Tech Digest - February 04, 2026

Quote for the day:

"The struggle you're in today is developing the strength you need for tomorrow." -- Elizabeth McCormick

A deep technical dive into going fully passwordless in hybrid enterprise environments

Before we can talk about passwordless authentication, we need to address what I call the “prerequisite triangle”: cloud Kerberos trust, device registration and Conditional Access policies. Skip any one of these, and your migration will stall before it gains momentum. ... Once your prerequisites are in place, you face critical architectural decisions that will shape your deployment for years to come. The primary decision point is whether to use Windows Hello for Business, FIDO2 security keys or phone sign-in as your primary authentication mechanism. ... The architectural decision also includes determining how you handle legacy applications that still require passwords. Your options are limited: implement a passwordless-compatible application gateway, deprecate the application entirely or use Entra ID’s smart lockout and password protection features to reduce risk while you transition. ... Start with a pilot group — I recommend between 50 and 200 users who are willing to accept some friction in exchange for security improvements. This group should include IT staff and security-conscious users who can provide meaningful feedback without becoming frustrated with early-stage issues. ... Recovery mechanisms deserve special attention. What happens when a user’s device is stolen? What if the TPM fails? What if they forget their PIN and can’t reach your self-service portal? Document these scenarios and test them with your help desk before full rollout.

When Cloud Outages Ripple Across the Internet

For consumers, these outages are often experienced as an inconvenience, such as being unable to order food, stream content, or access online services. For businesses, however, the impact is far more severe. When an airline’s booking system goes offline, lost availability translates directly into lost revenue, reputational damage, and operational disruption. These incidents highlight that cloud outages affect far more than compute or networking. One of the most critical and impactful areas is identity. When authentication and authorization are disrupted, the result is not just downtime; it is a core operational and security incident. ... Cloud providers are not identity systems. But modern identity architectures are deeply dependent on cloud-hosted infrastructure and shared services. Even when an authentication service itself remains functional, failures elsewhere in the dependency chain can render identity flows unusable. ... High availability is widely implemented and absolutely necessary, but it is often insufficient for identity systems. Most high-availability designs focus on regional failover: a primary deployment in one region with a secondary in another. If one region fails, traffic shifts to the backup. This approach breaks down when failures affect shared or global services. If identity systems in multiple regions depend on the same cloud control plane, DNS provider, or managed database service, regional failover provides little protection. In these scenarios, the backup system fails for the same reasons as the primary.

The Art of Lean Governance: Elevating Reconciliation to Primary Control for Data Risk

In today's environment comprising of continuous data ecosystems, governance based on periodic inspection is misaligned with how data risk emerges. The central question for boards, regulators, auditors, and risk committees has shifted: Can the institution demonstrate at the moment data is used that it is accurate, complete, and controlled? Lean governance answers this question by elevating data reconciliation from a back-office cleanup activity to the primary control mechanism for data risk reduction. ... Data profiling can tell you that a value looks unusual within one system. It cannot tell you whether that value aligns with upstream sources, downstream consumers, or parallel representations elsewhere in the enterprise. ... Lean governance reframes governance as a continual process-control discipline rather than a documentation exercise. It borrows from established control theory: Quality is achieved by controlling the process, not by inspecting outputs after failures. Three principles define this approach: Data risk emerges continuously, not periodically; Controls must operate at the same cadence as data movement; and Reconciliation is the control that proves process integrity. ... Data profiling is inherently inward-looking. It evaluates distributions, ranges, patterns, and anomalies within a single dataset. This is useful for hygiene, but insufficient for assessing risk. Reconciliation is inherently relational. It validates consistency between systems, across transformations, and through the lifecycle of data.

Working with Code Assistants: The Skeleton Architecture

Critical non-functional requirements- such as security, scalability, performance, and authentication- are system-wide invariants that cannot be fragmented. If every vertical slice is tasked with implementing its own authorization stack or caching strategy, the result is "Governance Drift": inconsistent security postures and massive code redundancy. This necessitates a new unifying concept: The Skeleton and The Tissue. ... The Stable Skeleton represents the rigid, immutable structures (Abstract Base Classes, Interfaces, Security Contexts) defined by the human although possibly built by the AI. The Vertical Tissue consists of the isolated, implementation-heavy features (Concrete Classes, Business Logic) generated by the AI. This architecture draws on two classical approaches: actor models and object-oriented inversion of control. It is no surprise that some of the world’s most reliable software is written in Erlang, which utilizes actor models to maintain system stability. Similarly, in inversion of control structures, the interaction between slices is managed by abstract base classes, ensuring that concrete implementation classes depend on stable abstractions rather than the other way around. ... Prompts are soft; architecture is hard. Consequently, the developer must monitor the agent with extreme vigilance. ... To make the "Director" role scalable, we must establish "Hard Guardrails"- constraints baked into the system that are physically difficult for the AI to bypass. These act as the immutable laws of the application.

8-Minute Access: AI Accelerates Breach of AWS Environment

A threat actor gained initial access to the environment via credentials discovered in public Simple Storage Service (S3) buckets and then quickly escalated privileges during the attack, which moved laterally across 19 unique AWS principals, the Sysdig Threat Research Team (TRT) revealed in a report published Tuesday. ... While the speed and apparent use of AI were among the most notable aspects of the attack, the researchers also called out the way that the attacker accessed exposed credentials as a cautionary tale for organizations with cloud environments. Indeed, stolen credentials are often an attacker's initial access point to attack a cloud environment. "Leaving access keys in public buckets is a huge mistake," the researchers wrote. "Organizations should prefer IAM roles instead, which use temporary credentials. If they really want to leverage IAM users with long-term credentials, they should secure them and implement a periodic rotation." Moreover, the affected S3 buckets were named using common AI tool naming conventions, they noted. The attackers actively searched for these conventions during reconnaissance, enabling them to find the credentials quite easily, they said. ... During this privilege-escalation part of the attack — which took a mere eight minutes — the actor wrote code in Serbian, suggesting their origin. Moreover, the use of comments, comprehensive exception handling, and the speed at which the script was written "strongly suggests LLM generation," the researchers wrote.

Ask the Experts: The cloud cost reckoning

According to the 2025 Azul CIO Cloud Trends Survey & Report, 83% of the 300 CIOs surveyed are spending an average of 30% more than what they had anticipated for cloud infrastructure and applications; 43% said their CEOs or boards of directors had concerns about cloud spend. Moreover, 13% of surveyed CIOs said their infrastructure and application costs increased with their cloud deployments, and 7% said they saw no savings at all. Other surveys show CIOs are rethinking their cloud strategies, with "repatriation" -- moving workloads from the cloud back to on-premises -- emerging as a viable option due to mounting costs. ... "At Laserfiche we still have a hybrid environment. So we still have a colocation facility, where we house a lot of our compute equipment. And of course, because of that, we need a DR site because you never want to put all your eggs in that one colo. We also have a lot of SaaS services. We're in a hyperscaler environment for Laserfiche cloud. "But the reason why we do both is because it actually costs us less money to run our own compute in a data center colo environment than it does to be all in on cloud." ,,, "The primary reason why the [cloud] costs have been increasing is because our use of cloud services has become much more sophisticated and much more integrated. "But another reason cloud consumption has increased is we're not as diligent in managing our cloud resources in provisioning and maintaining."

NIST develops playbook for online use cases of digital credentials in financial services

The objective is to develop what a panel description calls a “playbook of standards and best practices that all parties can use to set a high bar for privacy and security.” “We really wanted to be able to understand, what does it actually take for an organization to implement this stuff? How does it fit into workflows? And then start to think as well about what are the benefits to these organizations and to individuals.” “The question became, what was the best online use case?” Galuzzo says. “At which point our colleagues in Treasury kind of said, hey, our online banking customer identification program, how do we make that both more usable and more secure at the same time? And it seemed like a really nice fit. So that brought us to both the kind of scope of what we’re focused on, those online components, and the specific use case of financial services as well.” ... The model, he says, “should allow you to engage remotely, to not have to worry about showing up in person to your closest branch, should allow for a reduction in human error from our side and should allow for reduction in fraud and concern over forged documents.” It should also serve to fulfil the bank’s KYC and related compliance requirements. Beyond the bank, the major objective with mDLs remains getting people to use them. The AAMVA’s Maru points to his agency’s digital trust service, and to its efforts in outreach and education – which are as important in driving adoption as anything on the technical side.

Designing for the unknown: How flexibility is reshaping data center design

Rapid advances in compute architectures – particularly GPUs and AI-oriented systems – are compressing technology cycles faster than many design and delivery processes can respond. In response, flexibility has shifted from a desirable feature to the core principle of successful data center design. This evolution is reshaping how we think about structure, power distribution, equipment procurement, spatial layout, and long-term operability. ... From a design perspective, this means planning for change across several layers: Structural systems that can accommodate higher equipment loads without reinforcement; Spatial layouts that allow reconfiguration of white space and service zones; and Distribution pathways that support future modifications without disrupting live operations. The objective is not to overbuild for every possible scenario, but to provide a framework that can absorb change efficiently and economically. ... Another emerging challenge is equipment lead time. While delivery periods vary by system, generators can now carry lead times approaching 12 months, particularly for higher capacities, while other major infrastructure components – including transformers, UPS modules, and switchgear – typically fall within the 30- to 40-week range. Delays in securing these items can introduce significant risk when procurement decisions are deferred until late in the design cycle.

Onboarding new AI hires calls for context engineering - here's your 3-step action plan

In the AI world, the institutional knowledge is called context. AI agents are the new rockstar employees. You can onboard them in minutes, not months. And the more context that you can provide them with, the better they can perform. Now, when you hear reports that AI agents perform better when they have accurate data, think more broadly than customer data. The data that AI needs to do the job effectively also includes the data that describes the institutional knowledge: context. ... Your employees are good at interpreting it and filling in the gaps using their judgment and applying institutional knowledge. AI agents can now parse unstructured data, but are not as good at applying judgment when there are conflicts, nuances, ambiguity, or omissions. This is why we get hallucinations. ... The process maps provide visibility into manual activities between applications or within applications. The accuracy and completeness of the documented process diagrams vary wildly. Front-office processes are generally very poor. Back-office processes in regulated industries are typically very good. And to exploit the power of AI agents, organizations need to streamline them and optimize their business processes. This has sparked a process reengineering revolution that mirrors the one in the 1990s. This time around, the level of detail required by AI agents is higher than for humans.

Q&A: How Can Trust be Built in Open Source Security?

The security industry has already seen examples in 2025 of bad actors deploying AI in cyberattacks – I’m concerned that 2026 could bring a Heartbleed- or Log4Shell-style incident involving AI. The pace at which these tools operate may outstrip the ability of defenders to keep up in real time. Another focus for the year ahead: how the Cyber Resilience Act (CRA) will begin to reshape global compliance expectations. Starting in September 2026, manufacturers and open source maintainers must report exploited vulnerabilities and breaches to the EU. This is another step closer to CRA enforcement and other countries like Japan, India and Korea are exploring similar legislation. ... The human side of security should really be addressed just as urgently as the technical side. The way forward involves education, tooling and cultural change. Resilient human defences start with education. Courses from the Linux Foundation like Developing Secure Software and Secure AI/ML‑Driven Software Development equip users with the mindset and skills to make better decisions in an AI‑enhanced world. Beyond formal training, reinforcing awareness creating a vigilant community is critical. The goal is to embed security into culture and processes so that it’s not easily overlooked when new technology or tools roll around. ... Maintainers and the community projects they lead are struggling without support from those that use their software.

Daily Tech Digest - February 03, 2026

Quote for the day:

"In my whole life, I have known no wise people who didn't read all the time, none, zero." -- Charlie Munger

How risk culture turns cyber teams predictive

Reactive teams don’t choose chaos. Chaos chooses them, one small compromise at a time. A rushed change goes in late Friday. A privileged account sticks around “temporarily” for months. A patch slips because the product has a deadline, and security feels like the polite guest at the table. A supplier gets fast-tracked, and nobody circles back. Each event seems manageable. Together, they create a pattern. The pattern is what burns you. Most teams drown in noise because they treat every alert as equal and security’s job. You never develop direction. You develop reflexes. ... We’ve seen teams with expensive tooling and miserable outcomes because engineers learned one lesson. “If I raise a risk, I’ll get punished, slowed down or ignored.” So they keep quiet, and you get surprised. We’ve also seen teams with average tooling but strong habits. They didn’t pretend risk was comfortable. They made it speakable. Speakable risk is the start of foresight. Foresight enables the right action or inaction to achieve the best result! ... Top teams collect near misses like pilots collect flight data. Not for blame. For pattern. A near miss is the attacker who almost got in. The bad change that almost made it into production. The vendor who nearly exposed a secret. The credential that nearly shipped in code. Most organizations throw these away. “No harm done.” Ticket closed. Then harm arrives later, wearing the same outfit.

Why CIOs are turning to digital twins to future-proof the supply chain

The ways in which digital twin models differ from traditional models are that they can be run as what-if scenarios and simulated by creating models based on cause-and-effect. Examples of this would include a demand increase in volume of supply chain product in a short time frame, or changes involving a facility shutting down because of severe weather conditions. The model will look at how this will affect a supply chain’s inventory levels, shipping schedule and delivery date, and even worker availability if any. All of this allows companies to move their decision-making process away from reactive firefighting to the more proactive planning process. For a CIO, using a digital twin model eliminates the historical siloing of enterprise architecture of supply chain-related data. ... Although the value of the digital twin technology is evident, scaling digital twins remains a significant challenge. Integration of data from multiple sources including ERP, WMS, IoT, and partner systems is a primary challenge for all. High fidelity simulation requires high computational capacity, which in turn requires trade-offs between realism, performance, and cost. There are also governance issues associated with digital twins. As digital twin models drift or are modified due to the physical state of the model changing, potential security vulnerabilities also increase as continuing data is streamed from cloud and edge environments.

Quantum computing is getting closer, but quantum-proof encryption remains elusive

“Everybody’s well into the belief that we’re within five years of this cryptocalypse,” says Blair Canavan, director of alliances for the PKI and PQC portfolio at Thales, a French multinational company that develops technologies for aerospace, defense, and digital security. “I see it and hear it in almost every circle.” Fortunately, we already have new, quantum-safe encryption technology. NIST released its fifth quantum-safe encryption algorithm in early 2025. The recommended strategy is to build encryption systems that make it easy to swap out algorithms if they become obsolete and new algorithms are invented. And there’s also regulatory pressure to act. ... CISA is due to release its PQC category list, which will establish PQC standards for data management, networking, and endpoint security. And early this year, the Trump administration is expected to release a six-pillar cybersecurity strategy document that includes post-quantum cryptography. But, according to the Post Quantum Cryptography Coalition’s state of quantum migration report, when it comes to public standards, there’s only one area in which we have broad adoption of post-quantum encryption, and that’s with TLS 1.3, and only with hybrid encryption — not pre or post quantum encryption or signatures. ... The single biggest driver for PQC adoption is contractual agreements with customers and partners, cited by 22% of respondents.

From compliance to competitive edge: How tech leaders can turn data sovereignty into a business advantage

Data sovereignty - where data is subject to the laws and governing structures of the nation in which it is collected, processed, or held - means that now more than ever, it’s incredibly important that you understand where your organization’s data comes from, and how and where it’s being stored. Understandably, that effort is often seen through the lens of regulation and penalties. If you don’t comply with GDPR, for example, you risk fines, reputational damage, and operational disruption. But the real conversation should be about the opportunities it could bring, and that involves looking beyond ticking boxes, towards infrastructure and strategy. ... Complementing the hybrid hub-and-spoke model, distributed file systems synchronize data across multiple locations, either globally or only within the boundaries of jurisdictions. Instead of maintaining separate, siloed copies, these systems provide a consistent view of data wherever it is needed and help teams collaborate while keeping sensitive information within compliant zones. This reduces delays and duplication, so organizations can meet data sovereignty obligations without sacrificing agility or teamwork. Architecture and technology like this, built for agility and collaboration, are perfectly placed to transform data sovereignty from a barrier into a strategic enabler. They support organizations in staying compliant while preserving the speed and flexibility needed to adapt, compete, and grow.

Why digital transformation fails without an upskilled workforce

“Capability” isn’t simply knowing which buttons to click. It’s being able to troubleshoot when data doesn’t reconcile. It’s understanding how actions in the system cascade through downstream processes. It’s recognizing when something that’s technically possible in the system violates a business control. It’s making judgment calls when the system presents options that the training scenarios never covered. These capabilities can’t be developed through a three-day training session two weeks before go-live. They’re built through repeated practice, pattern recognition, feedback loops and reinforcement over time. ... When upskilling is delayed or treated superficially, specific operational risks emerge quickly. In fact, in the implementations I’ve supported, I’ve found that organizations routinely experience productivity declines of as much as 30-40% within the first 90 days of go-live if workforce capability hasn’t been adequately addressed. ... Start by asking your transformation team this question: “Show me the behavioral performance standards that define readiness for the roles, and show me the evidence that we’re meeting them.” If the answer is training completion dashboards, course evaluation scores or “we have a really good training vendor,” you have a problem. Next, spend time with actual end users not power users, not super users, but the people who will do this work day in and day out.

How Infrastructure Is Reshaping the U.S.–China AI Race

Most of the early chapters of the global AI race were written in model releases. As LLMs became more widely adopted, labs in the U.S. moved fast. They had support from big cloud companies and investors. They trained larger models and chased better results. For a while, progress meant one thing. Build bigger models, and get stronger output. That approach helped the U.S. move ahead at the frontier. However, China had other plans. Their progress may not have been as visible or flashy, but they quietly expanded AI research across universities and domestic companies. They steadily introduced machine learning into various industries and public sector systems. ... At the same time, something happened in China that sent shockwaves through the world, including tech companies in the West. DeepSeek burst out of nowhere to show how AI model performance may not be as contrained by hardware as many of us thought. This completely reshaped assumptions about what it takes to compete in the AI race. So, instead of being dependent on scale, Chinese teams increasingly focused on efficiency and practical deployment. Did powerful AI really need powerful hardware? Well, some experts thought DeepSeek developers were not being completely transparent on the methods used to develop it. However, there is no doubt that the emergence of DeepSeek created immense hype. ... There was no single turning point for the emergence of the infrastructure problem. Many things happened over time.

Why AI adoption keeps outrunning governance — and what to do about it

The first problem is structural. Governance was designed for centralized, slow-moving decisions. AI adoption is neither. Ericka Watson, CEO of consultancy Data Strategy Advisors and former chief privacy officer at Regeneron Pharmaceuticals, sees the same pattern across industries. “Companies still design governance as if decisions moved slowly and centrally,” she said. “But that’s not how AI is being adopted. Businesses are making decisions daily — using vendors, copilots, embedded AI features — while governance assumes someone will stop, fill out a form, and wait for approval.” That mismatch guarantees bypass. Even teams with good intentions route around governance because it doesn’t appear where work actually happens. ... “Classic governance was built for systems of record and known analytics pipelines,” he said. “That world is gone. Now you have systems creating systems — new data, new outputs, and much is done on the fly.” In that environment, point-in-time audits create false confidence. Output-focused controls miss where the real risk lives. ... Technology controls alone do not close the responsible-AI gap. Behavior matters more. Asha Palmer, SVP of Compliance Solutions at Skillsoft and a former US federal prosecutor, is often called in after AI incidents. She says the first uncomfortable truth leaders confront is that the outcome was predictable. “We knew this could happen,” she said. “The real question is: why didn’t we equip people to deal with it before it did?”

How AI Will ‘Surpass The Boldest Expectations’ Over The Next Decade And Why Partners Need To ‘Start Early’

The key to success in the AI era is delivering fast ROI and measurable productivity gains for clients. But integrating AI into enterprise workflows isn’t simple; it requires deep understanding of how work gets done and seamless connection to existing systems of record. That’s where IBM and our partners excel: embedding intelligence into processes like procurement, HR, and operations, with the right guardrails for trust and compliance. We’re already seeing signs of progress. A telecom client using AI in customer service achieved a 25-point Net Promoter Score (NPS) increase. In software development, AI tools are boosting developer productivity by 45 percent. And across finance and HR, AI is making processes more efficient, error-free, and fraud-resistant. ... Patience is key. We’re still in the early innings of enterprise AI adoption — the players are on the field, but the game is just beginning. If you’re not playing now, you’ll miss it entirely. The real risk isn’t underestimating AI; it’s failing to deploy it effectively. That means starting with low-risk, scalable use cases that deliver measurable results. We’re already seeing AI investments translate into real enterprise value, and that will accelerate in 2026. Over the next decade, AI will surpass today’s boldest expectations, driving a tenfold productivity revolution and long-term transformation. But the advantage will go to those who start early.

Five AI agent predictions for 2026: The year enterprises stop waiting and start winning

By mid-2026, the question won't be whether enterprises should embed AI agents in business processes—it will be what they're waiting for if they haven't already. DIY pilot projects will increasingly be viewed as a risker alternative to embedded pre-built capabilities that support day-to-day work. We're seeing the first wave of natively embedded agents in leading business applications across finance, HR, supply chain, and customer experience functions. ... Today's enterprise AI landscape is dominated by horizontal AI approaches: broad use cases that can be applied to common business processes and best practices. The next layer of intelligence - vertical AI - will help to solve complex industry-specific problems, delivering additional P&L impact. This shift fundamentally changes how enterprises deploy AI. Vertical AI requires deep integration with workflows, business data, and domain knowledge—but the transformative power is undeniable. ... Advanced enterprises in 2026 will orchestrate agent teams that automatically apply business rules, maintain a tight control on compliance, integrate seamlessly across their technology stack, and scale human expertise rather than replace it. This orchestration preserves institutional knowledge while dramatically multiplying its impact. Organizations that master multi-agent workflows will operate with fundamentally different economics than those managing point automation solutions.

How should AI agents consume external data?

Agents benefit from real-time information ranging from publicly accessible web data to integrated partner data. Useful external data might include product and inventory data, shipping status, customer behavior and history, job postings, scientific publications, news and opinions, competitive analysis, industry signals, or compliance updates, say the experts. With high-quality external data in hand, agents become far more actionable, more capable of complex decision-making and of engaging in complex, multi-party flows. ... According to Lenchner, the advantages of scraping are breadth, freshness, and independence. “You can reach the long tail of the public web, update continuously, and avoid single‑vendor dependencies,” he says. Today’s scraping tools grant agents impressive control, too. “Agents connected to the live web can navigate dynamic sites, render JavaScript, scroll, click, paginate, and complete multi-step tasks with human‑like behavior,” adds Lenchner. Scraping enables fast access to public data without negotiating partnership agreements or waiting for API approvals. It avoids the high per-call pricing models that often come with API integration, and sometimes it’s the only option, when formal integration points don’t exist. ... “Relying on official integrations can be positive because it offers high-quality, reliable data that is clean, structured, and predictable data through a stable API contract,” says Informatica’s Pathak. “There is also legal protection, as they operate under clear terms of service, providing legal clarity and mitigating risk.”

Daily Tech Digest - February 02, 2026

Quote for the day:

"How do you want your story to end? Begin with that end in mind." -- Elizabeth McCormick

Why Architecture Rots No Matter How Good Your Engineers Are

Every architect has seen it. The system starts clean. The design makes sense. Code reviews are sharp. Engineers are solid. Yet six months later, performance has slipped. A caching layer breaks quietly. Technical debt shows up despite everyone’s best intentions. The question isn’t why this happens to bad teams. The question is why it happens to good teams. ... Rot doesn’t usually come from bad judgment. It comes from lost context. The information needed to prevent many problems exists. It’s just scattered across too many files, too many people, and too many moments in time. No single mind can hold it all. ... Human working memory holds roughly four chunks of information at once. That isn’t a vibe. It’s a constraint. And it matters more than we like to admit. When developers read code, they’re juggling variable state, control flow, call chains, edge cases, and intent. As the number of mental models increases, onboarding slows and comprehension drops. Once cognitive load pushes beyond working memory capacity, understanding doesn’t degrade linearly. It collapses. ... Standards drift because good intentions don’t scale. The system allows degradation, and the information needed to prevent it is often invisible at the moment decisions are made. Architecture decision records are a good example. ADRs capture why you chose one path over another. They preserve context. In practice, when a developer is making a change, they rarely stop to consult ADRs.

Quantum Computing and Cybersecurity: The Way Forward for a Quantum-Safe Future

While the timeline for commercial production of a powerful quantum computer is uncertain, most industry insiders agree that it is only a matter of time. In its 2025 report, the Global Risk Institute posits a five to ten year timeframe for the development of Cryptographically Relevant Quantum Computers (CRQC). A quantum-powered adversary may decrypt traffic as it flows, impersonate endpoints or even intercept authentication credentials in transit. The foundational risk begins with intercepting VPN traffic around the world and compromising all HTTPS/SSL certificates. Beyond this, large, distributed Internet of Things (IoT) systems that rely on light-weight encryption would be compromised. Operational Technology (OT) and Industrial Control Systems (ICS) that cannot be upgraded swiftly are likely to be compromised too, jeopardizing vital sectors like healthcare, energy and transportation. HNDL poses a significant risk to long-lasting, sensitive data in finance, healthcare, government and critical infrastructure. These sectors are especially vulnerable due to their extended confidentiality requirements, most of which could be beyond the arrival of quantum computers. Enterprises ignoring this threat now risk future breaches, and regulatory or reputational damage when adversaries deploy quantum decryption. The downstream effects of such breaches could be catastrophic not just to the organization, but to entire ecosystems.

Chewing through data access is key to AI adoption

The fact that the generic nature of LLMs can be augmented by contextual data is a valuable solution to the bottleneck problem. But it presents another problem in the form of data access. Contextual data might exist, but it is typically scattered across multiple systems, held in multiple formats and generally stored heterogeneously. All of this makes data access difficult. Data silos, always a perennial problem for analytics, have now become a critical roadblock to AI adoption and value realisation. Another problem comes from compliance requirements. Many industries, organisations, and jurisdictions regulate how data is accessed and moved. This is particularly true in industries like financial services, healthcare, insurance, or government, but it is true to a greater or lesser extent in all industries. ... Evans suggests that data federation can provide access to context to feed and augment the generic training data of models. The result is likely the best approach that organisations have when facing their AI goals and contending with data access bottlenecks. “Moving data by default is really something of a brute force approach. It was needed during the heyday of the data warehouse, but technologies like Apache Iceberg and Trino make data lakehouses built around data federation more accessible than ever,” he said. “In the past, data federation was slower than data centralisation. But in recent years, advances in Massively Parallel Processing (MPP) mean that technologies built to take advantage of federation, like Trino, are finally able to make the data federation dream a reality.”

CSO Barry Hensley on staying a step ahead of the cyber threat landscape

Times have changed as more organizations have either experienced a significant incident firsthand or have seen enough third- and fourth-party breach notifications to take up arms. All these events drive awareness and give credibility to the threats and associated risks. However, there is still a challenge in establishing an appropriate risk tolerance that drives the right investments in effective security controls, especially for budget constrained organizations. ... We do see the evolution of third- and fourth-party risk management, especially in how we validate our security partner’s maturity and resilience. The evolution of risk is partly based on third and fourth parties swapping their underlying technologies to reduce cost or increase efficiencies that a customer has little to no understanding of the risks that might expose. So, for the security functions we’re going to provide internally, we’ll focus on the basics and do them well. With the controls/functions we outsource, we must reimagine not only how we verify our partner environments but how do we actively participate to improve their security programs as well as ours. ... Are we assessing the most relevant risks, rather than the risks of yesterday? And, because we can get so wrapped up in the playbook that we ran in our last organization, how do we ensure the current playbook is relevant to the organization at hand? An example would be how much time we focus on phishing training, which burdens our teammates to be the first line of defense, where we could instead leverage anomaly-based detection to automate the detection and response actions.

Dedicated Servers vs. Cloud: Which Is More Secure?

Because the resources under a dedicated server model are yours and yours alone, you won't have to worry about "noisy neighbor" interference or side-channel attacks originating from other tenants, which can be a real risk in cloud server management. With this physical exclusivity, dedicated servers are often attractive for high-risk, compliance-heavy workloads—for example, healthcare, financial services, or government systems. This isolation doesn't just provide a higher standard of performance, but also simplifies your servers' threat surface, especially when possible mechanisms for cyberattacks are removed. ... Cloud servers, by comparison, always operate under a multi-tenant architecture. This means that virtual servers on shared hardware are separated by a hypervisor layer, which creates and manages multiple isolated operating systems in a single server. ... With dedicated servers, you'll have complete control over your operating systems, firewalls, access policies, and encryption. You'll also have the flexibility to set the patch schedule, firewall rules, monitoring tools, and segmentation strategies. ... Cloud servers, on the other hand, always rely on a shared responsibility model. Your vendor will secure the infrastructure, networking, and some parts of the stack. However, you'll still have to manage everything from the operating system (OS) upwards yourself.

How threat actors are really using AI

Are we getting to a point where hackers are going to use AI to slowly but surely circumvent every defense we throw at it? Is this more a case of actors simply using capabilities, as they have with past technical advances? Or is this entire concern overblown, meaning the money in our wallets is perfectly safe ... if only we could remember where we put the darned thing? ... While these early examples stemmed from the spread of generative AI, the technology has been sprinkled across attacks as early as 2018. TaskRabbit, the commoditized services platform owned by Ikea, was the subject of a breach where AI was used to control a massive botnet that performed a distributed denial-of-service (DDoS) attack on its servers. The result? Names, passwords, and payment details of both clients and ‘taskers’ were stolen in an attack that employed machine learning to make it more efficient and ultimately effective than a simple automated script. ... The picture isn't uniformly alarming, however, with Meyers suggesting less sophisticated actors are actually using AI “to their detriment.” He pointed to a group that created malware called Funk Walker using an adversarial LLM called Worm GPT. “There was broken cryptography in that, and the adversary left their name in it,” he explained. “That's kind of on the lower end of the sophistication spectrum.” The reality, then, is a split between highly capable state actors leveraging AI for genuine operational advantages, to less skilled criminals whose efforts to get a leg up via AI assistance have the potential to backfire through either technical failures or operational security mistakes that make them that bit easier to track.

StrongestLayer: Top ‘Trusted’ Platforms are Key Attack Surfaces

Rather than relying on malware or obvious phishing techniques, today’s attackers exploit trust, authentication gaps, and operational dependency. The report provides rare visibility into the techniques that define modern email threats by examining only attacks that incumbent security controls missed. “Email security has reached an inflection point,” said Alan LeFort, CEO and co-founder, StrongestLayer. “The controls enterprises depend on were designed to detect patterns and known bad signals. But attackers are now exploiting trusted brands and legitimate infrastructure, areas that those systems were never built to reason about.” ... The report thinks that attackers are no longer trying to look legitimate – they are hiding behind platforms that already are. DocuSign alone accounted for more than one-fifth of all attacks analyzed, particularly targeting legal, financial and healthcare organizations where document-signing workflows are deeply embedded in daily operations. Google Calendar attacks represent an especially concerning trend. Because calendar invitations are delivered via calendar APIs rather than email, these attacks bypass secure email gateways entirely, creating a blind spot for most security teams. ... StrongestLayer’s analysis shows AI-assisted phishing has fundamentally changed the economics of detection. Traditional phishing campaigns reuse templates with high similarity, allowing pattern-based systems to work.

Enterprises are measuring the wrong part of RAG

Across enterprise deployments, the recurring pattern is that freshness failures rarely come from embedding quality; they emerge when source systems change continuously while indexing and embedding pipelines update asynchronously, leaving retrieval consumers unknowingly operating on stale context. ... In retrieval-centric architectures, governance must operate at semantic boundaries rather than only at storage or API layers. This requires policy enforcement tied to queries, embeddings and downstream consumers — not just datasets. ... In production environments, evaluation tends to break once retrieval becomes autonomous rather than human-triggered. Teams continue to score answer quality on sampled prompts, but lack visibility into what was retrieved, what was missed or whether stale or unauthorized context influenced decisions. As retrieval pathways evolve dynamically in production, silent drift accumulates upstream, and by the time issues surface, failures are often misattributed to model behavior rather than the retrieval system itself. Evaluation that ignores retrieval behavior leaves organizations blind to the true causes of system failure. ... Retrieval is no longer a supporting feature of enterprise AI systems. It is infrastructure. Freshness, governance and evaluation are not optional optimizations; they are prerequisites for deploying AI systems that operate reliably in real-world environments.

Data privacy urged as strategic board issue in AI era

"Data privacy is no longer a cybersecurity business control or a risk mitigation compliance checkbox. It reflects how deeply interconnected the modern world has become between businesses, governments, travellers, and citizens. Every interaction, financial transaction, remote authentication, and geolocation ping generates personal data. That data moves across borders, clouds, applications, partners, and marketing algorithms at machine speed and far beyond what most individuals realise in terms of data broker destinations. As a result, personal data privacy is harder to achieve than at any point in history, not because of negligence, but because of scale, dependency, design, and business models design to monetise the information itself," said Haber ... Bluntly, we have an unusual challenge. Data privacy strategies have not evolved at the same pace as data creation and monetised analytics. Organisations still focus on cyber security defences while data flows freely through APIs, SaaS platforms, AI models, and third-party ecosystems. True personal data privacy requires visibility into all of this data with control being assigned to the individual user and not the business or government entity based on regulations. Without the user knowing who and what is accessing data, why it is being accessed, and how long the data will be archived, data privacy will remain an abstract concept with individuals only loosely being able to opt of data storage and profiling.

Why workers are losing confidence in AI - and what businesses can do about it

While platforms like Claude Code are saving software developers at REACHUM significant time, not everything is as effective. Tinfow sees a disparity between how some AI tools are marketed and what they can actually do. Even working at a company built around AI, Tinfow's team has run into issues with tasks like text generation in images, where certain AI tools just didn't deliver. "There's so much noise, and I don't want our team to get distracted by that, so I'm the one who will take a look at something, decide whether it is reasonable or garbage, and then give it to the team to work with," Tinfow said. ... "If you're now starting to look at how you can use AI for the same task, you all of a sudden have to put a lot more mental effort into trying to figure out how to do this in a completely different way," Ginn said, "That loss of the routine, the confidence of how I'm doing it, that can also just go back to the human nature to avoid change." Additionally, Stefan discussed the role adequate training plays in maintaining confidence. ... Back at the digital marketing agency Candour, Farrar said the company has a variety of tactics to help balance the quest for innovation with the day-to-day challenges of a technology that still has a way to go. Candour builds in extra time to account for the fact that everyone is learning, frames experiments as "test and learn" to mitigate stress, and has appointed a "champion" to stay abreast of developments in AI.

Daily Tech Digest - February 01, 2026

Quote for the day:

"Successful leadership requires positive self-regard fused with optimism about a desired outcome." -- Warren Bennis

Forget the chief AI officer - why your business needs this 'magician

There's a lot of debate about who should be responsible for ensuring the business makes the most out of generative AI. Some experts suggest the CIO should oversee this crucial role, while others believe the responsibility should lie with a chief data officer. Beyond these existing roles, other experts champion the chief AI officer (CAIO), a newcomer to the C-suite who oversees key considerations, including governance, security, and identification of potential use cases. ... Many people across other business units are confused about the different roles of technology and data teams. When Panayi joined Howden in August last year, he decided to head off that issue at the pass. ... "I think companies are missing a trick if they've not got someone ensuring that people are using things like Copilot and so on. These tools are new enough that we do need people to help with adoption," he said. "And at the moment, I don't think we can assume the narrative is correct that people using AI at home to help them book holidays is the same as how it can help them be more productive at work." ... "It's like he's a magician, showing people who have to deal with thousands of pages of stuff, how to get the answers they need quickly," he said, outlining how the director of productivity highlights the benefits of gen AI to the firm's brokers. "These people are not at the computer all day. They are out in the market, talking and making decisions."

Just Relying on Data Doesn’t Make You Data-driven — Advantage Solutions CDO

O’Hazo then draws a line between measurement and transformation. Success in data programs, she explains, is not only about performance indicators; it is also about whether the organization is starting to internalize the mindset behind them. “Success for me in this data and AI space is all about, ‘Are my stakeholders starting to actually speak some of my language?’” When stakeholders begin to “believe” and “trust,” she says, the shift becomes visible not only in outcomes but also in demand. The moment data starts becoming embedded in the business is the moment the need for the CDO office outgrows its capacity. ... She ties true data-driven maturity to operational efficiency and responsiveness: Accurate, timely information; Faster decision-making cycles; Quicker reactions to market conditions; and Lower effort to extract value from data. In her view, strong data foundations should reduce friction instead of creating new burdens. Speed, however, is not just about moving fast, it’s about winning the race to insight. “Once you have that foundation built, to get to the answer quickly, you have to be the first one there. If you’re not the first one there, you’ve lost.” ... As the conversation returns to the governance part of transformation, O’Hazo underscores that governance becomes sustainable only when people are comfortable using data and confident enough to surface risks early. For her, the true differentiator is not policy; it is talent and environment.

The Three Mindsets That Shape Your Life, Work And Fulfillment

Mission Mindset is goal-oriented but not outcome-obsessed. It begins with clarity about a specific, measurable and time-bound goal. Decades of research on goal-setting, including the work of Stanford psychologist Carol Dweck, shows that how we interpret challenges influences how we engage with them—and that mindset creates very different psychological worlds for people facing the same obstacles. Here's where most people go wrong. ... If mission provides direction, identity provides stability. Identity Mindset is rooted in a healthy, coherent self-image that does not rise and fall with every outcome. It answers a deeper question: Who am I when the going gets tough or disappointment abounds? Many people identify with their performance. Success feels like validation, and failure feels personal. That volatility makes progress emotionally expensive because every result threatens their self-worth. In contrast, PsychCentral broadly defines resilience as adapting well to adversity; individuals who are stable in how they see themselves are better able to regulate emotions, process setbacks and continue forward without losing themselves in the struggle. ... Agency Mindset is where actual momentum lives. It is the lived belief that you are the author of your life, not a character reacting to circumstances. Agency does not deny reality or minimize hardship. It refuses to play the victim, make excuses or place blame.

Why We Can’t Let AI Take the Wheel of Cyber Defense

When we talk about fully autonomous systems, we are talking about a loop: the AI takes in data, makes a decision, generates an output, and then immediately consumes that output to make the next decision. The entire chain relies heavily on the quality and integrity of that initial data. The problem is that very few organizations can guarantee their data is perfect from start to finish. Supply chains are messy and chaotic. We lose track of where data originated. Models drift away from accuracy over time. If you take human oversight out of that loop, you aren’t building a better system; you are creating a single point of systemic failure and disguising it as sophistication. ... There is no magical self-healing feature that puts everything back together elegantly. When a breach happens, it is people who rebuild. Engineers are the ones trying to deal with the damage and restoring services. Incident commanders are the ones making the tough calls based on imperfect information. AI can and absolutely should support those teams—it’s great at surfacing weak signals, prioritizing the flood of alerts, or suggesting possible actions. But the idea that AI will independently put the pieces back together after a major attack is a fantasy. ... So, how do we actually do this? First, make “human-in-the-loop” the default setting for any AI that can act on your systems or data. Automated containment can save your skin in the first few seconds of an attack, but every autonomous process needs guardrails.

Connecting the dots on the ‘attachment economy’

In the attention economy paradigm, human attention is a currency with monetary value that people “spend.” The more a company like Meta can get people to “spend” their attention on Instagram or Facebook, the more successful that company will be. ... Tristan Harris at the Center for Humane Technology coined the phrase “attachment economy,” which he criticizes as the “next evolution” of the extractive-tech model; that’s where companies use advanced technologies to commodify the human capacity to form attached bonds with other people and pets. In August, the idea began to gain traction in business and academic circles with a London School of Economics and Political Science blog post entitled, “Humans emotionally dependent on AI? Welcome to the attachment economy” by Dr. Aurélie Jean and Dr. Mark Esposito. ... The rise of attachment-forming tech is similar to the rise in subscriptions. While posting an article or YouTube video may get attention, getting people to subscribe to a channel or newsletter is better. It’s “sticky,” assuring not only attention now, but attention in the future as well. Likewise, the attachment economy is the “sticky” version of the attention economy. Unlike content subscription models, the attachment idea causes real harm. It threatens genuine human connection by providing an easier alternative, fostering addictive emotional dependencies on AI, and exploiting the vulnerabilities of people with mental health issues.

From monitoring blind spots to autonomous action: Rethinking observability in an Agentic AI world

AI-supported observability tools help teams not only understand system performance but also uncover the reasons behind issues. By linking signals across interconnected parts, these tools provide actionable insights and usually resolve problems automatically, reducing Mean Time to Resolution (MTTR) and cutting the risk of outages. ... AI-driven observability can trace service dependencies from start to finish, connect signals across third-party platforms, and spot early signs of unusual behavior. By examining traffic patterns, error rates, and configuration changes in real-time, observability helps teams identify emerging issues sooner, understand the potential impact quickly, and respond before full disruptions occur. While observability cannot prevent every third-party outage, it can greatly reduce uncertainty and response time, allowing solutions to be introduced sooner and helping rebuild customer trust. ... When AI-driven applications fail, teams often lack clear visibility into what went wrong, putting significant AI investments at risk. Slow or incorrect responses turn troubleshooting into guesswork, as teams struggle to understand agent interactions, find delays, or identify the responsible agent or tool. This lack of clarity slows down root-cause analysis, extends downtime, diverts engineering efforts from innovation, and can ultimately lead to lost revenue and customer trust. Observability addresses this challenge by providing complete visibility into AI application behavior.

Architecture Testing in the Age of Agentic AI: Why It Matters Now More Than Ever

Historically, architecture testing functioned as a safeguard against emergent complexity in distributed systems. Whenever an organization deployed a network of interdependent services, message buses, caches, and APIs, the potential for unforeseen interactions grew. Even before AI entered the picture, architects confronted the reality that large systems behave in ways no single engineer fully anticipates. ... Agentic systems challenge traditional testing practices in several fundamental ways. First, these systems are inherently non‑deterministic. A test that succeeds at 9:00 might fail just minutes later simply because the agent followed a different reasoning path. This creates a widening ‘verification gap,’ where deterministic enterprise systems and probabilistic, adaptive agents operate according to fundamentally different reliability expectations. Second, these agents operate within environments that are constantly shifting—APIs, user interfaces, databases, and document stores all evolve independently of the agent itself. Because agents are expected to detect these changes and adapt their behavior, long‑held architectural assumptions about stability and interface contracts become far more fragile. ... Third, agentic AI introduces a new level of emergent behavior. Operating through multi‑step reasoning loops and tool interactions, agents can develop strategies or intermediate actions that were never explicitly designed or anticipated. While emergence has always existed in complex distributed systems, with agents it becomes the rule rather than the exception.

Data Privacy Day warns AI, cloud outpacing governance

Kornfeld commented, "Data Privacy Day is a reminder that protecting sensitive information requires consistent discipline, not just policies. This discipline starts with infrastructure choices. As organizations continue to evaluate cloud-first strategies, many are also reassessing where their most critical data should live. For workloads that demand predictable performance, strong governance and clear ownership, on-site infrastructure continues to play an essential role in a sound privacy strategy." ... Russel said, "Data Privacy Day often prompts the usual reminders: update policies, refresh consent language, and train staff on security and resilience strategies. These are important steps, but increasingly they are simply the baseline. In 2026, the board-level question leaders should also be asking is: can we demonstrate control of personal data and sustain trust through disruption, whether it stems from a compromise, misconfiguration, insider error, or a supplier incident?" ... Russell commented that identity controls and response processes sit at the core of this shift as attackers continue to exploit account compromise to reach sensitive information in cloud environments. "Identity is a privacy fault line. In cloud environments, compromised identities are often the fastest route to sensitive data. Resilience means detecting abnormal access early, limiting blast radius, and recovering confidently when identity controls are bypassed."

Security teams are carrying more tools with less confidence

Security leaders express mixed views about the performance of their SIEM platforms. Most say their SIEM contributes to faster detection and response, yet only half describe that contribution as strong. Confidence in long-term scalability follows a similar pattern, with many teams expressing partial confidence as data volumes and monitoring demands continue to grow. Satisfaction with log management and security analytics tools mirrors this split. Teams that express higher satisfaction also report stronger alignment between their tooling and application environments. ... Threat detection represents the most common use of AI and machine learning within security operations. Fewer teams apply AI to incident triage, automated response, or anomaly detection. Despite this limited scope, security leaders consistently associate AI with reduced alert fatigue and improved signal quality. Many also prioritize AI capabilities when evaluating SIEM platforms, alongside real-time analytics. ... Security leaders frequently describe operational cost as a top pain point. Multiple point solutions contribute to overlapping capabilities, siloed data, and increased alert noise. Data that remains isolated across tools complicates threat analysis and slows investigations, particularly when teams attempt to reconstruct activity across cloud, identity, and application layers.

Integrating Financial Counterparty Risk into Your Business Continuity Plan

Vendor defaults and liquidity issues can disrupt operations in ways that ripple across departments and delay recovery. If a key financial partner fails, access to working capital, credit or critical services can disappear overnight. For example, if your leasing company collapses, essential equipment could be repossessed, or service agreements could lapse. ... Financial counterparties show up across many areas of your business. You depend on banks for credit facilities and insurers for risk transfer. Payment processors, brokers and pension custodians handle everything from daily cash flow to long-term employee benefits. Clearinghouses are also vital in structured markets, such as stocks and futures. They sit between buyers and sellers to ensure both sides honor their contracts, which reduces your exposure to failure during high-volume or high-volatility periods. ... Not all financial counterparties pose the same level of risk, but the warning signs often follow familiar patterns. Monitoring a few high-impact indicators can help you identify problems and take action before disruptions escalate. ... Industry standards are raising the bar on how you manage financial counterparties. Frameworks like ISO 22301 stress the need to include financial dependencies in your continuity and risk programs. These standards define how regulators and stakeholders expect you to identify, assess and respond to financial exposure. If you treat financial partners like background support, you risk missing vulnerabilities that could surface under pressure.