Daily Tech Digest by Kannan Subbiah: 2026

Daily Tech Digest - March 02, 2026

Quote for the day:

“Winners are not afraid of losing. But losers are. Failure is part of the process of success. People who avoid failure also avoid success.” -- Robert T. Kiyosaki

Western Cybersecurity Experts Brace for Iranian Reprisal

Analysts at the threat intelligence firm Flashpoint on Sunday reported that the Iran-linked Handala Group was already targeting Israeli industrial control systems and claimed disruption of manufacturing and energy distribution in the country. Handala, which earlier in the week claimed on social media to have stolen data held by Israel's Clalit healthcare network, also claimed responsibility for a cyberattack on Jordanian fuel station infrastructure. ... "The inclusion of Gulf states such as the UAE, Qatar, and Bahrain in the potential crossfire underscores that this is not a localized exchange, but a high-risk regional security environment," said Austin Warnick, Flashpoint's director of national security intelligence, in an emailed statement. "Beyond the kinetic strikes themselves, the broader risk lies in the second-order effects - retaliatory cyber operations, attacks on critical infrastructure, and prolonged disruption to air and maritime corridors that underpin global commerce," Warnick added. The cybersecurity firm SentinelOne on Saturday observed that Iran has "historically incorporated cyber operations into periods of regional escalation." ... Concerns about retaliation in cyberspace come after what may have been the "largest cyberattack in history," which is how the Jerusalem Post characterized a plunge into digital darkness that accompanied missile strikes. Internet observatory NetBlocks observed a sudden decline in Iranian internet connectivity in a timeline coinciding with the onset of missile attacks.

Security debt is becoming a governance issue for CISOs

Security debt is a time problem as much as a volume problem. Older items tend to live in code that teams hesitate to change, such as legacy services, shared libraries, or apps tied to revenue workflows. That slows remediation, and it can make risk conversations feel repetitive for engineering leaders. Programs that track debt end up debating ownership, change windows, and acceptable exposure for systems with high business dependency. Governance often comes down to who owns remediation, what gets funded, and which teams can accept risk exceptions. ... Prioritization becomes an operational discipline when remediation capacity stays constrained. Programs need a repeatable way to tie issues to business criticality, reachable attack paths, and runtime exposure, so teams can focus effort on the highest impact weaknesses in the systems that matter most. Wysopal said organizations need to recalibrate how they rank and measure vulnerability reduction. “Success in reducing security debt is about focus. Direct teams to the small subset of vulnerabilities that are both highly exploitable and capable of causing catastrophic damage to the organisation if left unaddressed. By layering exploitability potential on top of the CVSS, organisations add critical business context and establish a ‘high-risk’ fast lane for vulnerabilities that demand immediate attention.”

Biometrics, big data and the new counterintelligence battlefield

Modern immigration enforcement relies on vast interconnected databases that contain fingerprints, facial images, travel histories, employment records, family relationships, and immigration status determinations. Much of this information is immutable. A compromised password can be reset. A compromised fingerprint cannot. That permanence gives biometric repositories enduring intelligence value. If accessed, such data could enable long term targeting, profiling, and exploitation of individuals both inside and outside the U.S. The risk is magnified by scale and distribution. Immigration data flows across multiple components within the Department of Homeland Security (DHS) and into partner agencies. Mobile devices capture biometrics in the field. Cloud environments host case management systems. Contractors provide infrastructure, analytics, and support services. ... The counterintelligence risk does not stop at static records. Immigration enforcement increasingly relies on advanced analytics, large scale data aggregation, and biometric matching systems that connect government holdings with commercial data streams. Location data derived from advertising technology ecosystems, social media analysis, and facial recognition tools can all be integrated into investigative workflows. As these ecosystems grow more interconnected, the intelligence payoff from breaching, de-anonymization, or manipulation increases.

Can you trust your AI to manage its own security

A pressing concern within many organizations is the disconnect between security teams and R&D departments. Managing NHIs effectively can bridge this gap. By fostering collaboration and communication between these teams, organizations can create a more secure and unified cloud environment. This integration ensures that security protocols align seamlessly with innovation efforts, mitigating risks at every turn. ... Have you ever contemplated the extent to which AI can autonomously manage its security infrastructure? Where organizations increasingly transition to cloud-based operations, the intersection of Non-Human Identities (NHIs) and AI-driven security becomes critically important. By understanding these key components, cybersecurity professionals can develop robust strategies that mitigate risks while bolstering AI’s role in maintaining a secure environment. ... How can organizations cultivate trust in AI systems? By implementing stringent protocols and maintaining transparency throughout the process, businesses can illustrate AI’s capacity for reliable and secure operations. Collaborative efforts that involve transparency between AI developers and end-users can also enhance understanding and trust. Incorporating AI-driven security measures requires careful consideration and ongoing evaluation to maintain efficacy. This commitment to excellence fortifies AI strategies and ensures organizations maintain a proactive stance on security challenges.

What if the real risk of AI isn’t deepfakes — but daily whispers?

AI is transitioning from tools we use to prosthetics we wear. This will create significant new threats we’re just not prepared for. No, I’m not talking about creepy brain implants. These AI-powered prosthetics will be mainstream products we buy from Amazon or the Apple Store ... They will provide real value in our lives — so much so that we will feel disadvantaged if others are wearing them and we are not. This will create rapid pressure for mass adoption. ... First and foremost, policymakers need to realize that conversational AI enables an entirely new form of media that is interactive, adaptive, individualized and increasingly context-aware. This new form of media will function as “active influence,” because it can adjust its tactics in real time to overcome user resistance. When deployed in wearable devices, these AI systems could be designed to manipulate our actions, sway our opinions and influence our beliefs — and do it all through seemingly casual dialog. Worse, these agents will learn over time what conversational tactics work best on each of us on a personal level. The fact is, conversational agents should not be allowed to form control loops around users. If this is not regulated, AI will be able to influence us with superhuman persuasiveness. In addition, AI agents should be required to inform users whenever they transition to expressing promotional content on behalf of a third party.

A peak at the future of AI and connectivity

2026 will mark the point where AI shifts from experimentation to fully commercialized, autonomous decision-making at scale. The acceleration in inference traffic alone will expose the limits of network architectures designed for linear data flows and predictable consumption. AI-driven workloads will generate volatile east-west traffic patterns, machine-to-machine exchanges, and microburst dynamics that current networks were never built to accommodate. Ultra-low latency, deterministic performance, and the ability to dynamically allocate bandwidth in milliseconds will move from “nice to have” to critical requirements. The drive to generate ROI from AI will also put a bigger spotlight on the network. ... The industry has long viewed non-terrestrial networks (NTNs) as a means to fill coverage gaps where terrestrial connectivity is too impractical or costly. However, conversations from recent industry meetings and events tell me that NTNs are set to play a far more important, and potentially disruptive role than originally expected. Tens of thousands of new satellites are set to launch in the coming years, with Musk alone securing licenses for 10,000 additional units. This rapidly expanding mesh of networks is evolving at pace and will soon reach a point where direct-to-cell services can offer performance competing with terrestrial coverage. It is important to note, however, that NTNs will never be able to compete on peak data throughput. They will be part of the broader connectivity ‘coverage package’.

How CISOs can build a resilient workforce

Ford has developed strategies to not only recruit talent but maintain their interests and get them through the ebbs and flows of daily life in cybersecurity. “I put a focus around monitoring the workforce and trying to get a good sense of the workloads that are coming in.” Having a team that’s properly staffed is important and this is where data is helpful to gauge the workload and make the argument to support resourcing. ... Burnout is an ongoing concern for many CISOs and their teams, especially when unpredictable events can trigger workload spikes, burnout can escalate fast. “It’s something that can overwhelm pretty quickly,” Ford says. Industry surveys continue to flash red on persistent burnout that leads to job dissatisfaction. ... Ford agrees it’s difficult to find top-tier talent across all the different cybersecurity disciplines, especially for a large organization like Rockwell. His strategy entails bringing in a key expert or two in different disciplines with years of experience and adding more junior, early career people. “Pairing them with seasoned experts allows you to build an effective, sustainable team over time, and I’ve seen that work extremely well for organizations with early career programs.” He also looks for experts from adjacent disciplines such as infrastructure, the data center space or application development keen to break into cyber. “I’m not recruiting for everyone. I’m recruiting for a few top experts and then building a pipeline either through early career or other similar activities from a technology space to get an effective cyber team,” he says.

Why Retries Are More Dangerous Than Failures

The system enters a state where retries eat all available capacity, starving even the requests that might've succeeded. It's a trap — the harder you struggle, the tighter it clamps down. AWS engineers lived this during an October 2025 database outage. Client apps did exactly what they were supposed to: aggressively retry failed database calls. The database was already wobbly — some internal resource thing, normally the kind of issue that resolves itself in minutes. But those minutes never came. The retry storm kept the system pinned in a failure state for hours. The outage dragged on not because the original problem was catastrophic, but because every well-meaning client was enthusiastically making it worse. ... But backoff alone won't save you. You need circuit breakers — the pattern where after N consecutive failures, you stop trying entirely for some cooldown window. Give the service room to recover. Requests fail fast instead of queuing up. This feels wrong the first time you implement it. You're programming the system to give up. But the alternative — letting it spin uselessly pretending the next retry will work — is worse. ... SRE teams talk about error budgets — how much failure you can tolerate before breaking SLOs. Same logic applies to retries. You need a retry budget: a system-wide cap on in-flight retries. Harder to implement than it sounds. Requires coordination. Maybe you emit metrics on retry rates and alert when they cross thresholds.

The Real Cost of Cutting Costs in Digital Banking

Digital banking platforms must maintain robust security protocols, stay current with evolving regulatory requirements, and respond quickly to emerging threats. This is especially true for community FIs, since fraudsters often target smaller FIs based on smaller security teams and budgets. Budget vendors often lack the resources to invest adequately in security infrastructure, maintain comprehensive compliance programs, or dedicate teams to proactive threat monitoring. ... Budget platforms frequently lack robust integration capabilities, forcing your team to manage endless workarounds, manual processes, and custom development projects. These integration gaps create multiple cost centers. Your IT team spends hours troubleshooting connection issues instead of driving strategic initiatives. ... One of the most overlooked costs of budget digital banking platforms emerges precisely when your institution is succeeding. Growth-minded credit unions and community banks need partners whose platforms can scale seamlessly as account holder numbers increase, transaction volumes surge, and service offerings expand. Budget vendors often hit performance ceilings that turn your growth trajectory into an operational crisis. The problem manifests in multiple ways. ... The direct costs of migration such as consulting fees, vendor implementation charges, and internal labor costs easily run into six figures for even small institutions. The indirect costs are equally significant. During migration, your team’s attention diverts from strategic initiatives to tactical execution.

Why privacy by design matters most in high-risk data ecosystems

The most fundamental shift, Vora argues, is mental rather than technical. Privacy by design is not a checklist to be validated post-facto—it is a constraint that must shape systems from inception. “We have to incorporate privacy into the core of our architecture,” she says. “That means rethinking legacy systems, reengineering data flows, and redesigning how consent, access, and retention are handled.” ... Data minimisation, therefore, becomes the first line of defense. organisation must clearly define the lifecycle of every data element—from collection to disposal—and ensure that end users retain the right to access, correct, or erase their data. ... Key to this is data tagging: assigning unique identifiers to track data across its entire journey. Complementing this is the creation of centralised data catalogs, which document what data is collected, its sensitivity, purpose, retention period, and access rights. “These catalogs become the backbone of governance,” Vora says, “ensuring transparency and accountability across departments.” Technology, of course, plays a critical role. ... If privacy by design is the foundation, dynamic consent management is the operating system. Vora is clear that consent cannot be treated as a one-time checkbox. “Consent must be layered, granular, and flexible,” she says. “Users should be able to update, revoke, or modify their consent at any point.” This requires centralised consent management platforms, standardised APIs with consent baked in, and user-centric controls across both new and legacy products.

Daily Tech Digest - March 01, 2026

Quote for the day:

"You can't be a leader if you can't influence others to act." -- Dale E. Zand

Meet your AI auditor: How this new job role monitors model behavior

The relentless rise of artificial intelligence (AI) is creating a new role for business and technology professionals to consider: AI auditor. The role bears a striking resemblance to that of financial auditors, with a major exception: AI auditors monitor and report on the behavior of AI transactions rather than monetary transactions. ... The closest role to an AI auditor is now seen within teams tasked with reviewing AI model behavior, but their work is more akin to quality assurance, Bronfman said. The reviews cover "outputs, outliers, and edge-cases, and audit training processes for data input properties, accuracy, and predictability." AI auditors will put more teeth into assuring AI is responsible and trustworthy. ... AI auditing jobs won't just be found within enterprises. Just as organizations tend to rely on outside financial auditors, there will be many roles within third-party AI auditing firms. "Independent third-party auditors provide structured oversight and prevent conflicts of interest," said Bronfman. AI auditing standards and codes of conduct may even be ultimately supported "by a UN-like body or a coalition of major states, where deployment will require ongoing behavioral audits and mandated transparency." ... To move into this type of role, budding AI auditors "will need to deeply understand AI and how the algorithm works in order to identify where the pitfalls are and test how it can fail," said Bronfman.

Ransomware is the invoice for compounding technical debt

Cybercriminals are continuing their aggressive campaign of credential theft, purchasing stolen usernames and passwords from the dark web to access personal email, social media or financial accounts, noted the report. At an organisational level, these same pathways are compounded by internal security gaps like identity sprawl, which increases the chance of compromise, said Niraj Naidu ... “Technical debt accumulates quickly and quietly,” he told ARN. “A lot of organisations rely on legacy backup systems that were never really designed to protect against cyber-attacks. ... Naidu believes the urgency to do something “isn’t really triggered until there’s a security event for a lot of organisations”. That then leads to the ransom note, which is like “the invoice coming due for years of technical debt”, he explained. “With that there’s downtime, strained investor relations, legal implications, customer churn, as well as brand damage and regulatory penalties,” Naidu said. ... What has led to the failure for organisations to address tech debt is a “lack of clear visibility” over what sensitive information they hold, where it resides and who can access it, explained Naidu. “A lot of organisations may believe they’ve eliminated technical debt, especially executives,” he said. “They may not necessarily have that level of visibility or transparency, particularly when you’re looking at cloud adoption.

Don’t Panic Yet: “Humanity’s Last Exam” Has Begun

Well-known benchmarks such as the Massive Multitask Language Understanding (MMLU) exam, previously viewed as rigorous, have become less effective at distinguishing true progress in AI capability. In response, an international group of nearly 1,000 researchers, including a professor from Texas A&M University, developed a far more demanding assessment. Their goal was to design an exam so comprehensive and grounded in specialized human expertise that today’s AI systems would struggle to pass it. The result is “Humanity’s Last Exam” (HLE), a 2,500-question test that covers mathematics, the humanities, natural sciences, ancient languages, and highly specialized academic fields. ... Despite its apocalyptic name, Humanity’s Last Exam isn’t meant to suggest the end of human relevance. Instead, it highlights how much knowledge remains uniquely human and how far AI systems still have to go. “This isn’t a race against AI,” Nguyen said. “It’s a method for understanding where these systems are strong and where they struggle. That understanding helps us build safer, more reliable technologies. And, importantly, it reminds us why human expertise still matters.” ... HLE is intended to serve as a long‑term, transparent benchmark for evaluating advanced AI systems. As part of that mission, the team has made some of the exam publicly available, while keeping most of the test questions hidden so AI models can’t memorize the answers.

Who really sets AI guardrails? How CIOs can shape AI governance policy

As Donald Farmer, futurist at Tranquilla AI, explains, the guardrails of a vendor's AI system reflect that vendor's assessment of acceptable risk -- not the enterprise's. "That is shaped by their legal own exposure, their broadest possible customer base and their own ethical assumptions," Farmer said. "This works for many customers, but at the edges there can be tension." ... "Every AI agent expands the attack surface." Without disciplined data management and segmentation, one compromised component can ripple across business functions. The more tightly integrated AI becomes, the greater the potential blast radius. This requires CIOs to engage actively with governance, even if it seems like they are being handed a list of preset rules. As Palmer said, "traditional IT governance assumes that products stay the same. AI governance has to assume that they will not." ... Caught between competing restrictions and changing mandates at the federal level, CIOs may feel powerless to influence much change -- but the experts reject this impotence. Turner-Williams described the CIO's influence as "significant, but not unilateral. The CIO acts as orchestrator and trust agent." This is especially true for CIOs working across multiple jurisdictions, making them accountable not only to U.S. law, but also to the EU AI Act, GDPR and other international frameworks. ... Ratcliffe offers a pragmatic lens, arguing that CIOs should approach this issue as one of reputational strategy, not a compliance exercise.

Why Responsible Orchestration Outperforms Aggressive Automation

In complex large businesses, automation decisions are rarely made in one place. Teams optimize locally, adopt tools independently and automate processes in isolation. This results in fragmented automation that delivers short-term wins but creates long-term complexity and risk. Over time, this fragmentation further reduces leadership visibility into what work has been done, making it harder to manage risk, govern change and understand the true state (and impact!) of automation. This is where automation strategies break down. ... Orchestration is both a technical and a leadership discipline in this context, as it ensures automation decisions are intentional, coordinated and aligned with the way the business operates. Without orchestration, even well-intentioned automation can erode institutional knowledge, duplicate effort and make it harder for the very top of the organization to understand the true impact. ... The impact of fragmented automation and poorly orchestrated decision-making is felt throughout the organization, particularly by employees affected by the day-to-day disruption, and enterprises often fail to account for the impact on their workforce. Alongside day-to-day adoption, longer-term plans and how AI will make an impact are important questions to address early on. Companies must communicate AI strategy clearly and avoid reflexive headcount cuts that destroy organizational knowledge and boomerang rehiring.

India’s trillion-dollar data center opportunity is taking shape

With expanding cloud adoption, evolving sovereign data frameworks, and rapidly increasing compute intensity across industries, the country’s datacenter sector is entering its most consequential phase of growth. What is unfolding is not a temporary expansion cycle, but a sustained build-out of the digital backbone required to support the next phase of economic development. ... The drivers of this shift are both domestic and global. India generates one of the largest volumes of digital data in the world and serves a rapidly expanding digital user base. Enterprises across financial services, manufacturing, healthcare, retail, and public services are embedding cloud into core operations rather than treating it as a peripheral IT layer. AI adoption is moving from experimentation into production environments, raising compute intensity and infrastructure complexity. ... Sovereign cloud considerations further reinforce the need for domestic infrastructure. Across jurisdictions, governments and enterprises are reassessing where critical workloads reside and how data governance frameworks evolve. For a country of India’s scale, digital sovereignty is not merely regulatory; it is strategic. Hosting critical data and AI workloads domestically enhances resilience, compliance, and long-term economic control over digital systems. As sectors such as financial services, healthcare, defence, and public administration deepen their digital integration, secure and high-availability domestic capacity becomes essential.

Anthropic vs. The Pentagon: what enterprises should do

The rupture stems from a fundamental dispute over "all lawful use." The Pentagon demanded unrestricted access to Claude for any mission deemed legal, while Anthropic CEO Dario Amodei refused to budge ... The fallout is immediate; the Department of War has ordered all contractors and partners to stop conducting commercial activity with Anthropic effectively at once, though the Pentagon itself has a 180-day window to transition to "more patriotic" providers. ... If your entire agentic workflow or customer-facing stack is hard-coded to a single provider's API, you aren't going to be nimble or flexible enough to meet the demands of a marketplace where some potential customers, such as the U.S. military or government, want you to use or avoid specific models as conditions of your contracts with them. The most prudent move right now isn't necessarily to hit the "delete" button on Claude—which remains a best-in-class model for coding and nuanced reasoning, and certainly can and should continue to be used for work outside of that with the U.S. military and government agencies—but to ensure you have a "warm standby." ... The takeaway is clear: if you plan to maintain business with federal agencies, you must be able to certify to them that your products aren't built on any single prohibited model provider — however sudden that designation may come down or how ultimately legally untenable it may prove.

Intelligence as Infrastructure: The Cloud Architecture Powering Enterprise AI

For over a decade, digital transformation has been treated as a portfolio of initiatives — cloud migration, platform consolidation, automation, data modernisation. The introduction of large-scale AI assistants signals a structural shift: intelligence is no longer a feature embedded within applications. It is becoming an organising principle of enterprise systems. This shift demands architectural literacy. Leaders responsible for digital infrastructure, service optimisation, and operational risk must understand how modern AI systems are constructed — and where control, exposure, and opportunity reside within them. ... Modern AI assistants are not monolithic systems. They are composite architectures composed of tightly integrated layers, each with distinct operational and governance responsibilities. ... In regulated industries, governance begins at the first prompt. Every interaction is both a productivity event and a potential compliance event. The architectural consequence is clear: AI entry points must be treated as critical infrastructure. ... Grounded intelligence reduces hallucination risk and ensures outputs align with current policy, documentation, and regulatory obligations. In knowledge-intensive sectors, this layer is central to operational credibility. ... Organisations that attempt to retrofit governance will encounter resistance from risk and compliance functions. Those that design governance into architecture will scale AI with institutional confidence.

Open source devs consider making hogs pay for every Git pull

Fox, who also oversees Apache Maven, a popular Java build tool, explained that its repository site is at risk of being overwhelmed by constant Git pulls. The team has dug into this and found that 82 percent of the demand comes from less than 1 percent of IPs. Digging deeper, they discovered that many companies are using open source repositories as if they were content delivery networks (CDNs). ... How bad is it? Fox revealed that last year, major repositories handled 10 trillion downloads. That's double Google's annual search queries if you're counting from home and they're doing it on a shoestring. Fox described this as a "tragedy of the commons," where the assumption of "free and infinite" resources leads to structural waste amplified by CI/CD pipelines, security scanners, and AI-driven code generation. Companies may think that they can rely on "free and infinite" infrastructure, when in reality the costs of bandwidth, storage, staffing, and compliance are accelerating. ... With AI-driven repository usage exploding, Fox urged checking bills, using caching proxies, and avoiding per-commit tests. He seeks endorsements: "We need you to help step up... so that when we go out to the rest of the wild world... you need to pay to keep doing what you've been doing." But, wait, there's more! Besides simply being overwhelmed by constant download demands, Winser said, "People conflate open source software and open source infrastructure.."

AI in higher education and the ‘erosion’ of learning

Hybrid systems are increasingly shaping day-to-day academic work. Students use them as writing companions, tutors, brainstorming partners and on-demand explainers. Faculty use them to generate rubrics, draft lectures and design syllabuses. Researchers use them to summarise papers, comment on drafts, design experiments and generate code. This is where the ‘cheating’ conversation belongs. With students and faculty alike increasingly leaning on technology for help, it is reasonable to wonder what kinds of learning might get lost along the way. But hybrid systems also raise more complex ethical questions. One has to do with transparency. ... A second ethical question relates to accountability and intellectual credit. If an instructor uses AI to draft an assignment and a student uses AI to draft a response, who is doing the evaluating, and what exactly is being evaluated? If feedback is partly machine-generated, who is responsible when it misleads, discourages or embeds hidden assumptions? And when AI contributes substantially to research synthesis or writing, universities will need clearer norms around authorship and responsibility – not only for students, but also for faculty. Finally, there is the critical question of cognitive offloading. AI can reduce drudgery, and that’s not inherently bad. But it can also shift users away from the parts of learning that build competence, such as generating ideas, struggling through confusion, revising a clumsy draft and learning to spot one’s own mistakes.

Daily Tech Digest - February 28, 2026

Quote for the day:

"Stories are the single most powerful weapon in a leader's arsenal." -- Howard Gardner

AI ambitions collide with legacy integration problems

Many enterprises have moved beyond experimentation and are preparing for formal deployment. The survey found that 85% have begun adopting AI or expect to do so within the next 12 months. Respondents also reported efforts to formalise AI governance, reflecting greater attention to risk, accountability and oversight. ... Integration sits at the centre of that tension. AI initiatives often depend on clean data, consistent definitions and reliable access across multiple applications, requirements that legacy estates can complicate. The survey links these constraints to compliance risks, including data retention, access controls and auditability across connected systems. ... Security and privacy concerns featured prominently. Data privacy across systems was cited as a top risk by 49% of respondents, while 48% said they were concerned about third parties handling sensitive data. The results highlight the difficulty of managing information flows when AI systems interact with multiple internal applications and external providers. Governance approaches varied. Fewer than half (47%) said board-level reporting forms part of risk management for AI and related technology work, suggesting uneven executive oversight as AI moves into operational settings where incidents can carry regulatory and reputational consequences. ... Despite pressure to move quickly on AI initiatives, respondents said engineering quality remains a priority.

Striking the Right Balance Between Automation and Manual Processes in IT

Rather than thinking of applying AI wherever possible and over-automating, leaders should think about the most beneficial uses of the technology and begin implementation of the technology in those areas first before expanding further. Automation is a powerful tool, but humans are the most powerful tool in the IT stack. Let’s discuss how today’s IT leaders can strike the right balance between automation and manual processes. ... Even with the many benefits of automation, human-led processes still reign supreme in certain areas. For example, optimal IT operations happen at the intersection of tools and teamwork. IT teams must still foster a collaborative culture, working with other departments to ensure cross team visibility and alignment on business goals. While the latest AI technology can help in these efforts, ultimately, humans must do this collaborative work. Team dynamics can also be complex at times. Conflict resolution and major team decisions are not things that automation can solve. Moreover, if there is a critical system issue, DBAs must be able to work with IT leaders to resolve this issue and forge a path forward. Finally, manual processes are often necessitated by convoluted workflows. Many DBA teams have workflows in which every step is a set of if-then-else decisions, with each possible outcome also encumbered with many if-then decisions cascading through multiple levels of decisions.

Translating data science capabilities into business ROI

The fundamental challenge in demonstrating data science ROI is that most analytics infrastructure feels optional until it becomes essential. During normal operations, executives tolerate delays in reporting and gaps in visibility. During a crisis, those same gaps become existential threats. ... The turning point came when I realized we weren’t facing a data problem or a technology problem. We were facing a decision-making problem. Our leadership needed to maintain operational stability for a multi-trillion-dollar asset manager during unprecedented disruption. Every day without visibility meant delayed decisions, missed opportunities, and compounding uncertainty. ... Speed-to-value often trumps technical sophistication. The COVID dashboard taught me this lesson definitively. We could have spent months building a comprehensive data warehouse with sophisticated ETL pipelines and machine learning-powered forecasting. Instead, we focused ruthlessly on the minimum viable solution that executives needed immediately. ... Strategic positioning creates a disproportionate impact. I served as strategic architect for a major product repositioning — a multi-million-dollar initiative essential for our competitive positioning. My data-backed strategies produced immediate, quantifiable market share gains and resulted in substantially larger deal sizes and accelerated acquisition rates that fundamentally altered our market position.

The reliability cost of default timeouts

Many widely used libraries and systems default to infinite or extremely large timeouts. In Java, common HTTP clients treat a timeout of zero as “wait indefinitely” unless explicitly configured. In Python, requests will wait indefinitely unless a timeout is set explicitly. The Fetch API does not define a built-in timeout at all. These defaults aren’t careless. They’re intentionally generic. Libraries optimize for the correctness of a single request because they can’t know what “too slow” means for your system. Survivability under partial failure is left to the application. ... Long timeouts can also mask deeper design problems. If a request regularly times out because it returns thousands of items, the issue isn’t the timeout itself. It’s missing pagination or poor request shaping. By optimizing for individual request success, teams unintentionally trade away system-level resilience. ... A timeout defines where a failure is allowed to stop. Without timeouts, a single slow dependency can quietly consume threads, connections and memory across the system. With well-chosen timeouts, slowness stays contained instead of spreading into a system-wide failure. ... A timeout is a decision about value. Past a certain point, waiting longer does not improve user experience. It increases the amount of wasted work a system performs after the user has already left. A timeout is also a decision about containment. Without bounded waits, partial failures turn into system-wide failures through resource exhaustion: blocked threads, saturated pools, growing queues and cascading latency.

From dashboards to decisions: How streaming data transforms vertical software

For years, the standard for vertical software has been the nightly sync. You collect data all day, run a massive batch job at 2:00 AM, and provide your customers with a clean report the next morning. In a world of 2026, that delay is becoming a liability rather than a best practice. ... Data streaming isn’t just about moving bits faster; it’s about changing the fundamental value proposition of your application. Instead of being a system of record that tells a user what happened, your software becomes a system of agency that tells them what is happening right now. This shift requires a mental move away from static databases toward event-driven architectures. You’re no longer just storing a “state” (like current inventory); you’re capturing every “event” (every scan, every sale, every sensor ping) that leads to that state. ... One of the biggest mistakes I see software leaders make is treating real-time data as a “table stakes” feature that they give away for free. Streaming infrastructure is expensive to run and even more expensive to maintain. If you bake these costs into your standard subscription without a clear monetization strategy, you’ll watch your gross margins shrink as your customers’ data volumes grow. ... When you process data at the edge, you’re also solving the “data gravity” problem. Sending thousands of high-frequency sensor pings from a factory floor to the cloud just to filter out the noise is a waste of bandwidth and money.

MCP leaves much to be desired when it comes to data privacy and security

From a data privacy standpoint, one of the major issues is data leakage, while from a security perspective, there are several things that may cause issues, including prompt injections, difficulty in distinguishing between verified and unverified servers, and the fact that MCP servers sit below typical security controls. ... Fulkerson went on to say that runtime execution is another issue, and legacy tools for enforcing policies and privacy are static and don’t get enforced at runtime. When you’re dealing with non-deterministic systems, there needs to be a way to verifiably enforce policies at runtime execution because the blast radius of runtime data access has outgrown the protection mechanisms organizations have. He believes that confidential AI is the solution to these problems. Confidential AI builds on the properties of confidential computing, which involves using hardware that has an encrypted cache, allowing data and inference to be run inside an encrypted environment. While this helps prove that data is encrypted and nobody can see it, it doesn’t help with the governance challenge, which is where Fulkerson says confidential AI comes in. Confidential AI treats everything as a resource with its own set of policies that are cryptographically encoded. For example, you could limit an agent to only be able to talk to a specific agent, or only allow it to communicate with resources on a particular subnet.

3 Ways OT-IT Integration Helps Energy and Utilities Providers Modernize Grid Operations

Increasingly, energy providers are turning to digital twins to model and simulate critical infrastructure across generation, transmission and distribution environments. By feeding live telemetry from supervisory control and data acquisition systems, intelligent electronic devices and other OT assets into IT-based simulation platforms, utilities can create real-time digital replicas of substations, turbines, transformers and even entire grid segments. This enables teams to test load-balancing strategies, maintenance schedules or DER integrations without disrupting service. ... Private 5G networks offer a compelling alternative. Designed for high reliability and low latency, private 5G can operate effectively in interference-heavy environments such as substations or generation facilities. When paired with TSN, utilities can achieve deterministic, sub-millisecond communication between protection systems, controllers and analytics platforms. ... Federated machine learning allows utilities to train AI models locally at the edge — analyzing equipment performance, detecting anomalies and refining predictive maintenance strategies — without centralizing raw operational data. For industries such as energy and oil, remote sites can run local anomaly detection models tailored to site-specific conditions, while still sharing insights that strengthen enterprisewide safety and operational protocols.

Even if AI demand fades, India need not worry - about data centres

AI pushes rack densities from ~5–10kW to 50–100kW+, making liquid cooling, greater power capacity, and purpose‑built ‘AI‑ready’ Data Centre campuses essential — whether for regional training clusters or dense inference. What makes a Data Centre AI-ready is the ability to support advanced cooling, predictable scalability and direct access to clouds, networks and partners in a sustainable manner. ... In India, enterprises are rapidly adopting hybrid and multi-cloud architectures as they modernise their digital infrastructure. Domestic enterprises, particularly in BFSI and broking, are moving away from in-house data centres toward third-party colocation facilities to gain scalability, efficient interconnection with their required ecosystem, operational efficiency and access to specialised talent. This shift is being further accelerated by distributed AI, hybrid multi-cloud architectures and a growing focus on sustainability. ... India’s Data Centre market is distinctive because of the scale of its digital consumption, combined with the early stage of ecosystem development. India generates a significant share of global data, yet its installed data centre capacity remains comparatively low, creating strong long-term growth potential. This growth is now being amplified by hyperscalers and AI-led demand. India aims to become a USD 1 T digital economy by 2028. It is already making significant progress, supported by the country’s thriving startup ecosystem, the third largest in the world, and initiatives like Startup India.

Surprise! The One Being Ripped Off by Your AI Agent Is You

It’s now happening all the time: in the sale of location data and browsing histories to brokers who assemble and sell our highly personal profiles, and in DOGE’s and other data grabs across the federal government, where housing, tax, and health information is being weaponized for immigration enforcement or misleading voter fraud “investigations.” With AI agents, it just gets worse. Data betrayal is an even more intimate act. Yet the people who granted OpenClaw access to their accounts were making a reasonable choice—to use a powerful tool on their behalf. ... The data aggregation capabilities of AI add another dimension of risk that rarely gets even a mention, but represent a change in scale that adds up to a sea change, making someone marketed as “productivity” software a menacing vector for data weaponization. The same capabilities that make agents useful—synthesizing enormous amounts of information across sources and acting autonomously across platforms with persistence and memory—make them extraordinarily powerful instruments for state surveillance and targeted repression. An autocratic government could build dossiers on dissidents, journalists, or voters from financial records, social media, location data, and communications metadata, acting in real time: micro-targeting people with persuasion campaigns, swarming targets with coordinated social media attacks, engineering entrapment schemes, or flagging individuals based on patterns no court ever authorized.

What makes Non-Human Identities in AI secure

By aligning security goals with technological advancements, NHIs offer a tangible solution to the challenges posed by AI and cloud-based architectures. Forward-thinking organizations are leveraging this strategic advantage to stay ahead of potential threats, ensuring that their digital remain both protected and resilient. ... Can businesses effectively integrate Non-Human Identities across diverse sectors? Where industries such as financial services, healthcare, and travel become increasingly dependent on digital transformation, the need for securing NHIs is paramount. Each sector presents unique challenges and requirements that necessitate tailored approaches to NHI management. In financial services, for example, the emphasis might be on protecting transactional data, while healthcare organizations focus on safeguarding patient information. Thus, versatile solutions that accommodate varying security demands while maintaining robust protection standards are essential. ... What greater role can NHIs play where emerging technologies unfold? The growing intersection of AI and IoT devices creates a complex web of interactions that requires robust security measures. Non-Human Identities provide a framework for securely managing the myriad connections and transactions occurring between devices. In IoT networks, NHIs authenticate and authorize communication between endpoints, thus safeguarding the integrity of both data and operations.

Daily Tech Digest - February 27, 2026

Quote for the day:

"The best leaders build teams that don’t rely on them. That’s true excellence." -- Gordon Tredgold

Ransomware groups switch to stealthy attacks and long-term access

“Ransomware groups no longer treat vulnerabilities as isolated entry points,” says Aviral Verma, lead threat intelligence analyst at penetration testing and cybersecurity services firm Securin. “They assemble them into deliberate exploitation chains, selecting weaknesses not just for severity, but for how effectively they can collapse trust, persistence, and operational control across entire platforms.” AI is now widely accessible to threat actors, but it primarily functions as a force multiplier rather than a driving force in ransomware attacks. ... Vasileios Mourtzinos, a member of the threat team at managed detection and response firm Quorum Cyber, says that more groups are moving away from high-impact encryption towards extortion-led models that prioritize data theft and prolonged, low-noise access. “This approach, popularized by actors such as Cl0p through large-scale exploitation of third-party and supply chain vulnerabilities, is now being mirrored more widely, alongside increased abuse of valid accounts, legitimate administrative tools to blend into normal activity, and in some cases attempts to recruit or incentivize insiders to facilitate access,” Mourtzinos says. ... “For CISOs, the priority should be strengthening identity controls, closely monitoring trusted applications and third-party integrations, and ensuring detection strategies focus on persistence and data exfiltration activity,” Mourtzinos advises.

Expert Maps Identity Risk and Multi-Cloud Complexity to Evolving Cloud Threats

Cavalancia began by noting that cloud adoption has fundamentally altered traditional security boundaries. With 88 percent of organizations now operating in hybrid or multi-cloud environments, the hardened network edge is no longer the primary control point. Instead, identity and privilege determine access across distributed systems. ... Discussing identity risk specifically, he underscored how central privilege is to modern attacks, saying, "If you don't have identity, you don't have identity, you don't have privilege, you don't have privilege, you don't have a threat." Excessive permissions and credential abuse create privilege escalation paths once access is obtained. ... Reducing exploitable attack paths requires prioritizing risk based on business impact. Rather than attempting to address every vulnerability equally, organizations should identify which exposures would cause the greatest operational or financial harm and focus there first. ... Looking ahead, Cavalancia argued that security must be built around continuous monitoring and identity-first principles. "Continuous monitoring, continuous validation, continuous improvement, maybe we should just have the word continuous here," he said. He also cautioned that AI-assisted attacks are already influencing the threat landscape, noting that "90% of the decisions being made by that attack were done solely by AI, no human intervention whatsoever."

Data Centers in Space: Pi in the Sky or AI Hallucination?

Space is a great place for data centers because it solves one of the biggest problems with locating data centers on Earth: power, argues Google’s Senior Director of Paradigms of Intelligence, Travis Beals. ... SpaceX is also on board with the idea of data centers in space. Last month, it filed a request with the Federal Communications Commission to launch a constellation of up to one million solar-powered satellites that it said will serve as data centers for artificial intelligence. ... “Data centers in space can access solar power 24/7 in certain ‘sun-synchronous’ orbits, giving them all the power they need to operate without putting immense strain on power grids here on Earth,” Scherer told TechNewsWorld. “This would alleviate concerns about consumers having to bear the costs of higher energy use.” “There is also less risk of running out of real estate in space, no complex permitting requirements, and no community pushback to new data centers being built in people’s backyards,” he added. ... “By some estimates, energy and land costs are only around 25% of the total cost for a data center,” Yoon told TechNewsWorld. “AI hardware is the real cost driver, and shifting to space only makes that hardware more expensive.” “Hardware cannot be repaired or upgraded at scale in space,” he explained. “Maintaining satellites is extremely hard, especially if you have hundreds of thousands of them. Maintaining a traditional data center is extremely easy.”

Centralized Security Can't Scale. It's Time to Embrace Federation

In a federated model, the organization recognizes that technology leaders, whether from across security, IT, and Engineering, have a deep understanding of the nuances of their assigned units. Their specialized knowledge helps them set strategies that match the goals, technologies, workflows, and risks they need. That in turn leads to benefits that a centralized security authority can't touch. To start with, security decisions happen faster when the people making them are closer to the action. Service and application owners already have the context and expertise to make the right calls based on their scopes. Delegated authority allows companies to seize market opportunities faster, deploy new tools more easily, manage fewer escalations, and reduce friction and delays. ... In practice, that might look like a CISO setting data classification standards, while partner teams take responsibility for implementing these standards via low-friction policies and capabilities at the source of record for the data. Netflix's security team figured this out early. Their "Paved Roads" philosophy offers a collection of secure options that meet corporate guidelines while being the easiest for developers to use. In other words, less saying no, more offering a secure path forward. Outside of engineering, organization-wide standards also need to provide flexibility and avoid becoming overly specific or too narrow.

Linux explores new way of authenticating developers and their code - here's how it works

Today, kernel maintainers who want a kernel.org account must find someone already in the PGP web of trust, meet them face‑to‑face, show government ID, and get their key signed. ... the kernel maintainers are working to replace this fragile PGP key‑signing web of trust with a decentralized, privacy‑preserving identity layer that can vouch for both developers and the code they sign. ... Linux ID is meant to give the kernel community a more flexible way to prove who people are, and who they're not, without falling back on brittle key‑signing parties or ad‑hoc video calls. ... At the core of Linux ID is a set of cryptographic "proofs of personhood" built on modern digital identity standards rather than traditional PGP key signing. Instead of a single monolithic web of trust, the system issues and exchanges personhood credentials and verifiable credentials that assert things like "this person is a real individual," "this person is employed by company X," or "this Linux maintainer has met this person and recognized them as a kernel maintainer." ... Technically, Linux ID is built around decentralized identifiers (DIDs). This is a W3C‑style mechanism for creating globally unique IDs and attaching public keys and service endpoints to them. Developers create DIDs, potentially using existing Curve25519‑based keys from today's PGP world, and publish DID documents via secure channels such as HTTPS‑based "did:web" endpoints that expose their public key infrastructure and where to send encrypted messages.

IT hiring is under relentless pressure. Here's how leaders are responding

The CIO's relationship with the chief human resources officer (CHRO) matters greatly, though historically, they've viewed recruitment through different lenses. HR professionals tend not to be technologists, so their approach to hiring tends to be generic. Conversely, IT leaders aren't HR professionals. Many of them were promoted to management or executive roles for their expert technical skills, not their managerial or people skills. ... The multigenerational workforce can be frustrating for everyone at times, simply because employees' lives and work experiences can be so different. While not all individuals in a demographic group are homogeneous, at a 30,000-foot view, Gen Z wants to work on interesting and innovative projects -- things that matter on a greater scale, such as climate change. They also expect more rapid advancement than previous generations, such as being promoted to a management role after a year or two versus five or seven years, for example. ... Most organizational leaders will tell you their companies have great cultures, but not all their employees would likely agree. Cultural decisions made behind closed doors by a few for the many tend to fail because too many assumptions are made, and not enough hypotheses tested. "Seeing how your job helps the company move forward has been a point of opacity for a long time, and after a certain point, it's like, 'Why am I still here?'" Skillsoft's Daly said.

Generative AI has ushered in a new era of fraud, say reports from Plaid, SEON

“Generative AI has lowered the barrier to creating fake personas, falsifying documents, and impersonating real people at scale,” says a new report from Plaid, “Rethinking fraud in the AI era.” “As a result, fraud losses are projected to reach $40 billion globally within the next few years, driven in large part by AI-enabled attacks.” The warning is familiar. What’s different about Plaid’s approach to the problem is “network insights” – “each person’s unique behavioral footprint across the broader financial and app ecosystem,” understood as a system of relationships and long-standing patterns. In these combined signals, the company says, can be found “a resilient, high-signal lens into intent, risk and legitimacy.” ... “The industry is overdue for its next wave of fraud-fighting innovation,” the report says. “The question is not whether change is needed, but what unique combination of data, insights, and analytics can meet this moment.” The AI era needs its weapon of choice, and it needs to work continuously. “AI driven fraud is exposing the limits of identity controls that were designed for point in time verification rather than continuous assurance,” says Sam Abadir, research director for risk, financial (crime & compliance) at IDC, as quoted in the Plaid report. ... The overarching message is that “AI is real, embedded and widely trusted, but it has not materially reduced the scope of fraud and AML operations.” Fraud continues to scale, enabled by the same AI boom.

The hidden cost of AI adoption: Why most companies overestimate readiness

Walk into enough leadership meetings and you’ll hear the same story told with different accents: “We need AI.” It shows up in board decks, annual strategy documents and that one slide with a hockey-stick curve that magically turns pilot into profit. ... When I talk about the hidden cost of AI adoption, I’m not talking about model pricing or vendor fees. Those are visible and negotiable. The real cost lives in the messy middle: data foundations, integration work, operating model changes, governance, security, compliance and the ongoing effort required to keep AI useful after the demo fades. ... If I had to summarize AI readiness in one sentence, it would be this: AI readiness is your organization’s ability to repeatedly take a business problem, turn it into a well-defined decision or workflow, feed it trustworthy data and ship a solution you can monitor, audit and improve. ... Having data is not the same as having usable data. AI systems amplify quality problems at scale. Until proven otherwise, “we already have the data” usually means duplicated records, inconsistent definitions, missing fields, sensitive data in the wrong places and unclear ownership. ... If it adds friction or produces unreliable outputs, adoption collapses fast. Vendor risk doesn’t disappear either. Pricing changes. Usage spikes. Workflows become coupled to tools you don’t fully control. Without internal ownership, you’re not building capability, you’re renting it.

Overcoming Security Challenges in Remote Energy Operations

The security landscape for remote facilities has shifted "dramatically," and energy providers can no longer rely on isolation for protection, said Nir Ayalon, founder and CEO of Cydome, a maritime and critical infrastructure cybersecurity firm. "These sites are just as exposed as a corporate office - but with far more complex operational challenges," Ayalon said. ... A recent PES Wind report by Cyber Energia found that only 1% of 11,000 wind assets worldwide have adequate cyber protection, while U.K.-based renewable assets face up to 1,000 attempted cyberattacks daily. Trustwave SpiderLabs also reported an 80% rise in ransomware attacks on energy and utilities in 2025, with average costs exceeding $5 million. Ransomware is the most common form of attack. ... Protecting offshore facilities is also costly and a major challenge. Sending a technician for on-site installation can run up to $200,000, including vessel rental. Ayalon said most sites lack specialized IT staff. The person managing the hardware is usually an operator or engineer and not necessarily a certified cybersecurity professional. Limited space for racks and equipment, as well as poor bandwidth poses major challenges, said Rick Kaun, global director of cybersecurity services at Rockwell Automation. ... Designing secure offshore energy systems and shipping vessels is no longer a choice but a necessity. Cybersecurity can't be an afterthought, said Guy Platten, secretary general of the International Chamber of Shipping.

How the CISO’s Role is Evolving From Technologist to Chief Educator

Regardless of structure, modern CISOs are embedded in executive decision-making, legal strategy and supply chain oversight. Their responsibilities have expanded from managing technical defenses to maintaining dynamic risk portfolios, where trade-offs must be weighed across business functions. Stakeholders now include regulators, customers and strategic partners, not just internal IT teams. ... Effective leaders accumulate knowledge and know when to go deep and when to delegate, ensuring subject-matter experts are empowered while key decisions remain aligned to business outcomes. This blend of technical insight and strategic judgment defines the CISO’s value in complex environments. ... As security becomes more embedded in daily operations, cultural leadership plays a defining role in long-term resilience. A positive cybersecurity culture is proactive and free from blame, creating an environment where employees feel safe to speak up and suggest improvements without fear of repercussions. This shift leads to earlier detection, better mitigation and stronger overall security posture. Teams asking for security input during the design phase and employees self-reporting suspicious activity signal a mature culture that understands protection is everyone’s job. ... The modern CISO operates at the intersection of technology, risk, leadership and influence. Leaders must navigate shifting business priorities and complex stakeholder relationships while building a strong security culture across the enterprise.

Daily Tech Digest - February 26, 2026

Quote for the day:

"It is not such a fierce something to lead once you see your leadership as part of God's overall plan for his world." -- Calvin Miller

Boards don’t need cyber metrics — they need risk signals

Decision-makers want to know whether risk is increasing or decreasing, whether controls are effective, and whether the organization can limit damage when prevention fails. Metrics are therefore useful when they clarify those questions. “Time is really the universal metric because everyone can understand time,” Richard Bejtlich, strategist and author in residence at Corelight, tells CSO. “How fast do we detect problems, and how fast do we contain them. Dwell time, containment time. That’s the whole game for me.” Organizations cannot prevent every intrusion, Bejtlich argues, but they can measure how quickly they recognize and contain one. ... Wendy Nather, a longtime CISO who is now an advisor at EPSD, cautions against equating measurement with understanding. “When you are reporting to the board, there are some things you just cannot count that you have to report anyway,” she tells CSO. She points to incidents, near misses, and changes in assumptions as examples. “Anything that changes your assumptions about how you’re managing your security program, you should be bringing those to the board, even if you can’t count them,” Nather says. Regular metrics can create a rhythm of predictability, and that predictability could lull board members into a false sense of security. “Metrics are very seductive,” she says. “They lead us toward things that can be counted, that happen on a regular basis.” The result may be a steady flow of data that obscures structural risk or emerging weaknesses, Nather warns.

The Enterprise AI Postmortem Playbook: Diagnosing Failures at the Data Layer

Your first rule of the playbook is to treat AI incidents as data incidents – until proven otherwise. You should start by tagging the failure type. Document whether it’s a structure issue, retrieval misalignment, conflict with metric definition, or other categories. Ideally, you want to assign the issue to an owner and attach evidence to force some discipline into the review. Try to classify the issue into clearly defined buckets. For example, you can classify into these four buckets: structural failure, retrieval misalignment, definition conflict, or freshness failure. Once this part is clear, the investigation becomes more focused. The goal with this step is to isolate the data fault line. ... The next step is to move one layer deeper. Identify the source table behind the retrieved context. You also want to confirm the timestamp of the last refresh. Check whether any ingestion jobs failed, partially completed, or ran late. Silent failures are common. A job may succeed technically while loading incomplete data. As you go through the playbook continue tracing upstream. Find the transformation job that shaped the dataset. Look at recent schema changes. Check whether any business rules were updated. The idea here is to rebuild the exact path that led to the output. Try to not make any assumptions at this stage about model behavior – simply keep tracing until the process is complete. Don’t be surprised if the model simply worked with what it was given.

Top Attacks On Biometric Systems (And How To Defend Against Them)

Presentation attacks, often referred to as spoofing attacks, occur when an attacker presents a fake biometric sample to a sensor (like a camera or microphone) in an attempt to impersonate a legitimate user. Common examples include printed photos, video replays, silicone masks, prosthetics or synthetic fingerprints. More recently, high-quality deepfake videos have become a powerful new tool in the attacker’s arsenal. ... Passive liveness techniques, which analyze subtle physiological and behavioral signals without requiring user interaction, are particularly effective because they reduce friction while improving security. However, liveness detection must be resilient to unknown attack methods, not just tuned to detect known spoof types. ... Not all biometric attacks happen in front of the sensor. Replay and injection attacks target the biometric data pipeline itself. In these scenarios, attackers intercept, replay or inject biometric data, such as images or templates, directly into the system, bypassing the sensor entirely. ... Defensive strategies must extend beyond the biometric algorithm. Secure transmission, encryption in transit, device attestation, trusted execution environments and validation that data originates from an authorized sensor are all essential. ... Although less visible to end users, attacks targeting biometric templates and databases can pose long-term risks. If biometric templates are compromised, the impact extends far beyond a single breach.

Open-source security debt grows across commercial software

High and critical risk findings remain widespread. Most codebases contain at least one high risk vulnerability, and nearly half contain at least one critical risk issue. Those rates dipped slightly from the prior year even as total vulnerability counts rose. Supply chain attacks add another layer of risk. Sixty five percent of surveyed organizations experienced a software supply chain attack in the past year. ... “As AI reshapes software development, security teams will have to continue to adapt in turn. Security budgets and security guidelines should reflect this new reality. Leaders should continue to invest in tooling and education required to equip teams to manage the drastic increase in velocity, volume, and complexity of applications,” Mackey said. Board level reporting also requires adjustment as vulnerability volumes rise. ... Outdated components appear in nearly every audited environment. More than nine in ten codebases contain components that are several years out of date or show no recent development activity. A large share of components run many versions behind current releases. Only a small fraction operate on the latest available version. This maintenance debt intersects with regulatory obligations. The EU Cyber Resilience Act entered into effect in late 2024, with key reporting requirements taking effect in 2026 and broader enforcement following in 2027.

The agentic enterprise: Why value streams and capability maps are your new governance control plane

The enterprise is currently undergoing a seismic pivot from generative AI, which focuses on content creation, to agentic AI, which focuses on goal execution. Unlike their predecessors, these agents possess “structured autonomy”: the ability to perceive contexts, plan actions and execute across systems without constant human intervention. For the CIO and the enterprise architect, this is not merely an upgrade in automation speed; it is a fundamental shift in the firm’s economic equation. We are moving from labor-centric workflows to digital labor capable of disassembling and reassembling entire value chains. ... In an agentic enterprise, the value stream map is no longer just a diagram; it is the control plane. It must explicitly define the handoff protocols between human and digital agents. In my opinion, Value stream maps must move from static documents stored in a repository to context documents used to drive agentic automation. ... If a value stream does not exist, you cannot automate it. For new agentic workflows, do not map the current human process. Instead, use an outcome-backwards approach. Work backward from the concrete deliverable (e.g., customer onboarded) to identify the minimum viable API calls required. Before granting write access, run the new agentic stream in shadow mode to validate agent decisions against human outcomes.

Beyond compliance: Building a culture of data security in the digital enterprise

Cyber compliance is something organisations across industrial sectors take seriously, especially with new regulations getting introduced and non-compliance having consequences such as hefty penalties. Hence, businesses are placing compliance among their top priorities. However, hyper-focusing only on compliance can lead to tunnel vision, crippling creativity, and innovation. It fails to offer a comprehensive risk assessment due to the checklist approach it follows, exposing organizations to vulnerabilities and fast-evolving threats. Having a compliance-first mindset can lead to incomplete risk assessment, creating blind spots and security gaps in security provisions. ... With businesses relying on data for operations, customer engagement, and decision-making, ensuring data security protects both users and organisations. Data breaches have severe consequences, including financial losses, reputational damage, customer churn, and regulatory penalties. With data moving across on-premises data centers, cloud platforms, third-party ecosystems, remote work environments, and AI-driven applications, there is a need for a holistic, culture-driven approach to cybersecurity. ... Data protection traditionally was focused on safeguarding the perimeter by securing networks and systems within the physical boundaries where data was normally stored.

If you thought RTO battles were bad, wait until AI mandates start taking hold across the industry

With the advent of generative AI and the incessant beating of the drum by executives hellbent on unlocking productivity gains, we could see a revival of the dreaded workforce mandate –- only this time with AI. We’ve already had a glimpse of the same RTO tactics being used with AI over the last year. In mid-2025, Microsoft introduced new rules aimed at boosting AI use across the company, with an internal memo warning staff that “using AI is no longer optional”. ... As with RTO mandates, we’re now reaching a point where upward mobility within the enterprise could be at risk as a result of AI use. It’s a tactic initially touted by Dell in 2024 when enforcing its own hybrid work rules, which prompted a fierce backlash among staff. Forcing workers to use AI or risk losing out on promotions will have the desired effect executives want, namely that employees will use the technology, but that’s missing the point entirely. AI has been framed by many big tech providers as a prime opportunity to supercharge productivity and streamline enterprise efficiency. We’ve all heard the marketing jargon. If business leaders are at the point where they’re forcing staff to use the technology, it begs the question of whether it’s actually having the desired effect, which recent analysis suggests it’s not. ... Recent analysis from CompTIA found roughly one-third of companies now require staff to complete AI training.

In perfect harmony: How Emerald AI is turning data centers into flexible grid assets

At the core of Emerald AI is its Emerald Conductor platform. Described by Sivaram as “an AI for AI,” the system orchestrates thousands of AI workloads across one or more data centers, dynamically adjusting operations to respond to grid conditions while ensuring the facility maintains performance. The system achieves this through a closed-loop orchestration platform comprising an autonomous agent and a digital twin simulator. ... A point keenly pointed out by Steve Smith, chief strategy and regulation officer at National Grid, at the time of the announcement: “As the UK’s digital economy grows, unlocking new ways to flexibly manage energy use is essential for connecting more data centers to our network efficiently.” The second reason was National Grid's transatlantic stature - as an American company active in both the UK and US markets - and its commitment to the technology. “They’ve invested in the program and agreed to a demo, which makes them the ideal partner for our first international launch,” says Sivaram. The final, and most important, factor, notes Sivaram, was the access to the NextGrid Alliance, a consortium of 150 utilities worldwide. By gaining access to such a robust partner network, the deal could serve as a springboard for further international projects. This aligns with the company’s broader partnership approach. Emerald AI has already leveraged Nvidia’s cloud partner network to test its technology across US data centers, laying the groundwork for broader deployment and continued global collaboration.

7 ways to tame multicloud chaos with generative AI

Architects have the difficult job of understanding tradeoffs between proprietary cloud services and cross-cloud platforms. For example, should developers use AWS Glue, Azure Data Factory, or Google Cloud Data Fusion to develop data pipelines on the respective platforms, or should they adopt a data integration platform that works across clouds? ... “Managing multicloud is like learning multiple languages from AWS, Azure, Oracle, and others, and it’s rare to have teams that can traverse these environments fluidly and effectively. Plus, services and concepts are not portable among clouds, especially in cloud-native PaaS services that go beyond IaaS,” says Harshit Omar, co-founder and CTO at FluidCloud. One way to work around this issue is to assign an AI agent to support the developer or architect in evaluating platform selections. ... Standardizing infrastructure and service configurations across different clouds requires expertise in different naming conventions, architecture, tools, APIs, and other paradigms. Look for genAI tools to act as a translator to streamline configurations, especially for organizations that can templatize their requirements. ... CI/CD, infrastructure-as-code, and process automation are key tools for driving efficiency, especially when tasks span multiple cloud environments. Many of these tools use basic flows and rules to streamline tasks or orchestrate operations, which can create boundary cases that cause process-blocking errors.

It’s Time To Reinforce Institutional Crypto Key Management With MPC: Sodot CEO

For years, crypto security operations were almost exclusively focused on finding a way to protect the private keys to crypto wallets. It’s known as the “custody risk,” and it will always be a concern to anyone holding digital assets. However, Sofer believes that custody is no longer the weakest link. Cyberattackers have come to realize that secure wallets, often held in cold storage, are far too difficult to crack. ... Sodot has built a self-hosted infrastructure platform that leverages a pair of cutting-edge security techniques – namely, Multi-Party Computation or MPC and Trusted Execution Environments or TEEs. With Sodot’s platform, API keys are never reassembled in full plaintext, eliminating one of the main weaknesses of traditional secrets managers, which typically expose the entire key to any authenticated machine. Instead, Sodot uses MPC to split each key into multiple “shares” that are held by different partners on different technology stacks, Sofer explained. Distributing risk in this way makes an attacker’s job exponentially more difficult, as it means they would have to compromise multiple isolated systems to gain access. ... “Keys are here to stay, and they will control more value and become more sensitive as technology progresses,” Sofer concluded. “As financial institutions get more involved in crypto, we believe demand for self-hosted solutions that secure them will only grow, driven by performance requirements, operational resilience, and control over security boundaries.”