Daily Tech Digest by Kannan Subbiah: Edge AI

Showing posts with label Edge AI. Show all posts

Daily Tech Digest - January 20, 2026

Quote for the day:

"The level of morale is a good barometer of how each of your people is experiencing your leadership." -- Danny Cox

The culture you can’t see is running your security operations

Non-observable culture is everything happening inside people’s heads. Their beliefs about cyber risk. Their attitudes toward security. Their values and priorities when security conflicts with convenience or speed. This is where the real decisions get made. You can’t see someone’s belief that “we’re too small to be targeted” or “security is IT’s job, not mine.” You can’t measure their assumption that compliance equals security. You can’t audit their gut feeling that reporting a mistake will hurt their career. But these invisible forces shape every security decision your people make. Non-observable culture includes beliefs about the likelihood and severity of threats. It includes how people weigh security against productivity. It includes their trust in leadership and their willingness to admit mistakes. It includes all the cognitive biases that distort risk perception. ... Implicit culture is the stuff nobody talks about because nobody even realizes it’s there. The unspoken assumptions. The invisible norms. The “way things are done here” that everyone knows but nobody questions. This is the most powerful layer because it operates below conscious awareness. People don’t choose to follow implicit norms. They do. Automatically. Without thinking. Implicit culture includes unspoken beliefs like “security slows us down” or “leadership doesn’t really care about this.” It contains hidden power dynamics that determine who can challenge security decisions and who can’t.

The top 6 project management mistakes — and what to do instead

Project managers are trained to solve project problems. Scope creep. Missed deadlines. Resource bottlenecks. ... Start by helping your teams understand the business context behind the work. What problem are we trying to solve? Why does this project matter to the organization? What outcome are we aiming for? Your teams can’t answer those questions unless you bring them into the strategy conversation. When they understand the business goals, not just the project goals, they can start making decisions differently. Their conversations change to ensure everyone knows why their work matters. ... Right from the start of the project, you need to define not just the business goal but how you’ll measure it was successful in business terms. Did the project reduce cost, increase revenue, improve the customer experience? That’s what you and your peers care about, but often that’s not the focus you ask the project people to drive toward. ... People don’t resist because they’re lazy or difficult. They resist because they don’t understand why it’s happening or what it means for them. And no amount of process will fix that. With an accelerated delivery plan designed to drive business value, your project teams can now turn their attention to bringing people with them through the change process. ... To keep people engaged in the project and help it keep accelerating toward business goals, you need purpose-driven communication designed to drive actions and decisions.

AI has static identity verification in its crosshairs. Now what?

Identity models based on “joiner–mover–leaver” workflows and static permission assignments cannot keep pace with the fluid and temporary nature of AI agents. These systems assume identities are created carefully, permissions are assigned deliberately, and changes rarely happen. AI changes all of that. An agent can be created, perform sensitive tasks, and terminate within seconds. If your verification model only checks identity at login, you’re leaving the entire session vulnerable. ... Securing AI-driven enterprises requires a shift similar to what we saw in the move from traditional firewalls to zero-trust architectures. We didn’t eliminate networks; we elevated policy and verification to operate continuously at runtime. Identity verification for AI must follow the same path. This means building a system that can: Assign verifiable identities to every human and machine actor; Evaluate permissions dynamically based on context and intent; Enforce least privilege at high velocity; Verify actions, not just entry points; ... This is why frameworks like SPIFFE and modern workload identity systems are receiving so much attention. They treat identity as a short-lived, cryptographically verifiable construct that can be created, used, and retired in seconds, exactly the model AI agents require. Human activity is becoming the minority as autonomous systems that can act faster than we can are being spun up and terminated before governance can keep up. That’s why identity verification must shift from a checkpoint to a real-time trust engine that evaluates every action from every actor, human or AI.

AWS European cloud service launch raises questions over sovereignty

AWS established a new legal entity to operate the European Sovereign Cloud under a separate governance and operational model. The new company is incorporated in Germany and run exclusively by EU residents, AWS said. ... “This is the elephant in the room,” said Rene Buest, senior director analyst at Gartner. There are two main concerns regarding the operation of AWS’s European Sovereign Cloud for businesses in Europe. The first relates to the 2018 US Cloud Act, which could require AWS to disclose customer data stored in Europe to the United States, if requested by US authorities. The second involves the possibility of US government sanctions: If a business that uses AWS services is subject to such sanctions, AWS may be compelled to block that company’s access to its cloud services, even if its data and operations are based in Europe. ... It’s an open question at this stage, said Dario Maisto, senior analyst at Forrester. “Cases will have to be tested in court before we can have a definite answer,” he said. “The legal ownership does matter, and this is one of the points that may not be addressed by the current setup of the AWS sovereign cloud.” AWS’s European Sovereign Cloud represents one of several ways that European business can approach the challenge of digital sovereignty. Gartner identifies a spectrum that ranges from global hyperscaler public cloud services through to regional cloud services that are based on non-hyperscaler technology.

Why peripheral automation is the missing link in end-to-end digital transformation?

While organisations have successfully modernized their digital cores, the “last mile” of business operations often remains fragmented, manual, and surprisingly analogue. This gap is why Peripheral Automation is emerging not merely as a tactical correction but as the critical missing link in achieving true, end-to-end digital transformation. ... Peripheral Automation offers a strategic resolution to this paradox. It’s an architectural philosophy that advocates “differential innovation.” Rather than disrupting stable cores to accommodate fleeting business needs, organisations build agile, tailored applications and workflows that sit on top of the core systems. This approach treats the enterprise as a layered ecosystem. The core remains the single source of truth, but the periphery becomes the “system of engagement”. By leveraging modern low-code platforms and composable architecture, leaders can deploy lightweight, purpose-built automation tools that address specific friction points without altering the underlying infrastructure. ... Peripheral automation reduces process latency, manual effort, and rework. By addressing specific pain points rather than attempting broad, multi-year system redesigns, companies unlock measurable efficiency in weeks. This precision improves throughput, reduces cycle times, and frees teams to focus on high-value work.

How does agentic ops transform IT troubleshooting?

AI Canvas introduces a fundamentally different user experience for network troubleshooting. Rather than navigating through multiple dashboards and CLI interfaces, engineers interact with a dynamic canvas that populates with relevant widgets as troubleshooting progresses. You could say that the ‘canvas’ part of the name AI Canvas is the most important part of it. That is, AI Canvas is actually a blank canvas every time you start troubleshooting. It fills the canvas with boxes and on the fly widgets, among other things, during the troubleshooting. Sampath confirms this: “When you ask a question, it’s using and picking the right types of tools that it can go and execute on a specific task and calls agents to be able to effectively take a task to completion and returns a response back.” The system can spin up monitoring agents that continuously provide updated information, creating a living troubleshooting environment rather than static reports. ... AI Canvas doesn’t exist in isolation. It builds on Cisco’s existing automation foundation. The company previously launched Workflows, a no-code network automation engine, and AI assistants with specific skills for network operations. “All of the automations that are already baked into the workflows, the skills that were built inside of the assistants, now manifest themselves inside of the canvas,” Sampath details. This creates a continuum from deterministic workflows to semi-autonomous assistants to fully autonomous agentic operations.

UK government launches industry 'ambassadors' scheme to champion software security improvements

"By acting as ambassadors, signatories are committing to a process of transparency, development and continuous improvement. The implementation of this code of practice will take time and, in doing so, may bring to light issues that need to be addressed," DSIT said in a statement confirming the announcement. "Signatories and policymakers will learn from these issues as well as the successes and challenges for each organization and, where appropriate, will share information to help develop and strengthen this government policy." ... The Software Security Code of Practice was unveiled by the NCSC in May last year, setting out a series of voluntary principles defining what good software security looks like across the entire software lifecycle. Aimed at technology providers and organizations that develop, sell, or procure software, the code offers best practices for secure design and development, build-environment security, and secure deployment and maintenance. The code also emphasizes the importance of transparent communication with customers on potential security risks and vulnerabilities. ... “The code moves software security beyond narrow compliance and elevates it to a board-level resilience priority. As supply chain attacks continue to grow in scale and impact, a shared baseline is essential and through our global community and expertise, ISC2 is committed to helping professionals build the skills needed to put secure-by-design principles into practice.”

Privacy teams feel the strain as AI, breaches, and budgets collide

Where boards prioritize privacy, AI use appears more frequently and follows defined direction. Larger enterprises, particularly those with broader risk and compliance functions, also report higher uptake. In smaller organizations, or those where privacy has limited visibility at the leadership level, AI adoption remains tentative. Teams that apply privacy principles throughout system development report higher use of AI for privacy tasks. In these environments, AI supports ongoing work rather than introducing new approaches. ... Respondents working in organizations where privacy has active board backing report more consistent use of privacy by design. Budget stability shows a similar pattern, with better-funded teams reporting stronger integration of privacy into design and engineering work. The study also shows that privacy by design on its own does not stop breaches. Organizations that experienced breaches report similar levels of design practice as those that did not. The data places privacy by design mainly in a governance and compliance role, with limited connection to incident prevention. ... Governance shapes how teams view that risk. Professionals in organizations where privacy lacks board priority report higher expectations of a breach in the coming year. Gaps between privacy strategy and broader business goals also appear alongside higher breach expectations, suggesting that structural alignment influences outlook as much as technical controls. Confidence remains common, even among organizations that have experienced breaches.

Cyber Insights 2026: Information Sharing

The sheer volume of cyber threat intelligence being generated today is overwhelming. “Information sharing channels often help condense inputs and highlight genuine signals amid industry noise,” says Caitlin Condon, VP of security research at VulnCheck. “The very nature of cyber threat intelligence demands validation, context, and comparison. Information sharing allows cybersecurity professionals to more rigorously assess rising threats, identify new trends and deviations, and develop technically comprehensive guidance.” ... “The importance of the Cybersecurity Information Sharing Act of 2015 for U.S. national security cannot be overstated,” says Crystal Morin, cybersecurity strategist at Sysdig. “Without legal protections, many legal departments would advise security teams to pull back from sharing threat intelligence, resulting in slower, more cautious processes. ...” CISOs have developed their own closed communities where they can discuss current incidents with other CISOs. This is done via channels such as Slack, WhatsApp and Signal. Security of the channels is a concern, but who better than multiple CISOs to monitor and control security? ... “Much of today’s threat intelligence remains reactive, driven by short-lived IoCs that do little to help agencies anticipate or disrupt cyberattacks,” comments BeyondTrust’s Greene. “We need to modernize our information-sharing framework to emphasize behavior-based analytics enriched with identity-centric context,” he continues.

Edge AI: The future of AI inference is smarter local compute

The bump in edge AI goes hand in hand with a broader shift in focus from AI training, the act of preparing machine learning (ML) models with the right data, to inference, the practice of actively using models to apply knowledge or make predictions in production. “Advancements in powerful, energy-efficient AI processors and the proliferation of IoT (internet of things) devices are also fueling this trend, enabling complex AI models to run directly on edge devices,” says Sumeet Agrawal ... “The primary driver behind the edge AI boom is the critical need for real-time data processing,” says David. The ability to analyze data on the edge, rather than using centralized cloud-based AI workloads, helps direct immediate decisions at the source. Others agree. “Interest in edge AI is experiencing massive growth,” says Informatica’s Agrawal. For him, reduced latency is a key factor, especially in industrial or automotive settings where split-second decisions are critical. There is also the desire to feed ML models personal or proprietary context without sending such data to the cloud. “Privacy is one powerful driver,” says Johann Schleier-Smith ... A smaller footprint for local AI is helpful for edge devices, where resources like processing capacity and bandwidth are constrained. As such, techniques to optimize SLMs will be a key area to aid AI on the edge. One strategy is quantization, a model compression technique that reduces model size and processing requirements.

Daily Tech Digest - November 13, 2025

Quote for the day:

“You get in life what you have the courage to ask for.” -- Nancy D. Solomon

Does your chatbot have 'brain rot'? 4 ways to tell

Oxford University Press, publisher of the Oxford English Dictionary, named "brain rot" as its 2024 Word of the Year, defining it as "the supposed deterioration of a person's mental or intellectual state, especially viewed as the result of overconsumption of material (now particularly online content) considered to be trivial or unchallenging." ... Trying to draw exact connections between human cognition and AI is always tricky, despite the fact that neural networks -- the digital architecture upon which modern AI chatbots are based -- were modeled upon networks of organic neurons in the brain. ... That said, there are some clear parallels: as the researchers note in the new paper, for example, models are prone to "overfitting" data and getting caught in attentional biases ... If the ideal AI chatbot is designed to be a completely objective and morally upstanding professional assistant, these junk-poisoned models were like hateful teenagers living in a dark basement who had drunk way too much Red Bull and watched way too many conspiracy theory videos on YouTube. Obviously, not the kind of technology we want to proliferate. ... Obviously, most of us don't have a say in what kind of data gets used to train the models that are becoming increasingly unavoidable in our day-to-day lives. AI developers themselves are notoriously tight-lipped about where they source their training data from, which means it's difficult to rank consumer-facing models

7 behaviors of the AI-Savvy CIO

"The single most critical takeaway for CIOs is that a strong data foundation isn't optional -- it's critical for AI success. AI has made it easy to build prototypes, but unless you have your data in a single place, up to date, secured, and well governed, you'll struggle to put those prototypes into production. The team laying the groundwork for that foundation and getting enterprises' data AI-ready is data engineering. CIOs who still see data engineering as a back-office function are already five years behind, and probably training their future competitors. ... "Your data will never be perfect. And it doesn't have to be. It needs to be indicative of your company's reality. But your data will get a lot better if you first use AI to improve the UX. Then people will use your systems more, and in the way intended, creating better data. That better data will enable better AI. And the virtuous cycle will have begun. But it starts with the human side of the equation, not the technological." ... CIOs don't need deep technical mastery such as coding in Python or tuning neural networks -- but they must understand AI fundamentals. This includes grasping core AI principles, machine learning concepts, statistical modeling, and ethical implications. Mastery starts with CIOs understanding AI as an umbrella of technologies that automate different things. With this foundational fluency, they can ask the right questions, interpret insights effectively, and make informed strategic decisions. Let's look at the three AI domains.

The economics of the software development business

Some software companies quietly tolerated piracy, figuring that the more their software spread—even illegally—the more legitimate sales would follow in the long run. The argument was that if students and hobbyists pirated the software, it would lead to business sales when those people entered the workforce. The catchphrase here was “piracy is cheaper than marketing.” This was never an official position, but piracy was often quietly tolerated. ... Over the years, the boxes got thinner and the documentation went onto the Internet. For a time, though, “online help” meant a *.hlp file on your hard drive. People fought hard to keep that type of online help well into the Internet age. “What if I’m on an airplane? What if I get stranded in northern British Columbia?” Eventually, the physical delivery of software went away as Internet bandwidth allowed for bigger and bigger downloads. ... SaaS too has interesting economic implications for software companies. The marketplace generally expects a free tier for a SaaS product, and delivering free services can become costly if not done carefully. The compute costs money, after all. An additional problem is making sure that your free tier is good enough to be useful, but not so good that no one wants to move up to the paid tiers. ... The economics of software have always been a bit peculiar because the product is maddeningly costly to design and produce, yet incredibly easy to replicate and distribute. The years go by, but the problem remains the same: how to turn ideas and code into a profitable business?

Beyond the checklist: Shifting from compliance frameworks to real-time risk assessments

One of the most overlooked aspects of risk assessments is cadence. While gap analyses are sometimes done yearly or to prepare for large-scale audits, risk assessments need to be continuous or performed on a regular schedule. Threats do not respect calendar cycles. Major changes, including new technologies, mergers, regulatory changes or implementing AI, need to trigger reassessments. ... Risk assessments should culminate in outputs that business leaders can act on. This includes a concise risk heat map, a prioritized remediation roadmap and clear asks, such as budget, ownership and timelines. These deliverables convert technical findings into strategic decisions. They also help build trust with stakeholders, especially in organizations that may be new to formal risk management. ... Targeted risk assessments can be viewed as a low-cost, fundamental option. They are best suited to companies that have limited budget or are not prepared for a full review of the framework. With reduced scope, shorter turnaround and transparent business value, such assessments enable rapid establishment of trust, delivering prioritized outcomes. ... Risk assessments are not just checkboxes. They are tools for making decisions. The best programs are aligned with the business, focused, consistent and made to change over time.

Legitimate Interest as a Lawful Basis: Pros, Cons and the Indian DPDP Act’s Stance

Under the EU’s General Data Protection Regulation (GDPR), for example, a company can process data if it is “necessary for the purposes of the legitimate interests pursued by the controller or by a third party” (Article 6(1)(f) GDPR), so long as those interests are not overridden by the individual’s rights. However, India’s new Digital Personal Data Protection Act, 2023 (DPDP Act) pointedly does not include legitimate interest as a standalone lawful ground for processing. Instead, the Indian law relies primarily on consent and a limited set of “legitimate uses” explicitly enumerated in the statute. This divergence raises important questions about the pros and cons of the legitimate interest basis, its impact on the free flow of data, and whether India might benefit from adopting a similar concept. ... India’s decision to omit a general legitimate interest clause has sparked debate. There are advantages and disadvantages to this approach, and its impact on data flows and innovation is a key consideration. Pros / Rationale for Omission: From a privacy rights perspective, the absence of an open-ended legitimate interest basis means stronger individual control and legal certainty. The law explicitly tells citizens and businesses what the non-consensual exceptions are mostly common-sense or public interest scenarios and everything else by default requires consent.

CIOs: Collect the right data now for future AI-enabled services

In its Technology Trends Outlook for 2025 report, McKinsey suggests the technology landscape continues to undergo significant innovation-sponsored shifts. The consultant says success will depend on executives identifying high-impact areas where their businesses can use AI, while addressing external factors such as regulatory shifts and ecosystem readiness. CIOs, as the guardians of enterprise technology, will be expected to embrace this challenge, but how? For Steve Lucas, CEO at technology specialist Boomi, digital leaders must start with a recognition that the surfeit of data held in modern enterprises is simply a starting point for what comes next. “There’s plenty of data,” he says. “In fact, there’s too much of it. We worry about collecting, storing, and accessing data. I think a successful approach is about determining the data that matters. As a CIO, do you understand what data matters today and what emerging technologies will matter tomorrow?” ... While it can be tough to find the wood for the trees, Corbridge suggests CIOs should search for established data roots within the enterprise. “It’s about going back to the huge volumes of data you’ve got already and working out how you put that information in the right place so it can be used in the right way for your AI projects,” he says. Focusing on the fine details is an approach that chimes with Ian Ruffle, head of data and insight at UK breakdown specialist RAC.

How TTP-based Defenses Outperform Traditional IoC Hunting

To fight modern ransomware, organizations must shift from chasing IoCs to detecting attacker behaviors — known as Tactics, Techniques, and Procedures (TTPs). The MITRE ATT&CK framework provides a detailed overview of these behaviors throughout the attack lifecycle, from initial access to impact. TTPs are challenging for attackers to modify because they represent core behavioral patterns and strategic approaches, unlike IoCs which are surface-level elements that can be easily altered. This shift is reinforced by the so-called ‘Pyramid of Pain’ – a conceptual model that ranks indicators by how difficult they are for adversaries to alter. At the base are easily changed elements like hash values and IP addresses. At the top are TTPs, which represent the attacker’s core behaviors and strategies. Disrupting TTPs forces adversaries to change their entire strategy, which makes behavior-based detection the most effective and resource-consuming method for them to avoid. ... When security and networking are natively integrated, policy enforcement is consistent, micro-segmentation is practical, and containment actions can be executed inline without stitching together multiple consoles. The cloud model also enables continuous, global updates to prevention logic and the ability to apply AI/ML on aggregated, high‑fidelity data feeds to reduce noise and improve detection quality. All this reminds me of the OODA military model that can help speed up incident response.

Healthcare security is broken because its systems can’t talk to each other

To maintain effectiveness, healthcare organizations should continually evaluate their security toolset for relevance, integration potential, and overall value to the security program. Prioritizing solutions that support open standards, and seamless integration helps minimize context switching and alert fatigue, while ensuring that the security team remains engaged and productive. Ultimately, the decision to balance specialized point solutions with broader integrated platforms must be guided by strategic priorities, resource capacity, and the need to support both operational efficiency and clinical excellence. ... A critical consideration is the interoperability of security tools across both cloud and on-premises environments. Healthcare organizations must assess if their security solutions need to span multiple cloud providers, support on-premises systems, or both, and determine how long on-premises support will be necessary as applications gradually shift to the cloud. Cloud providers are increasingly acquiring and integrating advanced security technologies, offering unified solutions that reduce the need for multiple monitoring platforms. This consolidation not only lessens alert fatigue but also enhances real-time visibility to security threats, an important advantage for healthcare, where timely detection is vital for protecting patient data and ensuring clinical continuity.

The Hard Truth About Trying to AI-ify the Enterprise

Every company has a few proofs of concept running. The problem isn't experimentation, it's scalability. How do you take those POCs and embed them into your business fabric? Many enterprises get trapped in "PowerPoint transformation": ambitious visions that never cross into operational workflows. "I've seen organizations invest millions in analytics platforms and data lakes. But when you ask how that's translating into faster underwriting, better risk models or smarter supply chains, there's often silence," Sen said. "That's because AI doesn't fail at the technology layer - it fails at the architecture of adoption." ... The central challenge, Sen argues, is that most enterprises treat AI as an overlay rather than an integral part of their operational core. "You can't bolt it on top of outdated systems and expect it to transform decision-making. The technology stack, data flow and governance model all need to evolve together," he said. That evolution is what Gartner describes as the pivot from "defending AI pilots to expanding into agentic AI ROI." Organizations that mastered generative AI in 2024 are now moving toward autonomous, interconnected systems that can execute tasks without human micromanagement. Sen points to his own experience at Linde plc as an early example. His team's first gen AI deployment at Linde was built for the audit department. "Our internal audit head wanted a semantic search database and a generative model to cut audit report generation time by half," he said.

Designing Edge AI That Works Where the Mission Happens

The fastest way to make federal AI deployments more reliable is to build on existing systems rather than start from scratch. Every mission already generates valuable data – drone imagery, satellite feeds, radar signals, logistics updates — that tells part of the operational story. AI at the edge helps teams interpret that data faster and more accurately. Instead of rebuilding infrastructure, agencies can embed lightweight, mission-specific AI models directly into their existing systems. ... When AI leaves the data center, its security model must accompany it. Systems operating at the edge face distinct risks, including limited oversight, contested networks, and the potential for physical compromise. Protection has to be built into every layer of the system, from the silicon up, to ensure full-scale security. That starts with end-to-end encryption, protecting data at rest, in transit, and during inference. Hardware-based features, such as secure boot and Trusted Execution Environments, prevent unauthorized code from running, while confidential computing keeps information encrypted even as it’s being processed. ... A decade ago, deploying AI in remote or contested locations required racks of hardware and constant connectivity. Today, a laptop or a single sensor array can deliver that same intelligence locally, securely, and autonomously. The power of AI and edge computing isn’t measured in size or speed; it’s in relevance.

Daily Tech Digest - October 10, 2025

Quote for the day:

“Whether you think you can or you think you can’t, you’re right.” -- Henry Ford

Has the value of data increased?

“We’ve seen that AI’s true potential is unlocked by connecting trusted, governed data – structured and unstructured – with real-time analytics and decision intelligence. With the rise of , the next wave of value creation will come from intelligent systems that don’t just interpret data, but continuously and autonomously act on it at scale. Put simply, AI isn’t a shortcut to insight – it’s a multiplier of value, if the data is ready. Enterprises that treat data as an afterthought will fall behind, while those that treat it as a strategic asset will lead,” added the . ... “In this , compute power may set the pace, but . raises that ceiling, transforming scattered, hard-to-reach datasets into a living, high-performance fabric that fuels every AI prompt and initiative. With MinIO AIStor, organizations gain the ability to store and understand. Data that is secure, fluid, and always ready for action is a competitive weapon,” added . ... “Data that is fresh, well described and beats bigger but blind datasets because it can be safely composed, reused and measured for impact, with the lineage to show teams what to trust and what to fix so they can ship faster,” said . ... While there is no question, really, of whether the value of data has increased and, further, whether the proliferation of AI has been fundamental to that value escalation, the mechanics as variously described here should point us towards the new wave of emerging truths in this space.

Whose Ops is it Anyway? How IDPs, AI and Security are Evolving Developer Culture

For many teams, the problem is not a lack of enthusiasm or ambition but a shortage of resources and skills. They want to automate more, streamline workflows, and adopt new practices, yet often find themselves already operating at full capacity just in keeping existing systems running. In that environment, the slightest of steps toward more can feel like a big leap forward. ... On the security side, the logic behind is compelling. More companies are realising that security has to be baked in from day one, not bolted on later. The difficulty lies in making that shift a practical reality, as integrating early in the pipeline often requires new tooling, changes to established workflows, and in some cases, rethinking the roles and responsibilities within the team. ... In many organisations, it is the existing DevOps or platform teams that are best positioned to take on this responsibility, extending their remit into what is often referred to as . These teams already have experience building and maintaining shared infrastructure, managing pipelines, and ensuring operational stability at scale, so expanding those capabilities to handle and can feel like a natural evolution. ... That said, as adoption grows, we can also expect to see more specialised MLOps roles appearing, particularly in larger enterprises or in organisations where AI is a major strategic focus.

The ultimate business resiliency test: Inside Kantsu’s ransomware response

Kantsu then began collaborating with the police, the cyberattack response teams of the company’s insurers, and security specialists to confirm the scope of cyber insurance coverage and estimate the amount of damage. ... when they began the actual recovery work, they encountered an unexpected pitfall. “We considered how to restore operations as quickly as possible. We did a variety of things, including asking other companies in the same industry to send packages, even ignoring our own profits,” says. ... To prevent reinfection with ransomware, the company prohibited use of old networks and PCs. Tethering was used, with smartphones as Wi-Fi routers. Where possible, this was used to facilitate shipping. New PCs were purchased to create an on-premises environment. ... “In times of emergency like this, the most important thing is cash to recover as quickly as possible, rather than cost reduction. However, insurance companies do not pay claims immediately. ... “In the end, many customers cooperated, which made me really happy. , in particular, offers a service called ‘Strongest Delivery,’ which allows for next-day delivery and delivery time specification, but they were considerate enough to allow us a grace period in consideration of the delay in delivery,” says President Tatsujo.

Stablecoins: The New Currency of Online Criminals

Practitioners say a cluster of market and technical factors are making stablecoins the payment of choice for cybercriminals and fraudsters. "It's not just the dollar peg that makes stablecoins attractive," said , vice president and global head of policy and government affairs at . "Liquidity is critical. There are deep pools of stablecoin liquidity on both centralized and decentralized platforms. Settlement speed and irreversibility are also appealing for criminals trying to move large sums quickly," he told Information Security Media Group. The perception of stability - knowing $1 today will likely be $1 tomorrow - often suffices for illicit actors, regardless of an issuer's exact collateral model, he said. This stability and on-chain plumbing create both opportunity and exposure. Redbord said the spike in stablecoin usage is partly because law enforcement agencies around the world have become "exceptionally effective at tracing and seizing bitcoin," and criminals "go where the liquidity and usability are." There is no technical attribute of stablecoins that makes them more appealing to criminals or harder to trace, compared to other cryptocurrencies, said. In practice, public ledgers keep transfers visible; the question then becomes whether investigators have the right tools and the cooperation of the ecosystem's gatekeepers to follow value across chains.

Zero Trust cuts incidents but firms slow to adopt AI security

Zero Trust is increasingly viewed as the standard going forward. As AI-driven threats accelerate, organisations must evaluate security holistically across identity, devices, networks, applications, and data. At , we're helping customers embed Zero Trust into their culture and technology to safeguard operations. Our end-to-end expertise makes it possible to both defend against AI threats and harness secure AI in the same decisive motion. ... New cybersecurity threats are the primary driver for updating Zero Trust frameworks, with 72% of respondents indicating that the evolving threat landscape pushes them to continuously upgrade policies and practices. In addition, more than half of responding organisations recognised improvements in user experience as a secondary benefit of adopting Zero Trust approaches, beyond the gains in security posture. ... Most enterprises already rely on and as the backbone of their IT environments. Building Zero Trust solutions alongside DXC extends that value, enabling tighter integration, simplified operations, and greater visibility and control. By consolidating around the Microsoft stack, organisations can reduce complexity, cut costs, and accelerate their Zero Trust journey. ... Participants in the study agreed that Zero Trust is not a project with a defined end point. Instead, it is an ongoing process that requires continuous monitoring, regular updates, and cultural adaptation.

Overcome Connectivity Challenges for Edge AI

The challenges of AI at the Edge are as large as the advantages, however. One of the biggest challenges and key enablement technologies is connectivity. Edge processing and AI at the Edge require reliability, low latency, and resiliency in the harshest of environments. Without good connections to the network, many of the advantages of Edge AI are diminished, or lost entirely. A truly rugged Edge AI system requires a dual focus on connectivity, according to the experts at . It needs both robust external I/O to interface with the outside world, and high-speed, resilient internal interconnects to manage data flow within the computing module. ... The transition to Edge AI is not just a software challenge; it is a hardware and systems engineering challenge. The key to overcoming this dual challenge is to engage with a partner like ATTEND, who will understand that the reliability of an advanced AI model is ultimately dependent on the physical-layer components that capture and transmit its data. By offering a comprehensive portfolio that addresses connectivity from the external sensor to the internal processor module, ATTEND can help you to build end-to-end systems that are both powerful and resilient. To meet with ATTEND and see all that they are doing to advance and enable true intelligence at the Edge, meet with them at embedded world North America in November at the Anaheim Convention Center.

AI Security Goes Mainstream as Vendors Spend Heavily on M&A

One of the most significant operational gaps in AI adoption is the lack of runtime observability, with organizations struggling to know what data a model is ingesting or what it's producing. Observability answers these questions by providing a live view of AI behavior across prompts, responses and system interactions, and it is a precursor to regulating or securing AI systems. ... One of the biggest risks of in the enterprise is data leakage, with workers inadvertently pasting confidential information into a chatbot, models regurgitating sensitive data it was exposed to during training, or adversaries crafting prompts to extract private information through jailbreaking. Allowing AI access without control is equivalent to opening an unsecured API to your crown jewels. ... Output is just as risky as input with GenAI since an LLM could generate sensitive content, malicious code or incorrect results that are trusted by downstream systems or users. ' noted the need for bi-directional inspection to watch not only what goes into large language models, but also what comes out. ... Another key challenge is defining identity in a non-human context, raising questions around how AI agents should be authenticated, what permissions AI agents should have and how to prevent escalation or impersonation. Enterprises must treat bots, copilots, model endpoints and LLM-backed workflows as identity-bearing entities that log in, take action, make decisions and access sensitive data.

Navigating the Techno-Future: Between Promise and Prudence

On one side are the techno-optimists: the believers in inexorable progress, the proponents of markets and innovation as self-correcting forces. They see every challenge as a technical problem and every failure as a design flaw waiting to be solved. On the other side are techno-pessimists: the prophets of collapse who warn that every new tool will inevitably accelerate inequality, erode democracy, or catalyze ecological catastrophe. They see history as a cautionary tale, and the present as a fragile prelude to systemic failure. Both perspectives share a common flaw: they treat the future as preordained. Optimists assume that progress will automatically yield good outcomes; pessimists assume that progress will inevitably lead to harm. Reality, however, is far less deterministic. Technology, in itself, is neutral. It amplifies human choices but does not dictate them. ... Just as a hammer can build a home or inflict injury, a powerful technology like artificial intelligence, gene editing, or blockchain can be used to improve lives or to exacerbate inequalities. The technology does not prescribe its use; humans do. This neutrality is both liberating and daunting. On the one hand, it affirms that progress is not predestined. The future is not a straight line determined by the mere existence of certain tools.

CISOs prioritise real-time visibility as AI reshapes cloud security

The top priority for CISOs is real-time threat monitoring and comprehensive visibility into all data in motion across their organisations, supporting a defence-in-depth strategy. However, 97 percent of CISOs acknowledged making compromises in areas such as visibility gaps, tool integration and data quality, which they say limit their ability to fully secure and manage hybrid cloud environments. ... The reliance on AI is also causing a revision of how () function. Almost one in five CISOs reported lacking the appropriate tools to manage the increased network data volumes created by AI, underscoring that legacy log-based tools may not be fit for purpose against AI-powered threats. ... Rising data breaches, with a 17 percent increase year on year, are translating into greater pressure on CISOs, 45 percent of whom said they are now the main person held accountable in the event of a breach. There is also concern about stress and burnout within cybersecurity teams, which is driving a greater embrace of AI-based security tools. ... The adoption of AI is expected to have practical impacts, such as enabling junior analysts to perform at the same level as more experienced team members, reducing training costs, speeding up analysis while investigating threats, and improving overall visibility for the security function.

Serverless Security Risks Are Real, and Hackers Know It

Many believe, “No servers, no security risks.” That’s a myth. Nowadays, attackers take advantage of the specific security weaknesses found in serverless platforms. ... All serverless applications need third-party libraries for operation. Each function that depends on the compromised component becomes vulnerable to attack. An npm package experienced a hijack attack when hackers inserted a secret entry into its system. The incorporation of code by resulted in the silent extraction of all environment variables. The unauthorized loss of API keys, credentials, and sensitive data, together with all other valuable information. The process finished in milliseconds, which was too brief for any security system to identify. ... As more companies are adopting serverless technologies, security risks become more widespread. So, it’s fundamental to validate that serverless environments are secure. Let’s explore the facts. Research dictates that serverless computing is expected to grow rapidly. According to ’s July 2025 forecast, global IT spending will climb to $5.43 trillion, with enterprises investing billions into AI-driven cloud and data center infrastructure, making serverless platforms an increasingly critical, but often overlooked, security target.

Daily Tech Digest - September 09, 2025

Quote for the day:

“The greatest leader is not necessarily the one who does the greatest things. He is the one that gets the people to do the greatest things.” -- Ronald Reagan

Neuromorphic computing and the future of edge AI

While QC captures the mainstream headlines, neuromorphic computing has positioned itself as a force in the next era of AI. While conventional AI relies heavily on GPU/TPU-based architectures, neuromorphic systems mimic the parallel and event-driven nature of the human brain. ... Neuromorphic hardware has shown promise in edge environments where power efficiency, latency and adaptability matter most. From wearable medical devices to battlefield robotics, systems that can “think locally” without requiring constant cloud connectivity offer clear advantages. ... As neuromorphic computing matures, ethical and sustainability considerations will shape adoption as much as raw performance. Spiking neural networks’ efficiency reduces carbon footprints by cutting energy demands compared to GPUs, aligning with global decarbonization targets. At the same time, ensuring that neuromorphic models are transparent, bias‑aware and auditable is critical for applications in healthcare, defense and finance. Calls for AI governance frameworks now explicitly include neuromorphic AI, reflecting its potential role in high‑stakes decision‑making. Embedding sustainability and ethics into the neuromorphic roadmap will ensure that efficiency gains do not come at the cost of fairness or accountability.

10 security leadership career-killers — and how to avoid them

“Security has evolved from being the end goal to being a business-enabling function,” says James Carder, CISO at software maker Benevity. “That means security strategies, communications, planning, and execution need to be aligned with business outcomes. If security efforts aren’t returning meaningful ROI, CISOs are likely doing something wrong. Security should not operate as a cost center, and if we act or report like one, we’re failing in our roles.” ... CISOs generally know that the security function can’t be the “department of no.” But some don’t quite get to a “yes,” either, which means they’re still failing their organizations in a way that could stymie their careers, says Aimee Cardwell, CISO in residence at tech company Transcend and former CISO of UnitedHealth Group. ... CISOs who are too rigid with the rules do a disservice to their organizations and their professional prospects, says Cardwell. Such a situation recently came up in her organization, where one of her team members initially declined to permit a third-party application from being used by workers, pointing to a security policy barring such apps. ... CISOs who don’t have a firm grasp on all that they must secure won’t succeed in their roles. “If they don’t have visibility, if they can’t talk about the effectiveness of the controls, then they won’t have credibility and the confidence in them among leadership will erode,” Knisley says.

A CIO's Evolving Role in the Generative AI Era

The dual mandate facing CIOs today is demanding but unavoidable. They must deliver quick AI pilots that boards can take to the shareholders while also enforcing guardrails on security, ethics and cost aspects. Too much caution can make CIOs irrelevant. This balancing act requires not only technical fluency but also narrative skill. The ability to translate AI experiments into business outcomes that CEOs and boards can trust can make CIOs a force. The MIT report highlights another critical decision point: whether to build or buy. Many enterprises attempt internal builds, but externally built AI partnerships succeed twice as often. CIOs, pressured for fast results, must be pragmatic about when to build and when to partner. Gen AI does not - and never will - replace the CIO role. But it demands corrections. The CIO who once focused on alignment must now lead business transformation. Those who succeed will act less as CIOs and more as AI diplomats, bridging hype with pragmatism, connecting technological opportunities to shareholder value and balancing the boardroom's urgency with the operational reality. As AI advances, so does the CIO's role - but only if they evolve. Their reporting line to the CEO symbolizes greater trust and higher stakes. Unlike previous technology cycles, AI has brought the CIO to the forefront of transformation.

Building an AI Team May Mean Hiring Where the Talent Is, Not Where Your Bank Is

Much of the adaptation of banking to AI approaches requires close collaboration between AI talent with people who understand how the banking processes involved need to work. This will put people closer together, literally, to facilitate both quick and in-depth but always frequent interactions to make collaboration work — paradoxically, increased automation needs more face-to-face dealings at the formative stages. However, the "where" of the space will also hinge on where AI and innovation talent can be recruited, where that talent is being bred and wants to work, and the types of offices that talent will be attracted to. ... "Banks are also recruiting for emerging specialties in responsible AI and AI governance, ensuring that their AI initiatives are ethical, compliant and risk-managed," the report says. "As ‘agentic AI’ — autonomous AI agents — and generative AI gain traction, firms will need experts in these cutting-edge fields too." ... Decisions don’t stop at the border anymore. Jesrani says that savvy banks look for pockets of talent as well. ... "Banks are contemplating their global strategies because emerging markets can provide them with talent and capabilities that they may not be able to obtain in the U.S.," says Haglund. "Or there may be things happening in those markets that they need to be a part of in order to advance their core business capabilities."

How Data Immaturity is Preventing Advanced AI

Data immaturity, in the context of AI, refers to an organisation’s underdeveloped or inadequate data practices, which limit its ability to leverage AI effectively. It encompasses issues with data quality, accessibility, governance, and infrastructure. Critical signs of data immaturity include inconsistent, incomplete, or outdated data leading to unreliable AI outcomes; data silos across departments hindering access and comprehensive analysis, as well as weak data governance caused by a lack of policies on data ownership, compliance and security, which introduces risks and restricts AI usage. ... Data immaturity also leads to a lack of trust in analysis and predictability of execution. That puts a damper on any plans to leverage AI in a more autonomous manner—whether for business or operational process automation. A recent study by Kearney found that organisations globally are expecting to increase data and analytics budgets by 22% in the next three years as AI adoption scales. Fragmented data limits the predictive accuracy and reliability of AI, which are crucial for autonomous functions where decisions are made without human intervention. As a result, organisations must get their data houses in order before they will be able to truly take advantage of AI’s potential to optimise workflows and free up valuable time for humans to focus on strategy and design, tasks for which most AI is not yet well suited.

From Reactive Tools to Intelligent Agents: Fulcrum Digital’s AI-First Transformation

To mature, LLM is just one layer. Then you require the integration layer, how you integrate it. Every customer has multiple assets in their business which have to connect with LLM layers. Every business has so many existing applications and new applications; businesses are also buying some new AI agents from the market. How do you bring new AI agents, existing old systems, and new modern systems of the business together — integrating with LLM? That is one aspect. The second aspect is every business has its own data. So LLM has to train on those datasets. Copilot and OpenAI are trained on zillions of data, but that is LLM. Industry wants SLM—small language models, private language models, and industry-orientated language models. So LLMs have to be fine-tuned according to the industry and also fine-tuned according to their data. Nowadays people come to realise that LLMs will never give you 100 per cent accurate solutions, no matter which LLM you choose. That is the phenomenon customers and everybody are now learning. The difference between us and others: many players who are new to the game deliver results with LLMs at 70–75 per cent. Because we have matured this game with multiple LLMs coexisting, and with those LLMs together maturing our Ryze platform, we are able to deliver more than 93–95 per cent accuracy.

You Didn't Get Phished — You Onboarded the Attacker

Many organizations respond by overcorrecting: "I want my entire company to be as locked down as my most sensitive resource." It seems sensible—until the work slows to a crawl. Without nuanced controls that allow your security policies to distinguish between legitimate workflows and unnecessary exposure, simply applying rigid controls that lock everything down across the organization will grind productivity to a halt. Employees need access to do their jobs. If security policies are too restrictive, employees are either going to find workarounds or continually ask for exceptions. Over time, risk creeps in as exceptions become the norm. This collection of internal exceptions slowly pushes you back towards "the castle and moat" approach. The walls are fortified from the outside, but open on the inside. And giving employees the key to unlock everything inside so they can do their jobs means you are giving one to Jordan, too. ... A practical way to begin is by piloting ZSP on your most sensitive system for two weeks. Measure how access requests, approvals, and audits flow in practice. Quick wins here can build momentum for wider adoption, and prove that security and productivity don't have to be at odds. ... When work demands more, employees can receive it on request through time-bound, auditable workflows. Just enough access is granted just in time, then removed. By taking steps to operationalize zero standing privileges, you empower legitimate users to move quickly—without leaving persistent privileges lying around for Jordan to find.

OT Security: When Shutting Down Is Not an Option

some of the most urgent and disruptive threats today are unfolding far from the keyboard, in operational technology environments that keep factories running, energy flowing and transportation systems moving. In these sectors, digital attacks can lead to physical consequences, and defending OT environments demands specialized skills. Real-world incidents across manufacturing and critical infrastructure show how quickly operations can be disrupted when OT systems are not adequately protected. Just this week, Jaguar Land Rover disclosed that a cyberattack "severely disrupted" its automotive manufacturing operations. ... OT environments present challenges that differ sharply from traditional IT. While security is improving, OT security teams must protect legacy control systems running outdated firmware, making them difficult to patch. Operators need to prioritize uptime and safety over system changes; and IT and OT teams frequently work in silos. These conditions mean that breaches can have physical as well as digital consequences, from halting production to endangering lives. Training tailored to OT is essential to secure critical systems while maintaining operational continuity. ... An OT cybersecurity learning ecosystem is not a one-time checklist but a continuous program. The following elements help organizations choose training that meets current needs while building capacity for ongoing improvement.

Connected cars are racing ahead, but security is stuck in neutral

Connected cars are essentially digital platforms with multiple entry points for attackers. The research highlights several areas of concern. Remote access attacks can target telematics systems, wireless interfaces, or mobile apps linked to the car. Data leaks are another major issue because connected cars collect sensitive information, including location history and driving behavior, which is often stored in the cloud. Sensors present their own set of risks. Cameras, radar, lidar, and GPS can be manipulated, creating confusion for driver assistance systems. Once inside a vehicle, attackers can move deeper by exploiting the CAN bus, which connects key systems such as brakes, steering, and acceleration. ... Most drivers want information about what data is collected and where it goes, yet very few said they have received that information. Brand perception also plays a role. Many participants prefer European or Japanese brands, while some expressed distrust toward vehicles from certain countries, citing political concerns, safety issues, or perceived quality gaps. ... Manufacturers are pushing out new software-defined features, integrating apps, and rolling out over the air updates. This speed increases the number of attack paths and makes it harder for security practices and rules to keep up.

Circular strategies for data centers

Digital infrastructure is scaling rapidly, with rising AI workloads and increased compute density shaping investment decisions. Growth on that scale can generate unnecessary waste unless sustainability is integrated into planning. Circular thinking makes it possible to expand capacity without locking facilities into perpetual hardware turnover. Operators can incorporate flexibility into refresh cycles by working with vendors that design modular platforms or by adopting service-based models that build in maintenance, refurbishment, and recovery. ... Sustainable planning also involves continuous evaluation. Instead of defaulting to wholesale replacement, facilities can test whether assets still meet operational requirements through reconfiguration, upgrades, or role reassignment. This kind of iterative approach gives operators a way to match innovation with responsibility, ensuring that capacity keeps pace with demand without discarding equipment prematurely. ... The transition to circular practices is more than an environmental gesture. For data centers, it is a strategic shift in how infrastructure is procured, maintained, and retired. Extending lifecycles, redeploying equipment internally, refurbishing where possible, and ensuring secure, responsible recycling at the end of use all contribute to a more resilient operation in a resource-constrained and tightly regulated industry.

Daily Tech Digest - May 22, 2025

Quote for the day:

"Knowledge is being aware of what you can do. Wisdom is knowing when not to do it." -- Anonymous

Consumer rights group: Why a 10-year ban on AI regulation will harm Americans

AI is a tool that can be used for significant good, but it can and already has been used for fraud and abuse, as well as in ways that can cause real harm, both intentional and unintentional — as was thoroughly discussed in the House’s own bipartisan AI Task Force Report. These harms can range from impacting employment opportunities and workers’ rights to threatening accuracy in medical diagnoses or criminal sentencing, and many current laws have gaps and loopholes that leave AI uses in gray areas. Refusing to enact reasonable regulations places AI developers and deployers into a lawless and unaccountable zone, which will ultimately undermine the trust of the public in their continued development and use. ... Proponents of the 10-year moratorium have argued that it would prevent a patchwork of regulations that could hinder the development of these technologies, and that Congress is the proper body to put rules in place. But Congress thus far has refused to establish such a framework, and instead it’s proposing to prevent any protections at any level of government, completely abdicating its responsibility to address the serious harms we know AI can cause. It is a gift to the largest technology companies at the expense of users — small or large — who increasingly rely on their services, as well as the American public who will be subject to unaccountable and inscrutable systems.

Putting agentic AI to work in Firebase Studio

An AI assistant is like power steering. The programmer, the driver, remains in control, and the tool magnifies that control. The developer types some code, and the assistant completes the function, speeding up the process. The next logical step is to empower the assistant to take action—to run tests, debug code, mock up a UI, or perform some other task on its own. In Firebase Studio, we get a seat in a hosted environment that lets us enter prompts that direct the agent to take meaningful action. ... Obviously, we are a long way off from a non-programmer frolicking around in Firebase Studio, or any similar AI-powered development environment, and building complex applications. Google Cloud Platform, Gemini, and Firebase Studio are best-in-class tools. These kinds of limits apply to all agentic AI development systems. Still, I would in no wise want to give up my Gemini assistant when coding. It takes a huge amount of busy work off my shoulders and brings much more possibility into scope by letting me focus on the larger picture. I wonder how the path will look, how long it will take for Firebase Studio and similar tools to mature. It seems clear that something along these lines, where the AI is framed in a tool that lets it take action, is part of the future. It may take longer than AI enthusiasts predict. It may never really, fully come to fruition in the way we envision.

Edge AI + Intelligence Hub: A Match in the Making

The shop floor looks nothing like a data lake. There is telemetry data from machines, historical data, MES data in SQL, some random CSV files, and most of it lacks context. Companies that realize this—or already have an Industrial DataOps strategy—move quickly beyond these issues. Companies that don’t end up creating a solution that works with only telemetry data (for example) and then find out they need other data. Or worse, when they get something working in the first factory, they find out factories 2, 3, and 4 have different technology stacks. ... In comes DataOps (again). Cloud AI and Edge AI have the same problems with industrial data. They need access to contextualized information across many systems. The only difference is there is no data lake in the factory—but that’s OK. DataOps can leave the data in the source systems and expose it over APIs, allowing edge AI to access the data needed for specific tasks. But just like IT, what happens if OT doesn’t use DataOps? It’s the same set of issues. If you try to integrate AI directly with data from your SCADA, historian, or even UNS/MQTT, you’ll limit the data and context to which the agent has access. SCADA/Historians only have telemetry data. UNS/MQTT is report by exception, and AI is request/response based (i.e., it can’t integrate). But again, I digress. Use DataOps.

AI-driven threats prompt IT leaders to rethink hybrid cloud security

Public cloud security risks are also undergoing renewed assessment. While the public cloud was widely adopted during the post-pandemic shift to digital operations, it is increasingly seen as a source of risk. According to the survey, 70 percent of Security and IT leaders now see the public cloud as a greater risk than any other environment. As a result, an equivalent proportion are actively considering moving data back from public to private cloud due to security concerns, and 54 percent are reluctant to use AI solutions in the public cloud citing apprehensions about intellectual property protection. The need for improved visibility is emphasised in the findings. Rising sophistication in cyberattacks has exposed the limitations of existing security tools—more than half (55 percent) of Security and IT leaders reported lacking confidence in their current toolsets' ability to detect breaches, mainly due to insufficient visibility. Accordingly, 64 percent say their primary objective for the next year is to achieve real-time threat monitoring through comprehensive real-time visibility into all data in motion. David Land, Vice President, APAC at Gigamon, commented: "Security teams are struggling to keep pace with the speed of AI adoption and the growing complexity of and vulnerability of public cloud environments.

Taming the Hacker Storm: Why Millions in Cybersecurity Spending Isn’t Enough

The key to taming the hacker storm is founded on the core principle of trust: that the individual or company you are dealing with is who or what they claim to be and behaves accordingly. Establishing a high-trust environment can largely hinder hacker success. ... For a pervasive selective trusted ecosystem, an organization requires something beyond trusted user IDs. A hacker can compromise a user’s device and steal the trusted user ID, making identity-based trust inadequate. A trust-verified device assures that the device is secure and can be trusted. But then again, a hacker stealing a user’s identity and password can also fake the user’s device. Confirming the device’s identity—whether it is or it isn’t the same device—hence becomes necessary. The best way to ensure the device is secure and trustworthy is to employ the device identity that is designed by its manufacturer and programmed into its TPM or Secure Enclave chip. ... Trusted actions are critical in ensuring a secure and pervasive trust environment. Different actions require different levels of authentication, generating different levels of trust, which the application vendor or the service provider has already defined. An action considered high risk would require stronger authentication, also known as dynamic authentication.

AWS clamping down on cloud capacity swapping; here’s what IT buyers need to know

For enterprises that sourced discounted cloud resources through a broker or value-added reseller (VAR), the arbitrage window shuts, Brunkard noted. Enterprises should expect a “modest price bump” on steady‑state workloads and a “brief scramble” to unwind pooled commitments. ... On the other hand, companies that buy their own RIs or SPs, or negotiate volume deals through AWS’s Enterprise Discount Program (EDP), shouldn’t be impacted, he said. Nothing changes except that pricing is now baselined. To get ahead of the change, organizations should audit their exposure and ask their managed service providers (MSPs) what commitments are pooled and when they renew, Brunkard advised. ... Ultimately, enterprises that have relied on vendor flexibility to manage overcommitment could face hits to gross margins, budget overruns, and a spike in “finance-engineering misalignment,” Barrow said. Those whose vendor models are based on RI and SP reallocation tactics will see their risk profile “changed overnight,” he said. New commitments will now essentially be non-cancellable financial obligations, and if cloud usage dips or pivots, they will be exposed. Many vendors won’t be able to offer protection as they have in the past.

The new C-Suite ally: Generative AI

While traditional GenAI applications focus on structured datasets, a significant frontier remains largely untapped — the vast swathes of unstructured "dark data" sitting in contracts, credit memos, regulatory reports, and risk assessments. Aashish Mehta, Founder and CEO of nRoad, emphasizes this critical gap.
"Most strategic decisions rely on data, but the reality is that a lot of that data sits in unstructured formats," he explained. nRoad’s platform, CONVUS, addresses this by transforming unstructured content into structured, contextual insights. ... Beyond risk management, OpsGPT automates time-intensive compliance tasks, offers multilingual capabilities, and eliminates the need for coding through intuitive design. Importantly, Broadridge has embedded a robust governance framework around all AI initiatives, ensuring security, regulatory compliance, and transparency. Trustworthiness is central to Broadridge’s approach. "We adopt a multi-layered governance framework grounded in data protection, informed consent, model accuracy, and regulatory compliance," Seshagiri explained. ... Despite the enthusiasm, CxOs remain cautious about overreliance on GenAI outputs. Concerns around model bias, data hallucination, and explainability persist. Many leaders are putting guardrails in place: enforcing human-in-the-loop systems, regular model audits, and ethical AI use policies.

Building a Proactive Defence Through Industry Collaboration

Trusted collaboration, whether through Information Sharing and Analysis Centres (ISACs), government agencies, or private-sector partnerships, is a highly effective way to enhance the defensive posture of all participating organisations. For this to work, however, organisations will need to establish operationally secure real-time communication channels that support the rapid sharing of threat and defence intelligence. In parallel, the community will also need to establish processes to enable them to efficiently disseminate indicators of compromise (IoCs) and tactics, techniques and procedures (TTPs), backed up with best practice information and incident reports. These collective defence communities can also leverage the centralised cyber fusion centre model that brings together all relevant security functions – threat intelligence, security automation, threat response, security orchestration and incident response – in a truly cohesive way. Providing an integrated sharing platform for exchanging information among multiple security functions, today’s next-generation cyber fusion centres enable organisations to leverage threat intelligence, identify threats in real-time, and take advantage of automated intelligence sharing within and beyond organisational boundaries.

3 Powerful Ways AI is Supercharging Cloud Threat Detection

AI’s strength lies in pattern recognition across vast datasets. By analysing historical and real-time data, AI can differentiate between benign anomalies and true threats, improving the signal-to-noise ratio for security teams. This means fewer false positives and more confidence when an alert does sound. ... When a security incidents strike, every second counts. Historically, responding to an incident involves significant human effort – analysts must comb through alerts, correlate logs, identify the root cause, and manually contain the threat. This approach is slow, prone to errors, and doesn’t scale well. It’s not uncommon for incident investigations to stretch hours or days when done manually. Meanwhile, the damage (data theft, service disruption) continues to accrue. Human responders also face cognitive overloads during crises, juggling tasks like notifying stakeholders, documenting events, and actually fixing the problem. ... It’s important to note that AI isn’t about eliminating the need for human experts but rather augmenting their capabilities. By taking over initial investigation steps and mundane tasks, AI frees up human analysts to focus on strategic decision-making and complex threats. Security teams can then spend time on thorough analysis of significant incidents, threat hunting, and improving security posture, instead of constant firefighting.

The hidden gaps in your asset inventory, and how to close them

The biggest blind spot isn’t a specific asset. It is trusting that what’s on paper is actually live and in production. Many organizations often solely focus on known assets within their documented environments, but this can create a false sense of security. Blind spots are not always the result of malicious intent, but rather of decentralized decision-making, forgotten infrastructure, or evolving technology that hasn’t been brought under central control. External applications, legacy technologies and abandoned cloud infrastructure, such as temporary test environments, may remain vulnerable long after their intended use. These assets pose a risk, particularly when they are unintentionally exposed due to misconfiguration or overly broad permissions. Third-party and supply chain integrations present another layer of complexity. ... Traditional discovery often misses anything that doesn’t leave a clear, traceable footprint inside the network perimeter. That includes subdomains spun up during campaigns or product launches; public-facing APIs without formal registration or change control; third-party login portals or assets tied to your brand and code repositories, or misconfigured services exposed via DNS. These assets live on the edge, connected to the organization but not owned in a traditional sense.