Daily Tech Digest by Kannan Subbiah: RPA

Showing posts with label RPA. Show all posts

Daily Tech Digest - August 13, 2026

Quote for the day:

“Personal growth is not a matter of learning new information but unlearning old limits.” -- Alan Cohen

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Duration: 21 mins • Perfect for listening on the go.

4 RPA lessons that still hold true in the AI boom

As companies rush to adopt new artificial intelligence tools, many are stumbling over the exact same hurdles they faced years ago with robotic process automation. To succeed with AI technology today, organizations should remember four vital lessons from the past. First, they must carefully choose what to automate. Applying new technology to a broken or inefficient process only speeds up the creation of bad results. Every automation project needs a clear, measurable business benefit before it begins. Second, automation is never a project you can simply turn on and ignore. Because artificial intelligence acts quickly and sounds confident, keeping human experts in the loop is essential to prevent small errors from becoming large failures. Third, the quality of the information you feed the system remains critical. While modern tools can read messy data, they can easily misunderstand context, leading to flawed decisions on a massive scale. Finally, managing how people adapt to the changes is the most difficult challenge of all. Most technology projects fail because of people and workflows, not the software itself. Rather than abandoning older, predictable automation methods entirely, smart organizations are combining them with new artificial intelligence to create highly reliable, cost-effective, and highly practical solutions.

The intelligent workplace (part 2): Technology’s next transformation of work

As artificial intelligence takes on a larger role in the modern workplace, organizations must rethink how they manage teams and measure performance. The traditional focus on the sheer volume of tasks completed, such as reports written or cases closed, is no longer effective when automated tools can generate that output almost instantly. Instead, managers need to prioritize the actual quality of work, accuracy, and the ability to solve the right problems. Rather than competing with machines on speed, employees should focus on areas where human judgment remains critical. Furthermore, managers are shifting from simply overseeing daily activity to deliberately designing workflows where people and technology support each other. This change requires establishing clear rules for when employees should rely on automated systems and when they need to step in and override them. Ultimately, accountability must always rest with humans. A major challenge is ensuring junior employees still develop necessary expertise, as the routine tasks they traditionally learned from are now handed off to software. Companies will need to create deliberate opportunities for practice, mentoring, and direct feedback. Finally, successfully integrating these tools relies heavily on trust and transparency. Leaders must maintain human oversight, protect time for learning, and ensure that automated metrics do not replace empathy and open communication.

AI, Digital Twins, and Cybersecurity in Industrial Remote Operations

The second part of this article series explores how artificial intelligence and virtual models—often called digital twins—are fundamentally changing remote industrial operations, while highlighting the serious cybersecurity challenges that come with them. Instead of waiting for machines to break down, AI allows manufacturers to shift from reactive monitoring to predictive maintenance. By analyzing patterns in temperature, vibration, and power use, these systems can spot equipment failures weeks in advance. This capability drastically reduces unplanned downtime and lowers maintenance costs. Meanwhile, digital twins serve as the virtual interface for these physical systems. Engineers can use these exact digital copies to run simulations, test adjustments, and manage entire production lines remotely, achieving a level of oversight that previously required being physically present on the factory floor. However, moving factory controls online introduces major network security risks. Manufacturing remains a prime target for cyberattacks, and every new remote connection is a potential entry point. This risk is complicated by a severe shortage of security professionals who actually understand industrial systems. Ultimately, building a secure foundation is what makes these remote capabilities possible. Organizations that proactively address their network security can safely unlock the very real efficiency and productivity benefits of these modern industrial tools.

Social engineering reshapes financial fraud as attacks scale

Social engineering has rapidly emerged as the primary method for financial fraud, moving away from complex technical hacking toward manipulating human behavior. Recent data reveals that impersonation scams in the United States have more than doubled over the past year. Fraudsters frequently pose as trusted organizations, celebrities, or relatives to deceive individuals into authorizing transactions themselves. Investment scams are currently causing the most financial damage, with criminals using fake websites and fabricated platforms to create a false sense of urgency. This trend is not limited to everyday consumers; major Wall Street firms, including hedge funds and private equity companies, are also defending against sophisticated phone-based attacks targeting their employees. Adding to the challenge is the growing commercial market for these scams. Rather than building malicious systems from the ground up, criminals can now purchase ready-made scam kits online. These affordable packages provide everything needed to launch convincing campaigns, such as fake cryptocurrency presales with personalized elements and countdown timers. By lowering the barrier to entry, these kits allow individuals with minimal technical skills to execute highly professional and persuasive scams. Ultimately, modern financial fraud relies less on defeating security software and more on exploiting human trust through highly convincing deception.

Tokenmaxxing: The strangest developer productivity metric of all time

A concerning trend called "tokenmaxxing" has emerged in software engineering, where developers are evaluated by how much AI computing power they consume rather than the quality of their code. Much like the outdated practice of measuring productivity by lines of code, this metric encourages the wrong behaviors. When companies reward raw token usage, developers are incentivized to generate massive amounts of unrefined code, stuff prompts with unnecessary text, and set up automated systems simply to climb internal leaderboards. This careless approach leads to higher code duplication, less thoughtful refinement, and software that is quickly discarded. Beyond degrading software quality, tokenmaxxing is financially destructive. The blind pursuit of AI usage has caused companies to burn through budgets rapidly, forcing some to restrict their access to these tools. Furthermore, this flawed measurement ignores the most valuable ways developers use AI, such as debugging complex issues or planning architectural designs, because these tasks do not generate high token counts. Ultimately, true software engineering requires careful planning and simplification. AI is a helpful tool for solving problems and learning, but using it effectively means focusing on meaningful outcomes rather than blindly treating the volume of AI interactions as a sign of success.

Architecting Multi-Cloud Networks to Survive Cryptographic Migrations under DORA Rules

The article outlines the critical intersection of the European Union’s Digital Operational Resilience Act, multi-cloud network strategies, and the impending shift toward post-quantum cryptography. Under DORA, financial institutions face strict mandates to ensure continuous operational resilience and to mitigate third-party concentration risks. This effectively makes multi-cloud and cloud-agnostic architectures a necessity rather than a mere option, as organizations can no longer rely on a single cloud provider without a tested, actionable exit strategy. As the financial industry prepares for complex cryptographic migrations to defend against advanced quantum computing threats, these multi-cloud network architectures will be put to the ultimate test. Updating long-lived trust chains, encryption protocols, and digital certificates across sprawling IT environments is an inherently risky process. The text explains that surviving this transition without violating DORA’s strict uptime requirements demands highly decoupled network designs. By strategically distributing workloads and avoiding deep dependencies on provider-specific services, financial entities can safely manage phased cryptographic updates. Ultimately, a well-architected multi-cloud environment is essential not just for avoiding vendor lock-in, but as a robust safety net. It allows institutions to implement sweeping security upgrades smoothly, ensuring total compliance and uninterrupted service delivery in a heavily regulated modern landscape.

The web’s newest weapon against AI scrapers is a font

Designers Isaque Seneda and Gabriel Abrucio have developed a new typeface called ShieldFont, designed to protect online content from unauthorized data extraction by artificial intelligence companies. The core mechanism relies on the traditional ligature feature found in standard typography. While a web page using ShieldFont appears perfectly normal and readable to human visitors, the underlying HTML source code is intentionally altered. When AI scrapers and automated web crawlers attempt to harvest the website text, they encounter only random, meaningless data instead of the actual content. This approach offers web publishers a practical technical method to prevent their work from being absorbed into AI training datasets without permission. Unlike earlier blocking methods that often disrupted the user experience or proved ineffective, ShieldFont specifically targets the data collection process by intentionally ruining the harvested text. Experts note that the success of this method depends on how well the substitution strategy is executed. If the replacements rely on simple patterns, such as direct synonyms or antonyms, advanced algorithms might learn to reverse the alterations. By focusing on random string generation and complex substitutions, ShieldFont aims to safeguard digital ownership and provide a reliable defense against the aggressive scraping tactics currently used across the internet.

Post-Quantum Deadlines Collide With OT Reality

The transition to post-quantum cryptography is becoming an urgent priority as looming regulatory deadlines clash with the practical constraints of operational technology environments. While government agencies and security bodies push for rapid adoption of quantum-resistant algorithms to protect critical infrastructure, the realities of operational technology present significant engineering and logistical hurdles. Unlike standard enterprise networks, operational technology systems like industrial control units, medical devices, and smart grids are built for longevity. They often run on older hardware with limited processing power and minimal memory. These strict constraints make it exceedingly difficult to implement complex new cryptographic standards without disrupting essential services or triggering massive hardware replacement cycles. Furthermore, the threat is not entirely theoretical. Adversaries are actively engaging in "harvest now, decrypt later" campaigns, collecting encrypted data today to break it once quantum computing matures. Consequently, securing these industrial environments requires a nuanced approach rather than a simple software update. Organizations must begin their planning immediately by conducting thorough inventories of their cryptographic assets. They should isolate vulnerable operational systems through strict network segmentation and adopt hybrid security models. Ultimately, building flexible encryption into aging infrastructure is crucial for navigating the tension between ambitious mandates and the slow-moving reality of industrial technology.

Beyond Cyber Protection: How European Companies Can Operate Through Cyber Disruption

European businesses face an evolving threat landscape where preventing cyberattacks entirely is simply no longer a realistic expectation. Driven by integrated supply chains and rapid artificial intelligence adoption, companies remain vulnerable despite heavy investments in traditional security. According to recent research, while many executives expect to recover from incidents like ransomware within days, actual disruptions often take months to resolve. To navigate this reality, leaders must transition their focus from basic protection to true operational resilience. This means acknowledging that some attacks will succeed and designing systems capable of operating under stress. Executives should start by identifying their essential operating core, which includes the critical services, data, and processes that must remain available during a crisis. Additionally, while strict regulations establish important security baselines, compliance should be viewed as a starting point rather than the ultimate goal. True resilience requires engineering robust recovery processes rather than simply hoping for a rapid response. It also demands making resilience a company wide responsibility, extending these practices across the entire value chain, and fully understanding the economic costs of a disruption. By accepting the inevitability of breaches and planning for continuity, organizations can confidently sustain their core functions and protect their stability during a severe disruption.

AI Agents Are Creating a New Identity Security Challenge for Enterprises

Morey Haber outlines the necessity of treating artificial intelligence agents as a unique class of non-human identity that requires strict security controls. Unlike standard software or human users, these agents operate autonomously, make independent decisions, and run on unpredictable schedules. Because they can reason and interact with other systems on their own, traditional access management is simply not enough. Organizations must assign each agent a specific identity tied to an accountable human owner. Instead of relying on permanent passwords, these agents should use temporary security secrets and be granted the absolute minimum access required to complete a specific task. Furthermore, security teams must monitor their behavior constantly rather than just checking their login credentials, looking for unusual activity or excessive data access. Proper management also means tracking an agent from the moment it is created to when it is retired. Crucially, companies need a reliable kill switch to instantly revoke an agent's access if it behaves improperly or is compromised by an attacker. By managing these tools with calm, steady oversight and limiting their permissions, organizations can prevent them from becoming dangerous entry points for cyber threats. Ultimately, an agent should never hold more power than you are prepared for it to misuse.

Daily Tech Digest - July 22, 2026

Quote for the day:

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

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Duration: 20 mins • Perfect for listening on the go.

Context bombing heralds a new AI era of deceptive defense

The article describes a defensive technique called “context bombing,” which uses the weaknesses of malicious AI agents against them. Attackers increasingly rely on autonomous AI models to speed up every stage of a cyberattack, from reconnaissance to exploitation. To counter this, defenders plant decoy files or secrets that contain short, carefully crafted prompts designed to trigger an AI model’s built‑in safety rules. When a rogue agent reads one of these prompts, it often stops executing its task entirely, halting the attack rather than simply alerting defenders. This builds on traditional “canary” techniques, where fake resources signal unauthorized access, but adds an active disruption layer. Tracebit, the firm behind the approach, tested context bombs in an AWS environment and found they reduced attack success rates by up to 90% by causing models to refuse further action . Because AI agents are vulnerable to prompt injection, hidden instructions placed in documents, DNS records, or environment variables can derail them mid‑operation. As one researcher explained, once the refusal enters the model’s context, “the model will often refuse to continue”. Context bombing heralds a new AI era of deceptive defense. The technique doesn’t replace other defenses, but it buys time, limits damage, and turns attackers’ reliance on AI into a practical point of failure.

Reskilling Mid-Career Leaders: What Senior Talent Needs to Stay Relevant

The discussion focuses on how mid‑career leaders can stay relevant as AI reshapes the workplace. Host Isaac Sacolick and guest Dean Cantave talk about the anxiety many senior professionals feel as their long‑held strengths no longer guarantee future opportunities. They emphasize that staying relevant now requires more than collecting certifications; leaders need to show clear, visible proof of their impact through thoughtful communication, public work, and practical results. Critical thinking, collaborative leadership, and strong data governance skills are highlighted as essential, along with understanding how AI agents and automation change decision‑making and team dynamics. The conversation also notes that leadership roles are becoming more cross‑functional, pushing senior talent to adapt their style, learn new tools, and work more fluidly across departments. Participants share personal stories about career transitions, stressing that credibility today comes from demonstrating how one’s experience translates into modern challenges rather than relying on past titles. They encourage leaders to build a recognizable professional presence, articulate their value clearly, and stay open to continuous learning. Overall, the session frames reskilling not as starting over but as evolving deliberately to match the demands of an AI‑driven workplace.

The Resilience Paradox – Why Autonomous Operations Require a New Approach to Governance

The article argues that as organizations move toward autonomous operations, their traditional governance models no longer fit the reality of how modern systems behave. It explains that observability has matured to the point where most companies can detect issues, but the real question now is how much decision‑making they are willing to hand over to AI. As environments grow more complex and produce more telemetry than humans can reasonably process, AIOps becomes essential for filtering noise and spotting patterns. However, each step toward autonomy reduces human workload while increasing the impact of a wrong automated decision. The piece notes that different teams often advance at different speeds, with platform groups embracing automation early while critical business systems remain manually governed. This uneven maturity creates a “resilience paradox”: delegating more to AI can strengthen reliability, but it also introduces new risks that governance frameworks were not designed to handle. The author stresses that resilience is no longer just about detecting problems but about deciding when systems should act on their own. As organizations shift from observation to autonomous action, they must rethink governance to ensure accountability, manage new categories of risk, and maintain trust in systems that increasingly make decisions without human intervention.

Technology moves faster than ecosystems

The article argues that many digital transformation efforts fail because technology evolves far faster than the ecosystems needed to support it. Companies invest heavily in advanced monitoring, automation, and predictive systems, yet execution performance often worsens. As the author notes, unplanned downtime rose to $1.4 trillion even as digital capability increased, revealing a structural gap where “technology advances faster than the ecosystems required to realize its value.” The paper explains that most industries operate across three maturity tiers, from highly digital enterprises to SMEs still dependent on spreadsheets and email. This mismatch means Tier‑1 intelligence layers can detect problems early, but Tier‑2 and Tier‑3 execution layers cannot respond at the same pace. The semiconductor shortage illustrates this clearly: Toyota’s deeper visibility helped for a time, but “the execution layer… still could not respond on the same timescale.” Workforce capability and physical infrastructure add further delays, evolving over years or decades while technology changes in months. To address this, the author proposes four architectural principles: design for graceful degradation, instrument for friction, build coordination layers, and orchestrate across the ecosystem rather than optimizing only within the enterprise. The core message is that digital transformation succeeds only when decision and execution architectures mature together.

SaaS will survive, but lazy SaaS is dead

The article argues that SaaS is not disappearing, but the old model of “lightweight” SaaS — tools that mainly provide a polished interface over simple workflows — is losing its footing. The author describes an internal review of AI meeting‑transcription tools where the products worked fine, yet the team kept asking, “what exactly are we paying for?” . Because they already had a secure AI environment, they could build the same workflow themselves in days and tailor it to their needs. This experience reflects a broader shift: AI and agentic systems have erased the old advantage SaaS once had, where buying was cheaper and faster than building. Large language models can now move data, call APIs, and automate logic with far less engineering effort, collapsing the integration friction that protected many SaaS categories. The SaaS most at risk are the thin workflow layers — dashboards, meeting tools, narrow productivity apps — whose value rested on simplifying implementation. Agents don’t use interfaces, and they don’t care about switching costs, which weakens the stickiness of these products. The SaaS that endures will be the kind that carries real operational burden for customers, such as compliance, regulatory complexity, or domain‑specific liability. In short, SaaS survives, but “lazy SaaS” — tools that exist mainly because integration used to be hard — does not.

Closing the Identity Gaps in Critical Infrastructure Security

Critical infrastructure remains highly vulnerable to identity‑based attacks, and the article explains why closing those gaps is now essential. It uses the Colonial Pipeline ransomware incident as a clear example, where attackers accessed the network through an inactive VPN account without MFA, leading to a shutdown that disrupted fuel supply across the U.S. East Coast . The piece notes that today’s threat actors, including state‑sponsored groups like Volt Typhoon, rely on stolen credentials, compromised devices, and legitimate remote‑access tools to blend into normal activity and maintain long‑term persistence inside critical infrastructure networks. Because these environments combine IT, cloud services, operational technology, and physical systems, implicit trust becomes dangerous. CISA’s guidance stresses that OT systems require careful handling due to safety and legacy constraints, but the article makes clear that business IT systems can be just as damaging when compromised. The core message is that MFA alone is not enough; organizations must verify both user identity and device trust, enforce segmentation, and continuously monitor for abnormal access patterns. Binding identities to trusted devices and eliminating unmanaged endpoints are highlighted as practical steps. Overall, the article urges critical‑infrastructure operators to adopt zero‑trust principles across both IT and OT so attackers cannot quietly enter, persist, and escalate into national‑level disruptions.

When your vehicle outlives its cloud: What happens next?

The article looks at what happens when a car’s cloud‑based features stop working long before the vehicle itself reaches the end of its life. Modern cars rely heavily on connected services for conveniences like remote locking, cabin pre‑conditioning, vehicle status checks, and emergency assistance. As Ars Technica notes, these features have become standard across brands, from HondaLink to BMW ConnectedDrive, and many owners willingly pay subscription fees to keep them active . The problem is that these services depend on backend systems, cellular networks, and telematics hardware that have much shorter lifespans than the vehicles they support. When networks shut down or manufacturers retire older platforms, owners can lose access to features overnight. A related report highlights how 3G shutdowns caused Lexus, Acura, and BMW to discontinue connected services for older models, sometimes leaving drivers with no upgrade path or costly hardware replacements. The mechanical car remains usable, but the digital layer quietly expires. The article suggests that this mismatch will only grow as more vehicles become internet‑dependent. Without modular hardware or long‑term support commitments, many drivers will eventually face a future where the car still runs but the cloud it depends on does not — raising practical questions about reliability, ownership, and the real lifespan of connected technology.

Designing Multi-Cloud Resiliency for Business Continuity

The piece explains why multi‑cloud strategies are becoming essential for business continuity, especially as outages, cyberattacks, and regional disruptions grow more frequent. It argues that relying on a single cloud provider creates a concentration risk: if that provider suffers a failure, the organization’s critical services may go down with it. Multi‑cloud architectures spread workloads across different providers, reducing the chance that one incident can halt operations. The article notes that this approach is not simply about redundancy; it is about designing systems that can operate even when parts of the environment are degraded. That includes planning for data portability, consistent security controls, and clear failover procedures. The author stresses that resilience requires more than technical configuration. Teams must understand how applications behave under stress, test recovery paths regularly, and ensure that governance policies support cross‑cloud operations. Multi‑cloud also introduces complexity, so organizations need strong visibility, shared standards, and disciplined architecture to avoid fragmentation. The core message is that resilience comes from intentional design: distributing risk, preparing for partial failures, and ensuring that critical functions can continue even when one cloud provider experiences trouble. In a world where disruptions are inevitable, multi‑cloud is presented as a practical way to keep essential services running with confidence.

From the bank branch to the mobile phone: India’s core banking journey

The article traces how India’s banking system evolved from branch‑centric operations to today’s mobile‑first experience, showing that this shift was gradual, uneven, and shaped by both technology and policy. It begins with the early core‑banking era, when banks moved from isolated branch systems to centralized platforms that allowed customers to access services from any branch. This foundation enabled nationwide expansion and consistent service delivery. As digital payments grew and smartphones became widespread, banks shifted again—this time from centralized infrastructure to digital channels that could support millions of small, real‑time transactions. The piece highlights how mobile banking, UPI, and app‑based services transformed customer expectations, pushing banks to modernize legacy systems, strengthen cybersecurity, and redesign processes for speed and reliability. It also notes that modernization is not only about technology; banks had to rethink architecture, improve integration, and adopt cloud‑ready platforms to keep pace with rising transaction volumes. The journey reflects India’s broader digital transformation: a move from physical branches to digital ecosystems that reach rural and urban customers alike. The article closes with a reminder that modernization is ongoing, and banks must continue refining their core systems to stay resilient and competitive in a fast‑changing financial landscape.

What is RPA? A revolution in business process automation

The article explains robotic process automation (RPA) in straightforward terms, focusing on what it is, how it works, and why organizations use it. RPA relies on software “bots” that mimic the steps a person takes on a computer—logging in, clicking buttons, copying data, moving files, and completing routine tasks much faster and without human error. These bots are best suited for high‑volume, rule‑based work on structured data, such as invoice processing, claims handling, report generation, and other repetitive back‑office activities. Because RPA operates at the user‑interface level, it works across existing applications without requiring deep system changes or complex integrations, making it practical for organizations with legacy systems. Sources note that RPA frees employees from tedious tasks so they can focus on work that requires judgment or creativity. RPA is not the same as AI; it cannot learn or make decisions outside its predefined workflow, though pairing it with AI enables more advanced “intelligent automation” capable of handling unstructured inputs or basic reasoning. The article also highlights that RPA can run unattended in the background or assist users directly, and its appeal continues to grow as businesses seek speed, accuracy, and consistency in routine operations. Overall, RPA is presented as a practical, dependable way to streamline repetitive digital work.

Daily Tech Digest - October 07, 2025

Quote for the day:

"There is only one success – to be able to spend your life in your own way." -- Christopher Morley

5 Critical Questions For Adopting an AI Security Solution

An AI-SPM solution must be capable of seamless AI model discovery, creating a centralized inventory for complete visibility into deployed models and associated resources. This helps organizations monitor model usage, ensure policy compliance, and proactively address any potential security vulnerabilities. By maintaining a detailed overview of models across environments, businesses can proactively mitigate risks, protect sensitive data, and optimize AI operations. ... An effective AI-SPM solution must tackle risks that are specific to AI systems. For instance, it should protect training data used in machine learning workflows, ensure that datasets remain compliant under privacy regulations, and identify anomalies or malicious activities that might compromise AI model integrity. Make sure to ask whether the solution includes built-in features to secure every stage of your AI lifecycle—from data ingestion to deployment. ... When evaluating an AI-SPM solution, ensure that it automatically maps your data and AI workflows to governance and compliance requirements. It should be capable of detecting non-compliant data and providing robust reporting features to enable audit readiness. Additionally, features like automated policy enforcement and real-time compliance monitoring are critical to keeping up with regulatory changes and preventing hefty fines or reputational damage.

The architecture of lies: Bot farms are running the disinformation war

As bots become more common and harder to tell from real users, people start to lose confidence in what they see online. This creates the liars dividend, where even authentic content is questioned simply because everyone knows fakes are out there. If any critical voice or inconvenient fact can be dismissed as just a bot or a deepfake, democratic debate takes a hit. AI-driven bots can also create the illusion of consensus. By making a hashtag or viewpoint trend, they create the impression that everyone is talking about it, or that an extreme position enjoys broader support than it appears to have. ... It’s still an open question how well online platforms stop malicious, bot-driven content, even though they are the ones responsible for policing their own networks. Harmful AI bots continue to get through the defenses of major social media platforms. Even though most have rules against automated manipulation, enforcement is weak and bots exploit the gaps to spread disinformation. Current detection systems and policies aren’t keeping up, and platforms will need stronger measures to address the problem. ... The EU and the US are both moving to address bot-driven disinformation. In the EU, the Digital Services Act obliges large online platforms to assess and mitigate systemic risks such as manipulation, and to provide vetted researchers with access to platform data.

Is the CISO chair becoming a revolving door?

“A CISO is interacting with a lot of interfaces, and you need to have soft skills and communicate well with others. In many cases, you need to drive others to take action, and that’s super tedious. It’s very difficult to keep doing it over time,” Geiger Maor says. “In many cases, you’re in direct conflict with company goals and your goals. You’re like a salmon fish going upstream against everybody else. This makes it very difficult to keep a long tenure.” ... That constant exposure to risk and blame is another reason some CISOs hesitate to take the role in the first place, according to Rona Spiegel, senior manager, security and trust, mergers and acquisitions at Autodesk and former cloud governance leader at Wells Fargo and Cisco. “The bad guys, especially now with AI and automation, they’re getting more sophisticated, and they only have to be right once, but the CISO has to be right all day every day. They only have to be wrong once, and they get blamed … you’re an operational cost centre no matter what because you’re not bringing in revenue, so if something goes wrong … all roads lead to the CISO,” Spiegel says. ... Chapman is also seeing a rise in fractional CISOs, brought in part-time to set up frameworks or oversee specific projects. “It really comes down to the individual,” he says. “Some want that top seat, speaking to the board, communicating risk. But I am also seeing some say, ‘It doesn’t have to be a CISO role.’”

RPA versus hyperautomation: Understanding accuracy (performance) benchmarks in practice

RPA is like that reliable coworker who never complains and does exactly what you ask. It loves repetitive, predictable tasks such as copying and pasting data, moving files between systems or generating standard reports. When everything goes according to plan, RPA is perfect. ... Hyperautomation is the next-level upgrade. It combines RPA with AI, natural language processing (NLP), intelligent document processing (IDP), process mining and workflow orchestration. In simple terms, it doesn’t just follow rules. It learns, adapts and keeps things moving even when the world throws curveballs. With hyperautomation, processes that would have stopped RPA cold continue without a hitch. ... RPA and Hyperautomation are not rivals. They are more like teammates with different strengths. RPA shines when tasks are stable and repetitive, quietly doing its job without fuss. Hyperautomation brings in intelligence, flexibility and the ability to handle entire processes from start to finish. When applied thoughtfully, hyperautomation cuts down on manual corrections, handles exceptions smoothly and delivers value at scale. All this happens without the IT team needing to hire extra coffee runners to fix errors or babysit the robots. The real goal is to build automation that works at the process level, adapts to change and keeps running even when things go off script.

The pros and cons of AI coding in the IT industry

Although now being used by the majority of programmers, AI tools were not universally welcomed upon their launch, and it has taken time to move beyond the initial doubts and suspicion surrounding generative AI. It’s important to note that risks remain when using AI-generated code, which organizations will have to mitigate. “Integrating AI into our coding processes was initially met with skepticism, both within our organization and across the industry,” Jain explains. “Concerns included AI's ability to comprehend complex codebases, the potential for generating buggy code, adherence to company standards, and issues surrounding code and data privacy.” However, since the launch of the first generative AI tools at the end of 2022, Jain says that the rapid evolution of AI technology’s implementation has alleviated many concerns, with features such as codebase indexing and secure training protocols addressing major concerns. “These advancements have enabled AI tools to understand code context, follow company standards, and maintain robust security measures,” Jain tells ITPro. Nevertheless, security and accountability are also major factors for any IT company to consider when looking to use AI as part of the development process, and research continues to show glaring vulnerabilities in AI code. There are certain steps that simply can’t be replaced by AI.

Why AI Is Forcing an Invisible Shift in Risk Management

Without the need for complex, technical coding knowledge, there are increasingly more departments within a business capable of driving and contributing to the development lifecycle, forcing a shift from centralized innovation to development that is fractalized across the entire organization. This shift has been revolutionary, driving more lucrative development by empowering technical teams and business leaders to align on goals and work hand-in-hand. Still, this transition has changed the organization’s relationship with risk. ... In the age of distributed application building, organizations have to raise more questions as it relates to governance and risk, which can mean many different things depending on where the technology sits in the business. Is the application going to be customer-facing? How sensitive is the data? How should it be stored? What are some other privacy considerations? These are all questions businesses must ask in the age of fractured development — and the answers will vary from case to case. ... The shift to decentralized development is not the first change technology has seen, and it’s certainly not the last. The key to staying ahead of the curve is paying attention to the invisible shifts that come with these disruptions, such as the changes that have recently come with the adoption of AI and low code. As these technologies reimagine the typical risk management and compliance model, it’s important for businesses to come to terms with adaptive governance and react as such.

How cross-functional teams rewrite the rules of IT collaboration

When done right, IT isn’t just an optional part of cross-functional collaboration, it’s an integral part of what makes collaboration possible. “There’s a lot of overlap now between IT, sales, finance and regulatory compliance,” says George Dimov, managing owner of Dimov Tax. ... What happens when IT plays a key role in breaking down barriers? First, getting IT involved in cross-functional teams means IT is at the table from day one. Rather than having an environment where a department requests a report or tool from IT after the fact, or has it digitize information later on, IT is present in all meetings. As more organizations recognize the inherent importance of digital transformation, the need for IT expertise — including perspectives from individuals with different types of IT experience — becomes more pronounced. It’s up to the CIO to provide the cross-functional leadership that ensures IT is involved in such efforts from the start. ... Even in situations when IT isn’t directly involved in day-to-day collaboration, it can still play a valuable role by providing technology resources that aid and facilitate collaboration. Ideally, IT should be part of the solution to eliminate barriers, whether that’s through digital sharing tools, reporting mechanisms, or something else. IT can and should be at the forefront of enabling cross-functional collaboration between teams and departments.

Service-as-software: The new control plane for business

Historically, enterprises ran on islands of automation — enterprise resource planning for the back office and, later, a proliferation of apps. Customer relationship management was the first to introduce a new operating model and a new business model. Today, the enterprise itself must begin to operate like a software company. That requires harmonizing those islands into a single unified layer where data and application logic collapse into an integrated System of Intelligence. Agents rely on this harmonized context to make decisions and, when needed, invoke legacy applications to execute workflows. Operating this way also demands a new operations model: a build-to-order assembly line for knowledge work that blends the customization of consulting with the efficiency of high-volume fulfillment. Humans supervise agents, and in doing so progressively encode their expertise into the system. ... The important point to remember is that islands of automation impede management’s core function – planning, resource allocation and orchestration with full visibility across levels of detail and business domains. Data lakes do not solve this by themselves; each star schema is another island. Near-term, organizations can start small and let agents interrogate a single domain (for example, the sales cube) and take limited actions by calling systems of record via MCP servers, for example, viewing a customer’s complaints and initiating a return authorization.

Companies are making the same mistake with AI that Tesla made with robots

Shai Ahrony, CEO of marketing agency Reboot Online, calls this phenomenon the "AI aftershock." "Companies that rushed to cut jobs in the name of AI savings are now facing massive, and often unexpected costs," he told ZDNET. "We've seen customers share examples of AI-generated errors -- like chatbots giving wrong answers, marketing emails misfiring, or content that misrepresents the brand -- and they notice when the human touch is missing." ... Some companies have already learned painful lessons about AI's shortcomings and adjusted course accordingly. In one early example from last year, McDonald's announced that it was retiring an automated order-taking technology that it had developed in partnership with IBM after the AI-powered system's mishaps went viral across social media. ... McDonalds' and Klarna's decisions to backtrack on AI in favor of humans is reminiscent of a similar about-face from Tesla. In 2018, after Tesla failed to meet production quotas for its Model 3, CEO Elon Musk admitted in a tweet that the electric vehicle company's reliance upon "excessive automation…was a mistake." "Humans are underrated," he added. Businesses aggressively pushing to deploy AI-powered customer service initiatives in the present could come to a similar conclusion: that even though the technology helps to cut spending and boost efficiency in some domains, it isn't able to completely replicate the human touch.

How Can the Usage of AI Help Boost DevOps Pipelines

In recent times, AI is playing a key role in CI/CD by using machine learning algorithms and intelligent automation to detect errors proactively, optimize resource usage and faster release cycles. With AI, CI/CD pipelines can learn, adapt and optimize themselves, redefining software development from start to finish. By combining AI and DevOps, you can eliminate silos, recover faster from outages and open up new business revenue streams. Today’s businesses are increasingly leveraging artificial intelligence capabilities throughout their DevOps pipelines to make their CI/CD pipelines intelligent, thereby enabling them to predict problems faster, optimize the pipelines if needed, and recover from failures without the need for any human intervention. ... When you adopt AI into the DevOps practices in your organization, you are applying specific technologies to automate, optimize, and enhance each stage of the software development lifecycle – coding, testing, deployment, and monitoring. Today’s organizations are using AI in their DevOps pipelines to drive innovation, enabling teams to work seamlessly and achieve rapid development and deployment cycles. ... AI can help in DevSecOps in ways such as automating security testing, automating threat detection, and streamlining incident response. You can use AI-powered tools to scan your application source code for security vulnerabilities, automate software patches, automate incident responses, and monitor in real-time to identify anomalies.

Daily Tech Digest - July 10, 2025

Quote for the day:

"Strive not to be a success, but rather to be of value." -- Albert Einstein

Domain-specific AI beats general models in business applications

Like many AI teams in the mid-2010s, Visma’s group initially relied on traditional deep learning methods such as recurrent neural networks (RNNs), similar to the systems that powered Google Translate back in 2015. But around 2020, the Visma team made a change. “We scrapped all of our development plans and have been transformer-only since then,” says Claus Dahl, Director ML Assets at Visma. “We realized transformers were the future of language and document processing, and decided to rebuild our stack from the ground up.” ... The team’s flagship product is a robust document extraction engine that processes documents in the countries where Visma companies are active. It supports a variety of languages. The AI could be used for documents such as invoices and receipts. The engine identifies key fields, such as dates, totals, and customer references, and feeds them directly into accounting workflows. ... “High-quality data is more valuable than high volumes. We’ve invested in a dedicated team that curates these datasets to ensure accuracy, which means our models can be fine-tuned very efficiently,” Dahl explains. This strategy mirrors the scaling laws used by large language models but tailors them for targeted enterprise applications. It allows the team to iterate quickly and deliver high performance in niche use cases without excessive compute costs.

The case for physical isolation in data centre security

Hardware-enforced physical isolation is fast becoming a cornerstone of modern cybersecurity strategy. These physical-layer security solutions allow your critical infrastructure – servers, storage and network segments – to be instantly disconnected on demand, using secure, out-of-band commands. This creates a last line of defence that holds even when everything else fails. After all, if malware can’t reach your system, it can’t compromise it. If a breach does occur, physical segmentation contains it in milliseconds, stopping lateral movement and keeping operations running without disruption. In stark contrast to software-only isolation, which relies on the very systems it seeks to protect, hardware isolation remains immune to tampering. ... When ransomware strikes, every second counts. In a colocation facility, traditional defences might flag the breach, but not before it worms its way across tenants. By the time alerts go out, the damage is done. With hardware isolation, there’s no waiting: the compromised tenant can be physically disconnected in milliseconds, before the threat spreads, before systems lock up, before wallets and reputations take a hit. What makes this model so effective is its simplicity. In an industry where complexity is the norm, physical isolation offers a simple, fundamental truth: you’re either connected or you’re not. No grey areas. No software dependency. Just total certainty.

Scaling without outside funding: Intuitive's unique approach to technology consulting

We think for any complex problem, a good 60–70% of it can be solved through innovation. That's always our first principle. Then where we see any inefficiencies; be it in workflows or process, automation works for the other 20% of the friction. The remaining 10–20% is where the engineering plays its important role, and it allows to touch on the scale, security and governance aspects. In data specifically, we are referencing the last 5–6 years of massive investments. We partner with platforms like Databricks and DataMiner and we've invested in companies like TESL and Strike AI for securing their AI models. ... In the cloud space, we see a shift from migration to modernisation (and platform engineering). Enterprises are focussing on modernisation of both applications and databases because those are critical levers of agility, security, and business value. In AI it is about data readiness; the majority of enterprise data is very fragmented or very poor quality which makes any AI effort difficult. Next is understanding existing processes—the way work is done at scale—which is critical for enabling GenAI. But the true ROI is Agentic AI—autonomous systems which don’t just tell you what to do, but just do it. We’ve been investing heavily in this space since 2018.

The Future of Professional Ethics in Computing

Recent work on ethics in computing has focused on artificial intelligence (AI) with its success in solving problems, processing large amounts of data, and with the award of Nobel Prizes to AI researchers. Large language models and chatbots such as ChatGPT suggest that AI will continue to develop rapidly, acquire new capabilities, and affect many aspects of human existence. Many of the issues raised in the ethics of AI overlap previous discussions. The discussion of ethical questions surrounding AI is reaching a much broader audience, has more societal impact, and is rapidly transitioning to action through guidelines and the development of organizational structure, regulation, and legislation. ... Ethics of digital technologies in modern societies raises questions that traditional ethical theories find difficult to answer. Current socio-technical arrangements are complex ecosystems with a multitude of human and non-human stakeholders, influences, and relationships. The questions of ethics in ecosystems include: Who are members? On what grounds are decisions made and how are they implemented and enforced? Which normative foundations are acceptable? These questions are not easily answered. Computing professionals have important contributions to make to these discussions and should use their privileges and insights to help societies navigate them.

AI Agents Vs RPA: What Every Business Leader Needs To Know

Technically speaking, RPA isn’t intelligent in the same way that we might consider an AI system like ChatGPT to mimic some functions of human intelligence. It simply follows the same rules over and over again in order to spare us the effort of doing it. RPA works best with structured data because, unlike AI, it doesn't have the ability to analyze and understand unstructured data, like pictures, videos, or human language. ... AI agents, on the other hand, use language models and other AI technologies like computer vision to understand and interpret the world around them. As well as simply analyzing and answering questions about data, they are capable of taking action by planning how to achieve the results they want and interacting with third-party services to get it done. ... Using RPA, it would be possible to extract details about who sent the mail, the subject line, and the time and date it was sent. This can be used to build email databases and broadly categorize emails according to keywords. An agent, on the other hand, could analyze the sentiment of the email using language processing, prioritize it according to urgency, and even draft and send a tailored response. Over time, it learns how to improve its actions in order to achieve better resolutions.

How To Keep AI From Making Your Employees Stupid

Treat AI-generated content like a highly caffeinated first draft – full of energy, but possibly a little messy and prone to making things up. Your job isn’t to just hit “generate” and walk away unless you enjoy explaining AI hallucinations or factual inaccuracies to your boss (or worse, your audience). Always, always edit aggressively, proofread and, most critically, fact-check every single output. This process isn’t just about catching AI’s mistakes; it actively engages your critical thinking skills, forcing you to verify information and refine expression. Think of it as intellectual calisthenics. ... Don’t settle for the first answer AI gives you. Engage in a dialogue. Refine your prompts, ask follow-up questions, request different perspectives and challenge its assumptions. This iterative process of refinement forces you to think more clearly about your own needs, to be precise in your instructions, and to critically evaluate the nuances of the AI’s response. ... The MIT study serves as a crucial wake-up call: over-reliance on AI can indeed make us “stupid” by atrophying our critical thinking skills. However, the solution isn’t to shun AI, but to engage with it intelligently and responsibly. By aggressively editing, proofreading and fact-checking AI outputs, by iteratively refining prompts and by strategically choosing the right AI tool for each task, we can ensure AI serves as a powerful enhancer, not a detrimental crutch.

What EU’s PQC roadmap means on the ground

The EU’s PQC roadmap is broadly aligned with that from NIST; both advise a phased migration to PQC with hybrid-PQC ciphers and hybrid digital certificates. These hybrid solutions provide the security promises of brand new PQC algorithms, whilst allowing legacy devices that do not support them, to continue using what’s now being called ‘classical cryptography’. In the first instance, both the EU and NIST are recommending that non-PQC encryption is removed by 2030 for critical systems, with all others following suit by 2035. While both acknowledge the ‘harvest now, decrypt later’ threat, neither emphasise the importance of understanding the cover time of data; nor reference the very recent advancements in quantum computing. With many now predicting the arrival of cryptographically relevant quantum computers (CRQC) by 2030, if organizations or governments have information with a cover time of five years or more, it is already too late for many to move to PQC in time. Perhaps the most significant difference that EU organizations will face compared to their American counterparts, is that the European roadmap is more than just advice; in time it will be enforced through various directives and regulations. PQC is not explicitly stated in EU regulations, although that is not surprising.

The trillion-dollar question: Who pays when the industry’s AI bill comes due?

“The CIO is going to be very, very busy for the next three, four years, and that’s going to be the biggest impact,” he says. “All of a sudden, businesspeople are starting to figure out that they can save a ton of money with AI, or they can enable their best performers to do the actual job.” Davidov doesn’t see workforce cuts matching AI productivity increases, even though some job cuts may be coming. ... “The costs of building out AI infrastructure will ultimately fall to enterprise users, and for CIOs, it’s only a question of when,” he says. “While hyperscalers and AI vendors are currently shouldering much of the expense to drive adoption, we expect to see pricing models evolve.” Bhathena advises CIOs to look beyond headline pricing because hidden costs, particularly around integrating AI with existing legacy systems, can quickly escalate. Organizations using AI will also need to invest in upskilling employees and be ready to navigate increasingly complex vendor ecosystems. “Now is the time for organizations to audit their vendor agreements, ensure contract flexibility, and prepare for potential cost increases as the full financial impact of AI adoption becomes clearer,” he says. ... Baker advises CIOs to be careful about their purchases of AI products and services and tie new deployments to business needs.

Multi-Cloud Adoption Rises to Boost Control, Cut Cost

Instead of building everything on one platform, IT leaders are spreading out their workloads, said Joe Warnimont, senior analyst at HostingAdvice. "It's no longer about chasing the latest innovation from a single provider. It's about building a resilient architecture that gives you control and flexibility for each workload." Cost is another major factor. Even though hyperscalers promote their pay-as-you-go pricing, many enterprises find it difficult to predict and manage costs at scale. This is true for companies running hundreds or thousands of workloads across different regions and teams. "You'd think that pay-as-you-go would fit any business model, but that's far from the case. Cost predictability is huge, especially for businesses managing complex budgets," Warnimont said. To gain more control over pricing and features, companies are turning to alternative cloud providers, such as DigitalOcean, Vultr and Backblaze. These platforms may not have the same global footprint as AWS or Azure but they offer specialized services, better pricing and flexibility for certain use cases. An organization needing specific development environments may go to DigitalOcean. Another may chose Vultr for edge computing. Sometimes the big players just don't offer what a specific workload requires.

How CISOs are training the next generation of cyber leaders

While Abousselham champions a personalized, hands-on approach to developing talent, other CISOs are building more formal pathways to support emerging leaders at scale. For others like PayPal CISO Shaun Khalfan, structured development was always part of his career. He participated in formal leadership training programs offered by the Department of Defense and those run by the American Council for Technology. ... Structured development is also happening inside companies like the insurance brokerage firm Brown & Brown. CISO Barry Hensley supports an internal cohort program designed to identify and grow emerging leaders early in their careers. “We look at our – I’m going to call it newer or younger – employees,” he explains. “And if you become recognized in your first, second, or third year as having the potential to [become a leader], you get put in a program,” he explains. ... Khalfan believes good CISOs should be able to dive deep with engineers while also leading boardroom conversations. “It’s been a long time since I’ve written code,” he says, “but I at least understand how to have a deep conversation and also be able to have a board discussion with someone.” Abousselham agrees that technical experience is only one part of the puzzle.

Daily Tech Digest - June 11, 2025

Quote for the day:

"The key to success is to focus on goals, not obstacles." -- Unknown

The future of RPA ties to AI agents

“Unlike RPA bots, that follow predefined rules, AI agents are learning from data, making decisions, and adapting to changing business logic,” Khan says. “AI agents are being used for more flexible tasks such as customer interactions, fraud detection, and predictive analytics.” Kahn sees RPA’s role shifting in the next three to five years, as AI agents become more prevalent. Many organizations will embrace hyperautomation, which uses multiple technologies, including RPA and AI, to automate business processes. “Use cases for RPA most likely will be integrated into broader AI-powered workflows instead of functioning as standalone solutions,” he says. ... “RPA isn’t dying — it’s evolving,” he says. “We’ve tested various AI solutions for process automation, but when you need something to work the same way every single time —without exceptions, without interpretations — RPA remains unmatched.” Radich and other automation experts see AI agents eventually controlling RPA bots, with various robotic processes in a toolbox for agents to choose from. “Today, we build separate RPA workflows for different scenarios,” Radich says. “Tomorrow, with our agentic capabilities, an agent will evaluate an incoming request and determine whether it needs RPA for data processing, API calls for system integration, or human handoff for complex decisions.”

The path to better cybersecurity isn’t more data, it’s less noise

SOCs deal with tens of thousands of alerts every day. It’s more than any person can realistically keep up with. When too much data comes in at once, things get missed. Responses slow down and, over time, the constant pressure can lead to burnout. ... The trick is to start spotting patterns. Look at what helped in past investigations. Was it a login from an odd location? An admin running commands they normally don’t? A device suddenly reaching out to strange domains? These are the kinds of details that stand out once you understand what typical system behavior looks like. At first, you won’t. That’s okay. Spend time reading through old incident reports. Watch how the team reacts to real alerts. Learn which ones actually spark investigations and which ones get dismissed without a second glance. ... Start by removing logs and alerts that don’t add value. Many logs are never looked at because they don’t contain useful information. Logs showing every successful login might not help if those logins are normal. Some logs repeat the same information, like system status messages. ... Next, think about how long to keep different types of logs. Not all logs need to be saved for the same amount of time. Network traffic logs might only be useful for a few days because threats usually show up quickly.

The EU challenges Google and Cloudflare with its very own DNS resolver that can filter dangerous traffic

The DNS4EU wants to be an alternative to major US-based public DNS services (like Google and Cloudflare) to boost the EU's digital autonomy by reducing European reliance on foreign infrastructure. This isn't only an EU-developed DNS, though. The DNS4EU comes with built-in filters against malicious domains, like those hosting malware, phishing, or other cybersecurity threats. The home user version also includes the possibility to block ads and/or adult content. ... The DNS4EU, which the EU ensures "will not be forced on anyone," has been developed to meet different users' needs. The home users' version is a public and free DNS resolver that comes with the option to add filters to block ads, malware, adult content, or all of these, or none. There's also a dedicated version for government entities and telecom providers that operate within the European Union. As mentioned earlier, the DNS4EU comes with a built-in filter to block dangerous traffic alongside the ability to provide regional threat intelligence. This means that a malicious threat discovered in one country could be blocked simultaneously across several regions and countries, de facto halting its spread. ... The Senior Director for European Government and Regulatory Affairs at the Internet Society, David Frautschy Heredia, also warns against potential risks related to content filtering, arguing that "safeguards should be developed to prevent abuse."

AgenticOps: How Cisco is Rewiring Network Operations for the AI Age

AI Canvas is where AgenticOps comes to life. It’s the industry’s first generative UI built for cross-domain IT operations, unifying NetOps, SecOps, IT, and executives into one collaborative environment. Powered by real-time telemetry from Meraki, ThousandEyes, Splunk, and more, AI Canvas brings together data from across the stack into one intelligent, always-on view. But this isn’t just visibility. It’s AI already operating. When a service issue hits, AI Canvas pulls in the right data, connects the dots, and surfaces a live picture of what matters—before anyone even asks. Every session starts with context, whether launched by AI or by an IT engineer. Embedded into the AI Canvas is the Cisco AI Assistant, your interface to the agentic system. Ask a question in natural language. Dig into root cause. Explore options. The AI Assistant guides you through diagnostics, decisions, and actions, all grounded in live telemetry. And when you’re ready to share, just drag your findings into AI Canvas. From there, with one click you can invite collaborators—and that’s when the canvas comes fully alive. Every insight becomes part of a shared investigation with AI Canvas actively thinking, collaborating, and evolving the UI at every step. But it doesn’t stop at diagnosis—AI Canvas acts. It applies changes, monitors impact and share outcomes in real time.

8 things CISOs have learned from cyber incidents

Brown believes there are often important lessons that come out of breaches, whether it’s high-profile ones that end up in textbooks and university courses, or experiences that can be shared among peers through conference panels and other events. “Always look for good to come from events. How can you help the industry forward? Can you help the CISO community?” he says. ... Many incident-hardened CISOs will shift their approach and their mindset about experiencing an attack first-hand. “You’ll develop an attack-minded perspective, where you want to understand your attack surface better than your adversary, and apply your resources accordingly to insulate against risk,” says Cory Michel, VP security and IT at AppOmni, who’s been on several incident response teams. In practice, shifting from defense to offence means preparing for different types of incidents, be it platform abuse, exploitation or APTs, and tailoring responses. ... The playbook needs clear guidance on communication, during and after an incident, because this can be overlooked while dealing with the crisis, but in the end, it may come to define the lasting impact of a breach that becomes common knowledge. “Every word matters during a crisis,” says Brown. “Of what you publish, what you say, how you say it. So, it’s very important to be prepared for that.”

The five security principles driving open source security apps at scale

Open-source AI’s ability to act as an innovation catalyst is proven. What is unknown is the downside or the paradox that’s being created with the all-out focus on performance and the ubiquity of platform development and support. At the center of the paradox for every company building with open-source AI is the need to keep it open to fuel innovation, yet gain control over security vulnerabilities and the complexity of compliance. ... Regulatory compliance is becoming more complex and expensive, further fueling the paradox. Startup founders, however, tell VentureBeat that the high costs of compliance can be offset by the data their systems generate. They’re quick to point out that they do not intend to deliver governance, risk, and compliance (GRC) solutions; however, their apps and platforms are meeting the needs of enterprises in this area, especially across Europe. ... “EU AI Act, for example, is starting its enforcement in February, and the pace of enforcement and fines is much higher and aggressive than GDPR. From our perspective, we want to help organizations navigate those frameworks, ensuring they’re aware of the tools available to leverage AI safely and map them to risk levels dictated by the Act.”

What We Wish We Knew About Container Security

Each container maps to a process ID in Linux. The illusion of separation is created using kernel namespaces. These namespaces hide resources like filesystems, network interfaces and process trees. But the kernel remains shared. That shared kernel becomes the attack surface. And in the event of a container escape, that attack surface becomes a liability. Common attack vectors include exploiting filesystem mounts, abusing symbolic links or leveraging misconfigured privileges. These exploits often target the host itself. Once inside the kernel, an attacker can affect other containers or the infrastructure that supports them. This is not just theoretical. Container escapes happen, and when they do, everything on that node becomes suspect. ... Virtual machines fell out of favor because of performance overhead and slow startup times. But many of those drawbacks have since been addressed. Projects leveraging paravirtualization, for example, now offer performance comparable to containers while restoring strong workload isolation. Paravirtualization modifies the guest OS to interact efficiently with the hypervisor. It eliminates the need to emulate hardware, reducing latency and improving resource usage. Several open source projects have explored this space, demonstrating that it’s possible to run containers within lightweight virtual machines.

The unseen risks of cloud data sharing and how companies can safeguard intellectual property

For many technology-driven sectors, intellectual property lies at their core. This is particular to the fields of software development, pharmaceuticals, and design innovation. For companies in these fields, IP theft can have serious consequences. Unfortunately, cybercriminals increasingly target valuable IP because it can be sold or used to undermine the original creators. According to the Verizon 2025 Data Breach Investigation Report, nearly 97 per cent of these attacks in the Asia-Pacific region are fuelled by social engineering, system intrusion and web app attacks. This alarming trend highlights the urgent need for stronger data protection measures. ... While cloud platforms present unique challenges for securing IP, they also offer some potential solutions. One of the most effective ways to protect data is through encryption. Encrypting files before they are uploaded to the cloud ensures that even if unauthorised access is gained, the data remains unreadable without the proper decryption key. For organisations that rely on cloud platforms for collaboration, file-level encryption is crucial. This form of encryption ensures that sensitive data is protected not just at rest but throughout its entire lifecycle in the cloud. Many cloud platforms offer built-in encryption tools, but companies can also implement third-party solutions to enhance the protection of their intellectual property.

The Critical Role of a Data Pipeline in Security

By implementing a data pipeline and prioritizing the optimization and reduction of data volume before it reaches the SIEM, organizations can stay on budget and still ensure that all necessary data can be thoroughly examined. Data pipelines also lead to tangible reductions in both storage and processing expenses. ... The decrease in the sheer volume of data that the SIEM must handle directly can significantly reduce the total cost of SIEM operations. In addition to volume reduction, data pipelines improve the quality of data delivered to SIEMs and other tools — filtering out repetitive noise and enriching logs for faster queries, increased relevance, and prioritization of the most critical security events. Data pipelines also introduce efficiency by automating the collection, processing, and routing of data. By reducing alert fatigue through intelligent anomaly detection and prioritization, data pipelines can significantly speed up incident resolution times. Beyond immediate threat detection and cost savings, data pipelines also aid in maintaining compliance with privacy regulations like GDPR, CCPA, and PCI. They help provide clear data lineage, making it easier to track the origin and transformations of data.

Why you need diverse third-party data to deliver trusted AI solutions

Data diversity refers to the variety and representation of different attributes, groups, conditions, or contexts within a dataset. It ensures that the dataset reflects the real-world variability in the population or phenomenon being studied. The diversity of your data helps ensure that the insights, predictions, and decisions derived from it are fair, accurate, and generalizable. ... Before you start your data analysis, it’s important to understand what you want to do with your data. A keen understanding of your use cases and data applications can help identify gaps and hypotheses you need to work to solve. It also gives you a method for seeking the data that fits your specific use case. In the same way, starting with a clear question provides direction, focus, and purpose to the whole process of text data analysis. Without one, you’ll inevitably gather irrelevant data, overlook key variables, or find yourself looking at a dataset that’s irrelevant to what you actually want to know. ... When certain voices, topics, or customer segments are over- or underrepresented in the data, models trained on that data may produce skewed results: misunderstanding user needs, overlooking key issues, or favoring one group over another. This can result in poor customer experiences, ineffective personalization efforts, and biased decision-making.