Daily Tech Digest by Kannan Subbiah: ESG

Showing posts with label ESG. Show all posts

Daily Tech Digest - March 15, 2026

Quote for the day:

"A leader must inspire or his team will expire." -- Orrin Woodward

🎧 Listen to this digest on YouTube Music

Duration: 24 mins • Perfect for listening on the go.

The Last Frontier: Navigating the Dawn of the Brain-Computer Interface Era

In the article "The Last Frontier: Navigating the Dawn of the Brain-Computer Interface Era," Kannan Subbiah explores the transformative rise of Brain-Computer Interfaces (BCIs) as they move from science fiction to strategic reality. BCIs function by bypassing traditional neural pathways to establish a direct communication link between the brain's electrical signals and external hardware. By 2026, the technology has transitioned from clinical trials—aimed at restoring mobility and sensory perception for the paralyzed—into the enterprise sector, where it is used to monitor cognitive load and optimize worker productivity. However, this deep integration between biological and digital intelligence introduces profound risks, including physical inflammation from invasive implants, cybersecurity threats like "brain-jacking," and ethical concerns regarding the erosion of personal agency. To address these vulnerabilities, a global movement for "neurorights" has emerged, led by frameworks from UNESCO and pioneer legislation in nations like Chile to protect mental privacy and integrity. Subbiah argues that while the potential for human augmentation is immense, society must establish rigorous ethical standards to ensure thoughts are treated as expressions of human dignity rather than mere harvestable data. Ultimately, navigating this frontier requires balancing rapid innovation with a "hybrid mind" philosophy that prioritizes psychological continuity and user autonomy.

Is your AI agent a security risk? NanoClaw wants to put it in a virtual cage

In the article "Is your AI agent a security risk? NanoClaw wants to put it in a virtual cage" on ZDNet, Charlie Osborne discusses the newly announced partnership between NanoClaw and Docker, designed to tackle the escalating security concerns surrounding autonomous AI agents. NanoClaw emerged as a lightweight, security-first alternative to OpenClaw, boasting a tiny codebase of fewer than 4,000 lines compared to its predecessor's massive 400,000. This simplicity allows for easier auditing and reduced risk. The integration enables NanoClaw agents to run within Docker Sandboxes, which utilize MicroVM-based, disposable isolation zones. Unlike traditional containers that share a kernel with the host, these MicroVMs provide a "hard boundary," ensuring that even if an agent misbehaves or is compromised, it remains contained and cannot access or damage the host system. This "secure-by-design" approach addresses critical enterprise obstacles, such as the potential for agents to accidentally delete files or leak sensitive credentials. By providing a controlled environment where agents can independently install tools and execute workflows without constant human oversight, the collaboration unlocks greater productivity while maintaining rigorous enterprise-grade safeguards. Ultimately, the partnership shifts the security paradigm from trusting an agent's behavior to enforcing OS-level isolation, making it safer for organizations to deploy powerful AI agents in production.

Banks Turn to Unified Data Platforms to Manage Risk Intelligence

In the article "Banks Turn to Unified Data Platforms to Manage Risk Intelligence," Sandhya Michu explores how financial institutions are addressing the complexities of digital banking by consolidating fragmented data environments into strategic unified platforms. The rapid growth of digital transactions has scattered operational and customer data across mobile apps and backend systems, creating a "brittle" infrastructure that often hinders the scalability of AI and analytics initiatives. To overcome this, leading banks are building centralized data lakes and unified digital layers to aggregate structured and unstructured information. These centralized environments empower business, compliance, and risk departments with shared datasets, significantly improving regulatory reporting and customer analytics. Additionally, unified platforms enhance operational observability by enabling faster incident analysis through log correlation across diverse systems. Beyond reliability, these data frameworks are revolutionizing credit risk management by providing real-time underwriting capabilities and early warning systems that ingest external market data. By digitizing legacy archives and investing in real-time data stores, banks are creating a robust foundation for advanced generative AI applications and continuous analytics. Ultimately, this shift toward a unified data architecture is essential for maintaining transparency, regulatory oversight, and enterprise-wide decision-making in an increasingly volatile and data-intensive financial landscape.

Why nobody cares about laptop touchscreens anymore

In the article "Why nobody cares about laptop touchscreens anymore," author Chris Hoffman argues that the once-coveted feature has become a neglected afterthought for both hardware manufacturers and Microsoft. While touchscreens remain prevalent on Windows 11 devices, they are rarely showcased in marketing because the industry has shifted focus toward performance, battery life, and AI integration. Hoffman posits that the initial appeal of touchscreens was largely a workaround for the poor-quality trackpads found on older Windows 10 machines. With the advent of highly responsive, "precision" touchpads across modern laptops, the functional necessity of reaching for the screen has vanished. Furthermore, Windows 11 lacks a truly optimized touch interface, and the ecosystem of touch-first applications has stagnated since the Windows 8 era. Even on 2-in-1 convertible devices, the "tablet mode" is described as an imperfect compromise with awkward ergonomics and watered-down software gestures. Unless a user specifically requires pen input for digital art or note-taking, Hoffman suggests that a touchscreen is now a "check-box" feature that adds little real-world value. Ultimately, the piece advises consumers to prioritize other specifications, as the current Windows environment remains firmly a mouse-and-keyboard-first experience, leaving the touchscreen as a redundant relic of past design ambitions.

How AI is changing your mind

In the Computerworld article "How AI is changing your mind," Mike Elgan warns that the widespread adoption of artificial intelligence is fundamentally altering human cognition and social interaction. Drawing on recent research from institutions like Cornell and USC, Elgan identifies two primary dangers: behavioral manipulation and the homogenization of thought. Studies show that biased AI autocomplete tools can successfully shift user opinions on controversial topics—even when individuals are warned of the bias—because the interactive nature of co-writing makes the influence feel internal. Simultaneously, the reliance on a few dominant Large Language Models (LLMs) is erasing linguistic and cultural diversity, nudging global expression toward a bland, Western-centric "hive mind" through a feedback loop of generic training data. These chatbots act as "co-reasoners," fostering sycophancy and simulated validation that can distort reality, particularly for isolated individuals. To combat this cognitive erosion, Elgan suggests practical strategies: disabling autocomplete, writing without AI to preserve individuality, and treating chatbots as intellectual sparring partners rather than authority figures. Ultimately, the piece argues that while AI offers immense utility, users must consciously protect their mental autonomy from being subtly rewritten by algorithms that prioritize consensus and efficiency over authentic human perspective and diversity of thought.

The value of reducing middle office emissions for ESG

In the Information Age article "The value of reducing middle-office emissions for ESG," Danielle Price explores how the modernization of middle-office functions—such as reconciliation, trade matching, and risk management—can significantly advance corporate sustainability. Historically, these processes have been energy-intensive, running continuously on legacy on-premise servers at peak capacity. As ESG performance increasingly influences a bank’s cost of capital, CIOs must view the middle office as a strategic asset for decarbonization. Migrating these data-heavy workloads to public, cloud-native infrastructure can reduce operational emissions by 60% to 80% without requiring fundamental changes to business processes. This transition is becoming essential as Pillar 3 disclosures demand more granular ESG reporting and evidence of measurable year-on-year reductions. Financially, high ESG scores are linked to lower credit spreads and reduced regulatory capital charges, making infrastructure efficiency a direct factor in a firm’s financial health. Furthermore, the shift to cloud-native platforms creates a powerful network effect; when shared systems lower their carbon footprint, the entire counter-party ecosystem benefits. Ultimately, the article argues that aligning operational efficiency with ESG objectives is no longer optional, but a strategic imperative that combines environmental stewardship with enhanced financial competitiveness in today's global capital markets.

New European Emissions Regs Include Cybersecurity Rules

The article from Data Breach Today details the integration of new cybersecurity requirements into the European Union's "Euro 7" emissions regulations, marking a significant shift in automotive compliance. Prompted by the "Dieselgate" scandal, these rules mandate that gas-powered vehicles feature on-board systems to monitor emissions data, which must be protected from tampering, spoofing, and unauthorized over-the-air updates. While the regulations primarily target malicious external hackers, they also aim to prevent corporate fraud. However, a major point of contention has emerged: the potential conflict with the "right-to-repair" movement. The same secure gateway technologies used to prevent unauthorized modifications to engine control units could effectively lock out independent mechanics, who require access to diagnostic data for legitimate repairs. Automotive experts warn that while most passenger vehicle manufacturers are prepared, the commercial sector lags behind, and the industry faces an immense architectural challenge in balancing security with equitable data access. Furthermore, as cars become increasingly connected, broader risks—including remote takeovers and sensitive data leaks—remain a concern for EU public safety, suggesting that current type-approval regimes may need to evolve to address nation-state threats and organized cybercrime.

Why Data Governance Fails in Many Organizations: The Accountability Crisis and Capability Gaps

In the article "Why Data Governance Fails in Many Organizations," Stanyslas Matayo explores the critical factors behind the high failure rate of data governance initiatives, specifically highlighting the "accountability crisis" and "capability gaps." Despite significant investments, many organizations engage in "governance theater," where committees exist on paper but lack the executive authority, seniority, and enforcement mechanisms to drive change. This accountability gap is exacerbated when governance roles report to mid-level IT rather than leadership, rendering them expendable scribes rather than strategic governors. Simultaneously, a "capability deficit" arises when initiatives are treated as purely technical projects. Teams often overlook essential non-technical skills like change management, ethics, and learning design, assuming technical expertise alone is sufficient for organizational transformation. To combat these failures, the author references the DMBOK framework, advocating for four pillars: formal role clarification (e.g., Data Owners and Stewards), governed metadata, explicit quality mechanisms, and aligned communication flows. Ultimately, success requires moving beyond technical delivery to establish a business-led discipline where data is managed as a strategic asset through senior-level sponsorship and a holistic integration of diverse organizational capabilities, ensuring that governance structures possess the actual power to resolve conflicts and enforce standards.

AI coding agents keep repeating decade-old security mistakes

The Help Net Security article "AI coding agents keep repeating decade-old security mistakes" details a 2026 study by DryRun Security that evaluated the security performance of Claude Code, OpenAI Codex, and Google Gemini. Researchers discovered that despite their rapid software generation capabilities, these AI agents introduced vulnerabilities in 87% of the pull requests they created. The study identified ten recurring vulnerability categories across all three agents, with broken access control, unauthenticated sensitive endpoints, and business logic failures being the most prevalent. For example, agents frequently failed to implement server-side validation for critical actions or neglected to wire authentication middleware into WebSocket handlers. While OpenAI Codex generally produced the fewest vulnerabilities, all agents struggled with secure JWT secret management and rate limiting. The report emphasizes that traditional regex-based static analysis tools often miss these complex logic and authorization flaws, as they cannot reason about data flows or trust boundaries effectively. Consequently, the study recommends that development teams scan every pull request, incorporate security reviews into the initial planning phase, and utilize contextual security analysis tools. Ultimately, while AI agents significantly accelerate development, their lack of inherent security-centric reasoning necessitates rigorous human oversight and advanced scanning to prevent the recurrence of foundational security errors.

Impact of Artificial Intelligence (AI) in Enterprise Architecture (EA) Discipline

The article "Impact of Artificial Intelligence (AI) in Enterprise Architecture (EA) Discipline" examines how AI is fundamentally reshaping the traditional responsibilities of enterprise architects. By integrating advanced AI tools into the EA framework, organizations can automate labor-intensive tasks such as data mapping and technical documentation, allowing architects to focus on higher-value strategic initiatives that drive business value. AI-driven analytics provide architects with deeper, real-time insights into complex system dependencies, enabling more accurate predictive modeling and significantly faster decision-making across the enterprise. This technological shift encourages a transition away from static, reactive architectures toward dynamic, proactive ecosystems that can autonomously adapt to rapid market changes and emerging digital threats. However, the author emphasizes that this transition is not without its hurdles; it necessitates a robust foundation in data governance, careful ethical considerations regarding AI bias, and a long-term commitment to upskilling the existing workforce. Ultimately, the fusion of AI and EA facilitates much better alignment between high-level business goals and underlying IT infrastructure, driving continuous innovation and operational efficiency. As the discipline evolves, the most successful enterprise architects will be those who leverage AI as a sophisticated collaborative partner to manage organizational complexity and provide strategic foresight in an increasingly competitive digital landscape.

Daily Tech Digest - May 12, 2025

Quote for the day:

"Our greatest fear should not be of failure but of succeeding at things in life that don't really matter." -- Francis Chan

The rise of vCISO as a viable cybersecurity career path

Companies that don’t have the means to hire a full-time CISO still face the same harsh realities their peers do — heightened compliance demands, escalating cyber incidents, and growing tech-related risks. A part-time security leader can help them assess their state of security and build out a program from scratch, or assist a full-time director-level security leader with a project. ... In some of these ongoing relationships this could be to fill the proverbial chair of the CISO, doing all the traditional work of the role on a part-time basis. This is the kind of arrangement most likely to be referred to as a fractional role. Other retainer arrangements may just be for an advisory position where the client is buying regular mindshare of the vCISO to supplement their tech team’s knowledge pool. They could be a strategic sounding board to the CIO or even a subject-matter expert to the director of security or newly installed CISO. But vCISOs can work on a project-by-project or hourly basis as well. “It’s really what works best for my potential client,” says Demoranville. “I don’t want to force them into a box. So, if a subscription model works or a retainer, cool. If they only want me here for a short engagement, maybe we’re trying to put in a compliance regimen for ISO 27001 or you need me to review NIST, that’s great too.”

Why Indian Banks Need a Sovereign Cloud Strategy

Enterprises need to not only implement better compliance strategies but also rethink the entire IT operating model. Managed sovereign cloud services can help enterprises address this need. ... The need for true sovereignty becomes crucial in a world where many global cloud providers, even when operating within Indian data centers, are subject to foreign laws such as the U.S. Clarifying Lawful Overseas Use of Data Act or the Foreign Intelligence Surveillance Act. These regulations can compel disclosure of Indian banking data to overseas governments, undermining trust and violating the spirit of data localization mandates. "When an Indian bank chooses a global cloud provider with U.S. exposure, they're essentially opening a backdoor for foreign jurisdictions to access sensitive Indian financial data," Rajgopal said. "Sovereignty is a strategic necessity." Managed sovereign clouds not only align with India's compliance frameworks but also reduce complexity by integrating regulatory controls directly into the cloud stack. Instead of treating compliance as an afterthought, it is incorporated in the architecture. ... "Banks today are not just managing money; they are managing trust, security and compliance at unprecedented levels. Sovereign cloud is no longer optional. It's the future of financial resilience," said Pai.

Study Suggests Quantum Entanglement May Rewrite the Rules of Gravity

Entanglement entropy measures the degree of quantum correlation between different regions of space and plays a key role in quantum information theory and quantum computing. Because entanglement captures how information is shared across spatial boundaries, it provides a natural bridge between quantum theory and the geometric fabric of spacetime. In conventional general relativity, the curvature of spacetime is determined by the energy and momentum of matter and radiation. The new framework adds another driver: the quantum information shared between fields. This extra term modifies Einstein’s equations and offers an explanation for some of gravity’s more elusive behaviors, including potential corrections to Newton’s gravitational constant. ... One of the more striking implications involves black hole thermodynamics. Traditional equations for black hole entropy and temperature rely on Newton’s constant being fixed. If gravity “runs” with energy scale — as the study proposes — then these thermodynamic quantities also shift. ... Ultimately, the study does not claim to resolve quantum gravity, but it does reframe the problem. By showing how entanglement entropy can be mathematically folded into Einstein’s equations, it opens a promising path that links spacetime to information — a concept familiar to quantum computer scientists and physicists alike.

Maximising business impact: Developing mission-critical skills for organisational success

Often, L&D is perceived merely as an HR-led function tasked with building workforce capabilities. However, this narrow framing extensively limits its potential impact. As Cathlea shared, “It’s time to educate leaders that L&D is not just a support role—it’s a business-critical responsibility that must be shared across the organisation. By understanding what success looks like through the eyes of different functions, L&D teams can design programmes that support those ambitions — and crucially, communicate value in language that business leaders understand. The panel referenced a case from a tech retailer with over 150,000 employees, where the central L&D team worked to identify cross-cutting capability needs, such as communication, project management, and leadership, while empowering local departments to shape their training solutions. This balance of central coordination and local autonomy enabled the organisation to scale learning in a way that was both relevant and impactful. ... The shift towards skill-based development is also transforming how learning experiences are designed and delivered. What matters most is whether these learning moments are recognised, supported, and meaningfully connected to broader organisational goals.

What software developers need to know about cybersecurity

Training developers to write secure code shouldn’t be looked at as a one-time assignment. It requires a cultural shift. Start by making secure coding techniques are the standard practice across your team. Two of the most critical (yet frequently overlooked) practices are input validation and input sanitization. Input validation ensures incoming data is appropriate and safe for its intended use, reducing the risk of logic errors and downstream failures. Input sanitization removes or neutralizes potentially malicious content—like script injections—to prevent exploits like cross-site scripting (XSS). ... Authentication and authorization aren’t just security check boxes—they define who can access what and how. This includes access to code bases, development tools, libraries, APIs, and other assets. ... APIs may be less visible, but they form the connective tissue of modern applications. APIs are now a primary attack vector, with API attacks growing 1,025% in 2024 alone. The top security risks? Broken authentication, broken authorization, and lax access controls. Make sure security is baked into API design from the start, not bolted on later. ... Application logging and monitoring are essential for detecting threats, ensuring compliance, and responding promptly to security incidents and policy violations. Logging is more than a check-the-box activity—for developers, logging can be a critical line of defense.

Why security teams cannot rely solely on AI guardrails

The core issue is that most guardrails are implemented as standalone NLP classifiers—often lightweight models fine-tuned on curated datasets—while the LLMs they are meant to protect are trained on far broader, more diverse corpora. This leads to misalignment between what the guardrail flags and how the LLM interprets inputs. Our findings show that prompts obfuscated with Unicode, emojis, or adversarial perturbations can bypass the classifier, yet still be parsed and executed as intended by the LLM. This is particularly problematic when guardrails fail silently, allowing semantically intact adversarial inputs through. Even emerging LLM-based judges, while promising, are subject to similar limitations. Unless explicitly trained to detect adversarial manipulations and evaluated across a representative threat landscape, they can inherit the same blind spots. To address this, security teams should move beyond static classification and implement dynamic, feedback-based defenses. Guardrails should be tested in-system with the actual LLM and application interface in place. Runtime monitoring of both inputs and outputs is critical to detect behavioral deviations and emergent attack patterns. Additionally, incorporating adversarial training and continual red teaming into the development cycle helps expose and patch weaknesses before deployment.

Finding the Right Architecture for AI-Powered ESG Analysis

Rather than choosing between competing approaches, we developed a hybrid architecture that leverages the strengths of both deterministic workflows and agentic AI: For report analysis: We implemented a structured workflow that removes the Intent Agent and Supervisor from the process, instead providing our own intention through a report workflow. This orchestrates the process using the uploaded sustainability file, synchronously chaining prompts and agents to obtain the company name and relevant materiality topics, then asynchronously producing a comprehensive analysis of environmental, social, and governance aspects. For interactive exploration: We maintained the conversational, agentic architecture as a core component of the solution. After reviewing the initial structured report, analysts can ask follow-up questions like, “How does this company’s emissions reduction claims compare to their industry peers?” ... By marrying these approaches, enterprise architects can build systems that maintain human oversight while leveraging AI to handle data-intensive tasks – keeping human analysts firmly in the driver’s seat with AI serving as powerful analytical tools rather than autonomous decision-makers. As we navigate the rapidly evolving landscape of AI implementation, this balanced approach offers a valuable pathway forward.

The Rise of xLMs: Why One-Size-Fits-All AI Models Are Fading

To reach its next evolution, the LLM market will follow all other widely implemented technologies and fragment into an “xLM” market of more specialized models, where the x stands for various models. Language models are being implemented in more places with application- and use case-specific demands, such as lower power or higher security and safety measures. Size is another factor, but we’ll also see varying functionality and models that are portable, remote, hybrid, and domain and region-specific. With this progression, greater versatility and diversity of use cases will emerge, with more options for pricing, security, and latency. ... We must rethink how AI models are trained to fully prepare for and embrace the xLM market. The future of more innovative AI models and the pursuit of artificial general intelligence hinge on advanced reasoning capabilities, but this necessitates restructuring data management practices. ... Preparing real-time data pipelines for the xLM age inherently increases pressure on data engineering resources, especially for organizations currently relying on static batch data uploads and fine-tuning. Historically, real-time accuracy has demanded specialized teams to complete regular batch uploads while maintaining data accuracy, which presents cost and resource barriers.

Ernst & Young exec details the good, bad and future of genAI deployments

“There is a huge skills gap in data science in terms of the number of people that can do that well, and that is not changing. Everywhere else we can talk about what jobs are changing and where the future is. But AI scientists, data scientists, continue to be the top two in terms of what we’re looking for. I do think organizations are moving to partner more in terms of trying to leverage those skills gap….” The more specific the case for the use of AI, the more easily you can calculate the ROI. “Healthcare is going to be ripe for it. I’ve talked to a number of doctors who are leveraging the power of AI and just doing their documentation requirements, using it in patient booking systems, workflow management tools, supply chain analysis. There, there are clear productivity gains, and they will be different per sector. “Are we also far enough along to see productivity gains in R&D and pharmaceuticals? Yes, we are. Is it the Holy Grail? Not yet, but we are seeing gains and that’s where I think it gets more interesting. “Are we far enough along to have systems completely automated and we just work with AI and ask the little fancy box in front of us to print out the balance sheet and everything’s good? No, we’re a hell of a long way away from that.

How Human-Machine Partnerships Are Evolving in 2025

“Soon, there will be no function that does not have AI as a fundamental ingredient. While it’s true that AI will replace some jobs, it will also create new ones and reduce the barrier of entry into many markets that have traditionally been closed to just a technical or specialized group,” says Bukhari. “AI becoming a part of day-to-day life will also force us to embrace our humanity more than ever before, as the soft skills AI can’t replace will become even more critical for success in the workplace and beyond.” ... CIOs and other executives must be data and AI literate, so they are better equipped to navigate complex regulations, lead teams through AI-driven transformations and ensure that AI implementations are aligned with business goals and values. Cross-functional collaboration is also critical. ... AI innovation is already outpacing organizational readiness, so continuous learning, proactive strategy alignment and iterative implementation approaches are important. CIOs must balance infrastructure investments, like GPU resource allocation, with flexibility in computing strategies to stay competitive without compromising financial stability. “As the enterprise landscape increasingly incorporates AI-driven processes, the C-suite must cultivate specific skills that will cascade effectively through their management structures and their entire human workforce,” says Miskawi.

Daily Tech Digest - May 14, 2024

Transforming 6G experience powered by AI/ML

While speed has been the driving force behind previous generations, 6G redefines the game. Yes, it will be incredibly fast, but raw bandwidth is just one piece of the puzzle. 6G aims for seamless and consistent connectivity everywhere. ... This will bridge the digital divide and empower remote areas to participate fully in the digital age. 6G networks will be intelligent entities, leveraging AI and ML algorithms to become: Adaptive: The network will constantly analyze traffic patterns, user demands, and even environmental factors. Based on this real-time data, it will autonomously adjust configurations, optimize resource allocation, and predict user needs for a truly proactive experience. Imagine a network that anticipates your VR gaming session and seamlessly allocates the necessary resources before you even put on the headset. Application-Aware: Gone are the days of one-size-fits-all connectivity. 6G will cater to a diverse range of applications, each with distinct requirements. The network will intelligently recognize the type of traffic – a high-resolution video stream, a critical IoT sensor reading, or a real-time AR overlay – and prioritize resources accordingly. This ensures flawless performance for all users, regardless of their activity.

How data centers can simultaneously enable AI growth and ESG progress

Unlocking AI’s full potential may require organizations to make significant concessions on their ESG goals unless the industry drastically reduces AI’s environmental footprint. This means all data center operators - including both in-house teams and third-party partners - must adopt innovative data center cooling capabilities that can simultaneously improve energy efficiency and reduce carbon emissions. The need for HPC capabilities is not unique to AI. Grid computing, clustering, and large-scale data processing are among the technologies that depend on HPC to facilitate distributed workloads, coordinate complex tasks, and handle immense amounts of data across multiple systems. However, with the rapid rise of AI, the demand for HPC resources has surged, intensifying the need for advanced infrastructure, energy efficiency, and sustainable solutions to manage the associated power and cooling requirements. In particular, the large graphics processing units (GPUs) required to support complex AI models and deep learning algorithms generate more heat than traditional CPUs, creating new challenges for data center design and operation.

Cutting the cord: Can Air-Gapping protect your data?

The first challenge is keeping systems up to date. Software requires patching and upgrading as bugs are found and new features needed. An Air-Gapped system can be updated via USB sticks and CD-Roms, but this is (a) time consuming and (b) introduces a partial connection with the outside world. Chris Hauk, Consumer Privacy Advocate at Pixel Privacy, has observed the havoc this can cause. “Yes, hardware and software both can be easily patched just like we did back in the day, before the internet,” says Hauk. “Patches can be ‘sneakernetted’ to machines on a USB stick. Unfortunately, USB sticks can be infected by malware if the stick used to update systems was created on a networked computer. “The Stuxnet worm, which did damage to Iran’s nuclear program and believed to have been created by the United States and Israel, was malware that targeted Air-Gapped systems, so no system that requires updating is absolutely safe from attacks, even if they are Air-Gapped.” The Air-Gap may suffer breaches. Users may want to take data home or have another reason to access systems. A temporary connection to the outside world, even via a USB stick, poses a serious risk.

Delivering Software Securely: Techniques for Building a Resilient and Secure Code Pipeline

Resilience in a pipeline embodies the system's ability to deal with unexpected events such as network latency, system failures, and resource limitations without causing interruptions. The aim is to design a pipeline that not only provides strength but also maintains self-healing and service continuity. By doing this, you can ensure that the development and deployment of applications can withstand the inevitable failures of any technical environment. ... To introduce fault tolerance into your pipeline, you have to diversify resources and automate recovery processes. ... When it comes to disaster recovery, it is crucial to have a well-organized plan that covers the procedures for data backup, resource provision, and restoration operations. This could include automating backups and using CloudFormation scripts to provision the infrastructure needed quickly. ... How can we ensure that these resilience strategies are not only theoretically effective but also practically effective? Through careful testing and validation. Use chaos engineering principles by intentionally introducing defects into the system to ensure that the pipeline responds as planned.

Cinterion IoT Cellular Modules Vulnerable to SMS Compromise

Cinterion cellular modems are used across a number of industrial IoT environments, including in the manufacturing and healthcare as well as financial services and telecommunications sectors. Telit Cinterion couldn't be immediately reached for comment about the status of its patching efforts or mitigation advice. Fixing the flaws would require the manufacturer of any specific device that includes a vulnerable Cinterion module to release a patch. Some devices, such as insulin monitors in hospitals or the programmable logic controllers and supervisory control and data acquisition systems used in industrial environments, might first need to be recertified with regulators before device manufacturers can push patches to users. The vulnerabilities pose a supply chain security risk, said Evgeny Goncharov, head of Kaspersky's ICS CERT. "Since the modems are typically integrated in a matryoshka-style within other solutions, with products from one vendor stacked atop those from another, compiling a list of affected end products is challenging," he said.

Automotive Radar Testing and Big Data: Safeguarding the Future of Driving

In radar EOL testing, one of the key verification parameters is the radar cross-section (RCS) detection accuracy, which represents the size of an object. Unlike passive objects that have fixed RCS, RTS allows the simulation of various levels of RCS, echoing a desired object size for radar detection. While RTS systems offer versatility for radar testing, they present challenges to overcome. One such challenge is the sensitivity of the system’s millimeter-wave (mmWave) components to temperature variations, which can significantly impact the ability to accurately simulate RCS values. Therefore, controlling the ambient temperature in a testing setup is important to ensuring that the RTS replicates the RCS expected for a given object size. Furthermore, the repercussions extend beyond the immediate operational setbacks with. the need to scrap a number of radar faulty module units. Not only does this represent a direct monetary loss and the overall profit margin, but it also contributes to waste and environmental concerns. All these adverse outcomes, from reduced output capacity to financial losses and environmental impact, highlight the critical importance of integrating analytics software into an automotive radar EOL testing solution.

Nvidia teases quantum accelerated supercomputers

The company revealed that sites in Germany, Japan, and Poland will use the platform to power quantum processing units (QPU) in their high performance computing systems. “Quantum accelerated supercomputing, in which quantum processors are integrated into accelerated supercomputers, represents a tremendous opportunity to solve scientific challenges that may otherwise be out of reach,” said Tim Costa, director, Quantum and HPC at Nvidia. “But there are a number of challenges between us, today, and useful quantum accelerated supercomputing. Today’s qubits are noisy and error prone. Integration with HPC systems remains unaddressed. Error correction algorithms and infrastructure need to be developed. And algorithms with exponential speed up actually need to be invented, among many other challenges.” ... “But another open frontier in quantum remains,” Costa said. “And that’s the deployment of quantum accelerated supercomputers – accelerated supercomputers that integrate a quantum processor to perform certain tasks that are best suited to quantum in collaboration with and supported by AI supercomputing. We’re really excited to announce today the world’s first quantum accelerated supercomputers.”

Tailoring responsible AI: Defining ethical guidelines for industry-specific use

As AI becomes increasingly embedded in business operations, organizations must ask themselves how to prepare for and prevent AI-related failures, such as AI-powered data breaches. AI tools are enabling hackers to develop highly effective social engineering attacks. Right now, having a strong foundation in place to protect customer data is a good place to start. Ensuring third-party AI model providers don’t use your customers’ data also adds protection and control. There are also opportunities for AI to help strengthen crisis management. The first relates to security crises, such as outages and failures, where AI can identify the root of an issue faster. AI can quickly sift through a ton of data to find the “needle in the haystack” that points to the source of the attack or the service that failed. It can also surface relevant data for you much faster using conversational prompts. In the future, an analyst might be able to ask an AI chatbot that’s embedded in its security framework questions about suspicious activity, such as, “What can you tell me about where this traffic originated from?” Or, “What kind of host was this on?”

Taking a ‘Machine-First’ Approach to Identity Management

With microservices, machine identities are proliferating at an alarming rate. Cyberark has reported that the ratio of machine identities to humans in organizations is 45 to 1. At the same time, 87% of respondents in its survey said they store secrets in multiple places across DevOps environments. Curity’s Michal Trojanowski previously wrote about the complex mesh of services comprising an API, adding that securing them is not just about authenticating the user. “A service that receives a request should validate the origin of the request. It should verify the external application that originally sent the request and use an allowlist of callers. ... Using agentless scanning of the identity repositories engineers are using and log analysis, the company first maps all the non-human identities throughout the infrastructure — Kubernetes, databases, applications, workloads, and servers. It creates what it calls attribution— a strong context of which workloads and which humans use each identity, including an understanding its dependencies. Mapping ownership of the various identities also is key. “Think about organizations that have thousands of developers. Security teams sometimes find issues but don’t know how to solve them because they don’t know who to talk with,” Apelblat said.

The limitations of model fine-tuning and RAG

Several factors limit what LLMs can learn via RAG. The first factor is the token allowance. With the undergrads, I could introduce only so much new information into a timed exam without overwhelming them. Similarly, LLMs tend to have a limit, generally between 4k and 32k tokens per prompt, which limits how much an LLM can learn on the fly. The cost of invoking an LLM is also based on the number of tokens, so being economical with the token budget is important to control the cost. The second limiting factor is the order in which RAG examples are presented to the LLM. The earlier a concept is introduced in the example, the more attention the LLM pays to it in general. While a system could reorder retrieval augmentation prompts automatically, token limits would still apply, potentially forcing the system to cut or downplay important facts. To address that risk, we could prompt the LLM with information ordered in three or four different ways to see if the response is consistent. ... The third challenge is to execute retrieval augmentation such that it doesn’t diminish the user experience. If an application is latency sensitive, RAG tends to make latency worse.

Quote for the day:

"What you do makes a difference, and you have to decide what kind of difference you want to make." -- Jane Goodall

Daily Tech Digest - April 25, 2024

The rise in CISO job dissatisfaction – what’s wrong and how can it be fixed?

“The reason for dissatisfaction is the lack of executive management support,” says Nikolay Chernavsky, CISO of ISSQUARED, which provides managed IT and security services as well as software products. He says he hears CISOs voice frustrations when their views on required security measures and acceptable risk are dismissed; when the board and CEO don’t define their positions on those issues; or when those leaders don’t recognize the CISOs work in reducing risk — especially as the CISO faces more accountability and liability. Understandably, CISOs shy away from interview requests to publicly share their frustrations on these issues. However, the IANS Research report speaks to these points, noting, for example, that only 36% of CISOs said they have clear guidance from their board on their risk tolerance. Adding to these issues today is the liability that CISOs now face with the new US Securities and Exchange Commission (SEC) cyber disclosure rules as well as other regulatory and legal requirements. That increased liability is coupled with the fact that many CISOs are not covered by their organization’s directors and officers (D&O) liability insurance.

How CIOs align with CFOs to build RevOps

CIOs who transition IT from being a cost center to being a driver of innovation, transformation, and new revenues, can become the leaders that the new economy needs. “We used to say that business runs technology,” says David Kadio-Morokro, EY Americas financial services innovation leader. “You tell me what you want, and I’ll code it and support you.” Now it’s switched, he says. “I really believe technology drives the business, because it’s going to impact business strategy and how the business survives,” he adds, and gen AI will force companies to rethink the value of their organizations to customers. “Developing and envisioning an AI-driven strategy is absolutely part of the equation,” he says. “And the CIO has this role of enabling these components, and they need to be part of the conversation and be able to drive that vision for the organization.” The CIO is also in a position to help the CFO evolve, too. CFOs are traditionally risk averse and expect certainty and accuracy from their technology. Not only is gen AI still a new and experimental technology that’s evolving quickly but is, by its very nature, probabilistic and nondeterministic.

Do you need to repatriate from the cloud?

It should be no surprise that repatriation has gained this hype. Cloud, which grew to maturity during an economic boom, is for the first time under downward pressure as companies seek to reduce spending. Amazon, Google, Microsoft, and other cloud providers have feasted on their customers’ willingness to spend. But the willingness has been tempered now by budget cuts. ... Transitioning back to on-premises is a heavy lift, and one that is hard to rescind should things go badly. And savings is yet to be seen until after the transition is complete. Switching to WebAssembly-powered serverless functions, in contrast, is less expensive and less risky. Because such functions can run inside of Kubernetes, the savings thesis can be tested merely by carving off a few representative services, rewriting them, and analyzing the results. Those already invested in a microservice-style architecture are already well setup to rebuild just fragments of a multi-service application. Similarly, those invested in event processing chains like data transformation pipelines will also find it easy to identify a step or two in a sequence that can become the testbed for experimentation.

ONDC’s blockchain is a Made-in-India visioning of global digital public infrastructures

ONDC Confidex is a transformative shift towards decentralised trust. Anchored in the blockchain’s nativity, this shift promotes a value exchange network of networks that enables the reuse of continuously assured data that is traceable, reliable, secure, transparent and immutable. Confidex provides a transparent ledger that tracks every phase in the supply chain from production to end consumption. This level of detail not only fosters trust but also aligns with the broader vision of creating a global standard for ensuring product history’s authenticity—a core aspect of continuous data assurance. In the realm of digital transactions, the reliability of data underpins the foundation of trust. Confidex enables data certainty, making each transaction verifiable and immutable. This paves the way for friction-free interactions within digital marketplaces, ensuring that every piece of data holds its integrity from the point of creation to consumption. The digital economy is plagued with issues of forgery and duplication. Confidex addresses this head-on by creating unique digital records that are impossible to replicate or alter.

How will AI-driven solutions affect the business landscape?

Redmond believes that the tech will quickly become embedded in normal business practice. “We won’t even think about asking gen AI to draft emails or documents or to generate images for our presentations.” He’s also looking forward to seeing how AI-driven video technology plays out, particularly OpenAI’s Sora. “I know that a lot of people in content generation are nervous about these tools replacing them, but I don’t think we hire an artist for their ability to draw, we hire them for their ability to draw what is in their imagination, and that is where their genius lies,” he says. “I am not sure that artists will ever stop creating wonderful works, and these technologies will just enhance that.” Tiscovschi agrees with Redmond’s outlook, stating that “this is just the beginning”. “We will continuously see more teams of humans and their AI agents or tools working together to achieve tasks,” he says. “A human quickly mining their organisation’s IP, automating repetitive tasks and then collaborating with their AI copilot on a report or piece of code will have a constantly growing multiplier on their productivity.”

5 Strategies for Better Results from an AI Code Assistant

The first step is to provide the GPT with high-level context. In her scenario, Phil demonstrates by building a Markdown editor. Since Copilot has no idea of the context, he has to provide it and he does this with a large prompt comment with step-by-step instructions. For instance, he tells the copilot, “Make sure we have support for bold, italics and bullet points” and “Can you use reactions in the React markdown package.” The prompt enables Copilot to create a functional but unsettled markdown editor. ... Follow up by providing the Copilot with specific details, Scarlett advised. “If he writes a column that says get data from [an] API, then GitHub Copilot may or may not know what he’s really trying to do, and it may not get the best result. It doesn’t know which API he wants to get the data from or what it should return,” Scarlett said. “Instead, you can write a more specific comment that says use the JSON placeholder API, pass in user IDs, and return the users as a JSON object. That way we can get more optimal results.”

ESG research unveils critical gaps in responsible AI practices across industries

In light of the ESG Research findings, Qlik recognises the imperative of aligning AI technologies with responsible AI principles. The company’s initiatives in this area are grounded in providing robust data management and analytics capabilities, essential for any organisation aiming to navigate the complexities of AI responsibly. Qlik underscores the importance of a solid data foundation, which is critical for ensuring transparency, accountability, and fairness in AI applications. Qlik’s commitment to responsible AI extends to its approach to innovation, where ethical considerations are integrated into the development and deployment of its solutions. By focusing on creating intuitive tools that enhance data literacy and governance, Qlik aims to address key challenges identified in the report, such as ensuring AI explainability and managing regulatory compliance effectively. Brendan Grady, General Manager, Analytics Business Unit at Qlik, said, “The ESG Research echoes our stance that the essence of AI adoption lies beyond technology—it’s about ensuring a solid data foundation for decision-making and innovation.

Applying DevSecOps principles to machine learning workloads

Unlike in a conventional software development environment with an integrated development environment (IDE), data scientists typically write code using Jupyter Notebooks. This takes place outside of an IDE, and often outside of the traditional DevSecOps lifecycle. As a result, it’s possible for a data scientist who is not trained on secure development techniques to put sensitive data at risk, by storing unprotected secrets or other sensitive information in a notebook. Simply put, the same tools and protections used in the DevSecOps world aren’t effective for ML workloads. The complexity of the environment also matters. Conventional development cycles usually lead directly to a software application interface or API. In the machine learning space, the focus is iterative, building a trainable model that leads to better outcomes. ML environments produce large quantities of serialized files necessary for a dynamic environment. The upshot? Organizations can become overwhelmed by the inherent complexities of versioning and integration.

Introducing Wi-Fi 7 access points that deliver more

This idea that the access point (AP) can do more than just route traffic is a core part of our product philosophy, and we’ve consistently expanded on that over multiple Wi-Fi generations with the addition of location services, IoT protocol support, and extensive network telemetry for security and AIOps. As organizations continue to innovate, and leverage applications that require more bandwidth or more IoT devices to support new digital use cases, the AP must continue to do more. Delivering solutions that go beyond standards is part of HPE Aruba Networking’s history and future. Now, with the introduction of 700 series access points that support Wi-Fi 7, we are doubling IoT capabilities with dual BLE 5.4 or 802.15.4/Zigbee radios and dual USB interfaces and improving location precision for use cases such as asset tracking and real-time inventory tracking. Moreover, we are using both the resources and the management of the AP to its full potential by delivering ubiquitous high-performance connectivity and processing at the edge. What this means is that these access points not only have optimal support for the 2.4, 5, and 6 GHz spectrum but also enough memory and compute capacity to run containers.

Why Your Enterprise Should Create an Internal Talent Marketplace

Strategically, an internal talent marketplace is a way to empower employees to be in the driver’s seat of their career journey, says Gretchen Alarcon, senior vice president and general manager of employee workflows at software and cloud platform provider ServiceNow, via email. "Tactically, it's a platform driven by technology that uses AI to match existing talent to open roles or projects within the organization," she explains. "It provides a more transparent view of new opportunities for employees and identifies untapped employee potential based on skills rather than anecdotes." ... A talent marketplace is only as good as the information it contains, Williamson warns. "Organizations should emphasize to employees that it's in their interest to keep the skills and preferences in their profiles up to date," he says. Managers. meanwhile, need to define the exact critical skills needed to be successful in a particular job or role. "That information drives recommended opportunities for employees and increases their chances of being identified by project managers to fill roles."

Quote for the day:

"Rarely have I seen a situation where doing less than the other guy is a good strategy." -- Jimmy Spithill

Daily Tech Digest - February 04, 2024

Prepare now for when quantum computers break biometric encryption: Trust Stamp

While experts expect quantum computers will not be able to scale to defeat such systems for at least another ten years, the white paper claims, entities should address “harvest now, decrypt later” (HNDL) attacks proactively. Through an HNDL approach, an attacker could capture encrypted data pending the availability of quantum computing-enabled decryption. It is worth noting that this cyber threat would be heavily resource-intensive to perform. Such an attack would most likely only be feasible by a nation-state and would target information that would remain extremely valuable for decades in the future. Still, HDNL is an especially concerning threat for biometric PII, due to its relative permanence. Certain data encryption methods are particularly vulnerable. Asymmetric, or public-key cryptography, uses a public and private key to encrypt and decrypt information. One of the keys can be stored in the public domain, which enables connections between “strangers” to be established quickly. Because the keys are mathematically related, it is theoretically possible to calculate a private key from a public key.

Managing the hidden risks of shadow APIs

In today's dynamic API landscape, maintaining comprehensive visibility into the security posture of API endpoints is paramount. All critical app and API security controls necessary to protect an app's entire ecosystem can be deployed and managed through the unified API security console of the F5 Distributed Cloud Platform. This allows DevOps and SecOps teams to observe and quickly identify suspected API abuse as anomalies are detected as well as create policies to stop misuse. This requires the use of ML models to create baselines of normal API usage patterns. Continuous ML-based traffic monitoring allows API security to predict and block suspicious activity over time. Deviations from these baselines and other anomalies trigger alerts or automated responses to detect outliers, including rogue and shadow APIs. Dashboards play a crucial role in providing the visibility required to monitor and assess the security of APIs. The F5 Distributed Cloud WAAP platform extends beyond basic API inventory management by presenting essential security information based on actual and attack traffic.

Cybersecurity Frontline: Securing India’s digital finance infrastructure in 2024

Fintech companies are progressively allowing AI to handle routine tasks, freeing human resources for more complex challenges. AI systems are also being used to simulate cyberattacks, testing systems for vulnerabilities. This shift highlights the critical role of AI and ML in modern cybersecurity, moving beyond mere automation to proactive threat detection and system fortification. The human element, often the weakest link in cybersecurity, is receiving increased attention. Fintech firms are investing in employee training to build resilience against cyberattacks, focusing on areas such as phishing, social engineering, and password security. One of the most notable advancements in this domain is the use of AI-powered fraud detection systems. For instance, a global fintech leader has implemented a deep learning model that analyses around 75 billion annual transactions across 45 million locations to detect and prevent card-related fraud. Despite, financial institutions keep on educating the customers on social engineering frauds, but the challenge is when customers willingly provide OTPs, payment/banking credentials which resulted misuse in the account.

The evolving challenge of complexity in cybersecurity

One of the biggest challenges when it comes to cybersecurity is the complexity that has evolved due to the need to use an increasing array of products and services to secure our businesses. This is largely due to the underlying complexity of our IT environments and the broad attack surface this creates. With the growing adoption of cloud and the more dispersed nature of our workforces, the perimeter approach to security that worked well in the 20th century is no longer adequate. In the same way the moats and castle walls of the Middle Ages gave good protection then but would not stand up to a modern attack, traditional firewalls and VPNs are no longer suitable now and invariably need to be augmented with lots of other layers of security tools. Modern, more flexible and (arguably) simpler zero-trust approaches such as secure access service edge, zero-trust network access and microsegmentation need to be adopted. These technologies ensure that access to applications and data, no matter where they reside, is governed by simple, identity based policies that are easy to manage while delivering levels of security and visibility that legacy approaches cannot.

CIOs rise to the ESG reporting challenge

To achieve success, CIOs must first understand how ESG reporting fits within the company’s business strategy, Sterling’s Kaur says. Then they need to engage and align with the right people in the organization. The CFO and CSO top that list, but CIOs should branch out further, as “upstream processes is where the vast majority of sustainability and ESG story really happens,” says Marsha Reppy, GRC technology leader for EY Global and EY Americas. “You will not be successful without procurement, R&D, supply chain, manufacturing, sales, human resources, legal, and tax at the table.” Because ESG data is broadly dispersed throughout the organization, CIOs will need broad consensus on an ESG reporting strategy, but the triumvirate of CIO, CFO, and CHRO should be driving ESG reporting forward, Kaur says. “Business goals matter, financials matter, and employee engagement matters,” she says. “Creating this partnership has the benefit of bringing a cohesive view forward with the right goals.” CIOs must also educate themselves on the nitty gritty of ESG reporting to fully understand the complexity and breadth of the problem they’re trying to solve, EY’s Reppy says.

How to Get Platform Engineering Just Right

In the land of digital transformation, seeing is believing, which is where observability has a role to play. Improving observability is crucial for gaining insights into the platform’s performance and behavior, which involves integrating tools like event and project monitoring, cloud cost transparency, application performance, infrastructure health and user interactions. In a rapidly growing cloud environment, observability enables teams to keep track of what is happening in terms of cost, usage, availability, performance and security across a constantly transforming cloud infrastructure. Once a project has been deployed, it needs to be managed and maintained across all cloud providers, something which is critical for keeping costs to a minimum but is often a huge and messy task. Managing this effectively requires monitoring key performance indicators (KPIs) and setting up alerts for critical events, and using logs and analysis tools to gain visibility into application behavior, track errors, and troubleshoot issues more effectively. Finally, implementing tracing systems that can track the flow of requests across various microservices and components helps to identify performance bottlenecks, understand latency issues and optimize system behavior.

AI Officer Is the Hot New Job That Pays Over $1 Million

Executives spearheading metaverse efforts at Walt Disney Co., Procter & Gamble Co. and Creative Artists Agency left. Leon's LinkedIn profile (yes, he had one), no longer exists, and there's no mention of him on the company's website, other than his introductory press release. Publicis Groupe declined to comment on the record. Instead, businesses are scrambling to appoint AI leaders, with Accenture and GE HealthCare making recent hires. A few metaverse executives have even reinvented themselves as AI experts, deftly switching from one hot technology to the next. Compensation packages average well above $1 million, according to a survey from executive-search and leadership advisory firm Heidrick & Struggles. Last week, Publicis said it would invest 300 million euros ($327 million) over the next three years on artificial intelligence technology and talent.Play Video "It's been a long time since I have had a conversation with a client about the metaverse," said Fawad Bajwa, the global AI practice leader at the Russell Reynolds Associates executive search and advisory firm. "The metaverse might still be there, but it's a lonely place."

Heart of the Matter: Demystifying Copying in the Training of LLMs

A characteristic of generative AI models is the massive consumption of data inputs, which could consist of text, images, audio files, video files, or any combination of the inputs (a case usually referred to as “multi-modal”). From a copyright perspective, an important question (of many important questions) to ask is whether training materials are retained in the large language model (LLM) produced by various LLM vendors. To help answer that question, we need to understand how the textual materials are processed. Focusing on text, what follows is a brief, non-technical description of exactly that aspect of LLM training. Humans communicate in natural language by placing words in sequences; the rules about the sequencing and specific form of a word are dictated by the specific language (e.g., English). An essential part of the architecture for all software systems that process text (and therefore for all AI systems that do so) is how to represent that text so that the functions of the system can be performed most efficiently. Therefore, a key step in the processing of a textual input in language models is the splitting of the user input into special “words” that the AI system can understand.

2024: The year quantum moves past its hype?

By contrast, today’s quantum computers are capable of a just few hundred error-free operations. This leap may sound like a return to the irrational exuberance of previous years. But there are many tangible reasons to believe. The quantum computing industry is now connecting these short-term testbeds with long-term moonshots as it starts to aim for middle-term, incremental goals. As we approach this threshold, we’ll start to more intrinsically understand errors and fix them. We can start to model simple molecules and systems, developing more powerful quantum algorithms. Then, we can work on more interesting (and impactful) applications with each new generation/testbed of quantum computer. What will those applications be? We don’t know. And that’s OK. ... But first we need to develop better quantum algorithms and QEC techniques. Then, we will need fewer qubits to run the same quantum calculations and we can unlock useful quantum computing, sooner. As progress and pace continues to accelerate, 2024 will be the year when the conversation around quantum applications has real substance as we follow tangible goals, commit to realistic ambitions and unlock real results.

Adaptive AI: The Promise and Perils for Healthcare CTOs

Adaptive AI is a subset of artificial intelligence that can learn and adjust its behavior based on new data and changing circumstances. Unlike traditional AI systems, which are static and rule-based, Adaptive AI algorithms can continually improve and adapt to evolving situations. This technology draws inspiration from the human brain's capacity for learning and adaptation. ... Adaptive AI plays a pivotal role in identifying and mitigating security threats. CTOs can leverage AI to monitor network traffic continuously, identify anomalies including software flaws and misconfigurations, and respond to threats in real time, bolstering their organization's security. It can prioritize these vulnerabilities based on the potential impact and likelihood of exploitation, allowing CTOs to allocate resources for patching and remediation efforts effectively. ... CTOs can drive innovation in customer engagement and personalization with Adaptive AI algorithms. In the case of virtual healthcare, Adaptive AI can be used to power virtual care platforms that allow patients to connect with healthcare providers from anywhere. This can improve access to care, especially for rural or underserved populations.

Quote for the day:

“Things work out best for those who make the best of how things work out.” -- John Wooden