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Author: DEROBS

  • Outmarket AI launches self-serve platform for insurance agencies

    Outmarket AI launches self-serve platform for insurance agencies

    Outmarket AI, a company specializing in AI platforms for the insurance industry, has announced the launch of Outmarket Lite. This new offering provides a self-serve mechanism intended to allow insurance agencies and teams to integrate AI-native workflows into their operations on the same day they sign up.

    By introducing a self-serve model, the company aims to make its AI technology accessible to insurance providers across a broader spectrum of organizational sizes. The platform is designed to automate specific tasks associated with insurance agency management through AI integration.

    Source: Finextra
    Original article: https://www.finextra.com/pressarticle/111393/outmarket-ai-brings-ai-native-insurance-workflows-to-agencies-of-all-sizes?utm_medium=rssfinextra&utm_source=finextrafeed

  • OCC issues final rule for implementation of the GENIUS Act

    OCC issues final rule for implementation of the GENIUS Act

    The Office of the Comptroller of the Currency (OCC) has announced the adoption of a final rule aimed at implementing the Guiding and Establishing National Innovation for U.S. Stablecoins (GENIUS) Act.

    This regulatory action represents the latest development in the federal oversight of digital assets. While the announcement confirms the procedural step of finalizing the rule to enact the legislation, specific technical details regarding the new regulatory requirements for market participants remain limited based on the initial disclosure.

    The implementation of the GENIUS Act is expected to establish a structured framework for the issuance and management of stablecoins within the U.S. financial system, reflecting ongoing efforts by federal regulators to address the growing role of digital currencies in the economy.

    Source: Finextra
    Original article: https://www.finextra.com/pressarticle/111394/occ-sets-out-final-rule-to-implement-genius-act?utm_medium=rssfinextra&utm_source=finextrafeed

  • Nvidia introduces Kumo relational for zero-training data analysis

    Nvidia introduces Kumo relational for zero-training data analysis

    Nvidia has announced the release of Kumo Relational, a technology designed to handle relational data analysis without requiring the traditional supervised machine learning training process. The development aims to streamline workflows involving structured data sets, allowing for predictive insights and analytical tasks to be executed more efficiently.

    By removing the training phase, the tool simplifies the deployment of machine learning models on relational structures, potentially reducing the computational overhead and time-to-insight for data-intensive applications. Kumo Relational is positioned as a specialized tool for organizations leveraging structured data for AI-driven decision-making.

    While specific technical specifications regarding its underlying architecture remain part of ongoing documentation, the release signifies an shift toward optimizing AI tools for immediate application on complex, interconnected data schemas.

    Source: Hugging Face
    Original article: https://huggingface.co/blog/nvidia/kumo-relational

  • CISA warns of hackers chaining Plex vulnerabilities in ‘Plex2Shell’ attacks

    CISA warns of hackers chaining Plex vulnerabilities in ‘Plex2Shell’ attacks

    The U.S. Cybersecurity and Infrastructure Security Agency (CISA) has issued a security warning concerning active exploitation of Plex Media Server installations. According to the agency, attackers are chaining four distinct vulnerabilities to perform unauthorized server breaches, a campaign currently being referred to as “Plex2Shell” attacks.

    The coordinated use of these vulnerabilities allows malicious actors to execute commands or gain unauthorized access to underlying server infrastructure. Given the widespread use of Plex for media management, these findings highlight a significant risk to organizations and individuals running the software on internet-facing devices.

    As CISA continues to monitor the situation, users and administrators are advised to review the official guidance to ensure their instances are properly secured against these exploitation attempts. Further details regarding the specific technical mechanisms of the vulnerabilities and recommended mitigations remain a focus of ongoing security efforts.

    Source: BleepingComputer
    Original article: https://www.bleepingcomputer.com/news/security/cisa-hackers-chain-plex-flaws-in-plex2shell-attacks-to-breach-servers/

  • Security teams address critical PHP vulnerabilities amid exploitation reports

    Security teams address critical PHP vulnerabilities amid exploitation reports

    Security organizations are responding to a heightened alert regarding two critical vulnerabilities within the PHP programming language. Following the disclosure of leaked technical details, security professionals have reported instances of active exploitation attempts targeting these flaws.

    As these vulnerabilities could potentially allow unauthorized access or remote code execution, remediation efforts are underway across the industry. Organizations utilizing PHP are encouraged to review their security posture and prioritize patching to mitigate the risk posed by the active exploitation of these identified bugs.

    Source: Dark Reading
    Original article: https://www.darkreading.com/application-security/php2shell-bugs-red-alert-security-industry

  • U.S. Hunger scales mission impact through Amazon Quick data integration

    U.S. Hunger scales mission impact through Amazon Quick data integration

    U.S. Hunger, a nonprofit organization dedicated to addressing food insecurity, is leveraging Amazon Quick to centralize its operational and financial data. The initiative aims to support the organization’s mission by providing leaders with direct access to information, facilitating faster decision-making for its food-assistance program, Full Cart.

    Data-Driven Mission Scaling

    With a history of delivering over 250 million meals, U.S. Hunger has experienced 500 percent growth over four years with only minimal headcount expansion. To maintain this pace, the organization integrated Amazon Quick to pull data from SQL Server into the SPICE in-memory engine. This setup allows CEO Rick Whitted and other leaders to explore core datasets, including survey responses and financial performance indicators, without requiring constant custom data pulls from the technology team.

    Centralized Collaboration via Quick Spaces

    The implementation of “Quick Spaces” has served as a central hub for the leadership team. By establishing shared definitions for key performance indicators (KPIs) and providing access to financial documents and planning tools, the organization has improved coordination across departments such as finance, technology, and program management. This approach allows staff to align on business logic and performance goals, reducing internal friction in planning and analysis.

    Social Impact and Future Plans

    Beyond internal operations, U.S. Hunger is analyzing data to understand the complex pressures behind food insecurity, such as income, housing, and health. The organization utilizes classification codes, known as Z codes, to visualize these social determinants of health. These insights help leadership identify early warning signals of food instability, enabling more proactive intervention.

    Looking ahead, U.S. Hunger plans to expand its data capabilities by building an enterprise KPI framework. This upcoming infrastructure will integrate data from diverse systems—including NetSuite, Salesforce, and Impact Manager—into Amazon Redshift. The organization also envisions transforming its internal data resources into a broader, publicly accessible platform to support external research on hunger, while simultaneously exploring enterprise offerings to help sustain its mission.

    Reflecting on the integration of AI tools, Rick Whitted emphasized that the primary goal is not merely to automate, but to bridge the gap between complex data and the human-centric needs of the families served. “This is not about bringing humans into the AI loop. It’s about bringing AI into the human loop,” he noted.

    Source: aws.amazon.com
    Original article: https://aws.amazon.com/blogs/machine-learning/how-u-s-hunger-connects-leaders-and-data-using-amazon-quick/

  • Evaluating the limitations of AI in modern lending practices

    Evaluating the limitations of AI in modern lending practices

    Artificial intelligence is playing an increasingly central role in the evolution of the lending sector, streamlining decision-making processes and risk assessment. However, as these technologies become more deeply embedded in financial services, questions persist regarding the potential limitations and blind spots inherent in automated lending systems.

    While machine learning models can process vast amounts of data to evaluate creditworthiness more rapidly than traditional manual methods, the reliance on these automated tools raises concerns about what might be overlooked during the assessment process. The shift toward algorithmic lending necessitates a critical look at how these systems interpret data and the impact they may have on financial inclusivity and accuracy in risk management.

    As the digital economy continues to integrate AI into core financial operations, stakeholders are increasingly focused on identifying which critical human insights or nuanced financial indicators may be missed by current automated models.

    Source: Finextra
    Original article: https://www.finextra.com/blogposting/33246/ai-is-transforming-lending-but-what-could-it-miss?utm_medium=rssfinextra&utm_source=finextrafeed

  • Walmart introduces new AI purchasing technology as Amazon restricts automated shopping

    Walmart introduces new AI purchasing technology as Amazon restricts automated shopping

    Retail giants are taking divergent paths regarding the use of AI agents in e-commerce. As Amazon reportedly implements measures to block AI agents from interacting with its digital marketplace, Walmart has announced the introduction of new technology designed to facilitate AI-assisted shopping.

    Walmart’s new offering allows AI agents to execute purchases on behalf of consumers. To address potential concerns regarding automated spending, the system includes integrated controls that provide users with oversight over transactions and authorization requirements for purchases.

    This development marks a significant contrast in the competitive landscape of digital retail. While Amazon seeks to restrict automated agent access to its platform, Walmart is actively building infrastructure to support AI agents as part of the customer shopping experience.

    Source: TechCrunch
    Original article: https://techcrunch.com/2026/10/21/while-amazon-blocks-ai-agents-walmart-rolls-out-the-welcome-mat/

  • AWS demonstrates Qwen3 training on Trainium hardware using torchtitan

    AWS demonstrates Qwen3 training on Trainium hardware using torchtitan

    Amazon Web Services (AWS) has published a technical guide detailing how to train Qwen3 models on its Trainium hardware using torchtitan, a library designed for large-scale model training. The documentation illustrates how developers can leverage TorchNeuron to integrate Trainium accelerators with standard PyTorch interfaces, enabling both pre-training and fine-tuning workloads.

    Training Configurations

    The guide outlines three distinct methods for training Qwen3 on a trn2.48xlarge instance, which features 16 Trainium2 accelerators and 128 physical NeuronCores:

    • Transformers v5 Integration: This approach uses the Transformers library as the modeling backend. It enables the direct use of Hugging Face Hub models through AutoModelForCausalLM, allowing for the direct reading and writing of safetensors.
    • Native torchtitan Models: By adjusting the model configuration, users can utilize the native Qwen3 implementation provided within torchtitan, offering an alternative to standard Hugging Face workflows.
    • Mixture-of-Experts (MoE) with NKI Kernels: This configuration incorporates Neuron Kernel Interface (NKI) recipes to handle mixture-of-experts layers. Developers can use the torchtitan override registry to attach custom NKI operators, providing flexibility for specialized workload optimization.

    Technical Implementation

    For these configurations, AWS utilizes torchrun to manage 64 training processes across the logical NeuronCores. The implementation supports both eager mode and torch.compile, with TorchNeuron handling the registration of Trainium as a native PyTorch device. The guide also details how to create and register custom NKI kernels, such as a specialized rotary embedding kernel, to optimize performance for specific tensor shapes and sequence lengths.

    AWS notes that these configurations maintain consistent launch patterns and structural requirements, allowing users to move between different training approaches without altering their underlying infrastructure setup. The documentation emphasizes that the override registry allows for granular control over model components, enabling developers to replace standard PyTorch operations with high-performance NKI-based kernels while maintaining autograd compatibility.

    As of the publication, these methods rely on specific versions of torch, transformers, and the torchtitan-neuron package to ensure compatibility with the Neuron driver, runtime, and compiler environments.

    Source: aws.amazon.com
    Original article: https://aws.amazon.com/blogs/machine-learning/train-qwen3-on-aws-trainium-with-torchtitan/

  • Cloud Security Alliance survey finds most DLP solutions fail to stop data loss before it occurs

    Cloud Security Alliance survey finds most DLP solutions fail to stop data loss before it occurs

    New research from the Cloud Security Alliance (CSA) highlights a significant gap in the effectiveness of current data loss prevention (DLP) technologies. According to the survey, among organizations that reported experiencing data loss, only 16% were able to use their DLP tools to successfully block their most significant incidents before the data actually left the organization’s control.

    The findings suggest that many enterprise DLP implementations may be functioning as reactive measures rather than proactive barriers, often failing to detect or prevent unauthorized data movement in real time. For organizations relying on these systems to secure sensitive information, the data points to a persistent challenge in mitigating data exfiltration effectively.

    Source: Dark Reading
    Original article: https://www.darkreading.com/cloud-security/cloud-security-alliance-survey-data-loss-prevention-detects-data-loss