When combining Large Language Models (LLMs) with autonomous agents, used in network monitoring and decision-making systems, this will create serious security issues. In this research, the MAESTRO framework consisting of the seven layers threat modeling architecture in the system was used to expose, evaluate, and eliminate vulnerabilities of agentic AI. The prototype agent system was constructed and implemented, using Python, LangChain, and telemetry in WebSockets, and deployed with inference, memory, parameter tuning, and anomaly detection modules. Two practical threat cases were confirmed as follows: (i) resource denial of service by traffic replay denial-of-service, and (ii) memory poisoning by tampering with the historical log file maintained by the agent. These situations resulted in measurable levels of performance degradation, i.e. telemetry updates were delayed, and computational loads were increased, as a result of poor system adaptations. It was suggested to use a multilayered defense-in-depth approach with memory isolation, validation of planners and anomaly response systems in real-time. These findings verify that MAESTRO is viable in operational threat mapping, prospective risk scoring, and the basis of the resilient system design. The authors bring attention to the importance of the enforcement of memory integrity, paying attention to the adaptation logic monitoring, and cross-layer communication protection that guarantee the agentic AI reliability in adversarial settings.

As Agentic AI systems evolve from basic workflows to complex multi agent collaboration, robust protocols such as Google's Agent2Agent (A2A) become essential enablers. To foster secure adoption and ensure the reliability of these complex interactions, understanding the secure implementation of A2A is essential. This paper addresses this goal by providing a comprehensive security analysis centered on the A2A protocol. We examine its fundamental elements and operational dynamics, situating it within the framework of agent communication development. Utilizing the MAESTRO framework, specifically designed for AI risks, we apply proactive threat modeling to assess potential security issues in A2A deployments, focusing on aspects such as Agent Card management, task execution integrity, and authentication methodologies. Based on these insights, we recommend practical secure development methodologies and architectural best practices designed to build resilient and effective A2A systems. Our analysis also explores how the synergy between A2A and the Model Context Protocol (MCP) can further enhance secure interoperability. This paper equips developers and architects with the knowledge and practical guidance needed to confidently leverage the A2A protocol for building robust and secure next generation agentic applications.

We investigate the reasoning capabilities of large language models (LLMs) for automatically generating data-cleaning workflows. To evaluate LLMs' ability to complete data-cleaning tasks, we implemented a pipeline for LLM-based Auto Data Cleaning Workflow (AutoDCWorkflow), prompting LLMs on data cleaning operations to repair three types of data quality issues: duplicates, missing values, and inconsistent data formats. Given a dirty table and a purpose (expressed as a query), this pipeline generates a minimal, clean table sufficient to address the purpose and the data cleaning workflow used to produce the table. The planning process involves three main LLM-driven components: (1) Select Target Columns: Identifies a set of target columns related to the purpose. (2) Inspect Column Quality: Assesses the data quality for each target column and generates a Data Quality Report as operation objectives. (3) Generate Operation & Arguments: Predicts the next operation and arguments based on the data quality report results. Additionally, we propose a data cleaning benchmark to evaluate the capability of LLM agents to automatically generate workflows that address data cleaning purposes of varying difficulty levels. The benchmark comprises the annotated datasets as a collection of purpose, raw table, clean table, data cleaning workflow, and answer set. In our experiments, we evaluated three LLMs that auto-generate purpose-driven data cleaning workflows. The results indicate that LLMs perform well in planning and generating data-cleaning workflows without the need for fine-tuning.

To address the challenges associated with data processing at scale, we propose Dataverse, a unified open-source Extract-Transform-Load (ETL) pipeline for large language models (LLMs) with a user-friendly design at its core. Easy addition of custom processors with block-based interface in Dataverse allows users to readily and efficiently use Dataverse to build their own ETL pipeline. We hope that Dataverse will serve as a vital tool for LLM development and open source the entire library to welcome community contribution. Additionally, we provide a concise, two-minute video demonstration of our system, illustrating its capabilities and implementation.

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LendingTree is seeking a Senior Manager of Data Operations for our Enterprise Data Management Organization. This person will be tasked with establishing a robust Data Operations operating model to include Incident and Change Management, Database Administration and establish an Observability framework. Additionally, the leader will proactively work to attract/train and grow talent across the data operations team with admin and SRE experts who will be the'go to' resources for the data organization. In this role we are seeking a strong agilest who excels in fast-paced environments and is willing to push outside your comfort zone. You are ready to find your purpose at work and possess an exceptional ability to collaborate with multiple stakeholders across business verticals and technical teams.

Artificial intelligence presents a major challenge to conventional computing architecture. In standard models, memory storage and computing take place in different parts of the machine, and data must move from its area of storage to a CPU or GPU for processing. The problem with this design is that movement takes time. You can have the most powerful processing unit on the market, but its performance will be limited as it idles waiting for data, a problem known as the "memory wall" or "bottleneck." When computing outperforms memory transfer, latency is unavoidable.

When Diane Chang began working at Intuit, maker of Turbo Tax and Quick Books, more than a decade ago in 2009 as a data scientist, there were only a few other people performing that role at the company. Being a data scientist back then reminded her of a time when she had worked for a consulting firm. "We had to convince people that they should work with us, and that they'd want to work with us, and that we could help provide value. There was a lot of selling initially and explaining and describing what we could do," she says. There's more demand than there is supply for data scientists." Chang, now director of data science at the company, provided an inside view into how Intuit is leveraging data science today. The company says it has evolved into an AI-driven platform company. Chang provided insights into the business trends have impacted the company and what data science trends are getting the attention of the C-suite. One of the major business trends that has impacted Intuit's ...

The AIPPF, established in 2020 by the Bank of England and the Financial Conduct Authority (FCA), was set up to facilitate dialogue between the private sector, public sector, and academia regarding AI. Data comes first: The report describes data as foundational for AI, attributing most of AI's recent growth to a surge in the availability of data to contribute to models. Data is more connected to AI's pros and cons than other aspects, and "many of the benefits and risks can be traced back to the data, rather than the AI systems or algorithms themselves." Data is also among "the defining features of AI," which can process massive quantities of data and find patterns from it. The report honed in on key areas for banks' data operations for AI, such as: Data quality: This covers accuracy, timeliness, transparency, and completeness. Banks' quality challenges include needing to update their controls and processes for AI, and handling complex data sources.