Regeneron Pharmaceuticals is acquiring 23andMe for $256 million, gaining access to the genetic data of around 15 million customers. This raises data privacy concerns, despite assurances from Regeneron to honor existing privacy practices. The sale also brings up questions about potential compensation for customers if the data leads to profitable medications.
This article discusses the importance of integrating responsible AI practices with security measures, particularly within organizations like Grammarly. It emphasizes treating responsible AI as a product principle, securing the AI supply chain, and the interconnectedness of responsible AI and security. It also touches on the future of AI customization and control.
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The LinkedIn article, “Leading With Trust: When Responsible AI and Security Collide,” by Grammarly’s CISO Sacha Faust, argues that responsible AI isn’t just an ethical or compliance issue, but a critical security imperative.
**Key takeaways:**
* **Responsible AI as a Product Principle:** Organizations should integrate responsible AI into product design, asking questions about values alignment, employee enablement, and proactive risk identification.
* **Secure the AI Supply Chain:** Organizations must trace AI model origins, evaluate vendors, and control key components (moderation, data governance, deployment) to mitigate risks.
* **Blur the Lines:** Responsible AI and AI security are intertwined – security ensures systems *work* as intended, while responsible AI ensures they *should* behave a certain way.
* **Certification & Transparency:** Frameworks like ISO/IEC 42001:2023 can signal commitment to AI governance and build trust.
* **Future Focus: Customization vs. Control:** Leaders need to address policies and safeguards for increasingly customized and autonomous AI systems, balancing freedom with oversight.
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The LinkedIn article, “Leading With Trust: When Responsible AI and Security Collide,” by Grammarly’s CISO Sacha Faust, argues that responsible AI isn’t just an ethical or compliance issue, but a critical security imperative.
**Key takeaways:**
* **Responsible AI as a Product Principle:** Organizations should integrate responsible AI into product design, asking questions about values alignment, employee enablement, and proactive risk identification.
* **Secure the AI Supply Chain:** Organizations must trace AI model origins, evaluate vendors, and control key components (moderation, data governance, deployment) to mitigate risks.
* **Blur the Lines:** Responsible AI and AI security are intertwined – security ensures systems *work* as intended, while responsible AI ensures they *should* behave a certain way.
* **Certification & Transparency:** Frameworks like ISO/IEC 42001:2023 can signal commitment to AI governance and build trust.
* **Future Focus: Customization vs. Control:** Leaders need to address policies and safeguards for increasingly customized and autonomous AI systems, balancing freedom with oversight.
A user with a Russian IP address attempted to log into NLRB systems shortly after DOGE gained access, raising concerns about potential foreign intelligence operations. A whistleblower alleges DOGE exfiltrated data and disabled security monitoring, and received threats after raising concerns internally.
A deep dive into the structure and performance benefits of Parquet files, including columnar storage, partitioning strategies, and row groups.
The article discusses how AI agents are transforming the way data is organized and utilized, moving away from rigid tabular structures to more flexible, interconnected data models. This shift is driven by the need for applications to be more intelligent and context-aware, requiring vast and complex datasets. The database layer is becoming increasingly critical, with AI agents necessitating richly structured data to enable near-human levels of logic and intuition.
LlamaExtract is a powerful, easy-to-use tool that allows users to extract structured data from unstructured documents with minimal effort, available through LlamaCloud’s web UI and Python SDK.
The article discusses the security risks and challenges associated with the increasing use of AI agents in enterprise workflows. It highlights concerns about data access, privacy, and the potential for new vulnerabilities in multi-agent systems. Experts emphasize the need for careful management of agent identities and access permissions to mitigate risks.
OpenSanctions helps investigators find leads, allows companies to manage risk, and enables technologists to build data-driven products by providing a clean, de-duplicated dataset from 276 global sources.
This article introduces Streamlit, a Python library for building data dashboards, as a solution for Python programmers to create graphical front-ends without needing to delve into CSS, HTML, or JavaScript. The author, a seasoned data engineer, explains how Streamlit and similar tools enable the creation of attractive dashboards, marking a shift from traditional tools like Tableau or Quicksight. This piece serves as the first in a series focusing on Streamlit, with future articles planned on Gradio and Taipy. The author aims to replicate similar layouts and functionalities across dashboards using consistent data.
Breser stands for Business Rules & Expression Syntax for Easy Retrieval. It is a powerful and flexible query language designed for efficient log processing and structured data filtering.
