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Samsung Electronics announced plans to turn all its manufacturing operations into AI-driven factories by 2030. The company will fully integrate AI throughout the manufacturing process, from material, logistics, and production to quality checks and final shipment, creating a new autonomous production environment.  

To support this change, Samsung will use digital twin simulations in its manufacturing and introduce specialized AI agents for quality control, production, and logistics. These agents will help improve data analysis and pre-validation, raising quality, efficiency, and productivity throughout Samsung’s global factories.  

Samsung will also bring more AI into its environmental, health, and safety operations through proactive detection and automated hazard prevention. The company intends to raise safety standards at its production sites worldwide.  

The core of this change is agentic AI, first seen in the Galaxy S26 series. This AI can plan, execute, and optimize decisions on its own to meet set goals. Samsung is now using its mobile AI expertise to build a strong base for self-governance in manufacturing.  

With custom AI agents, Samsung will improve production workflows, predictive maintenance, repairs, and logistics. This will help ensure high standards and consistent quality at all its sites worldwide.  

To accelerate the transition from automation to advanced autonomy, Samsung is adding humanoid and specialized robots to its production lines. These include operating robots for managing lines and facilities, logistic robots for moving materials, and assembly robots for precise manufacturing. In places where it is hard or unsafe for people to work, Samsung will use environmental safety robots with digital twin technology to monitor conditions, spot risks, and stop hazards.  

The next phase of manufacturing innovation is about creating autonomous situations where AI understands operations in real time and makes the best decisions on its own, said Young-Soo Lee, executive vice president and head of global technology research at Samsung Electronics. We are committed to leading the way in AI-powered global manufacturing innovation.  

Global Industry Engagement 

Samsung will present its industrial AI strategy and digital twin manufacturing vision at MWC 2026 in Barcelona. The company will show how industrial AI can improve safety and efficiency in actual environments.  

At the Samsung Mobile Business Summit, which marks its 10th anniversary this year, the company will share its governance strategy for expanding AI autonomy. This approach includes adding safety features from the start, making sure industrial AI grows responsibly and can be trusted by customers and partners worldwide.  

SFBS is a private invitation-only event for key B2B customers and partners. Samsung uses this event to share its B2B strategy, latest technology plans, and to explore new ways to work together across industries.

Source: Samsung Electronics Announces Strategy To Transition Global Manufacturing Into ‘AI-Driven Factories’ by 2030 

Samsung Electronics brought together global experts for a panel called In Tech, We Trust: Rethinking Security and Privacy in the AI Age ” during its tech forum at CES 2026. The event, held at the Wynn in Las Vegas, focused on how trust is becoming a key factor in how people use and accept AI as it becomes a bigger part of everyday life.  

Making Invisible Intelligence Feel Trustworthy 

As AI increasingly anticipates needs, curates routines, and operates autonomously across devices, panelists Allie K. Miller (CEO of Open Machine), Amy Webb (CEO of The Future Now Strategy Group), Zach Kass (Global AI Advisor at ZKAI Advisory and former Head of Go-to-Market at OpenAI), and Shin Baik (AI Platform Center Group Head at Samsung Electronics) emphasized that trust must be earned not through promises but via consistent, understandable behavior. Each expert contributed their view on how transparency and reliability are critical for earning user trust.  

During the session, Samsung explained its trust-by-design approach, stressing that AI should be predictable, transparent, and easy for users to control. Allie Miller said when it comes to AI, users are looking for openness and control. They want to be leaders in their own personalized experiences, to understand whether an AI model is running locally or in the cloud, to know that their data is secure, and to clearly see what is powered by AI and what is not. That level of visibility builds confidence in the provider’s head. There is a responsibility to show up for users by designing personalized experiences around the core components of trust, clarity, security, and accountability.  

Samsung also pointed out that on-device AI keeps personal data on the device whenever possible. Cloud-based AI is used only when more speed or scale is needed, so users get flexibility without sacrificing privacy.  

Security for an AI-Driven World 

The panel discussed how security needs to change as AI spreads across phones, TVs, and home appliances. Samsung presented its Knox security platform, which now protects billions of devices through chip-level security and the Knox Matrix, a system that enables devices to authenticate and protect one another.  

Trust in AI starts with security, thus proven, not promised. Shin Baik said that, for more than a decade, Samsung Knox has provided a deeply embedded security platform that protects sensitive data at every layer. But trust does go beyond a single device  it requires an ecosystem that protects itself. Samsung Knox devices continuously authenticate and monitor one another, so each device acts as a shield for the rest, creating a resilient, secure environment users can rely on.  

Across Industry, Look At The Future Of Trust. 

Shin Baik emphasized that trust grows when AI behaves predictably and securely across devices, arguing that users need visible signals of control rather than black-box systems. Samsung pointed to its alliances with industry leaders such as Google and Microsoft as a way to strengthen shared security research, interoperability, and ecosystem-wide protection, while Allie Miller pointed out the value of transparency for users, including clear visibility into where AI models run, how data is used, and explicit labels that show what is powered by AI and what is not. Meanwhile, Zach Kass added that while misinformation and misuse present real challenges, for every risk there is also a countermeasure, and technology itself will play a critical part in mitigating AI’s downsides.  

Amy Webb evaluated the relationship between trust and consumer purchasing habits. I don’t think they’re making decisions based solely on trust. She said people aren’t paying for trust. They don’t buy things because of trust. They buy things because of convenience. So if the AI piece of this hooks people in, it makes their lives easier and more convenient.  

As AI becomes part of daily life, the panel agreed on one point. The technologies people trust most will be those that prioritize security, transparency, and user choice from the start.

Source: Samsung Explores How Trust, Security and Privacy Shape the Future of AI at CES 2026 

Apple’s OpenELM 2.0 models work with the MLX framework to deliver fast, private, local retrieval-augmented generation (RAG) on Apple Silicon in optimized setups. The back-end processes over 150 tokens/sec by using layer-wise scaling and unified memory. This configuration delivers secure, high-speed AI inference directly on your device, eliminating the need for the cloud.  

Key Aspects Of The OpenELM And MLX Stack 

• High-speed local inference: The MLX framework is designed for Apple Silicon and efficiently runs models, processing over 150 tokens/sec in some cases for real-time responses.  

• Privacy-focused RAG: Build local RAG servers using the MLX framework to keep sensitive data on your MacBook and protect privacy.  

• OpenELM architecture: Layer-wise scaling improves the accuracy of OpenELM models by optimizing parameter distribution across transformer layers.  

• Optimized Deployment: MLX provides libraries like MLX-LM for fast model deployment with minimal code and supports fine-tuning for on-device machine learning.  

1. Index documents by splitting them into small text chunks.  

2. Generate embeddings: Use MLX to turn chunks into numerical representations that help models interpret meaning.  

3. Store in vector database: Store embeddings, numerical text representations, locally in a vector database, a system for storing and searching embeddings efficiently.  

4. Retrieve and generate: Locate the best context for your queries, then use Apple or Open ELM models to generate responses.  

Note: these results are based on ideal conditions and specific Apple Silicon hardware.  

On-device AI has long promised a local-first approach, but hardware limits and slow speeds have held it back. With the release of Apple’s Open ELM 2.0 MLX framework, things are changing as Apple pairs the open-source, efficient language model Open ELM with the MLX array framework. Apple now delivers private, high-speed local RAG (retrieval-augmented generation) at 150 tokens per second on everyday consumer hardware.  

For developers and privacy-focused businesses, this update is more than just faster for performance. It changes how sensitive data from medical records to proprietary code is handled and searched, keeping everything on the device and protected by private RAG.  

The Architecture of OpenELM 2.0: Layer-wise Scaling Reimagined 

The key innovation in OpenELM 2.0 is its new layer-wise scaling approach. Instead of using the same layer sizes throughout, it spreads parameters unevenly. Layers near the input and output have different sizes and headcounts, allowing the model to use its resources more efficiently.  

When used with the MLX framework, Apple’s open-source library for Apple Silicon, Open ELM 2.0, leverages the unified memory architecture. Here, the CPU and GPU share a single fast memory pool, eliminating the usual PCIe bottleneck found with separate GPUs. Thanks to 4-bit quantization with MLX-LM, the model fits easily in a base MacBook AS memory living room for the vector database needed for RAG.  

Enabling 150 Token/Sec Performance: Speculative Decoding And MLX Kernels. 

OpenELM 2.0 reaches 150 tokens/sec, which feels almost instant to users, by using two main updates: optimized Metal kernels for Group Query Attention (GQA) and RMSNorm. These kernels reduce how often the processor needs to access memory, which is usually the main slowdown for large language models (LLMs).  

For speculative generation, OpenELM 2.0 uses a draft-and-verify method: a model like the 270M version predicts tokens first, and then the larger 3B model checks them in parallel. This lets the system generate several tokens at once, reaching speeds of 150 tokens/sec.  

The speed is especially important for local RAG workflows. In these pipelines, the model needs to read and summarize the context it finds before answering. High throughput means that, even with extensive documentation, the wait for the first token is barely noticeable.  

The Private RAG Advantage: Security at the Edge 

The private int private 150 token/SEC local RAG is more than just a label. It’s a technical guarantee based on the local-first design. In typical RAG setups, you send data, such as health records or company spreadsheets, to the cloud for processing.  

With OpenELM 2.0 and MLX, the whole process stays on your device.  

• Local embedding: data is converted into vectors using MLX-optimized embedding models, such as Hugging Face’s Sentence Transformers, and the results are saved locally in an encrypted format as microservices.  

• Local inference: the open ELM 2.0 model searches the local store and creates responses using the device’s GPU. This reduces ongoing subscription costs and keeps the system running even when offline. For fields like healthcare or law, this is the only practical way to use generative AI every day.  

Developer Implementation: The MLX LM Ecosystem 

Apple has made it easy for engineers to get started. The Mix LM package lets you add OpenELM 2.0 to a Swift or Python project and handles converting and quantizing Hugging Face rates with just one CLI command or a few lines of Python code. It uses modern containerization to manage memory limits, allowing the system to adjust its GPU cache for devices ranging from iPhones with 8 GB of RAM to M3 Max workstations with 128 GB.  

Final Thoughts 

The Apple Open ELM 2.0 MLX Framework signals a new era for local machine learning with a private 150-token/SEC local RAG. Apple allows that retina-level AI, which delivers instant responses, to run without a huge server farm. As the open-source community continues to improve these models and the MLX framework gains more support, cloud-only LLMs will lose their edge for anyone who cares about both privacy and performance. Local elements are not just here they’re running 150 tokens per second. 

Source: OpenELM: An Efficient Language Model Family with Open Training and Inference Framework 

Today, Microsoft released its March 2026 Patch Tuesday updates addressing 79 security flaws. This includes two zero-day vulnerabilities that have been publicly disclosed.  

Transitioning from the initial overview, Dispatch Tuesday also fixes three serious vulnerabilities. Two are remote code execution issues, and one is an information disclosure flaw.  

Here is a breakdown of the number of bugs in each vulnerability category. In cybersecurity, a vulnerability is a weakness that could allow unauthorized actions in a system.  

• 46 elevation of privilege vulnerabilities.  

• Two security features bypass vulnerabilities  

• Eighteen, remote code execution on the line of notice.  

• Information Disclosure Vulnerabilities  

• Four denial-of-service vulnerabilities.  

• Four spoofing vulnerabilities.  

Bleeping Computer only counts Patch Tuesday security updates released by Microsoft on the day itself. This means the total does not include 9 Microsoft Edge flaws or issues in Marina Payment Orchestrator Service, Azure, and Microsoft Devices Pricing Program that were fixed earlier this month. Thank you.  

Two Zero-Day Vulnerabilities and Microsoft Office Flaws 

This month’s Patch Tuesday addresses two publicly disclosed zero-day vulnerabilities with no reports of active exploitation.  

Microsoft defines a zero-day flaw as one that is either publicly disclosed or actively exploited before an official fix is available.  

The two publicly disclosed zero-day vulnerabilities are:  

CVE-2026-21262 – SQL Server Elevation of Privilege Vulnerability  

Microsoft has fixed this publicly disclosed SQL Server flaw.  

Improper access control in SQL Server allows an authorized attacker to elevate their privileges on a network, Microsoft explains.  

Microsoft credited Erland Sommarskog for discovering this flaw. He told Bleeping Computer that it was first disclosed in the “Packaging Permissions in Stored Procedures” article.   

CVE-2026-2612. net denial of service vulnerability.  

An “Out of bounds read” in .NET allows an unauthorized attacker to deny service over a network, Microsoft explains.  

An anonymous researcher is credited with finding this flaw.  

Microsoft has also addressed two remote code execution bugs (CVE-2026-26110 and CVE-2026-26113) in Microsoft Office. Remote code execution (RCE) refers to bugs that could allow attackers to run programs on your computer from a distance. These can be exploited through the preview pane, so users should update the application as soon as possible.  

One remarkable issue is the Microsoft Excel Information Disclosure flaw (CVE-2026-26144), which could allow attackers to steal data using Microsoft Copilot. An information disclosure occurs when unauthorized people can access confidential data.   

An attacker who successfully exploited this vulnerability could cause Copilot Agent Mode to exfiltrate data via unintended network egress, enabling a zero-click information disclosure attack, Microsoft explains.  

Turning to Azure Cloud Services, Microsoft has also fixed a vulnerability in Azure Container Instances (ACI) that could have allowed users to access information belonging to other Azure customers.  

Microsoft did not share technical details about the vulnerability. However, Palo Alto Networks researchers say attackers could have used the bug to run code on other users’ containers, steal sensitive data like crypto secrets, or even install crypto-mining malware.   

Microsoft said it notified customers who might have been affected through service health notifications in the Azure portal. The company added that anyone who did not receive a notification does not need to take any action.  

There is no indication that any customer data was accessed as a result of this vulnerability. Out of an abundance of caution, Microsoft sent notifications to customers potentially affected by the researcher’s activities, advising them to revoke any privileged credentials deployed to the platform before August 31, 2021, Microsoft said in a statement.  

Microsoft recommends that all Azure customers regularly rotate their privileged credentials as a precaution. Credential rotation involves frequently changing passwords or secure keys to reduce the risk of theft.  

Palo Alto Networks researchers say the issue known as Azure escape could allow attackers to compromise Kubernetes clusters that host ACI. This would give them full control over other Azure customers’ containers.  

ACI was designed to prevent attacks from malicious neighboring containers, such as cross-account or cross-tenant attacks. However, it used an older version of runc (the standard container runtime), which was vulnerable to several container escape flaws.  

The researchers used a modified proof-of-concept code for CVE-2019-5736 to escape from a container and establish a root reverse shell on the host system. They then found token permissions in the Kube system namespace that allowed them to run commands on any pod in the cluster and carry out the cross-account attack.  

A malicious Azure user could have compromised the multi-tenant Kubernetes cluster hosting ACI. As a cluster administrator, an attacker could execute commands in other customers’ containers, exfiltrate secrets and private images deployed to the platform, or deploy crypto miners. A sophisticated adversary would further investigate the detection protocols protecting ACI to avoid detection, Palo Alto said.  

ACI, launched in July 2017, is a container-as-a-service (CaaS) that runs on multi-tenant clusters such as Kubernetes and Service Fabric. It lets users deploy containers without managing the underlying infrastructure.  

In a related development, two weeks ago, Microsoft fixed a similar problem in Azure Cosmos DB. In that case, users could access other customers’ databases with full administrative rights, potentially gaining full control of them.

Source: Microsoft March 2026 Patch Tuesday fixes 2 zero-days, 79 flaws 

Microsoft Warns of Information Leak Flaw in Azure Container Instances

Silicon Motion has launched the SM8008A PCIe Gen5 x4 NVMe SSD controller. This controller delivers high performance and sets industry-leading energy efficiency for data-center boot drives and other enterprise storage.  

With the growth of AI and cloud services, data centers are adding more servers than ever. Each server requires a system drive to run its software. In large-scale data centers, the energy used by these systems accumulates and can account for a significant portion of total power consumption.  

Silicon Motion SM8008 Features And Support 

The SM8008 directly tackles the challenge of balancing high performance with low power draw in modern data centers. Built with TSMC’s 6nm process, it reaches up to 14 GB/s in sequential speed, delivers over 2.3 million random IOPS for 4 KB tasks, and keeps power consumption under 5 watts. With 8 NAND channels and support for ONFI and DDR5.0 interfaces up to 3600 MT/s, the S8 offers robust and efficient storage management.  

The controller uses a PCIe Gen5 x4 interface and runs on the NVMe 2.0a protocol, delivering the required performance for new server platforms. Depending on the SSD configuration, it supports up to 16 TB of storage.  

To help data centers cut energy costs when running thousands of drives, the controller supports DDR4-3200 or LPDDR4-3200 DRAM featuring built-in error correction. This approach reduces power consumption while maintaining reliability, making it cost-effective for enterprise environments.  

Silicon Motion SM-80008 Security 

With built-in security features like TCG, Profile 2.0, encryption, and hardware support for AES-256, SHA-512, and RSA-3072, the SM-8008 helps enterprises ensure robust protection for critical data while supporting secure boot and firmware integrity.  

The controller also incorporates security schemes such as DICE and SPDM and already meets the CNSA 2.0 cryptographic standard. As a result, it will satisfy new requirements expected for some government and business users starting in 2027.  

Silicon Motion SM8008 Storage Capabilities 

For storage, the controller uses Silicon Motion, NAMD command technology, and a strong LDPC error-correction engine. These features protect data and help the drive last longer, even with TLC and QLC memory. The controller’s hardware also supports NAMD processing methods that increase speed and throughput.  

The SM8008 enables flexible deployment in demanding environments by supporting standards used in large-scale data centers and enterprises. Its compatibility with the NVMe 2.0a protocol and the OCP Hyperscale NVMe Boot SSD Specification version 1.0 ensures seamless interoperability. The controllers’ support for multiple SSD form factors such as M.2, U.2, E1.S, and E3.S allows vendors to streamline server and storage architectures for diverse applications.  

Silicon Motion SM8008 Specifications 

Silicon Motion SM8008, Availability 

Interest in controllers built specifically for boot storage is growing as hyperscale infrastructure evolves. Early deployments of the SM8008 are underway with companies such as ATP and Exascend integrating the controller into new enterprise SSD platforms for large server environments. Silicon Motion’s boot storage lineup now includes SLTA, PCIe Gen3/Gen4 controllers, PCIe NVMe BGA SSDs, and the SM8008 Gen5 controller, underscoring its focus on dedicated enterprise boot storage.t.  

About Silicon Motion 

Silicon Motion Technology Corporation is a global leader in NAND flash controllers for solid-state storage devices. The company ships more SSD controllers than any other supplier worldwide for servers, PCs, and other devices. It also dominates as the leading merchant provider of eMMC and UFS, as well as embedded storage controllers for smartphones, IoT products, and automotive applications.  

Silicon Motion delivers custom high-efficiency solutions for hyperscale data centers, industrial systems, and automotive SSDs. The company designs its controllers for advanced AI, cloud, and enterprise storage platforms, ensuring high performance, low power use, and reliable operation.  

Many of the world’s NAND flash vendors, data center and enterprise storage providers, storage device makers, and top OEMs rely on Silicon Motion’s controller technologies to achieve innovative, high-quality storage solutions. www.siliconmotion.com

Source: Silicon Motion Launches SM8008, a Purpose-Built PCIe Gen5 Controller for Enterprise Boot Drives and Ultra-Low-Power Storage