Difficult to capture, this mysterious, squid-shaped interstellar cloud spans nearly three full moons in planet Earth’s sky. Discovered in 2011 by French astro-imager Nicolas Outters, the Squid Nebula’s bipolar shape is distinguished here by the telltale blue emission from doubly ionized oxygen atoms. Though apparently surrounded by the reddish hydrogen emission region Sh2-129, the true distance and nature of the Squid Nebula have been difficult to determine. Still, one investigation suggests Ou4 really does lie within Sh2-129 some 2,300 light-years away. Consistent with that scenario, the cosmic squid would represent a spectacular outflow of material driven by a triple system of hot, massive stars, cataloged as HR8119, seen near the center of the nebula. If so, this truly giant squid nebula would physically be over 50 light-years across.
Massistant is the presumed successor to Chinese forensics tool, “MFSocket”, reported in 2019 and attributed to publicly traded cybersecurity company, Meiya Pico.
The forensics tool works in tandem with a corresponding desktop software.
Massistant gains access to device GPS location data, SMS messages, images, audio, contacts and phone services.
Meiya Pico maintains partnerships with domestic and international law enforcement partners, both as a surveillance hardware and software provider, as well as through training programs for law enforcement personnel.
The good news, per Balaam, is that Massistant leaves evidence of its compromise on the seized device, meaning users can potentially identify and delete the malware, either because the hacking tool appears as an app, or can be found and deleted using more sophisticated tools such as the Android Debug Bridge, a command line tool that lets a user connect to a device through their computer.
The bad news is that at the time of installing Massistant, the damage is done, and authorities already have the person’s data.
The developer of ICEBlock, an iOS app for anonymously reporting sightings of US Immigration and Customs Enforcement (ICE) officials, promises that it “ensures user privacy by storing no personal data.” But that claim has come under scrutiny. ICEBlock creator Joshua Aaron has been accused of making false promises regarding user anonymity and privacy, being “misguided” about the privacy offered by iOS, and of being an Apple fanboy. The issue isn’t what ICEBlock stores. It’s about what it could accidentally reveal through its tight integration with iOS.
Seems like an old system system that predates any care about security:
The flaw has to do with the protocol used in a train system known as the End-of-Train and Head-of-Train. A Flashing Rear End Device (FRED), also known as an End-of-Train (EOT) device, is attached to the back of a train and sends data via radio signals to a corresponding device in the locomotive called the Head-of-Train (HOT). Commands can also be sent to the FRED to apply the brakes at the rear of the train.
These devices were first installed in the 1980s as a replacement for caboose cars, and unfortunately, they lack encryption and authentication protocols. Instead, the current system uses data packets sent between the front and back of a train that include a simple BCH checksum to detect errors or interference. But now, the CISA is warning that someone using a software-defined radio could potentially send fake data packets and interfere with train operations.
As organizations scale their development efforts, AI coding assistants that understand organization-specific patterns and standards lead to more efficient development processes and higher quality software delivery. Amazon Q Developer Pro helps address this challenge by allowing organizations to customize the AI assistant with their proprietary code and development practices. Through Amazon Q Developer profiles, teams can efficiently manage access to Amazon Q customizations across different regions and AWS Identity Centers.
In this post, we will explore different approaches for implementing and managing Amazon Q Developer profiles and Amazon Q customizations across large organizations. Using an example with multiple business units, we will explore methods for managing access controls and customization governance while addressing security and compliance requirements.
Amazon Q customization is now available in both the US East (N. Virginia) and EU Central (Frankfurt) regions, giving teams more flexibility to create and deploy customizations closer to their operational hubs while meeting regional data residency requirements.
This blog is not intended to provide recommendations on how to structure your AWS accounts or divide Q Developer subscriptions. Rather, our aim is to explore the full capabilities of Q Developer Customizations in a comprehensive scenario that shows the current art of the possible.
A distributed Amazon Q Developer Pro subscriptions scenario
The following diagram illustrates a sample AWS Organizations structure with a Management Account and four Organizational Units (OUs). This is a common enterprise scenario with three business units, each business unit requiring their own Amazon Q Developer Pro subscription and customizations.
Figure 1: AWS Organizations Structure and Resource Hierarchy
The Infrastructure OU has a Delegated Admin Account with delegated access to the AWS IAM Identity Center. There are three additional OUs: Alpha, Bravo, and Charlie, each with at least one Amazon Q Developer Pro subscription. Alpha account has Amazon Q Developer subscriptions both in US East (N. Virginia) and EU Central (Frankfurt) region.
Think of each business unit as its own ecosystem within your organization. When you provide dedicated Q Developer Pro subscriptions to different OUs, you’re essentially giving each unit its own personalized AI assistant. This separation is valuable because it allows each team to work independently while maintaining their specific requirements and workflows.
The Charlie OU maintains its own account instance of IAM Identity Center for Amazon Q Developer Pro. In most cases, we recommend using an organization instance of IAM Identity Center with Amazon Q Developer Pro, there are a few situations where member account instances might make sense, for example: when you do not have a single identity provider, or when you haven’t yet decided to deploy it to the whole organization and want to use Amazon Q just for the AWS account you control.
Note: When a developer has a user within an Amazon Q profile tied to two different IAM Identity Center instances (Bravo and Charlie), they will have two user subscriptions and be billed twice. However, if they belong to two different Amazon Q profiles in two different accounts (Alpha and Bravo) but under the same IAM Identity Center, they will only be billed once.
In our example, the Charlie OU requires additional operational overhead in managing separate credentials and authentication flows. Additionally, the dashboard and administrative settings will only be associated with users and groups within this account. From an administrative perspective, instead of trying to manage one centralized configuration that attempts to serve everyone’s needs, you can distribute administration to each business unit and delegate responsibility to individual teams.
It’s like having different specialized departments in a hospital – while they’re all part of the same organization and can work together when needed, each department has its own specialized tools and protocols that help them perform their specific functions more effectively.
A strategic approach to Customizations through Q Developer profiles
Figure 2 Developers association to Amazon Q Developer Pro Subscriptions, Customizations and IAM Identity Centers
Amazon Q Developer profiles are the way developers connect to different Amazon Q Developer subscriptions through their IDE. Each profile represents a unique combination of an Amazon Q Developer subscription and its associated customizations. After authentication, developers can simply select or switch between profiles in their IDE to access different customizations.
Let’s walk through some scenarios in this architecture.
Scenario 1 – Users accessing two different customizations tied to a single IAM Identity Instance in the management account
Developers from the Orange team with access to Alpha account customizations can configure two different Amazon Q Developer profiles in their IDE:
A “US Profile” connected to the US East subscription in the Alpha account
An “EU Profile” connected to the EU Central subscription in the Alpha account
Switching between different sets of customizations involves selecting the relevant profile within their IDE.
Figure 3 IDE showing customizations available for Team Orange developers switching between US and EU Profile and their customizations
Note: While developers can access multiple customizations through different Amazon Q Developer profiles, they only incur a single user subscription cost since they are using the organization instance of IAM Identity Center. This is because the subscription is tied to their user identity in the IAM Identity Center organization instance, not to the number of profiles or customizations they access.
Scenario 2 – Users accessing two different customizations tied to a single IAM Identity Instance in the management account Similarly, developers from the Blue team can also configure multiple profiles:
One profile for accessing Alpha and Bravo customizations through the management account AWS IAM Identity Center instance
A separate profile for accessing Charlie customizations through the AWS IAM Identity Center member account Instance
When developers have access to multiple customizations within the same IAM Identity Center configuration and region, they can switch between profiles in their IDE without requiring reauthentication.
Figure 4 IDE showing customizations available for Team Blue developers when authenticated to AWS IAM Identity center Organization
However, as demonstrated in the blue developers’ case, switching between profiles that use different IAM Identity Center configurations (Organization vs Account Instance) still requires reauthentication.
Note: In this scenario, developers will incur two separate user subscription charges since they are accessing customizations through two different IAM Identity Center configurations (organization and account instance). As mentioned above, this scenario is not recommended except for situations it might make sense and is shown here purely to illustrate how the authentication and profile switching mechanisms work across different IAM Identity Center configurations.
Figure 5 IDE showing customizations available for Team Blue developers when authenticated to AWS IAM Identity center Account Instance
One scenario for creating code customizations specific to each profile is that the developers on the Alpha team might need Q to understand specific libraries and internal coding conventions for Java, while Bravo team developers might need Q to be well-versed in your proprietary technologies and development standards with Python. With separate profiles and customizations, each team gets their own “flavored” version of Q that understands their context.
For Blue developers who have access to Alpha, Bravo and Charlie customizations, they need to set up separate profiles since these customizations belong to different IAM Identity Center configurations and AWS Regions. Switching between these profiles requires reauthentication due to the different IAM Identity Center configurations involved.
Developer Team
AWS IAM Identity Center
Customizations
Orange
Organization instance
Alpha customizations in US East (N. Virginia)
Alpha customizations in EU Central (Frankfurt)
Blue
Organization instance
Alpha customizations in US East (N. Virginia)
Bravo customizations
Account instance
Charlie customizations
Grey
Organization instance
Bravo customizations
You can manage access to specific Amazon Q Developer Pro customizations by adding selected users and groups who already have access to Amazon Q Developer Pro subscriptions within the same Identity Center. This granular access control allows you to create targeted customizations that are only accessible to specific team members or groups within your organization.
Conclusion
In this post, we explored comprehensive strategies for implementing Amazon Q Developer customizations across large organizations. We demonstrated how Amazon Q Developer profiles provide a flexible way to manage access to different customizations across AWS regions and IAM Identity Center configurations. By integrating proprietary code repositories, establishing customization governance, and implementing continuous feedback loops, enterprises can maximize the value of their AI-powered development assistant while maintaining code quality and development standards.
The path forward depends on where you are in your Amazon Q Developer customization journey. If you’re just starting, begin with a clear assessment of your codebase and map out your customization approach before implementation. For existing users, review your current customizations and profile configurations to identify optimization opportunities.
In both cases, implement the customization governance we discussed, tailoring them to your specific development patterns and team structures. Remember that customization evolves with your codebase – regular refinements help ensure your AI assistant remains effective as your applications grow and development practices mature. Whether you’re new to Amazon Q Developer customizations or optimizing existing implementations, these practices can help develop an AI assistant that truly understands and aligns with your organization’s unique development environment.
Ready to get started? Visit the Amazon Q Developer guide to learn more about setting up profiles and customizations for your organization. If you need help planning your customization strategy, contact your AWS account team or find an AWS Partner in the AWS Partner Network.
Los clientes de hoy esperan que las organizaciones satisfagan proactivamente sus necesidades con contenido personalizado, entregado en el momento, lugar y forma de su elección. Buscan interacciones dinámicas y conscientes del contexto con conversaciones sofisticadas a través de todos los canales de comunicación. Esta creciente demanda ejerce presión sobre las organizaciones para transformar sus flujos de trabajo de experiencia del cliente para mejorar la lealtad y aumentar la eficiencia operativa. Si bien los avances recientes en Generative AI (GenAI), incluida la hiperpersonalización y Agentic AI, ofrecen posibilidades interesantes, también presentan nuevos desafíos. Las organizaciones necesitan una arquitectura flexible y reutilizable que les permita incorporar GenAI en sus sistemas existentes de participación del cliente sin requerir una revisión completa de sus soluciones dispares actuales.
Esta publicación de blog explora cómo construir un centro de comunicaciones moderno impulsado por IA utilizando ejemplos de GitHub de código abierto que integran servicios de SMS/MMS y WhatsApp con capacidades de GenAI. Las organizaciones pueden crear experiencias innovadoras de cliente impulsadas por IA con una rápida prueba de concepto sin interrumpir los sistemas existentes.
En combinación con Vector Databases y Retrieval Augmented Generation (RAG), GenAI hace posible reorganizar el conocimiento en un solo sistema y consultar desde una única interfaz de usuario a través de conversación en lenguaje natural con un chatbot o asistente virtual. Canalizar las comunicaciones de los clientes a través de un centro de comunicaciones multicanal vinculado con capacidades de GenAI ayuda a unificar los mecanismos de participación del cliente y agiliza la creación de experiencias ricas para el cliente. Los clientes interactúan con agentes de IA y bots de preguntas y respuestas en el canal de comunicación que les resulta conveniente para autogestionar sus necesidades. Las organizaciones pueden construir experiencias de cliente agnósticas al canal de comunicación mientras recopilan eventos de participación del canal y datos conversacionales en un almacén de datos centralizado para obtener información en tiempo real, consultas ad-hoc, análisis y entrenamiento de ML.
Descripción general de la solución
En el núcleo de la solución se encuentra el Centro de Comunicaciones Moderno que conecta los canales de comunicación digital con servicios clave de GenAI, como Amazon Bedrock y Amazon Q, junto con servicios de AWS ML, bases de datos, almacenamiento y computación sin servidor.
AWS End User Messaging y Amazon SES proporcionan acceso a nivel de API a canales de comunicación digital, ofreciendo servicios seguros, escalables, de alto rendimiento y rentables para que las aplicaciones empresariales intercambien SMS/MMS, WhatsApp, notificaciones push y de voz, y correo electrónico con los clientes.
Una colección de código de muestra de código abierto, publicada en el repositorio AWS-samples de GitHub, ilustra cómo facilitar conversaciones generativas en canales SMS/MMS y WhatsApp. Esto se extenderá para incluir servicios de correo electrónico. Dos componentes clave forman la base de las Muestras de Integración de GenAI: el Orquestrador de chat Multicanal con Agentes de IA, y la Base de Datos de Participación y Análisis para End User Messaging y SES. Nos referiremos a estos simplemente como el Procesador de Conversaciones y la Base de Datos de Participación en el diagrama de la solución.
El Procesador de Conversaciones recibe mensajes de clientes a través de AWS End User Messaging y Amazon Simple Email Service (SES), almacena los detalles de la conversación e invoca al Agente de Amazon Bedrock relevante. Los Agentes de Amazon Bedrock utilizan Modelos de Lenguaje Grandes (LLMs) y bases de conocimiento para analizar tareas, dividirlas en pasos accionables, ejecutar esos pasos o buscar en la base de conocimiento, observar resultados y refinar iterativamente su enfoque hasta completar la tarea junto con una respuesta. Alternativamente, el Procesador de Conversaciones puede funcionar como un bot de preguntas y respuestas, en cuyo caso utiliza Amazon Bedrock Knowledge Bases junto con su función RAG para generar una respuesta LLM y enviarla por el mismo canal que el mensaje del cliente.
La Base de Datos de Participación recopila y combina datos de participación del cliente y registros conversacionales de todos los canales de comunicación, almacenando la información en un data lake centralizado en Amazon S3. Al convertir los datos a un formato común y canónico, la solución simplifica la consulta y el análisis de estos eventos entrantes. Una función Lambda Transformer aprovecha las Plantillas Apache Velocity para transformar los datos JSON entrantes, permitiendo obtener información en tiempo real.
Los datos de eventos sin procesar almacenados en el data lake de Amazon S3 pueden luego alimentar otros servicios de AWS para su procesamiento posterior. Por ejemplo, los datos pueden fluir hacia Amazon Connect Customer Data Profiles o Amazon SageMaker para apoyar el entrenamiento de modelos de machine learning. Los analistas de datos pueden usar Amazon Athena para realizar consultas directas para informes detallados ad-hoc, o enviar los datos a Amazon QuickSight para visualizaciones avanzadas y capacidades de consulta en lenguaje natural a través de Amazon Q en QuickSight.
Amazon Bedrock proporciona capacidades centrales de GenAI como LLMs, Knowledge Bases, Retrieval Augmented Generation (RAG), agentes de IA y Guardrails, para comprender las solicitudes de los clientes, determinar qué acción tomar y qué comunicar de vuelta. Amazon Bedrock Knowledge Bases proporciona conocimiento y razonamiento específico de la organización, mientras que los Agentes de Amazon Bedrock automatizan tareas de múltiples pasos conectándose perfectamente con los sistemas, APIs y fuentes de datos de la empresa.
Requisitos previos
Los siguientes requisitos previos son necesarios para construir su centro de comunicaciones moderno:
Una cuenta de AWS. Regístrese para obtener una cuenta de AWS en el sitio web de AWS si no tiene una.
Roles y permisos apropiados de AWS Identity and Access Management (IAM) para Amazon Bedrock, AWS End User Messaging y Amazon S3. Para más información, consulte Create a service role for model import.
Configuración de AWS End User Messaging: Necesitará configurar la identidad de origen necesaria en el servicio AWS End User Messaging para entregar mensajes a través de SMS o WhatsApp. Si configura SMS, se debe aprovisionar un Número de Teléfono de Origen SMS registrado y activo en AWS End User Messaging SMS. (Dentro de Estados Unidos, use 10DLC o Números Gratuitos (TFNs)). Si configura WhatsApp, se debe aprovisionar un número activo que haya sido registrado con Meta/WhatsApp en AWS End User Messaging Social.
Modelos de Amazon Bedrock: Bedrock Anthropic Claude 3.0 Sonnet y Titan Text Embeddings V2 habilitados en su región. Tenga en cuenta que estos son los modelos predeterminados utilizados por la solución; sin embargo, puede experimentar con diferentes modelos.
Docker instalado y en ejecución – Se utiliza localmente para empaquetar recursos para el despliegue.
Node (> v18) y NPM (> v8.19) instalados y configurados en su computadora
AWS CDK (v2) instalado y configurado en su computadora.
Implementación del Procesador de Conversaciones y Base de Datos de Participación
Implemente las siguientes dos soluciones. Si bien no es obligatorio, es mejor implementarlas en este orden, ya que las salidas de la Base de Datos de Participación pueden utilizarse en el ejemplo de Chat Multicanal:
Cada solución contiene instrucciones detalladas para implementar los servicios requeridos usando AWS Cloud Development Kit (CDK). La primera solución de Base de Datos de Participación creará un flujo de Amazon Data Firehose que puede utilizarse como entrada para la segunda aplicación de Chat Multicanal, de modo que los datos puedan almacenarse y consultarse en la Base de Datos de Participación.
Orquestrador de chat Multicanal con Agentes de IA
Esta solución demuestra cómo los usuarios pueden interactuar con tres diferentes fuentes de conocimiento. Puede que no necesite las tres, sin embargo, esto debería servir como un buen ejemplo para construir la fuente de conocimiento adecuada para su caso de uso particular:
Construya sus Bases de Conocimiento en Amazon Bedrock usando Amazon S3. Por defecto, la solución creará Bases de Conocimiento usando un Bucket de Amazon S3 como fuente de datos. Esta solución le permite cargar documentos a un bucket de Amazon S3 para poblar la base de conocimiento.
NOTA: El proyecto inicial crea un bucket S3 para almacenar los documentos utilizados para la Base de Conocimiento de Bedrock. Por favor, considere usar Amazon Macie para ayudar en el descubrimiento de datos potencialmente sensibles en buckets S3. Amazon Macie puede habilitarse en una prueba gratuita durante 30 días, hasta 150GB por cuenta.
Agentes de Amazon Bedrock: Opcionalmente permita que sus usuarios chateen con Agentes de Amazon Bedrock. Los agentes tienen el beneficio adicional de soportar bases de conocimiento para responder preguntas y guiar a los usuarios a través de la recopilación de información necesaria para automatizar una tarea como hacer una reserva. Hay agentes de ejemplo disponibles en el repositorio Amazon Bedrock Agent Samples. Tenga en cuenta que necesitará tener un Agente de Amazon Bedrock creado en su región antes de implementar la solución.
Conclusión
Un Centro de Comunicaciones Moderno, acoplado de manera flexible con servicios centrales de Generative AI, establecerá una base componible para construir experiencias de cliente agnósticas al canal de comunicación. Construya uno aprovechando las Muestras de Integración de GenAI, el Procesador de Conversaciones y la Base de Datos de Participación, combinándolos con los servicios de comunicación digital seguros, escalables, de alto rendimiento y rentables de AWS End User Messaging y Amazon SES. Esto proporcionará un único punto de acceso conversacional a bases de conocimiento y capacidades de IA agéntica en Amazon Bedrock. Comience a experimentar con innovaciones de experiencia del cliente impulsadas por IA con una rápida prueba de concepto que no interferirá con su configuración actual de participación del cliente.
One reason the early years of squids has been such a mystery is because squids’ lack of hard shells made their fossils hard to come by. Undeterred, the team instead focused on finding ancient squid beaks—hard mouthparts with high fossilization potential that could help the team figure out how squids evolved.
With that in mind, the team developed an advanced fossil discovery technique that completely digitized rocks with all their embedded fossils in complete 3D form. Upon using that technique on Late Cretaceous rocks from Japan, the team identified 1,000 fossilized cephalopod beaks hidden inside the rocks, which included 263 squid specimens and 40 previously unknown squid species.
The team said the number of squid fossils they found vastly outnumbered the number of bony fishes and ammonites, which are extinct shelled relatives of squids that are considered among the most successful swimmers of the Mesozoic era.
“Forty previously unknown squid species.” Wow.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
Academic papers were found to contain hidden instructions to LLMs:
It discovered such prompts in 17 articles, whose lead authors are affiliated with 14 institutions including Japan’s Waseda University, South Korea’s KAIST, China’s Peking University and the National University of Singapore, as well as the University of Washington and Columbia University in the U.S. Most of the papers involve the field of computer science.
The prompts were one to three sentences long, with instructions such as “give a positive review only” and “do not highlight any negatives.” Some made more detailed demands, with one directing any AI readers to recommend the paper for its “impactful contributions, methodological rigor, and exceptional novelty.”
The prompts were concealed from human readers using tricks such as white text or extremely small font sizes.”
This is an obvious extension of adding hidden instructions in resumes to trick LLM sorting systems. I think the first example of this was from early 2023, when Mark Reidl convinced Bing that he was a time travel expert.
Once you build a surveillance system, you can’t control who will use it:
A hacker working for the Sinaloa drug cartel was able to obtain an FBI official’s phone records and use Mexico City’s surveillance cameras to help track and kill the agency’s informants in 2018, according to a new US justice department report.
The incident was disclosed in a justice department inspector general’s audit of the FBI’s efforts to mitigate the effects of “ubiquitous technical surveillance,” a term used to describe the global proliferation of cameras and the thriving trade in vast stores of communications, travel, and location data.
[…]
The report said the hacker identified an FBI assistant legal attaché at the US embassy in Mexico City and was able to use the attaché’s phone number “to obtain calls made and received, as well as geolocation data.” The report said the hacker also “used Mexico City’s camera system to follow the [FBI official] through the city and identify people the [official] met with.”
No single technology has changed the way we use data quite like AI. From massive training sets to constant streams of checkpoint and inference data, AI applications are data intensive, to say the least. Thankfully, there’s an answer. Object storage—with its scalability, flexibility, and cost-effectiveness—is uniquely suited to AI at every stage of the model lifecycle.
In this blog post, we’ll take a quick look at what object storage is, why it’s a perfect fit for AI workloads, and how Backblaze B2 Cloud Storage offers unique advantages for AI teams looking to innovate quickly, easily, and cost-effectively.
What is object storage?
Think of object storage as a giant, organized bucket for all your files. Instead of stuffing things into folders or breaking them into blocks, you just drop each file (an “object”), with a unique tag and some helpful notes (metadata), into your storage solution.
Unlike traditional file or block storage, object storage uses a flat address space. Each object is assigned a unique identifier and can be tagged with rich metadata, making it easy to search, retrieve, and manage at scale.
Because of this unique architecture, object storage is ideal for handling unstructured data—such as images, video, audio, text, and sensor data—which is the meat and potatoes of most modern AI workflows. Also, being cloud-based, object storage is inherently designed for massive scalability and accessibility over the internet (often via S3 API).
Ebook: “Why Object Storage Is Ideal for AI Workflows”
Want to take a deeper dive into the world of object storage? Check out our latest ebook, “Why Object Storage is Ideal for AI Workloads,” and discover the advantages this architecture has to offer across the model lifecycle.
Understanding AI’s data storage needs at each stage of the model lifecycle
Before diving into the benefits of object storage, let’s first define and outline the AI model lifecycle. While some may slice and dice it a little differently, generally speaking, we can break the AI model lifecycle down into the following stages:
Data ingestion and collection: Massive, often petabyte-scale datasets are gathered from a diversity of sources.
Data preparation and storage: Raw data is cleaned, labeled, transformed, and stored for future retrieval and processing.
Model training: Data is fed into AI training algorithms, typically deployed across many nodes in a GPU cluster—usually requiring high throughput, parallel access, and lengthy processing times.
Deployment and inference: Trained models are deployed into live applications where they take in new data and make inferences based on that data.
Monitoring and archiving: Continuous monitoring generates substantial amounts of log data and performance metrics that must be versioned, stored, and archived for compliance or retraining purposes.
As you can see, each stage of the model lifecycle presents its own unique set of data demands—with each one requiring plenty of planning, work, and preparation. And at every one of these stages, matters of scale, speed, accessibility, and cost are mission-critical to a project’s success.
Where object storage excels: Scalability for data ingestion and collection
Object storage offers virtually unlimited scalability for large, and ever-expanding datasets, making it an ideal solution for the earliest stages of AI development. With no need to create volumes or file systems, organizations can quickly start uploading data to object storage. In addition to this seamless scalability, object storage also shines in its ability to support a diverse range of structured and unstructured data types without the need for rigid hierarchies. In this way, AI teams can ingest all sorts of data to support whatever their unique application needs; and do it quickly and efficiently.
Flexible data preparation and storage
Cloud-based object storage systems are excellent for maintaining easily-accessible, version-controlled datasets that allow for lightning fast iteration and collaboration. Capabilities like version recovery (which allows teams to easily revert datasets to previous states with simple API calls) and concurrent access (which gives multiple team members the ability to work on the same datasets simultaneously without conflicts) are also key to the data preparation and storage phase of AI development.
Reliable, high-performance data storage for model training
For the model training stage of the AI lifecycle, object storage supports parallel access and high throughput, both of which are absolutely essential for GPU-intensive training workloads. Reliable shuttling of large datasets to GPU clusters, wherever they may be, is key for keeping things efficient. Meanwhile, streamlined storage of model checkpoints from those clusters gives teams peace of mind in knowing that a mid-training failure state will not place them all the way back at square one.
Plus, lifecycle management features allow completed or outdated training datasets to be automatically archived—reducing clutter and optimizing storage costs, all while keeping active training data easily accessible.
Efficient versioning for deployment and inference
AI models are always a work in progress. Once deployed and operational, they have to be routinely evaluated and tuned. To that end, object storage makes it easy to store and retrieve a range of valuable information, including model checkpoints, test results, and inference data.
Built-in versioning and object immutability features support reproducibility and audit trails, so you can always trace which data and models produced which results. Together, these capabilities make for robust and effective lifecycle management, significantly boosting reliability and compliance.
Cost-effectiveness and durability for monitoring and archiving
When in the field, continuous monitoring of AI models generates a whole lot of log data and performance metrics. Object storage automates the management of these resources through customizable lifecycle rules, automatically deleting or archiving out-of-date inference logs based on predefined timelines (e.g., after 30–180 days).
This significantly reduces the need for manual oversight, conserves engineering resources, and ensures that relevant performance data remains accessible for compliance and regulatory auditing.
Meanwhile, with the right vendor, object storage solutions can offer competitive pricing models—sometimes including the separation of compute from storage—to ensure cost-effectiveness throughout the late stages of the AI lifecycle. Finally, high durability (of 11 nines or more) and redundancies protect models and datasets which become increasingly valuable over time.
Backblaze B2: Cost-effective, high-performance object storage for your AI workloads
Backblaze B2 Cloud Storage takes all the inherent advantages of cloud-based object storage for AI workloads and amplifies them—through competitive, transparent pricing; reliable, high performance; and seamless integration and support to ensure your project is not only efficient and affordable, but most importantly, successful.
Competitive, transparent pricing: One-fifth of the cost of most hyperscalers’ solutions, with no hidden costs and three times your total storage volume in free egress included. Plus, fully-transparent, predictable pricing models ensure your organization is fully aware and prepared for the costs associated with your applications.
High performance and reliability: Upload speeds up to 30% faster than AWS S3 for many workloads, plus a 99.9% uptime SLA with 11 nines of durability, ensure always-hot, instantly accessible data for demanding AI workloads.
Seamless adoption and integration, accompanied by expert support: With features like Universal Data Migration and no hidden delete fees, B2 Cloud Storage uniquely streamlines cost-effective data management for AI. Backblaze B2 also boasts S3 API compatibility for true plug-and-play functionality with leading AI and machine learning ops (MLOps) tools and technologies.
Plus, our truly agnostic solution allows organizations to freely and easily connect to any compute or GPU environment (or environments), free of vendor lock-in and fees. And in case you want some support along the way, our team of dedicated solution engineers are available to tailor and fine-tune your architecture and operations to best suit whatever the unique needs of your AI project may be.
Optimize your AI lifecycle with cloud object storage from Backblaze B2
Data is one of the most important, and most challenging, aspects of AI development. And with their unprecedented data demands, traditional block and file storage systems frequently come up short in supporting modern AI applications. At the same time, legacy cloud storage solutions come with enormous burdens of cost, inflexibility, and the ever-looming threat of lock-in.
Cloud-based object storage offers the perfect solution to all these challenges—with the right mixture of performance, efficiency, and cost-effectiveness that AI projects need.It’s so-well-suited, in fact, that we’ve written an entire white paper on the subject! So, if you’re interested in taking a deeper dive into the topic, check out our ebook, Why Object Storage is Ideal for AI Workloads, today.
A whole class of speculative execution attacks against CPUs were published in 2018. They seemed pretty catastrophic at the time. But the fixes were as well. Speculative execution was a way to speed up CPUs, and removing those enhancements resulted in significant performance drops.
Now, people are rethinking the trade-off. Ubuntu has disabled some protections, resulting in 20% performance boost.
After discussion between Intel and Canonical’s security teams, we are in agreement that Spectre no longer needs to be mitigated for the GPU at the Compute Runtime level. At this point, Spectre has been mitigated in the kernel, and a clear warning from the Compute Runtime build serves as a notification for those running modified kernels without those patches. For these reasons, we feel that Spectre mitigations in Compute Runtime no longer offer enough security impact to justify the current performance tradeoff.
I agree with this trade-off. These attacks are hard to get working, and it’s not easy to exfiltrate useful data. There are way easier ways to attack systems.
American democracy runs on trust, and that trust is cracking.
Nearly half of Americans, both Democrats and Republicans, question whether elections are conducted fairly. Some voters accept election results only when their side wins. The problem isn’t just political polarization—it’s a creeping erosion of trust in the machinery of democracy itself.
Commentators blame ideological tribalism, misinformation campaigns and partisan echo chambers for this crisis of trust. But these explanations miss a critical piece of the puzzle: a growing unease with the digital infrastructure that now underpins nearly every aspect of how Americans vote.
The digital transformation of American elections has been swift and sweeping. Just two decades ago, most people voted using mechanical levers or punch cards. Today, over 95% of ballots are counted electronically. Digital systems have replaced poll books, taken over voter identity verification processes and are integrated into registration, counting, auditing and voting systems.
This technological leap has made voting more accessible and efficient, and sometimes more secure. But these new systems are also more complex. And that complexity plays into the hands of those looking to undermine democracy.
In recent years, authoritarian regimes have refined a chillingly effective strategy to chip away at Americans’ faith in democracy by relentlessly sowing doubt about the tools U.S. states use to conduct elections. It’s a sustained campaign to fracture civic faith and make Americans believe that democracy is rigged, especially when their side loses.
This is not cyberwar in the traditional sense. There’s no evidence that anyone has managed to break into voting machines and alter votes. But cyberattacks on election systems don’t need to succeed to have an effect. Even a single failed intrusion, magnified by sensational headlines and political echo chambers, is enough to shake public trust. By feeding into existing anxiety about the complexity and opacity of digital systems, adversaries create fertile ground for disinformation and conspiracy theories.
Testing cyber fears
To test this dynamic, we launched a study to uncover precisely how cyberattacks corroded trust in the vote during the 2024 U.S. presidential race. We surveyed more than 3,000 voters before and after election day, testing them using a series of fictional but highly realistic breaking news reports depicting cyberattacks against critical infrastructure. We randomly assigned participants to watch different types of news reports: some depicting cyberattacks on election systems, others on unrelated infrastructure such as the power grid, and a third, neutral control group.
The results, which are under peer review, were both striking and sobering. Mere exposure to reports of cyberattacks undermined trust in the electoral process—regardless of partisanship. Voters who supported the losing candidate experienced the greatest drop in trust, with two-thirds of Democratic voters showing heightened skepticism toward the election results.
But winners too showed diminished confidence. Even though most Republican voters, buoyed by their victory, accepted the overall security of the election, the majority of those who viewed news reports about cyberattacks remained suspicious.
The attacks didn’t even have to be related to the election. Even cyberattacks against critical infrastructure such as utilities had spillover effects. Voters seemed to extrapolate: “If the power grid can be hacked, why should I believe that voting machines are secure?”
Strikingly, voters who used digital machines to cast their ballots were the most rattled. For this group of people, belief in the accuracy of the vote count fell by nearly twice as much as that of voters who cast their ballots by mail and who didn’t use any technology. Their firsthand experience with the sorts of systems being portrayed as vulnerable personalized the threat.
It’s not hard to see why. When you’ve just used a touchscreen to vote, and then you see a news report about a digital system being breached, the leap in logic isn’t far.
Our data suggests that in a digital society, perceptions of trust—and distrust—are fluid, contagious and easily activated. The cyber domain isn’t just about networks and code. It’s also about emotions: fear, vulnerability and uncertainty.
Firewall of trust
Does this mean we should scrap electronic voting machines? Not necessarily.
Every election system, digital or analog, has flaws. And in many respects, today’s high-tech systems have solved the problems of the past with voter-verifiable paper ballots. Modern voting machines reduce human error, increase accessibility and speed up the vote count. No one misses the hanging chads of 2000.
But technology, no matter how advanced, cannot instill legitimacy on its own. It must be paired with something harder to code: public trust. In an environment where foreign adversaries amplify every flaw, cyberattacks can trigger spirals of suspicion. It is no longer enough for elections to be secure – voters must also perceive them to be secure.
That’s why public education surrounding elections is now as vital to election security as firewalls and encrypted networks. It’s vital that voters understand how elections are run, how they’re protected and how failures are caught and corrected. Election officials, civil society groups and researchers can teach how audits work, host open-source verification demonstrations and ensure that high-tech electoral processes are comprehensible to voters.
We believe this is an essential investment in democratic resilience. But it needs to be proactive, not reactive. By the time the doubt takes hold, it’s already too late.
Just as crucially, we are convinced that it’s time to rethink the very nature of cyber threats. People often imagine them in military terms. But that framework misses the true power of these threats. The danger of cyberattacks is not only that they can destroy infrastructure or steal classified secrets, but that they chip away at societal cohesion, sow anxiety and fray citizens’ confidence in democratic institutions. These attacks erode the very idea of truth itself by making people doubt that anything can be trusted.
If trust is the target, then we believe that elected officials should start to treat trust as a national asset: something to be built, renewed and defended. Because in the end, elections aren’t just about votes being counted—they’re about people believing that those votes count.
And in that belief lies the true firewall of democracy.
This essay was written with Ryan Shandler and Anthony J. DeMattee, and originally appeared in The Conversation.
Narrowly, the term refers to ensuring that data isn’t tampered with, either in transit or in storage. Manipulating account balances in bank databases, removing entries from criminal records, and murder by removing notations about allergies from medical records are all integrity attacks.
More broadly, integrity refers to ensuring that data is correct and accurate from the point it is collected, through all the ways it is used, modified, transformed, and eventually deleted. Integrity-related incidents include malicious actions, but also inadvertent mistakes.
We tend not to think of them this way, but we have many primitive integrity measures built into our computer systems. The reboot process, which returns a computer to a known good state, is an integrity measure. The undo button is another integrity measure. Any of our systems that detect hard drive errors, file corruption, or dropped internet packets are integrity measures.
Just as a website leaving personal data exposed even if no one accessed it counts as a privacy breach, a system that fails to guarantee the accuracy of its data counts as an integrity breach – even if no one deliberately manipulated that data.
Integrity has always been important, but as we start using massive amounts of data to both train and operate AI systems, data integrity will become more critical than ever.
Most of the attacks against AI systems are integrity attacks. Affixing small stickers on road signs to fool AI driving systems is an integrity violation. Prompt injection attacks are another integrity violation. In both cases, the AI model can’t distinguish between legitimate data and malicious input: visual in the first case, text instructions in the second. Even worse, the AI model can’t distinguish between legitimate data and malicious commands.
Any attacks that manipulate the training data, the model, the input, the output, or the feedback from the interaction back into the model is an integrity violation. If you’re building an AI system, integrity is your biggest security problem. And it’s one we’re going to need to think about, talk about, and figure out how to solve.
Web 3.0 – the distributed, decentralized, intelligent web of tomorrow – is all about data integrity. It’s not just AI. Verifiable, trustworthy, accurate data and computation are necessary parts of cloud computing, peer-to-peer social networking, and distributed data storage. Imagine a world of driverless cars, where the cars communicate with each other about their intentions and road conditions. That doesn’t work without integrity. And neither does a smart power grid, or reliable mesh networking. There are no trustworthy AI agents without integrity.
We’re going to have to solve a small language problem first, though. Confidentiality is to confidential, and availability is to available, as integrity is to what? The analogous word is “integrous,” but that’s such an obscure word that it’s not in the Merriam-Webster dictionary, even in its unabridged version. I propose that we re-popularize the word, starting here.
We need research into integrous system design.
We need research into a series of hard problems that encompass both data and computational integrity. How do we test and measure integrity? How do we build verifiable sensors with auditable system outputs? How to we build integrous data processing units? How do we recover from an integrity breach? These are just a few of the questions we will need to answer once we start poking around at integrity.
There are deep questions here, deep as the internet. Back in the 1960s, the internet was designed to answer a basic security question: Can we build an available network in a world of availability failures? More recently, we turned to the question of privacy: Can we build a confidential network in a world of confidentiality failures? I propose that the current version of this question needs to be this: Can we build an integrous network in a world of integrity failures? Like the two version of this question that came before: the answer isn’t obviously “yes,” but it’s not obviously “no,” either.
Let’s start thinking about integrous system design. And let’s start using the word in conversation. The more we use it, the less weird it will sound. And, who knows, maybe someday the American Dialect Society will choose it as the word of the year.
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