#ConfidentialComputing

[govindhtech]

How GCP Confidential Computing develops trusted AI

How Confidential Computing Builds Trusted AI

GCP Confidential Computing changed enterprise cloud-based sensitive workload management. Google's hardware ecosystem development makes Confidential Computing compatible with privacy-preserving AI and multi-party data analytics, boosting adoption.

Google Cloud unveils its latest Confidential Computing advancements, illustrating how clients protect their most sensitive workloads, including AI.

Newest innovations

A glance at GKE Nodes and private virtual machines with NVIDIA H100 GPUs for AI applications.

A private Vertex AI Workbench preview

An overview of Confidential Space using the widely available NVIDIA H100 and Intel TDX GPUs.

General access, secret GKE Nodes on C3 systems with integrated acceleration and Intel TDX.

On AMD SEV-SNP N2D computers, confidential GKE nodes are available.

Private AMD SEV virtual machines on C4D machines preview

Speed up your Confidential Compute trip with Gemini Cloud Assist's preview.

Confidential Computing New Applications

Businesses using GCP Confidential Computing to unlock commercial breakthroughs affect all major industries.

AiGenomix

AiGenomix uses Google Cloud Confidential Computing to provide unique infectious disease surveillance, early cancer detection, and pharmaceuticals intelligence with a global network of public and private partners.

Google Ads

Google Ads uses confidential matching to link first-party customer data for marketing. Google Ads products are the first to employ confidential computing, and additional companies will follow suit.

Swift

Swift is using GCP Confidential Computing to power a money laundering detection model while protecting the privacy of some of the biggest banks.

Confidential Computing

Confidential VMs, GKE, Dataflow, Dataproc, and Space safeguard data in use.

Encrypt data during processing.

A simple, user-friendly implementation does not sacrifice performance.

Confident collaboration with data ownership

Benefits

A secrecy breakthrough

Confidential Virtual Machines, a breakthrough technology, lets customers secure their cloud data during processing.

Simply understood by all

Google Cloud lets users encrypt data in-app without changing app code or affecting speed.

Creating new possibilities

Confidential Computing opens new doors. Companies can collaborate while protecting data.

GCP Confidential Computing

Secret Computing Platform

Secret VMs

Cloud data can be protected by confidential virtual machines (VMs) encrypting data-in-use during processing. Modern AMD, Intel, and other CPUs have security protections for secret virtual machines. With GCP Confidential Computing, customers can trust their cloud data to remain private.

Google Cloud uses the Intel AMX CPU accelerator on the general-purpose C3 machine class for Confidential VMs for AI/ML applications. Confidential VMs on the C3 machine series secure AI data and models at the hardware level and boost deep learning and inference performance.

Confidential H100 GPU VMs

Companies can use AI and machine learning while securing sensitive data using accelerator-optimized A3 machine series with NVIDIA H100 GPUs' confidential virtual machines (VMs). With H100 GPUs, confidential virtual machines (VMs) protect data from GPU entry to output. This reduces the risk of privileged users or malicious actors gaining unauthorised access. Confidential VMs on the A3 machine line provide a trusted execution environment for AI applications, enabling businesses to collaborate more freely and securely.

Secret GKE Nodes

Confidential GKE Nodes encrypt GKE cluster data while retaining performance. The same technology powers Confidential GKE Nodes and VMs. This functionality lets you encrypt memory with processor-created, node-specific keys. Since the keys are produced in hardware during node formation and only live in the CPU, Google and other host nodes cannot access them.

Private Area

Confidentiality Space protects sensitive data while allowing businesses to aggregate and analyse it. Organisations can perform collaborative data analysis and machine learning (ML) model training with data security from all parties, including cloud service provider access. Confidential Space integration with Privacy Sandbox provides a trusted execution environment for privacy-preserving ad campaign analytics and retargeting post-cookie.

Secret Dataflow and Dataproc

Fully controlled Dataflow supports several machine learning and streaming analytics use cases at scale. Dataflow supports Compute Engine Confidential VMs, which allow inline memory encryption, for data pipelines.

Dataproc manages Spark, Hadoop, and other open source technologies and frameworks for huge data processing. Confidential Dataproc lets you secure inline memory using Compute Engine Confidential virtual machines. This improves security, especially for sensitive data.

All features

Using real-time encryption

Google Cloud users can encrypt data while using cloud services for hidden computing and AMD, Intel, and other CPU security capabilities. Customers may trust that their data will be encrypted while processing.

Elevate and move privately

Google Cloud wants to simplify GCP Confidential Computing. All your workloads—new and old—can run as Confidential VMs, making the transfer easy. You don't need to change app code to use Confidential VMs. Simple as checking one box.

Detecting sophisticated persistent assaults

GCP Confidential Computing enhances Shielded virtual machine rootkit and bootkit protection. This simplifies ensuring the integrity of your private virtual machine's operating system.

Enhanced creativity

GCP Confidential Computing could enable impossible computing scenarios. Cloud-based enterprises can now collaborate on critical data in secrecy.

Great performance

Regular N2D virtual machines function like confidential ones.

GCP Private Computing Cost

Confidential VM costs depend on machine kinds, persistent discs, and other resources.

[fortanix]

Fortanix for AppDev Teams | Demo

Watch this demo video and learn how to embed security and data privacy into your applications and also to learn how to enable safe data usage and drive global regulatory compliance. Enable secure DevOps— Securely store, control, and manage secrets and certificates for leading code signing tools to “shift-left” security in your software delivery lifecycle. Automate and Integrate—Leverage readily available REST APIs and SDKs to boost quality and productivity. Centralize governance—Manage and apply consistent policies across all environments from a single central console.

[source-soft]

Amazon announces the general availability of AWS Nitro Enclaves for confidential computing in Amazon EC2. AWS Nitro Enclaves uses a security chip that can easily isolate data of each user running on a host.

[sourceinfotech]

Google Cloud has introduced a Confidential Computing service that allows data to remain encrypted while it’s being processed.

If you are looking to build a secure web application, mobile app, or website, contact us: https://bit.ly/3fBmPpy

[shikha-11-22]

Confidential computing is a new security approach to encrypt workloads while being processed, it limits access and ensures a 360° data protection and uses the Trusted Execution Environment (TEEs) to safeguard the confidentiality of your data and code.

[devopsdeveloper]

@devopsdotcom : Google has kicked off an effort to make it easier to build secure applications using an open source framework for confidential computing. https://t.co/H2nndf26VZ @mvizard #confidentialcomputing #google #projectasylo https://t.co/w27hKYYyN5

[govindhtech]

What Is Confidential Computing? How It Works In Google Cloud

What is Confidential Computing?

Cloud computing technology that can isolate data within a secured central processing unit (CPU) while it is being processed is known as Confidential Computing. The data the CPU processes and the techniques it employs to do so are both part of its environment. Only those with specific authorization may access this in order to provide programming code for privileged access. Otherwise, the CPU’s resources are undetectable and undiscovered by any software or anybody, even the cloud provider.

Businesses using public and hybrid cloud services need data security solutions more than ever. Confidential computing aims to reassure organizations about data security. Before customers can feel at ease transferring information to a cloud environment, they must be certain that data is secure and kept private.

When it comes to delicate or business-critical tasks, this assurance is equally crucial. Moving to the cloud requires many businesses to put their faith in an unknown technology. This might lead to challenging issues, especially if their digital assets are accessible to unidentified parties, like the cloud provider. The goal of confidential computing is to ease these worries.

Cloud computing is not a novel use of data encryption. Cloud service companies have been encrypting data while it is in storage or in a database for years. Additionally, they have encrypted data traveling across a network. These have been essential components of cloud security for a long time. However, confidential computing encrypts data in use as well as in transit and at rest.

How Confidential Computing Works

Applications connect to a computer’s memory in order to process data. An program must first decrypt data in memory before it can process it. The data is accessible since it is momentarily unencrypted. Before, during, and immediately after processing, it is accessible without encryption. This exposes it to dangers such as memory dump attacks, which, in the case of an irretrievable mistake, entail capturing and utilizing random access memory (RAM) placed on a storage device.

As part of the assault, the attacker causes this mistake, which makes the data vulnerable. Additionally, data is vulnerable to root user breaches, which happen when an unauthorized individual obtains administrator capabilities and may access data before to, during, and after processing.

By using a hardware-based architecture known as a trusted execution environment (TEE), confidential computing resolves this problem. Within a CPU, this is a secure coprocessor. TEEs have integrated encryption keys. The coprocessor employs built-in attestation techniques to ensure that the TEEs are only accessible by the application code that has been allowed for them. The TEE will reject the attempt at access and stop the calculation if malware or unauthorized code attacks the system while it is attempting to access the encryption keys.

This keeps private information safe while it’s in memory. The data is made available for processing after the application instructs the TEE to decrypt it. Everything and everyone else cannot see the data while it is encrypted and being processed by the computer. This covers the operating system, virtual machines, hypervisors, other computer resources, and the cloud provider.

Why is Confidential Computing a Breakthrough Technology?

Because it addresses a requirement specific to cloud computing and one that is becoming more and more popular trustless security in a cloud computing environment confidential computing is a game-changing technology. For private users who want to ensure that their data, software, and computational tasks are not left vulnerable to cloud providers or other individuals they do not like to interact with, cloud computing is probably going to remain the preferred option.

Currently, a bad actor may access important processes, data, and software if they are able to effectively get or fake the credentials of a cloud provider. The most direct method of reaching the core infrastructure in a conventional on-premises computer system is to carry out an in-person assault, unless the infrastructure is unprotected at its perimeter. Therefore, users feel secure knowing that the internal data center is locked.

It doesn’t matter whether their confidence is warranted or advisable. Trust is still fostered by the sensation of control over the computer environment. With cloud computing, when the digital assets are located hundreds of kilometers away, the same degree of confidence may be established. Without having to worry about data protection or other regulatory concerns, this might open the door for businesses to embrace the newest cloud technology.

Businesses that must adhere to compliance rules could feel much more at ease moving their workloads to the cloud. A company may face severe fines or perhaps legal action for even an unintentional violation. Services like Google Cloud and Kubernetes can only provide people who are concerned about cloud security so much trust without confidential computing. Sensitive information is protected from unwanted access by programs and processes on the computer as well as by individuals with to solutions like Microsoft Azure secret cloud computing.

Read more on Govindhtech.com

[fortanix]

Discover how Fortanix empowers data backup and recovery companies to protect sensitive information with advanced technologies like confidential computing, secure key management, and tamper-proof audit logs. Ensure robust data protection and compliance with seamless integration into existing platforms. Safeguard your data at every stage and prevent costly data breaches. Learn more about Fortanix's innovative solutions today!

#DataSecurity #BackupRecovery #Fortanix #ConfidentialComputing #CyberSecurity

[govindhtech]

Accelerate Insights with Intel Confidential AI

Intel’s Confidential AI

Large language models (LLMs) and generative artificial intelligence (AI) tools have exploded in the market, enabling businesses to become more efficient globally by streamlining operations and optimizing workflows.

Companies are becoming more conscious of how data processing affects their Zero Trust policies, which aim to protect sensitive, proprietary, or confidential data, as well as their compliance obligations in light of recently enacted laws like the U.S. Executive Order on the Safe, Secure, and Trustworthy AI and the European Union’s AI Act, as they adopt this technology. AI models themselves have intrinsic value, which makes them worthy of protection. Intellectual property, like custom algorithms and LLMs, is the product of years of research and development and millions of dollars of financial commitment.

Confidential AI contributes to the protection of this data and can help businesses continue to use AI’s capabilities while adhering to the security, privacy, and compliance requirements necessary to conduct business. Additionally, it shields confidential generative models from prying eyes, safeguarding priceless intellectual property.

Confidential AI: What Is It?

Confidential artificial intelligence (AI) is a hybrid technology that straddles the divide between generative AI, which frequently depends on cloud compute power to be trained and handle complex tasks and requests, and Zero Trust policies, which are intended to protect private data. Businesses need technology that protects against exposure to inputs, trained data, generative models, and proprietary algorithms before they can trust AI tools. Confidential AI facilitates that process.

Confidential AI protects the data used to train LLMs, the output produced by these models, and the proprietary models themselves while they are in use by utilizing technologies and principles of confidential computing. Confidential AI thwarts malicious actors from gaining access to and disclosing data from both inside and outside the chain of execution through strict isolation, encryption, and attestation.

Intel’s Strategy for Confidential AI

Only when AI is developed in an ethical and responsible manner will it truly be available to everyone. In order to provide cutting-edge ecosystem tools and solutions that will make using AI more secure while assisting businesses in addressing important privacy and regulatory concerns at scale, Intel works with leading technology companies in the sector.

Intel Confidential Computing’s Confidential AI: Safeguarding Data and Models

With Intel’s confidential AI technology, data and models are protected and the legitimacy of assets and the computing environments in which they are used is verified. Proven solutions like Intel Trust Domain Extensions (Intel TDX) and Intel Software Guard Extensions (Intel SGX) are combined. To enable customers to secure a variety of AI workloads throughout the ecosystem, Intel develops platforms and technologies that propel the convergence of artificial intelligence (AI) and confidential computing. Today’s industry’s most extensive portfolio of confidential computing products is provided by Intel:

Using Intel Software Guard Extensions for Application Isolation (Intel SGX)

Intel Trust Domain Extensions (Intel TDX)

For Virtual Machine Isolation; Intel Trust Authority for Independent Trust Attestation Services

Impact in the Real World

Businesses like healthcare, government, finance, and retail that depend on processing and storing sensitive data stand to gain from Intel’s creative and all-encompassing approach to confidential computing and AI. Businesses can quickly process massive volumes of data through their training models with confidential AI while upholding higher security and compliance standards.

FAQS

What is Confidential AI?

Confidential AI combines AI and confidential computing. This protects AI models and data while processing sensitive data in the cloud and other untrusted environments.

Why is Confidential AI important?

Security concerns are the reason why many organizations are reluctant to use AI. Confidential AI lets them use AI for sensitive data tasks like financial analysis and healthcare.

How does Intel’s technology achieve Confidential AI?

Intel Software Guard Extensions (SGX) and Intel Trust Domain Extensions encrypt data and models during processing. This guarantees their confidentiality even in a risky setting.

What is included with confidential computing?

Data in use is safeguarded through confidential computing. Confidential computing helps prevent data access by cloud operators, malicious admins, and privileged software by encrypting data in memory and processing it only after the cloud environment is confirmed to be a trusted execution environment.

What’s new in confidential computing?

Now, businesses can work together on regulated and sensitive data in the cloud while maintaining confidentiality. Standard N2D VM performance is comparable to that of confidential VMs. Confidential Computing opens up computing possibilities that were previously unattainable.

What are the benefits of Intel Confidential AI?

Security: Preserves private information and model sets for AI inference and training. Privacy: Facilitates teamwork on AI initiatives without jeopardizing sensitive data. Trust: Guarantees that the computer environment in which your AI workloads are executed is clean.

How does Intel Confidential AI work?

Intel offers technologies like Intel Trust Domain Extensions (Intel TDX) and Intel Software Guard Extensions (Intel SGX) to achieve Confidential AI. These technologies create isolated enclaves that protect your data and models even when they’re being processed in the cloud.

What is included with confidential computing?

Data in use is safeguarded through confidential computing. Confidential computing helps prevent data access by cloud operators, malicious admins, and privileged software by encrypting data in memory and processing it only after the cloud environment is confirmed to be a trusted execution environment.

Read more on govindhtech.com

[fortanix]

Companies are exploring the opportunities with #AI, but there's a big concern — #dataprivacy 🔐 👉That's where Fortanix steps in, with its advanced technology - #ConfidentialComputing, which helps #datasecurity teams tackle these challenges head-on 🙌 #artificialintelligence