#NVIDIA Morpheus cybersecurity AI

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NVIDIA Cybersecurity Revolution: Protecting AI Workloads

NVIDIA Cybersecurity

The NVIDIA DOCA Argus architecture seamlessly integrates with business security systems to give real-time insights and fix AI workload vulnerabilities.

Security in AI factories where complex, agentic tasks are performed is more crucial than ever as firms use AI.

New DOCA software framework from NVIDIA cybersecurity AI platform provides real-time cybersecurity for AI factories. NVIDIA DOCA Argus, which uses NVIDIA BlueField networking, detects and stops AI workload attacks on all nodes. It easily connects with enterprise security solutions for real-time threat analytics.

DOCA Argus uses advanced memory forensics to monitor assaults in real time and detect threats 1,000 times quicker than agentless solutions without affecting system performance.

Unlike previous solutions, Argus does not require agents, integration, or host-based resources. A containerised or multi-tenant AI computing environment benefits from this agentless, zero-overhead approach to system efficiency and security. Argus runs outside the host, making it undetectable to attackers in a system breach.

Easily connecting the framework with SIEM, SOAR, and XDR security systems allows NVIDIA cybersecurity specialists to enhance AI infrastructure cybersecurity capabilities and enable automated threat mitigation and continuous monitoring.

Since it provides comprehensive, data-centric protection for huge AI workloads, NVIDIA BlueField is essential for any AI factory. Using DOCA Argus' proactive threat detection and BlueField's acceleration, AI factories may be protected without losing performance.

Cisco and NVIDIA are building a protect AI Factory with NVIDIA architecture to help organisations install and protect AI infrastructure. Runtime protection is built into the AI factory design from the start, ensuring that every layer is safe.

Enterprises should advance AI now, but safety and security are vital to unlocking novel use cases and allowing wider adoption. Cisco and NVIDIA give organisations the foundation to deploy AI confidently while securing their most essential data.”

DOCA Argus and BlueField are part of the NVIDIA cybersecurity AI platform, a full-stack, accelerated computing platform for AI-driven defence. It uses NVIDIA AI Enterprise technologies including Morpheus, BlueField's data-centric security, and Argus' vision to give visibility and control over an AI factory. real-time threat detection. It also senses, reasons, and responds to dangers autonomously via agentic AI.

Improved AI Workload Security

Businesses are inundated with data, making it hard to see threats. The rising use of agentic AI models and autonomous agents at corporate scale to seamlessly connect data, apps, and users offers unprecedented opportunities for data insights and requires cutting-edge security.

DOCA Argus, improved by NVIDIA's security team, displays only validated threats. By targeting known threat actors and reducing false positives, the technology decreases warning fatigue and simplifies security operations.

Based on real-world threat intelligence and validation, Argus protects containerised workloads like NVIDIA NIM microservices and all AI application stack layers.

Cyber defenders need powerful tools to protect AI factories, which underpin agentic reasoning. “The DOCA Argus framework provides real-time security insights to enable autonomous detection and response, giving defenders a data advantage.”

Host and Platform Deployments

BlueField Networking Platform DOCA

DOCA powers massively scaled software-defined networking, storage, security, and management services at fast speeds on NVIDIA BlueField networking platform, an innovative data centre infrastructure computing platform.

Hosting DOCA

NVIDIA BlueField and NVIDIA Connect-X deliver 800 Gb/s Ethernet and InfiniBand connection with DOCA. The open-source DOCA-host package offers networking drivers and utilities to boost performance and functionality. For all major operating systems, DOCA software is available without an OS package for Arm and x86 platforms.

Unpack Stack

BlueField software bundle

BlueField includes a certified bootloader, OS kernel, NIC firmware, NVIDIA drivers, sample filesystem, and tools.

BlueField features Ubuntu 22.04, a commercial Linux OS and security update.

Key SDK Elements

GPUDirect, verbs, UCC, and UCX are in DOCA RDMA acceleration SDK.

VirtIO, P4, 5T for 5G, Firefly time synchronisation, and NVIDIA ASAP2 SDN are network acceleration SDKs.

Inline cryptography acceleration SDK, App Shield runtime security

SDK for storage acceleration: compression, encryption, emulation, virtualisation

The Data Path Acceleration (DPA) SDK accelerates workloads that demand fast NIC engine access.

Management SDK: provisioning, deployment, service orchestration

Industry-standard Linux Netlink, P4, SPDK, DPDK APIs User and kernel space

Forward/Backward Compatibility

DOCA provides multi-generational support to ensure that applications built today will function reliably and perform better on future BlueField generations.

Offload, Accelerate, Isolate Infrastructure

BlueField offloads, accelerates, and isolates network, storage, and security services while safely delivering data to applications at wire speed.

Open Ecosystem

DOCA helps software applications grow ecosystems.

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Nvidia makes its move into zero-trust

Nvidia makes its move into zero-trust

Nvidia is moving into Zero Trust with its own platform, the company has revealed.Looking to create something for other companies to build solutions on, the Zero Trust platform is based on three distinct technologies — NVIDIA BlueField Data Processing Units (DPU), NVIDIA DOCA and the NVIDIA Morpheus cybersecurity AI framework.Using technology it obtained in the acquisition of Mellanox earlier in

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Tech Behind Morpheus, NVIDIA’s AI Cybersecurity Framework

Tech Behind Morpheus, NVIDIA’s AI Cybersecurity Framework

Recently, NVIDIA launched Morpheus, a new open AI application framework powered by NVIDIA GPUs combined with NVIDIA® BlueField®-3 DPUs detect and prevent cybersecurity breaches instantaneously. NVIDIA Morpheus provides a framework that draws inferences from a large amount of cybersecurity data in real-time. With the launch of Morpheus, NVIDIA is bringing the powers of artificial intelligence to…

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Why Cybersecurity AI Requires Generative AI Guardrails

Three Strategies to Take Off on the Cybersecurity Flywheel AI Large language models provide security issues that Generative AI guardrails can resolve, including information breaches, access restrictions, and quick injections.

Cybersecurity AI

In a kind of progress flywheel, the commercial changes brought about by generative AI also carry dangers that AI itself may help safeguard. Businesses that adopted the open internet early on, over 20 years ago, were among the first to experience its advantages and develop expertise in contemporary network security.

These days, enterprise AI follows a similar trajectory. Businesses who are following its developments, particularly those with strong generative AI capabilities, are using the lessons learned to improve security.

For those who are just beginning this path, here are three major security vulnerabilities for large language models (LLMs) that industry experts have identified and how to handle them using AI.

Gen AI guardrails

AI Restraints Avoid Sudden Injections

Malicious suggestions that want to sabotage the LLM underlying generative AI systems or get access to its data may attack them.

Generative AI guardrails that are included into or positioned near LLMs are the greatest defense against prompt injections. Generative AI guardrails, like concrete curbs and metal safety barriers, keep LLM applications on course and on topic.

NVIDIA NeMo Guardrails

The industry has produced these solutions and is still working on them. The NVIDIA NeMo Generative AI guardrails program, for instance, enables developers to safeguard the dependability, security, and safety of generative AI services.

AI Recognizes and Preserves Private Information

Sometimes confidential information is revealed by the answers LLMs provide in response to prompts. Credentials are becoming more and more complicated thanks to multifactor authentication and other best practices, expanding the definition of what constitutes sensitive data.

All sensitive material should be properly deleted or concealed from AI training data to prevent leaks. AI algorithms find it simple to assure an efficient data cleansing procedure, while humans find it difficult given the magnitude of datasets utilized in training.

Anything private that was unintentionally left in an LLM’s training data may be protected against by using an AI model trained to identify and conceal sensitive information.

Businesses may use NVIDIA Morpheus, an AI framework for developing cybersecurity apps, to develop AI models and expedited pipelines that locate and safeguard critical data on their networks. AI can now follow and analyze the vast amounts of data flowing across a whole corporate network thanks to Morpheus, something that is not possible for a person using standard rule-based analytics.

AI Could Strengthen Access Control

Lastly, hackers could attempt to get access to an organization’s assets by using LLMs. Thus, companies must make sure their generative AI services don’t go beyond what’s appropriate.

The easiest way to mitigate this risk is to use security-by-design best practices. In particular, give an LLM the fewest rights possible and regularly review those privileges so that it can access just the information and tools required to carry out its specified tasks. In this instance, most users probably just need to adopt this straightforward, typical way.

On the other hand, AI can help with LLM access restrictions. By analyzing an LLM’s outputs, an independent inline model may be trained to identify privilege escalation.

Begin Your Path to AI-Powered Cybersecurity

Security remains to be about developing measures and counters; no one approach is a panacea. Those that employ the newest tools and technology are the most successful on that quest.

Organizations must understand AI in order to protect it, and the best way to accomplish this is by implementing it in relevant use cases. Full-stack AI, cybersecurity, NVIDIA and partners provide AI solutions.

In the future, cybersecurity and AI will be linked in a positive feedback loop. Users will eventually learn to trust it as just another automated process.

Find out more about the applications of NVIDIA’s cybersecurity AI technology. And attend the NVIDIA AI Summit in October to hear presentations on cybersecurity from professionals.

NVIDIA Morpheus

Cut the time and expense it takes to recognize, seize, and respond to threats and irregularities.

NVIDIA Morpheus: What Is It?

NVIDIA Morpheus is an end-to-end AI platform that runs on GPUs that enables corporate developers to create, modify, and grow cybersecurity applications at a reduced cost, wherever they are. The API that powers the analysis of massive amounts of data in real time for quicker detection and enhances human analysts’ skills with generative AI for maximum efficiency is the Morpheus development framework.

Advantages of NVIDIA Morpheus

Complete Data Visibility for Instantaneous Threat Identification

Enterprises can now monitor and analyze all data and traffic throughout the whole network, including data centers, edges, gateways, and centralized computing, thanks to Morpheus GPU acceleration, which offers the best performance at a vast scale.

Increase Productivity Through Generative AI

Morpheus expands the capabilities of security analysts, enables quicker automated detection and reaction, creates synthetic data to train AI models that more precisely identify dangers, and simulates “what-if” scenarios to avert possible attacks by integrating generative AI powered by NVIDIA NeMo.

Increased Efficiency at a Reduced Cost

The first cybersecurity AI framework that uses GPU acceleration and inferencing at a scale up to 600X quicker than CPU-only solutions, cutting detection times from weeks to minutes and significantly decreasing operating expenses.

Complete AI-Powered Cybersecurity Solution

An all-in-one, GPU-accelerated SDK toolset that uses AI to handle different cybersecurity use cases and streamline management. Install security copilots with generative AI capabilities, fight ransomware and phishing assaults, and forecast and identify risks by deploying your own models or using ones that have already been established.

AI at the Enterprise Level

Enterprise-grade AI must be manageable, dependable, and secure. The end-to-end, cloud-native software platform NVIDIA AI Enterprise speeds up data science workflows and simplifies the creation and implementation of production-grade AI applications, such as voice, computer vision, and generative AI.

Applications for Morpheus

AI Workflows: Quicken the Development Process

Users may begin developing AI-based cybersecurity solutions with the assistance of NVIDIA cybersecurity processes. The processes include cloud-native deployment Helm charts, training and inference pipelines for NVIDIA AI frameworks, and instructions on how to configure and train the system for a given use case. The procedures may boost trust in AI results, save development times and cut costs, and enhance accuracy and performance.

AI Framework for Cybersecurity

A platform for doing inference in real-time over enormous volumes of cybersecurity data is offered by Morpheus.

Data agnostic, Morpheus may broadcast and receive telemetry data from several sources, including an NVIDIA BlueField DPU directly. This enables continuous, real-time, and varied feedback, which can be used to modify rules, change policies, tweak sensing, and carry out other tasks.

AI Cybersecurity

Online safety Artificial Intelligence (AI) is the development and implementation of machine learning and accelerated computing applications to identify abnormalities, risks, and vulnerabilities in vast volumes of data more rapidly.

How AI Works in Cybersecurity

Cybersecurity is an issue with language and data. AI can immediately filter, analyze, and classify vast quantities of streaming cybersecurity data to identify and handle cyber threats. Generative AI may improve cybersecurity operations, automate tasks, and speed up threat detection and response.

AI infrastructure may be secured by enterprises via expedited implementation of AI. Platforms for networking and secure computing may use zero-trust security to protect models, data, and infrastructure.

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NVIDIA AIOps Partner Ecosystem Combines AI for Businesses

AIOps software

IT professionals deal with a never-ending stream of problems in today’s intricate corporate contexts, ranging from minor problems like employee account lockouts to serious security concerns. The task of ensuring seamless and safe operations becomes more difficult when faced with scenarios that call for both tactical defenses and fast remedies.

AIOps benefits This is where AIOps enters the picture, fusing IT operations and artificial intelligence to improve security protocols while also automating repetitive jobs. Teams can address small problems quickly with this effective technique, but more crucially, they can detect and react to security risks more accurately and swiftly than previously.

Best AIOps tools AIOps becomes a vital tool for both enhancing overall security and optimizing operations via the use of machine learning. Businesses trying to incorporate sophisticated AI into their teams are finding that it changes everything and keeps them one step ahead of any security threats.

IDC projects that the market for IT operations management software will expand at a 10.3% annual pace and reach $28.4 billion in sales by 2027. This expansion highlights the growing dependence on AIOps for improved operational effectiveness and as a vital part of contemporary cybersecurity plans.

A wide range of NVIDIA partners are providing AIOps solutions that use NVIDIA AI to enhance IT operations, as the fast expansion of machine learning operations continues to revolutionize the generative AI era.

NVIDIA provides accelerated computation and AI software to a wide range of AIOps partners. This includes tools such as NVIDIA NIM for rapid inference of AI modes, NVIDIA Morpheus for AI-based cybersecurity, and NVIDIA NeMo for bespoke generative AI. NVIDIA AI Enterprise is a cloud-native stack that can operate anywhere and serves as a foundation for AIOps. This program enables search, summarization, and chatbot capabilities powered by GenAI.

AIOps strategy By combining Davis CoPilot with causal, predictive, and generative AI approaches, Dynatrace Davis hypermodal AI enhances AIOps. This combination provides accurate and actionable, AI-driven solutions and automation, which improves observability and security across IT, development, security, and business processes.

For semantic and vector search, Elastic provides Elasticsearch Relevance Engine (ESRE), which combines with well-known LLMs like GPT-4 to enable AI Assistants in their Observability and Security products. A next-generation AI operations tool called the Observability AI Assistant aids IT teams in comprehending complicated systems, keeping an eye on system health, and automating the resolution of operational problems.

By using its machine learning, generative AI assistant frameworks, and extensive experience with observability, New Relic is developing AIOps. IT teams may minimize alarm noise, enhance mean time to detect and mean time to fix, automate root cause investigation, and create retrospectives with the aid of its machine learning and sophisticated logic. With the ability to recognize, clarify, and rectify problems without switching contexts, as well as propose and apply code solutions straight inside a developer’s integrated development environment, New Relic AI, its GenAI assistant, expedites the process of resolving issues.

By automatically generating high-level system health reports, evaluating and summarizing dashboards, and providing plain-language answers on a user’s apps, infrastructure, and services, it also increases issue visibility and prevention for non-technical teams. Additionally, full-stack observability is offered by New Relic for AI-powered apps that take use of NVIDIA GPUs.

With the integration of a generative AI assistant inside Slack, PagerDuty has unveiled a new feature in PagerDuty Copilot that streamlines the incident lifecycle and lessens the amount of human work that IT teams must do.

ServiceNow’s dedication to developing proactive IT operations includes improving service management, identifying abnormalities, and automating insights for quick issue response. It is now advancing toward generative AI in partnership with NVIDIA to better enhance technology services and operations.

AIOps services

Through the use of artificial intelligence and machine learning, Splunk’s technology platform improves IT productivity and security posture by automating the processes of detecting, evaluating, and addressing operational problems and threats. The main AIOps service from Splunk is called IT Service Intelligence, and it offers integrated AI-driven issue prediction, detection, and resolution all in one location.

By using the scalability and flexibility of cloud resources, cloud service providers like Microsoft Azure, Google Cloud, and Amazon Web Services (AWS) allow businesses to automate and improve their IT processes.

AWS provides a range of services that are helpful for AIOps, such as AWS Lambda for serverless computing, which enables response action automation based on triggers, Amazon SageMaker for repeatable and responsible machine learning workflows, AWS CloudTrail for tracking user activity and API usage, and Amazon CloudWatch for monitoring and serviceability.

Through services like Google Cloud Operations, which offers monitoring, logging, and diagnostics for both on-premises and cloud-based applications, Google Cloud enables AIOps. Vertex AI, which trains and predicts models, and BigQuery, which quickly searches SQL databases by using Google’s infrastructure’s processing capacity, are two of Google Cloud’s machine learning and AI offerings.

Azure Monitor, a tool from Microsoft Azure that allows for thorough application, service, and infrastructure monitoring, makes AIOps easier. The integrated AIOps features of Azure Monitor aid in capacity utilization prediction, autoscaling enabling, identifying application performance problems, and seeing unusual behavior in virtual machines, containers, and other resources. A cloud-based MLOps platform for properly training, deploying, and maintaining machine learning models at scale is provided by Microsoft Azure Machine Learning (AzureML).

The primary goal of platforms that specialize in MLOps is to streamline the machine learning model lifecycle, from development to deployment and monitoring. Although their primary goal is to increase machine learning’s accessibility, effectiveness, and scalability, their tools and processes also help AIOps by strengthening AI’s role in IT operations:

The Ray-based platform from Anyscale makes it simple to scale AI and machine learning applications, such as those used in AIOps for automatic remediation and anomaly detection. Anyscale enables AIOps systems handle massive amounts of operational data more effectively by providing distributed computing, which allows for real-time analytics and decision-making.

Models that anticipate IT system failures or improve resource allocation may be developed using Dataiku, which has capabilities that enable IT teams to rapidly implement and refine these models in real-world settings.

Users may create AI applications with their data thanks to Dataloop’s platform, which offers complete data lifecycle management and a flexible approach to plug in AI models for an end-to-end workflow.

IT operations teams can quickly develop, implement, and manage AI solutions with DataRobot, a complete AI lifecycle platform that boosts productivity and performance.

With the help of Domino Data Lab’s platform, businesses and their data scientists can create, implement, and oversee AI on a single, comprehensive platform. Teams can work together, keep an eye on production models, and define best practices for controlled AI innovation by having data, tools, computation, models, and projects centrally managed across all environments. This method is essential to AIOps because it strikes a compromise between perfect reproducibility, comprehensive cost monitoring, proactive governance for IT operational demands, and the self-service required by data science teams.

Weights & Biases offers collaboration, experiment tracking, and model optimization tools all essential for creating and optimizing AI models used in AIOps. Weights & Biases ensures that AI models used for IT operations are transparent and efficient by providing in-depth insights into model performance and encouraging team collaboration.

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