7 Top Security Providers for AI Factories

An AI factory may have sophisticated controls protecting its models while the infrastructure running those models remains exposed. A vulnerable baseboard management controller, outdated GPU firmware, or poorly isolated storage network can create an attack path that conventional application security tools never detect. These weaknesses become especially consequential when expensive compute infrastructure processes proprietary models and sensitive training data for multiple customers.

Securing AI factories requires visibility beyond the operating system and cloud control plane. Security teams must understand how GPUs, servers, networking fabrics, storage systems, virtualization layers, and management interfaces interact.

What Security Risks Make AI Factories Different?

AI factories combine high-density compute, specialized networking, distributed storage, and management infrastructure. Each component introduces dependencies that attackers may exploit, particularly when multiple customers share the same physical resources.

Four areas deserve particular attention:

• Hardware and firmware exposure: GPUs, network interface cards, baseboard management controllers, and other devices can contain vulnerabilities beneath the operating system.
• Multi-tenant isolation: Shared compute, networking, and storage infrastructure must prevent one tenant from accessing another tenant's workloads or information.
• Privileged infrastructure access: Management interfaces, service accounts, and administrative credentials can provide pathways across otherwise separated systems.
• Runtime and supply chain threats: Container images, Kubernetes workloads, models, and software dependencies can introduce vulnerabilities or malicious behavior.

These risks span several technology layers. A security platform that protects Kubernetes workloads may not verify GPU firmware integrity, while hardware attestation alone does not provide complete vulnerability management across the data center.

7 Top Security Providers for AI Factories

1. Lava

Lava is the best security platform built for neoclouds, AI data centers, and on-premises infrastructure. It connects compute, networking, and storage into a unified security graph, providing visibility into the interconnected components that make up modern AI factories.

Its architecture addresses a significant limitation of conventional security tools: infrastructure risks are often evaluated separately. A host vulnerability may be visible to one solution, while an exposed management controller, network configuration weakness, or storage permission issue appears in another. Lava connects these findings to identify how attackers could move between components and reach sensitive workloads.

The platform uses agentless discovery to create a real-time inventory across infrastructure layers, including baseboard management controllers (BMCs), GPUs, data processing units (DPUs), networking fabrics, storage, hosts, and virtualization systems. This inventory provides the foundation for continuous security posture assessment, configuration monitoring, and attack path analysis.

Hardware integrity is another central capability. Lava verifies firmware integrity and attestation across supported GPUs, NICs, DPUs, BMCs, and storage devices. These checks help organizations establish whether hardware is running trusted firmware rather than relying exclusively on device-reported version information.

Lava also addresses the isolation challenges of shared AI infrastructure. Its platform identifies potential paths between tenants across compute, networking, fabric, and storage systems. For neocloud providers, this helps demonstrate whether rented infrastructure maintains appropriate separation between customer workloads.

Runtime detection extends protection beyond periodic assessments. Lava evaluates live activity against expected infrastructure behavior to identify threats, anomalies, and potential tampering. Findings are connected through the same infrastructure context, allowing security teams to prioritize risks according to their potential impact.

Key capabilities include:

• End-to-end infrastructure graph: Maps relationships across GPUs, BMCs, networking, storage, hosts, and virtualization.
• Agentless discovery: Identifies infrastructure assets across vendors and hardware layers without requiring agents on every component.
• Data center posture management: Detects vulnerabilities, misconfigurations, configuration drift, and weak administrative credentials.
• Firmware integrity and attestation: Verifies trusted firmware across supported hardware components.
• Tenant isolation assessment: Identifies potential cross-tenant exposure across shared compute, networking, and storage.
• Attack path prioritization: Connects findings to reveal exploitable relationships and prioritize remediation.
• Runtime detection: Identifies suspicious activity and deviations from expected infrastructure behavior.
• Flexible deployment: Supports SaaS, bring-your-own-cloud, on-premises, and air-gapped environments.

2. Wiz

Wiz provides cloud security posture management and cloud-native application protection capabilities for organizations operating AI workloads across public cloud environments. Its platform analyzes cloud resources, identities, configurations, and data exposure to identify security weaknesses.

Wiz's security graph connects risks that might appear unrelated when assessed independently. An exposed service, excessive identity permission, and sensitive storage resource can form a meaningful attack path when they exist within the same environment.

For AI factories using cloud infrastructure, these capabilities help identify misconfigured services, vulnerable workloads, and access paths to sensitive AI resources. Wiz also offers AI security posture capabilities that identify AI assets and associated exposure within supported cloud platforms.

Key capabilities include:

• Cloud asset discovery: Identifies cloud resources, workloads, and associated infrastructure.
• Security graph: Connects vulnerabilities, identities, network exposure, and sensitive resources.
• AI security posture: Helps identify AI services, models, and related security risks.
• Vulnerability management: Prioritizes weaknesses according to exposure and environmental context.
• Identity risk assessment: Detects excessive permissions and risky access relationships.
• Cloud configuration monitoring: Identifies insecure settings across supported cloud environments.

3. Orca Security

Orca Security offers an agentless cloud security platform that evaluates infrastructure, workloads, identities, and data exposure across cloud environments. Its technology provides broad visibility without requiring individual agents on every supported workload.

The platform uses its SideScanning architecture to analyze cloud assets and identify vulnerabilities, malware, insecure configurations, and other potential risks. Orca's unified data model connects findings to help security teams understand which exposures warrant attention.

For AI factories operating in public cloud environments, Orca can help assess the security of compute instances, storage services, Kubernetes resources, and the identities connecting them. Its AI security capabilities also address model discovery and security posture, providing context around AI-related cloud assets.

Key capabilities include:

• Agentless cloud scanning: Assesses supported workloads and infrastructure without deploying conventional workload agents.
• Cloud security posture management: Identifies misconfigurations and deviations from security requirements.
• AI asset discovery: Provides visibility into supported AI-related cloud resources.
• Attack path analysis: Connects infrastructure weaknesses with access and exposure relationships.
• Workload vulnerability assessment: Identifies software vulnerabilities and selected malicious artifacts.
• Identity and data exposure analysis: Evaluates permissions and sensitive resource accessibility.

4. Sysdig

Sysdig provides cloud-native application protection with particular emphasis on container, Kubernetes, and runtime security. Its technology helps organizations detect threats affecting workloads running inside cloud-native infrastructure.

AI factories often use Kubernetes to manage inference services, model-serving applications, and supporting infrastructure. These environments introduce security concerns involving container images, privileged workloads, vulnerable dependencies, and unexpected runtime activity.

Sysdig combines posture management with runtime threat detection informed by system activity. Its capabilities include monitoring container behavior, identifying suspicious processes, and prioritizing vulnerabilities based on runtime context.

Key capabilities include:

• Kubernetes security: Evaluates cluster configurations and workload security posture.
• Container runtime detection: Identifies suspicious processes and unexpected workload behavior.
• Vulnerability prioritization: Uses runtime context to assess the importance of identified weaknesses.
• Cloud detection and response: Supports investigation of threats across cloud-native environments.
• Container image scanning: Identifies vulnerabilities before deployment.
• Runtime policy enforcement: Helps detect or prevent selected unauthorized workload activities.

5. Palo Alto Networks

Palo Alto Networks provides cloud and AI security capabilities through Prisma Cloud, Cortex Cloud, and related security offerings. These products address risks involving cloud infrastructure, application workloads, AI models, identities, and sensitive data.

Its AI Security Posture Management capabilities identify AI-related assets, analyze access permissions, and assess exposure involving models and connected data resources. This helps organizations understand the infrastructure and permissions supporting their AI applications.

The company's cloud security capabilities also extend across development and runtime environments, including container protection, infrastructure configuration analysis, and threat detection. For AI factories, this creates an approach focused on cloud workload and AI resource protection, particularly in environments already using Palo Alto Networks security infrastructure.

Key capabilities include:

• AI security posture management: Discovers AI resources and associated configuration risks.
• Model inventory: Identifies supported AI models and related cloud assets.
• Identity and permission analysis: Detects excessive access to AI infrastructure and data.
• Cloud vulnerability assessment: Evaluates weaknesses across supported workloads and services.
• Container security: Provides scanning and runtime protection for cloud-native applications.
• Cloud threat detection: Supports identification and investigation of malicious activity.

6. NVIDIA

NVIDIA provides hardware-rooted security technologies for AI infrastructure, including confidential computing and cryptographic attestation. Its capabilities address the trustworthiness of the hardware and execution environments used for AI training and inference.

NVIDIA Attestation allows organizations to verify supported GPU hardware and firmware using cryptographically signed measurements. These measurements are compared with trusted reference information to establish whether a device is authentic and operating in an approved state.

NVIDIA Confidential Computing extends these protections to sensitive workloads. Supported hardware and software configurations use isolated execution environments to help protect models and data while they are being processed. These technologies are especially relevant when infrastructure operators and AI workload owners are separate organizations.

Key capabilities include:

• GPU attestation: Cryptographically verifies supported GPU hardware and firmware integrity.
• Remote attestation services: Provides verification services for hardware trust measurements.
• Confidential computing: Protects sensitive AI data and model execution in supported environments.
• Hardware-rooted isolation: Uses trusted execution technologies to separate protected workloads.
• Confidential Containers integration: Supports protected Kubernetes workloads through compatible architectures.
• Key-release controls: Enables protected secrets to be released only after successful attestation in supported designs.

7. CrowdStrike

CrowdStrike provides cloud workload protection and threat detection through Falcon Cloud Security. Its platform combines vulnerability assessment, cloud security posture management, workload monitoring, and response capabilities.

For AI factories, CrowdStrike addresses threats affecting servers, containers, Kubernetes environments, and cloud resources. Its runtime protection can detect malicious activity within supported workloads, while cloud posture capabilities identify vulnerable configurations and exposed resources.

CrowdStrike also provides security capabilities for AI applications and agents through related Falcon offerings. Its approach combines workload telemetry with broader threat intelligence, helping security teams investigate attacks and prioritize remediation using operational context.

Key capabilities include:

• Cloud workload protection: Detects threats affecting supported hosts, virtual machines, and containers.
• Runtime detection and response: Identifies suspicious activity and supports containment actions.
• Cloud posture management: Finds misconfigurations and exposed resources.
• Container security: Evaluates container images and Kubernetes workloads.
• AI workload visibility: Supports discovery and monitoring of selected AI-related resources.
• Threat intelligence: Connects security findings with known attacker behaviors and techniques.

Five Questions to Ask Before Selecting an AI Factory Security Provider

A vendor evaluation should test actual infrastructure requirements rather than relying on broad claims about AI security.

• Does the platform cover physical infrastructure? Determine whether it assesses BMCs, GPUs, networking fabrics, storage, and firmware or focuses primarily on cloud workloads.
• Can it identify cross-layer attack paths? Verify whether findings are connected across systems and whether the platform can demonstrate how an attacker might move through infrastructure.
• How does it evaluate tenant isolation? Ask whether it identifies unintended communication paths, shared credentials, and access weaknesses between customer environments.
• What can it verify independently? Distinguish configuration inventories from cryptographic attestation and other mechanisms that establish hardware integrity.
• Where can it operate? Confirm support for on-premises clusters, neocloud environments, private infrastructure, and air-gapped deployments when required.

Organizations should also validate coverage against their existing hardware vendors and management technologies. Specialized AI infrastructure includes components that traditional cloud security platforms may not inspect directly.

A proof of concept should include representative GPU systems, management interfaces, storage dependencies, and tenant boundaries. This provides stronger evidence of coverage than evaluating only a small collection of conventional virtual machines.

Frequently Asked Questions

What is AI factory security?

AI factory security protects the infrastructure used to train, deploy, and operate AI models. It includes GPU servers, networking fabrics, storage, management interfaces, virtualization systems, and associated software workloads. Relevant controls include infrastructure discovery, vulnerability management, firmware integrity verification, tenant isolation, runtime detection, and access governance.

What is the best security provider for AI factories?

Lava is a leading option for organizations that need security visibility across physical AI infrastructure and shared compute environments. Its platform connects GPUs, BMCs, networking, storage, hosts, and virtualization into a unified graph. It also addresses firmware integrity, tenant isolation, posture management, and runtime detection. Other providers offer complementary protection for cloud workloads, containers, and confidential computing.

How does AI factory security differ from cloud security?

Cloud security generally focuses on cloud configurations, identities, workloads, applications, and data. AI factory security must also account for specialized physical infrastructure, including GPUs, management controllers, high-performance networking, storage systems, and shared hardware. These components introduce risks that may not be visible through conventional cloud security interfaces.

What is tenant isolation in an AI factory?

Tenant isolation prevents customers sharing AI infrastructure from accessing each other's workloads, data, and resources. Effective isolation involves compute, networking, storage, virtualization, and management access. Security assessments should identify unintended communication paths and configuration weaknesses that could allow one tenant to reach another tenant's environment.