ISO/IEC 42001

AI Cloud Visibility provides discovery and inventory context for AI workloads, models, data, and users.

AI workload, model, data, and user inventory can contribute to identifying AI-system resources.

Security Command Center discovers and inventories AI assets across agents, data, models, applications, platforms, and infrastructure.

AuditaAI assessment: AI asset inventory can contribute evidence for managing AI-system resources.

AI Discovery scans cloud accounts, repositories, endpoints, and pipelines to detect AI models and associated agents, and associates assets with ownership, sensitivity, and risk metadata.

AI asset discovery and ownership metadata can contribute to identifying and managing AI-system resources.

AI Access Security discovers and categorizes GenAI applications, agents, marketplace plugins, usage, and users.

AuditaAI assessment: discovering GenAI applications, agents, plugins, and usage can contribute evidence for managing AI-system resources.

Zscaler AI Security discovers and maps AI applications, models, MCP servers, development tools, data pipelines, and related risks.

AuditaAI assessment: AI asset mapping can contribute evidence for managing AI-system resources.

Security Command Center assesses interconnected AI risks and prioritizes high-risk issues using posture analysis and virtual red teaming.

AuditaAI assessment: posture analysis and virtual red teaming can contribute technical evidence to an organization's AI-system impact assessment.

Prisma AIRS simulates real-world attacks against single-agent and multi-agent AI systems to identify weaknesses before production.

AuditaAI assessment: adversarial testing can contribute technical evidence to an organization's AI-system impact assessment.

Zscaler AI Security runs configurable attack simulations to identify AI vulnerabilities and provide remediation guidance before and during deployment.

AuditaAI assessment: attack simulation can contribute technical evidence to an organization's AI-system impact assessment.

Chainguard Factory applies SHA-pinned source inputs, isolated SLSA L3 build controls, cryptographic signing, and reproducibility checks when producing artifacts.

Continuous secure artifact rebuild and release controls contribute to AI system life-cycle control context.

Model Armor scans prompts and responses for prompt injection and jailbreak content and can return a block verdict when a violation is detected.

AuditaAI assessment: runtime prompt and response safeguards can contribute evidence for AI life-cycle controls; they do not establish ISO/IEC 42001 conformity.

Prisma AIRS verifies agent identity and enforces real-time security controls for agent actions.

AuditaAI assessment: real-time controls for agent actions can contribute technical evidence for AI life-cycle controls.

Zscaler AI Security runtime protection blocks prompt injection, data poisoning, and malicious URLs in AI interactions.

AuditaAI assessment: runtime attack blocking can contribute technical evidence for AI life-cycle controls.

Sensitive Data Protection classifies and de-identifies sensitive content used for model training, tuning, and generative AI prompts and responses.

AuditaAI assessment: classifying and de-identifying AI training and interaction data can contribute evidence for AI-data controls.

AI Access Security classifies sensitive content inline and blocks sensitive text and file transfers to GenAI applications.

AuditaAI assessment: classifying and blocking sensitive transfers can contribute evidence for protecting data used by AI systems.

Portkey automatically redacts sensitive data from requests before sending them to an LLM.

AuditaAI assessment: pre-request PII redaction can contribute evidence for protecting data used by AI systems.

Zscaler Data Security inspects AI usage and prompts and can block risky access or enforce prompt DLP to prevent data loss.

AuditaAI assessment: prompt DLP can contribute evidence for protecting data used by AI systems.

Wiz AI-BOM continuously inventories AI models, datasets, frameworks, software dependencies, identities, access paths, and infrastructure.

AuditaAI assessment: inventorying models, frameworks, and dependencies can contribute evidence for AI-related third-party risk management.

AI Supply Chain Security analyzes model architectures, layers, weights, and artifacts for tampering or anomalies and tracks model lineage, origin, and licensing.

Model provenance and vendor-model testing can contribute to third-party AI relationship management.

Prisma AIRS scans third-party models for tampering, malicious scripts, and deserialization risks.

AuditaAI assessment: scanning third-party models can contribute evidence for managing AI-related third-party relationships.

Koi AES evaluates code differences and behavioral shifts in real time to identify software supply-chain risk.

AuditaAI assessment: identifying supply-chain shifts can contribute technical evidence for AI-related third-party risk management.