Overview
Product details compiled from public sources, each with a citation.
- Description
- Zero-trust platform that uncovers shadow AI, classifies and moderates AI prompts and responses inline, and enforces DLP to block sensitive data from leaving for generative-AI apps and tools.2
- Deployment
- SaaS2
- Status
- Active2
- Compliance
- SOC 2 Type 2, SOC 3, ISO 27001, ISO 27017, ISO 27018, ISO 27701, CSA STAR Level 2, HITRUST, HIPAA, GDPR1 (company-level, see Methodology)
Matrix Coverage
Where this product defends, by asset class and NIST CSF function. The Coverage column shows whether each asset is Primary, Secondary, or Adjacent to what the product does. The table omits empty rows and columns.
| Asset class | Identify | Protect | Detect | Coverage | Source |
|---|---|---|---|---|---|
| AI Traffic | Protect: Not covered | Detect: Not covered | Primary | 2 | |
| AI Model | Identify: Not covered | Protect: Not covered | Secondary | 3 | |
| Runtime AI Data | Primary | 2 |
Framework Relevance
These frameworks include controls relevant to the asset classes Zscaler defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Zscaler implements these controls or is certified against them.
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| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | AI Traffic | AI data flows; APIs; inference endpoints (traffic side); model registries and dataset sources |
| AI Model | Models; Algorithms (model configuration) | |
| Runtime AI Data | Prompts (runtime); inference data | |
| CSA AI Controls Matrix | AI Traffic | Infrastructure Security; Interoperability and Portability |
| AI Model | Model Security; Governance, Risk and Compliance | |
| Runtime AI Data | Data Security and Privacy Lifecycle Management; Application and Interface Security | |
| ISO 42001 | AI Traffic | A.8 Information for interested parties; A.9 Use of AI systems; A.10 Third-party and customer relationships |
| AI Model | A.6 AI system life cycle; A.10 Third-party and customer relationships; A.5 Assessing impacts of AI systems | |
| Runtime AI Data | A.7 Data for AI systems; A.8 Information for interested parties | |
| Google SAIF | AI Traffic | Harden and monitor infrastructure; network-level access and egress controls |
| AI Model | Protect the AI model; ensure model integrity, provenance, and weight security | |
| Runtime AI Data | Expand AI red-teaming; runtime input and output safety; prompt defense | |
| SANS Critical AI Security Guidelines | AI Traffic | Conventional Security Controls (authenticate and control access to inference APIs; API key management); Model I/O Handling (rate limiting; egress output filtering); Monitoring (interaction and API-usage logging) |
| AI Model | Conventional Security Controls (protect model parameters with least privilege, encryption at rest, runtime obfuscation, and trusted execution environments); Data/Model Engineering Controls (adversarial training; alignment and fine-tuning); AI Supply Chain Management (public-model caution; transfer-attack exposure) | |
| Runtime AI Data | Model I/O Handling (sanitize, validate, and filter inputs and outputs; segregate user and system prompts; multilayered prompt-injection defense); Conventional Security Controls (protect augmentation and RAG data with vector-store access controls and validation); Data Minimization and Obfuscation (limit sensitive prompt content; context-window management); Limit Model Behavior (AI guardrails) | |
| MITRE ATLAS | AI Traffic | AML.T0057 LLM Data Leakage; AML.T0024 Exfiltration via AI Inference API (network-side observation) |
| AI Model | AML.T0043 Craft Adversarial Data; AML.T0024 Exfiltration via AI Inference API (subtechniques: AML.T0024.001 Invert AI Model and AML.T0024.002 Extract AI Model); AML.T0018 Manipulate AI Model (integrity and backdoor) | |
| Runtime AI Data | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0056 Extract LLM System Prompt (system prompt leaks as inference data; the AI Coding and Orchestration Tools row owns it as configuration) | |
| OWASP AI Exchange | AI Traffic | Runtime threats: data leakage via AI egress; network-level access control gaps |
| AI Model | Development-time and runtime model threats: model inversion, extraction, evasion, poisoning | |
| Runtime AI Data | Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering | |
| OWASP LLM Top 10 | AI Traffic | LLM10 Unbounded Consumption (cost and rate control); shadow AI egress and output handling |
| AI Model | LLM03 Supply Chain; LLM04 Data and Model Poisoning; LLM09 Misinformation | |
| Runtime AI Data | LLM01 Prompt Injection; LLM02 Sensitive Information Disclosure; LLM08 Vector and Embedding Weaknesses; LLM05 Improper Output Handling | |
| OWASP Agentic Security Top 10 | AI Traffic | ASI07 Insecure Inter-Agent Communication; ASI02 Tool Misuse and Exploitation (egress and tool-invocation scope); ASI04 Agentic Supply Chain Vulnerabilities (MCP and tool-registry trust) |
| AI Model | ASI04 Agentic Supply Chain Vulnerabilities (model provenance, weights, and dynamic loading) | |
| Runtime AI Data | ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs) |
Provenance
Last sourced 2026-10-03.
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Sources
- Zscaler Compliance
- “ISO 27001, ISO 27701, SOC 2, and various others”
- Zscaler AI Access Security
- “Block the loss of sensitive data in prompts with powerful inline DLP across 100+ DLP dictionaries like Source Code, PII, PCI, PHI, and more.”
- “Discover which AI apps are being used and by which users.”
- Zscaler AI Security
- “AI red teaming tests and secures AI systems, especially large language models (LLMs), by simulating real-world attacks and vulnerabilities like prompt injection or data poisoning.”
Changelog
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Corrected matrix coverage (erratum): added AI app discovery to the AI Traffic row and refreshed a source quote.
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Enriched from the Zscaler AI security documentation.
Found an error? Corrections are welcome. Suggest an edit.