Azure AI Content Safety
Overview
Product details compiled from public sources, each with a citation.
- Vendor
- Microsoft1
- Description
- Microsoft content-safety service for generative AI that uses Prompt Shields to detect and block jailbreaks and indirect prompt injection, and filters prompts and responses across harm categories.1
- Deployment
- SaaS1
- Status
- Active1
- Compliance
- SOC 2 Type 2, ISO 27001, ISO 27017, ISO 27018, ISO 277013 (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 | Protect | Detect | Coverage | Source |
|---|---|---|---|---|
| Runtime AI Data | Primary | 2 |
Framework Relevance
These frameworks include controls relevant to the asset classes Azure AI Content Safety defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Microsoft implements these controls or is certified against them.
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| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | Runtime AI Data | Prompts (runtime); inference data |
| CSA AI Controls Matrix | Runtime AI Data | Data Security and Privacy Lifecycle Management; Application and Interface Security |
| ISO 42001 | Runtime AI Data | A.7 Data for AI systems; A.8 Information for interested parties |
| Google SAIF | Runtime AI Data | Expand AI red-teaming; runtime input and output safety; prompt defense |
| SANS Critical AI Security Guidelines | 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 | Runtime AI Data | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0056 Extract LLM System Prompt |
| OWASP AI Exchange | Runtime AI Data | Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering |
| OWASP LLM Top 10 | Runtime AI Data | LLM01 Prompt Injection; LLM02 Sensitive Information Disclosure; LLM08 Vector and Embedding Weaknesses; LLM05 Improper Output Handling |
| OWASP Agentic Security Top 10 | Runtime AI Data | ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs) |
Provenance
Last sourced 2026-06-09.
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Sources
- Azure AI Content Safety
- Prompt Shields in Azure AI Content Safety
- “If a prompt or document is detected as likely to generate inappropriate educational content, the shield blocks it and suggests alternative, safe inputs.”
- Microsoft Azure SOC 2 compliance offering
- “see the Azure SOC 2 Type 2 attestation report or Cloud services in audit scope”
Changelog
-
Added to the catalog from the Azure AI Content Safety documentation.
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