FortiAIGate
Overview
Product details compiled from public sources, each with a citation.
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 |
|---|---|---|---|---|
| AI Gateways and Routers | Primary | 1 | ||
| AI Model | Detect: Not covered | Secondary | 1 | |
| Runtime AI Data | Primary | 1 |
Framework Relevance
These frameworks include controls relevant to the asset classes FortiAIGate defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Fortinet implements these controls or is certified against them.
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| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | AI Gateways and Routers | 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 Gateways and Routers | 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 Gateways and Routers | 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 Gateways and Routers | 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 Gateways and Routers | 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 Gateways and Routers | 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 | |
| OWASP AI Exchange | AI Gateways and Routers | 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 Gateways and Routers | 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 Gateways and Routers | 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 2026-06-10.
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Sources
- FortiAIGate product page
- “FortiAIGate provides protection, visibility, and cost control.”
- “A context-aware DLP engine in the critical path monitors inbound and outbound traffic to prevent extraction of PII and sensitive data, and model extraction.”
- “Accelerate AI adoption and enforce security guardrails on large language models.”
Changelog
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Added to the catalog from the Fortinet documentation.
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