# IRI FieldShield vs K2View Test Data Management: which do AI models recommend for data masking, October 2026

IT AI Recommendation Index, October 2026 Edition, Data masking and test data management. Three of fourteen models named IRI FieldShield first on the direct prompt; one named K2View Test Data Management. Page: https://it-ai-index.com/it-data/data-masking-and-test-data/iri-fieldshield-vs-k2view-test-data-management/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| IRI FieldShield | 9% | #3 of 10 | 0% | 21 | 10 of 14 |
| K2View Test Data Management | 4% | #6 of 10 | 4% | 23 | 13 of 14 |

## The direct prompt, model by model

- Claude Haiku 4.5: iri fieldshield first (first choices: IRI FieldShield) (alternatives: DATPROF, Informatica Cloud Data Masking, Oracle Data Masking and Subsetting, Tonic.ai)
- DeepSeek V4 Flash: iri fieldshield first (first choices: IRI FieldShield) (alternatives: DATPROF, K2View Test Data Management, Microsoft SQL Server)
- Kimi K2: iri fieldshield first (first choices: IRI FieldShield) (alternatives: DataMasque, K2View Test Data Management, Satori Data Security Platform, Tonic.ai)
- GPT-5.4 mini: k2view test data management first (first choices: K2View Test Data Management) (alternatives: Broadcom, HushHush Data Masking Components, Oracle Data Safe)
- Perplexity Sonar: neither first, one named (first choices: Oracle Data Masking and Subsetting) (alternatives: IRI FieldShield, K2View Test Data Management)
- GPT-6 Luna: neither first, one named (first choices: Tonic Structural) (alternatives: K2View Test Data Management, Perforce Delphix, Redgate Data Masker)
- Muse Glimmer 30B: neither first, one named (first choices: Tonic.ai) (alternatives: Camouflage, Delphix, IRI FieldShield)
- Gemini 3.5 Flash: neither named (first choices: Tonic.ai) (alternatives: ALTR, Accutive Data Discovery & Masking, Immuta, Satori Cyber)
- Grok 4.1 Fast: neither named (first choices: Oracle Data Masking and Subsetting, Tonic.ai) (alternatives: DATPROF, Delphix, Microsoft SQL Data Masking)
- Mistral Small: neither named (first choices: Oracle Data Masking and Subsetting)
- Llama 4 Maverick: neither named (first choices: Oracle Data Masking and Subsetting) (alternatives: IBM InfoSphere Optim Data Privacy, Informatica Data Privacy Tool)
- Qwen 3.7 Flash: neither named (first choices: Armatix) (alternatives: AWS Data Sync / AWS Glue, GoodData, GreatMasking, Informatica Intelligent Data Management Cloud, Microsoft Purview)
- GLM 4.7 FlashX: neither named (first choices: Tonic.ai) (alternatives: Delphix, VGS Platform)
- MiniMax M2.5: neither named (first choices: Delphix, Oracle Data Masking and Subsetting) (alternatives: DataSunrise, IBM InfoSphere Optim)

## What the models said about IRI FieldShield

- "IRI FieldShield masks structured data across databases and files... offers strong coverage at a mid-market price" (Claude Haiku 4.5, direct prompt, first choice)
- "IRI FieldShield is widely considered the best value option for budget-conscious companies." (Claude Haiku 4.5, budget prompt, first choice)
- "IRI FieldShield is the strongest low-cost commercial option mentioned in the results" (Perplexity Sonar, budget prompt, first choice)

## What the models said about K2View Test Data Management

- "powerful, well-supported, and expensive... designed for large organizations with dedicated data engineering teams" (Muse Glimmer 30B, direct prompt, soft negative)
- "Best overall for modern, cross-system masking + synthetic data: K2view" (GPT-5.4 mini, direct prompt, first choice)
- "K2View Test Data Management is recommended for mid-sized B2B companies" (Llama 4 Maverick, paraphrase prompt, first choice)
- "Go with K2view if your QA team struggles to sync and mask test data that spans mainframes, cloud databases, and APIs" (Gemini 3.5 Flash, comparative prompt, alternative)

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.