# Wing Security vs Obsidian Security: which do AI models recommend for SSPM, October 2026

IT AI Recommendation Index, October 2026 Edition, SaaS security posture management. Nine of fourteen models named Wing Security first on the direct prompt; zero named Obsidian Security. Page: https://it-ai-index.com/security/saas-security-posture-management/wing-security-vs-obsidian-security/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Wing Security | 30% | #1 of 11 | 3% | 31 | 12 of 14 |
| Obsidian Security | 4% | #5 of 11 | 9% | 32 | 13 of 14 |

## The direct prompt, model by model

- Claude Haiku 4.5: wing security first (first choices: Wing Security) (alternatives: CrowdStrike Shield, Obsidian Security, Spin.AI)
- Gemini 3.5 Flash: wing security first (first choices: Wing Security) (alternatives: CrowdStrike Falcon Shield, Nudge Security, Obsidian Security)
- Perplexity Sonar: wing security first (first choices: Wing Security) (alternatives: Obsidian Security, Reco, Spin.AI)
- Grok 4.1 Fast: wing security first (first choices: Wing Security) (alternatives: CrowdStrike Falcon Shield, Spin.AI)
- Qwen 3.7 Flash: wing security first (first choices: Wing Security) (alternatives: Adaptive Shield, Grip Security)
- Kimi K2: wing security first (first choices: Wing Security) (alternatives: Obsidian Security, Valence Security)
- GLM 4.7 FlashX: wing security first (first choices: Wing Security) (alternatives: CrowdStrike Falcon Shield)
- MiniMax M2.5: wing security first (first choices: Wing Security) (alternatives: AppOmni, BetterCloud, CrowdStrike, Obsidian Security, SpinOne)
- Muse Glimmer 30B: wing security first (first choices: Wing Security) (alternatives: CrowdStrike Falcon Shield, Spin.AI / SpinOne)
- DeepSeek V4 Flash: neither first, one named (first choices: Adaptive Shield) (alternatives: AppOmni, Nudge Security, Wing Security)
- GPT-5.4 mini: neither named (first choices: AppOmni)
- Mistral Small: neither named (first choices: Grip Security, Wing) (alternatives: Nudge Security)
- Llama 4 Maverick: neither named
- GPT-6 Luna: neither named (first choices: Valence Security) (alternatives: CrowdStrike Falcon Shield)

## What the models said about Wing Security

- "Less depth than Nudge or Obsidian" (Kimi K2, paraphrase prompt, soft negative)
- "Wing Security stands out as particularly well-suited for mid-market teams due to its balance of affordability, ease of deployment, and practical SaaS discovery capabilities." (Claude Haiku 4.5, direct prompt, first choice)
- "Vendors like Wing Security are highly popular for mid-sized organizations because they focus heavily on ease-of-use, low operational overhead" (Gemini 3.5 Flash, scale prompt, first choice)
- "the strongest single pick is Wing Security if you want the best balance of mid-market fit, value, and SaaS discovery" (Perplexity Sonar, direct prompt, first choice)

## What the models said about Obsidian Security

- "Avoid: Enterprise-focused platforms like ... Obsidian Security" (DeepSeek V4 Flash, budget prompt, hard negative)
- "No built-in remediation or enforcement — identifies threats but doesn't automate response" (Kimi K2, negative prompt, soft negative)
- "Stronger for enterprise budgets; adds UEBA/behavioral analytics" (DeepSeek V4 Flash, direct prompt, soft negative)
- "Obsidian Security is the strongest budget-friendly starting point because it advertises a $0/month free tier for up to 1,000 users" (GPT-5.4 mini, budget prompt, first choice)
- "Obsidian is currently the standout SaaS vendor for small teams due to its genuinely useful free tier." (GLM 4.7 FlashX, budget prompt, first choice)
- "Choose AppOmni or Obsidian if you want deep, enterprise-grade SSPM across many SaaS applications." (Perplexity Sonar, comparative prompt, first choice)

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.