# Acalvio ShadowPlex vs OpenCanary: which do AI models recommend for deception, October 2026

IT AI Recommendation Index, October 2026 Edition, Deception technology. Four of fourteen models named Acalvio ShadowPlex first on the direct prompt; zero named OpenCanary. Page: https://it-ai-index.com/security/deception-technology/acalvio-shadowplex-vs-opencanary/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Acalvio ShadowPlex | 9% | #2 of 11 | 14% | 42 | 14 of 14 |
| OpenCanary | 4% | #4 of 11 | 0% | 17 | 10 of 14 |

## The direct prompt, model by model

- Perplexity Sonar: acalvio shadowplex first (first choices: Acalvio ShadowPlex) (alternatives: Fortinet FortiDeceptor, Thinkst Canary)
- Grok 4.1 Fast: acalvio shadowplex first (first choices: Acalvio ShadowPlex) (alternatives: Fortinet FortiDeceptor, Thinkst Canary)
- Mistral Small: acalvio shadowplex first (first choices: Acalvio ShadowPlex, Attivo Networks ThreatDefend)
- MiniMax M2.5: acalvio shadowplex first (first choices: Acalvio ShadowPlex, Illusive Networks)
- GPT-5.4 mini: neither first, one named (first choices: Thinkst Canary) (alternatives: Acalvio ShadowPlex)
- DeepSeek V4 Flash: neither first, one named (first choices: Tracebit) (alternatives: Acalvio ShadowPlex, Fidelis Deception, FortiDeceptor)
- GLM 4.7 FlashX: neither first, one named (first choices: Rapid7 InsightIDR) (alternatives: Acalvio ShadowPlex, Fortinet FortiDeceptor)
- GPT-6 Luna: neither first, one named (first choices: Tracebit) (alternatives: Acalvio ShadowPlex, Thinkst Canary, Zscaler Deception)
- Muse Glimmer 30B: neither first, one named (first choices: Fortinet FortiDeceptor, Rapid7 Incident Command) (alternatives: Acalvio ShadowPlex, Proofpoint, Thinkst Canary)
- Claude Haiku 4.5: neither named (first choices: Deceptive Bytes) (alternatives: Acalvio 360 Deception, Illusive, Thinkst Canary)
- Gemini 3.5 Flash: neither named (first choices: Thinkst Canary) (alternatives: SentinelOne Singularity Hologram, Tracebit)
- Llama 4 Maverick: neither named
- Qwen 3.7 Flash: neither named (first choices: Honeypot.io) (alternatives: Attivo Networks, Cymulate, Microsoft Defender XDR)
- Kimi K2: neither named (first choices: Thinkst Canary) (alternatives: TrapEye, Trapster)

## What the models said about Acalvio ShadowPlex

- "look out for architectural red flags when evaluating active deception platforms (such as those from Acalvio, CounterCraft, or Fidelis Security)" (Gemini 3.5 Flash, negative prompt, soft negative)
- "massive enterprise "deception grids" (such as Acalvio, Fidelis, or SentinelOne Attivo) are highly effective, they are cost-prohibitive" (Gemini 3.5 Flash, budget prompt, soft negative)
- "this complexity often translates to higher pricing and more required administrative upkeep" (Gemini 3.5 Flash, scale prompt, soft negative)
- "Consistently ranked as a leader in GigaOm’s Radar Report (2023–2024)... stands out for its centralized Deception Farm architecture, cloud-native design, and AI-driven countermeasures" (GLM 4.7 FlashX, comparative prompt, first choice)
- "I recommend requesting demos from Illusive Networks and Acalvio specifically, as they appear most focused on the mid-market segment." (MiniMax M2.5, direct prompt, first choice)
- "If you want enterprise-scale automation with very low operational burden, Acalvio is the recognized leader." (DeepSeek V4 Flash, comparative prompt, first choice)

## What the models said about OpenCanary

- "Start with Canarytokens.org (free) + OpenCanary (free)" (Kimi K2, budget prompt, first choice)
- "For a genuinely limited budget, start with OpenCanary." (GPT-6 Luna, budget prompt, first choice)
- "Another strong open-source option is OpenCanary, also from Thinkst, which is free and supports multiple protocols." (Mistral Small, paraphrase 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.