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Original Research

AIsecurityPlatform.com publishes original research about the AI security category. We collect data ourselves, document methodology, and refresh each piece on a published cadence. Our goal is the same as everywhere else on the site: give buyers ground truth they can audit.

Forthcoming research

  • AI Security Methodology Transparency Benchmark (2026) — a planned scoring of how clearly each vendor documents what their product does and how it is tested.
  • AI Security Compliance Mapping Benchmark (2027) — a planned scoring of vendor mappings to NIST AI RMF, ISO/IEC 42001, EU AI Act, and OWASP LLM/Agentic Top 10
  • AI Security Latency Benchmark (2027) — a planned independent latency test across runtime LLM security products under controlled load.

Published research

AI Security Pricing Transparency Benchmark 2026 — We checked 25 AI security vendors for public pricing and scored each on a four-tier transparency scale. Read at /research/ai-security-pricing-transparency-2026/.

How we publish

Each research piece is published with full methodology, a public dataset where licensing allows, and an annual refresh window. Vendors can request re-evaluation if their public posture changes between refreshes.

Pitches and feedback: editorial@aisecurityplatform.com.