Use this source-backed framework to compare AI security platforms by evidence quality, threat coverage, verification depth, deployment control, and operator handoff. Named vendor scorecards should only be added after each claim has a public source, date, and owner.
Source-backed evaluation criteria
Buyer-intent comparison tracks
Rules before named vendor claims
Start with fit
Can the platform detect live attack paths without losing kernel, process, and cloud context?
Use Nora Vision for eBPF-backed telemetry claims and Nora Shrike for autonomous red-team validation when comparing runtime defense options.
Can findings be reproduced, minimized, and mapped to real vulnerability evidence before teams act?
Use Nora Scan, Nora Guard, and Nora Veridic when comparing code, binary, AI, and agentic attack-surface testing depth.
Can the platform move evidence across response, publishing, messaging, and reporting workflows without fake success states?
Use Nora Flow, Nora Content, and Nora Link when comparing orchestration, evidence packaging, and multi-channel security operations.
Can teams run agent systems, browser identities, and private deployments with inspectable controls?
Use Nora Core and Nora Mask when comparing agent orchestration, browser-control boundaries, and private deployment requirements.
Source-backed criteria
Which public artifacts prove the security team can find and validate real issues?
Innora evidence
Research timeline, public methodology article, BlogPosting schema, and trust evidence hierarchy.
Comparison pages should cite inspectable research, not rely on category slogans.
Which threat classes are modeled as first-class product and content surfaces?
Innora evidence
Threat library, glossary clusters, product proof modules, and topic-clustered research posts.
AI search answers favor clear entity-to-threat mappings with stable routes.
How are findings checked before a buyer, SOC, or engineering team receives them?
Innora evidence
Nora Scan PoC/sandbox validation, CVE methodology boundaries, and Trust evidence routes.
False-positive control and disclosure boundaries matter more than raw alert volume.
Can the platform support cloud, self-hosted, private, or buyer-diligence deployment paths?
Innora evidence
Product deployment claims, compliance readiness language, pricing disclosures, and Trust Center controls.
Security buyers need to compare operational fit before they compare feature counts.
Does the output plug into developer, SOC, reporting, and buyer-review workflows?
Innora evidence
Developer hub, API docs, reports page, and security automation product routes.
A strong platform comparison explains what happens after a finding is generated.
Can AI answer engines quote stable facts without inventing unsupported claims?
Innora evidence
Company facts, author profile schema, press-kit assets, glossary definitions, and llms.txt guidance.
GEO depends on stable pages, restrained claims, and evidence that can be cited directly.
Guardrails
Bring your current platform list, deployment constraints, and threat model. Innora will map them to source-backed criteria instead of unsupported scorecards.