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The real test of AI-native code analysis: is your security debt shrinking? | Blog | Endor Labs

Endor Labs argues that AI-native code analysis tools should be judged by whether they actually shrink security debt, not by how many findings or patches they generate. It cites data showing coding agents increase code duplication and reduce review rigor (GitClear, DORA, Faros AI), that AI-generated findings are often false positives (Comcast: 44% of validated critical/high findings), and that agents can pass functional tests while shipping insecure code (Agent Security League: 84.4% correct vs 7.8% secure), then lays out six requirements for analysis tools (deterministic evidence, impact-ranked findings, remediation-grade context, fix validation, shift-left coverage, independent measurement) and six questions to ask vendors, tying back to Endor Labs' own AURI product.