Benchmarking AI for
Enterprise Security Remediation
Evaluate whether AI models can recommend, execute, and validate security remediation actions across code, cloud, supply chain, Kubernetes, runtime, APIs, and identity.
Why benchmarks matter
Enterprise security has moved beyond detection. Modern AppSec, CSPM, CWPP, and DSPM platforms surface tens of thousands of findings every week. The bottleneck is no longer discovery — it's remediation at scale.
AI agents are now being deployed to triage, prioritize, and even execute remediation. But before security and platform leaders can trust them, they need objective evaluation. Which models actually fix the problem? Which models break production? Which models can produce executable patches?
Remediation Labs benchmarks are designed to answer those questions across every major security remediation domain — with realistic findings, real system context, and rubrics calibrated against human experts.
Benchmark taxonomy
Eight remediation domains spanning the full enterprise security surface — from code to runtime.
Published benchmarks
These benchmarks are live with version v0.1. Model leaderboards and full reports are available on request.
Planned benchmarks
These benchmarks are in active development. Request early-access reports or join the design review.
RL Data for Security Remediation Agents
Remediation Labs generates expert-grade datasets for training and evaluating security remediation agents — combining findings, system context, expert reasoning, ideal answers, scoring rubrics, and validation criteria.
Findings
Real and synthesized security findings sourced from production tools and curated by experts.
System Context
Code, IaC, runtime topology, ownership, and policy context that surrounds each finding.
Expert Reasoning
Step-by-step rationale captured from security and engineering experts during annotation.
Ideal Answers
Reviewed remediation outputs — code patches, config changes, runbooks — ready as gold standard.
Scoring Rubrics
Per-dimension rubrics used to score model responses and train reward models for RLHF.
Validation Criteria
Executable validation tests that determine whether a proposed remediation actually works.
How the benchmarks run
Every benchmark follows the same six-stage evaluation flow — deterministic, reproducible, and calibrated against human experts.
Raw security finding from production-grade tools
Structured prompt including finding + optional context
Candidate model generates its proposed remediation
Rubric-driven judge calibrated against expert labels
Per-dimension scores aggregated into a final result
Public model ranking, versioned per benchmark release
Get the benchmarks, the data, or both.
Request a benchmark report, evaluate your own models, or partner with us on remediation datasets.
Or email us at info@remediationlabs.com