Expert datasets for
security remediation agents
Remediation Labs generates expert remediation datasets including findings, context, expert reasoning, ideal answers, scoring rubrics, and validation criteria — purpose-built for training and evaluating security remediation AI.
What's in the dataset
Each remediation record contains six layers of expert-curated data.
Findings
Real and synthesized security findings from production-grade tools, curated and de-duplicated by domain experts.
System Context
Source code, IaC, runtime topology, dependency graph, ownership, and policy context that surrounds each finding.
Expert Reasoning
Step-by-step rationale captured from security and engineering experts during annotation, including trade-off analysis.
Ideal Answers
Reviewed remediation outputs — code patches, config changes, runbook steps — ready as gold standard for training.
Scoring Rubrics
Per-dimension rubrics used to score model responses and train reward models for RLHF and DPO pipelines.
Validation Criteria
Executable tests that determine whether a proposed remediation actually resolves the issue without regressions.
How teams use RL Data
From pre-training to evaluation, the same dataset supports the full AI development lifecycle.
Pre-training & Fine-tuning
Use the dataset to train remediation-specialized models or fine-tune general-purpose LLMs for security domains.
RLHF / DPO Reward Models
Train preference models from expert-ranked remediation outputs and rubric scores.
Internal Eval Harnesses
Drop into your evaluation pipelines to score in-house models, agents, or fine-tunes against expert baselines.
Custom Domain Datasets
Commission proprietary datasets covering your specific tools, stacks, and remediation workflows.
Domain coverage
Every Remediation Labs benchmark has a corresponding dataset that can be licensed or extended.
Build remediation AI on a real foundation.
License our datasets, partner on proprietary corpora, or evaluate your models against expert baselines.
Or email us at info@remediationlabs.com