This model is a 3-model ensemble for ordinal tier classification of AI security control pairs across multiple frameworks. Given a pair of security controls (one from each framework), it assigns one of four ordinal relationship tiers:

Tier	Meaning
UNRELATED	No meaningful overlap
PARTIAL	Some topical overlap but different scope or depth
RELATED	Substantial overlap; controls address similar threats
EQUIVALENT	Near-identical coverage; either control satisfies the other

Built by Rock Lambros at the University of Denver as part of dissertation research on AI security framework alignment.

Intended Use

Primary use: Scoring cross-framework control mappings for AI security frameworks (NIST AI RMF, OWASP AI, MITRE ATLAS, ISO/IEC 42001, EU AI Act, ENISA, NIST SP 800-218A, NIST CSF 2.0, and others).

Out of scope: General natural language inference (NLI), non-security domains, or any application where automated output replaces human judgment without review.

Model Architecture

The ensemble combines three transformer-based encoders, each with a 4-class linear classification head:

Component	Base Model	Parameters	Embedding Dim
RoBERTa-large	`roberta-large`	355M	1024-dim CLS
DeBERTa-v3-base	`microsoft/deberta-v3-base`	86M	768-dim CLS
BGE-large-v1.5	`BAAI/bge-large-en-v1.5`	335M	1024-dim CLS

Combination strategy: Softmax averaging of the three models' output probability distributions. No learnable parameters in the combination layer — purely post-hoc ensemble.

Each head is a single linear layer (Linear(hidden_size, 4)) trained independently per model.

Training Details

Dataset: 5,920 expert-labeled control pairs drawn from 9 AI security frameworks. Labels were assigned by Rock Lambros using a structured annotation rubric.

Data cleaning: Mapping-level deduplication removed 56% contamination (cross-split leakage) from the raw dataset before train/val/test splits were finalized.

Loss functions: Ordinal-aware losses used during training:

KL divergence against soft ordinal label distributions
CORN (conditional ordinal ranking net) loss
Focal loss with class-balanced weighting

Compute: 3x NVIDIA H100 80GB SXM GPUs, BF16 mixed precision, ~4 hours total wall-clock time.

Hyperparameters: AdamW optimizer, linear warmup + cosine decay, per-model learning rates tuned via Optuna (tracked in Sacred).

Evaluation Results

Overall Metrics (179-pair test set)

Metric	Value
Exact Accuracy	79.9%
Adjacent Accuracy (±1 tier)	92.2%
Macro F1	0.558

Per-Class F1 Scores

Class	F1	Support
UNRELATED	0.928	~87
PARTIAL	0.526	~55
RELATED	0.378	~30
EQUIVALENT	0.400	7

Conformal Prediction Coverage

Conformal prediction sets (calibrated at 90% target coverage) achieve >90% empirical coverage for all four classes on the held-out test set.

Limitations and Biases

Small test set: 179 pairs total, with only 7 EQUIVALENT examples. Per-class metrics for EQUIVALENT are high-variance.
English-only: All frameworks and controls are in English; no multilingual support.
Framework coverage: Trained on 9 specific AI security frameworks. Performance on out-of-distribution frameworks (e.g., sector-specific CISA guidance) is unknown.
Expert labeler bias: A single expert (Rock Lambros) labeled all training data; inter-annotator agreement was not formally measured.
Ordinal collapsing: The PARTIAL/RELATED boundary is the hardest to learn (lowest F1s), reflecting genuine annotation ambiguity in the middle tiers.

Ethical Considerations

No personal data of any kind was used in training or evaluation.
All training data consists of publicly available security framework control text.
This is a security-domain tool; outputs should be treated as advisory scores requiring human review before use in compliance or procurement decisions.
The model does not generate free text and cannot be used for content generation or harmful repurposing.

Environmental Impact

Resource	Value
GPUs	3x NVIDIA H100 80GB SXM
Estimated TDP per GPU	~700W
Training wall time	~4 hours
Estimated energy	~8.4 kWh
Estimated CO2e	~3.4 kg (US average grid)

Estimate assumes 400g CO2/kWh US average grid intensity. Actual emissions may differ based on datacenter location and energy mix.

How to Use

python

import torch
from transformers import AutoTokenizer, AutoModel

# Load one encoder (RoBERTa shown; repeat for deberta_base and bge)
encoder = AutoModel.from_pretrained("rockCO78/ai-security-crosswalk-vfinal/roberta/encoder")
tokenizer = AutoTokenizer.from_pretrained("rockCO78/ai-security-crosswalk-vfinal/roberta/encoder")

# Load the classification head
head_state = torch.load("roberta/head.pt", map_location="cpu")
head = torch.nn.Linear(1024, 4)
head.load_state_dict(head_state)
head.eval()
encoder.eval()

LABELS = ["UNRELATED", "PARTIAL", "RELATED", "EQUIVALENT"]

def predict_pair(control_a: str, control_b: str) -> dict:
    """Predict ordinal relationship tier for a control pair."""
    text = f"{control_a} [SEP] {control_b}"
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        hidden = encoder(**inputs).last_hidden_state[:, 0, :]  # CLS token
        logits = head(hidden)
        probs = torch.softmax(logits, dim=-1).squeeze()
    return {label: round(prob.item(), 4) for label, prob in zip(LABELS, probs)}

# Example usage
result = predict_pair(
    "Implement data minimization for AI training datasets",
    "Limit collection of personal data to what is necessary for the stated purpose"
)
print(result)
# For full ensemble, average softmax outputs from all three models

How to Train on New Frameworks

To fine-tune or extend this model on additional AI security frameworks:

Collect control pairs. Enumerate all cross-framework control combinations (or sample strategically using embedding similarity pre-filtering).
Label using the rubric. Assign UNRELATED / PARTIAL / RELATED / EQUIVALENT using the annotation guide in scripts/ (see predict_edges.py for label definitions).
Deduplicate at mapping level. Remove any pairs where the same mapping appears in both train and test splits to prevent leakage.
Fine-tune each encoder independently. Use ordinal losses (KL soft-label, CORN, or focal) rather than standard cross-entropy to preserve tier ordering.
Evaluate with adjacent accuracy. Exact accuracy understates model quality for ordinal tasks; report adjacent accuracy (±1 tier) alongside macro F1.

Citation

bibtex

@misc{lambros2026crosswalk,
  author       = {Lambros, Rock},
  title        = {AI Security Framework Crosswalk: Ordinal Classification of Control Relationships},
  year         = {2026},
  institution  = {University of Denver},
  howpublished = {\url{https://huggingface.co/rockCO78/ai-security-crosswalk-vfinal}},
  note         = {Dissertation research; 3-model ensemble for ordinal tier classification across AI security frameworks}
}

Safety and Risk Assessment

Outputs are advisory scores, not authoritative compliance determinations.

The model assigns probabilistic tiers; human expert review is required before using predictions to inform compliance decisions, procurement, or framework adoption.
Tier predictions should be validated against primary framework documentation before any downstream use.
The model has no knowledge of organizational context, implementation details, or regulatory jurisdiction — factors that are essential for real compliance assessment.
Users operating in regulated environments (finance, healthcare, critical infrastructure) must apply additional human review commensurate with their risk tolerance.