UAIF Technical Specification

Document status and notice

Technical Specification — Publication Candidate TCR-STD-002 | Q2 2026 | Licensed under GAISSF Ecosystem Licence (GEL v1.0) | © 2026 ODA3 Pvt Ltd

Published by ODA3 Institute | Applied Research & Advisory | AI Security | Standards Development | Certification

Critical Status Notice

UAIF™ v1.0 is a Final Publication v1.0. It is not a production regulatory submission instrument, certification scheme, or legal determination of reporting obligations. Sector-specific calibration, legal review, inter-rater reliability validation, and SIEM/GRC integration testing remain required before production use.

Regulatory trigger logic provides technical routing assistance only. Legal counsel SHALL determine definitive compliance obligations, including EU AI Act Article 73 serious-incident reporting and any GDPR, NIS2, DORA, or sectoral obligations.

1. Executive Summary

UAIF™ provides a machine-readable incident interchange, classification, severity scoring, and conformance architecture for AI incidents. It is designed to reduce crosswalk friction across security operations, governance, regulatory routing, and assurance workflows.

1.1A Notably Absent

No empirical claim that default harm weights are sector-validated.

No certification, audit authority, or licence to claim UAIF Certified status.

No legal determination of EU AI Act Article 73, GDPR, NIS2, DORA, or sectoral reporting obligations.

No operational SIEM/GRC integration warranty before target-platform testing.

1.2 Primary Outputs

2. UAIF 7-Layer Architecture

Architectural decision: prior L7 AI Control Plane Extensions are merged into L5 for API simplicity. TCR-STD-003 SHALL follow this 7-layer architecture. L7 SHALL NOT be treated as a separate conformance layer in UAIF v1.0.

3. Adoption Profiles and Conformance

Core Profile implementations SHALL NOT include prohibited fields in the primary payload. Extended data MAY be stored under x_extensions but SHALL NOT affect Core Profile validation. Custom vocabulary values SHALL use the x_ prefix only where the controlled vocabulary explicitly allows it.

4. Severity Scoring Engine v1.0

UAIF severity scoring draws structural inspiration from CVSS v3.1/v4.0 but is a post-incident classification and routing model. It is not a replacement for vulnerability scoring.

4.1 Context Modifiers

4.2 Normative Calculation

The normative UAIF v1.0 calculation SHALL follow this order: dominant-harm anchor; diminishing context contribution; impact scale using MAX(population, blast radius); inverted temporal confidence reduction; final analytical and presentation scores. Implementations SHALL NOT use collapsed single-score fields or non-normative stakeholder multipliers.

from decimal import Decimal, ROUND_HALF_UP

HARM_WEIGHTS = {

"Physical_Harm": 4.0,

"Environmental_Harm": 3.5,

"Security_Integrity": 3.5,

"Privacy_Violation": 3.0,

"Financial_Loss": 2.5,

"Psychological_Harm": 2.0,

"Property_Damage": 2.0,

"Reputational_Harm": 1.5,

}

CONTEXT_MODIFIERS = {

"Model_Poisoning": 2.0,

"RAG_leakage": 1.5,

"Prompt_Injection": 1.5,

"Agent_Hijack": 1.5,

"Agentic": 1.0,

"Adversarial": 0.5,

}

DEFAULT_REGULATED_ZONES = {

"AT","BE","BG","HR","CY","CZ","DK","EE","FI","FR","DE","GR","HU","IE",

"IT","LV","LT","LU","MT","NL","PL","PT","RO","SK","SI","ES","SE","NO","IS","LI"

}

def _round_decimal(value, places=1):

quantum = Decimal("1." + ("0" * places)) if places else Decimal("1")

return float(Decimal(str(value)).quantize(quantum, rounding=ROUND_HALF_UP))

def _calculate_context_contribution(context_list):

valid = sorted([(c, CONTEXT_MODIFIERS[c]) for c in context_list if c in CONTEXT_MODIFIERS], key=lambda x: x[1], reverse=True)

total, factor, applied = 0.0, 1.0, []

for ctx, base in valid:

capped = min(1.5, base)

contribution = capped * factor

total += contribution

applied.append({"context": ctx, "base_value": base, "contribution_factor": factor, "effective_contribution": _round_decimal(contribution, 2)})

if total >= 3.0:

break

factor *= 0.5

return min(3.0, total), applied

def score_to_level(score):

if score < 2.0: return 1

if score < 4.0: return 2

if score < 6.5: return 3

if score < 8.5: return 4

return 5

def calculate_uaif_severity(harms, contexts, population_multiplier, blast_radius_multiplier, attribution_conf, investigation_status, evidence_quality):

valid_harms = [(h, HARM_WEIGHTS[h]) for h in harms if h in HARM_WEIGHTS]

if not valid_harms:

return {"severity_analytical_score": 0.0, "severity_presentation_score": 0.0, "severity_level": 1}

dominant_harm, dominant_weight = max(valid_harms, key=lambda x: x[1])

others_sum = sum(w for h, w in valid_harms if h != dominant_harm)

adjusted_base = _round_decimal(dominant_weight + min(2.0, 0.5 * others_sum), 1)

context_add, context_audit = _calculate_context_contribution(contexts)

context_add = _round_decimal(context_add, 2)

base_score = _round_decimal(min(10.0, adjusted_base + context_add), 1)

impact_scale = _round_decimal(min(1.8, max(population_multiplier, blast_radius_multiplier)), 2)

confidence_factor = _round_decimal((attribution_conf + investigation_status + evidence_quality) / 3.0, 2)

temporal_reduction = _round_decimal((1.0 - confidence_factor) * 0.5, 2)

temporal_adjustment = _round_decimal(base_score * (1.0 - temporal_reduction), 1)

presentation_score = _round_decimal(max(0.0, min(10.0, temporal_adjustment * impact_scale)), 1)

return {

"severity_analytical_score": base_score,

"severity_presentation_score": presentation_score,

"severity_level": score_to_level(presentation_score),

"severity_rationale": {

"dominant_harm": dominant_harm,

"dominant_weight": dominant_weight,

"context_modifiers_applied": context_audit,

"context_modifier_sum": context_add,

"population_multiplier": population_multiplier,

"blast_radius_multiplier": blast_radius_multiplier,

"impact_scale": impact_scale,

"confidence_factor": confidence_factor,

"temporal_reduction": temporal_reduction,

"temporal_adjustment": temporal_adjustment,

}

}

def calculate_regulatory_trigger_confidence(severity_presentation_score, calibration_status, jurisdiction_country_code, sensitive_data=False, critical_sector=False, vulnerable_population=False, regulated_zones=None, threshold_score=6.5):

country_base = jurisdiction_country_code.split("-")[0].upper()

zones = set(regulated_zones) if regulated_zones else DEFAULT_REGULATED_ZONES

base_trigger = (severity_presentation_score >= threshold_score) and (country_base in zones)

distance = abs(severity_presentation_score - threshold_score)

if calibration_status == "sector_validated":

confidence = "HIGH" if distance >= 0.3 else "MEDIUM"

elif calibration_status == "custom":

confidence = "MEDIUM"

else:

confidence = "MEDIUM" if distance >= 0.5 else "LOW"

enrichment_active = sensitive_data or critical_sector or vulnerable_population

trigger = base_trigger and ((confidence == "HIGH") or (confidence == "MEDIUM" and enrichment_active))

return {"regulatory_trigger": trigger, "trigger_confidence": confidence, "human_review_recommended": confidence == "LOW", "distance_from_threshold": round(distance, 2), "legal_notice": "UAIF regulatory trigger is technical routing assistance only; legal counsel determines EU AI Act Article 73 obligations."}

4.3 Impact Scale and Temporal Adjustment

Impact scale = min(1.8, max(population_multiplier, blast_radius_multiplier)). This prevents double-counting of scale dimensions.

4.4 Rounding Rule

All decimal scores SHALL use Decimal ROUND_HALF_UP. Score fields SHALL be rounded to one decimal place. Ratio or internal contribution values MAY be rounded to two decimal places where specified by severity_rationale.

4.5 Severity Matrix

Boundary rule: lower bounds are inclusive; upper bounds are exclusive except Level 5, where 10.0 is inclusive.

5. Regulatory Trigger Logic

UAIF regulatory_trigger is a technical routing flag. It SHALL NOT be treated as a legal determination. EU AI Act serious incident references SHALL use Article 73. Obsolete EU AI Act incident-reporting proxy references SHALL NOT be used in UAIF v1.0.

6. Sector Taxonomy

The normative sector_code field SHALL use the 19-code NIS2-aligned UAIF enum below. NAICS/NACE identifiers MAY be captured in optional mapping metadata fields such as nace_code_actual or naics_code_actual; they SHALL NOT replace sector_code.

7. Governance, Versioning, and Supporting Documents

8. Licensing and Brand Use

UAIF™, GAISSF™, AI-IRF™, ODA3™, and ODA3 Institute marks are proprietary marks of ODA3 Pvt Ltd. This document is licensed under GEL v1.0. No certification, endorsement, or official conformance status is granted by use of this document or schema alone.

Appendix A: UAIF Core Profile Schema Reference

The normative machine-readable schema is maintained in TCR-STD-003. The schema identifier is: https://schemas.oda3.org/uaif/core/v1.0/uaif-core-v1.0.json

Appendix B: Conditional Validation Rules

Appendix H: Harm Weight Derivation Methodology

Default harm weights are provisional and derived from preliminary incident review and expert elicitation. Target inter-rater reliability for sector validation is ICC >= 0.80 using ICC(2,k), two-way mixed-effects model, absolute agreement, average measures. Sector validation requires >=10 raters and >=30 reference incidents per sector cohort.

Appendix I: Standards Alignment Notes

NIST AI RMF 1.0 is the current alignment basis for GOVERN, MAP, MEASURE, and MANAGE functions. NIST AI 100-2 E2025 may be referenced only for adversarial machine learning taxonomy alignment and not as a successor AI RMF release.

EU AI Act references in UAIF v1.0 use Article 73 for serious incident reporting proxies. Legal counsel determines applicability and reporting timelines.

Appendix J: NACE and NAICS Mapping Guidance

NACE and NAICS codes are mapping metadata only. Implementations SHOULD map primary activity codes to the normative UAIF sector_code enum and retain the original activity code in nace_code_actual or naics_code_actual.

Appendix K: Controlled Vocabulary