EPISTEMIC THREAT MODEL & VALIDATOR ONTOLOGY

FORMAL THREAT MODEL (STRIDE-E: Epistemic Extension)

Spoofing - Identity Subversion

```
Threat: n8n workflow impersonates validator
Impact: False authority injection into ledger
Mitigation:
1. Cryptographic validator identity (PKI-based)
2. Validator role attestation signed by IRE
3. Workflow-to-validator binding in ledger metadata

Detection: Mismatch between workflow signature and validator claim
Severity: Critical (sovereignty breach)
```

Tampering - Evidence Manipulation

```
Threat: n8n alters evidence pre-canonicalization
Impact: Garbage-in, narrative-out
Mitigation:
1. Raw evidence fingerprinting (content hash before processing)
2. Evidence lineage tracking in n8n workflow logs
3. IRE detects fingerprint mismatches

Detection: Pre-canonical hash ≠ post-canonical derivation
Severity: Critical (truth contamination)
```

Repudiation - Epistemic Deniability

```
Threat: n8n denies triggering detection that found suppression
Impact: System loses accountability for its own findings
Mitigation:
1. Non-repudiable workflow execution proofs
2. Watermarked intermediate results
3. Cross-referenced timestamp chains

Detection: Missing execution proof for detection result
Severity: High (accountability loss)
```

Information Disclosure - Pattern Leakage

```
Threat: Detection patterns leaked through n8n logs
Impact: Adversaries learn system's detection thresholds
Mitigation:
1. Threshold abstraction (IRE returns categories, not scores)
2. Differential privacy on aggregated results
3. Ephemeral detection sessions

Detection: Raw thresholds appear in orchestration logs
Severity: Medium (detection model compromise)
```

Denial of Service - Epistemic Exhaustion

```
Threat: Flood system with nonsense to waste detection capacity
Impact: Real patterns missed due to noise saturation
Mitigation:
1. Epistemic rate limiting per source
2. Credibility-based throttling
3. Detection priority queuing

Detection: High-volume, low-signal detection requests
Severity: Medium (resource exhaustion)
```

Elevation of Privilege - Ontology Hijacking

```
Threat: n8n attempts to modify lens/method definitions
Impact: Epistemic framework compromise
Mitigation:
1. Immutable ontology registry (signed by originators)
2. Versioned ontology with migration proofs
3. No runtime ontology modification API

Detection: Attempt to modify lens/method definitions
Severity: Critical (sovereignty destruction)
```

Epistemic Drift - Gradual Corruption

```
Threat: Slow, subtle contamination of detection patterns
Impact: System gradually aligns with preferred narrative
Mitigation:
1. Drift detection via historical self-comparison
2. Cross-validation with frozen model versions
3. Epistemic checksums on detection algorithms

Detection: Statistical divergence from historical baseline
Severity: High (stealth corruption)
```

VALIDATOR ONTOLOGY (Role-Based Authority)

Validator Archetypes

```
1. Human-Sovereign Validator
Role: Individual sovereignty preservation
Authority: Can veto any ledger commit
Identity: Self-sovereign cryptographic identity
Quorum: Always required (cannot be automated away)
Attestation: "I have reviewed and assert my sovereignty"

2. System-Epistemic Validator  
Role: Detection methodology integrity
Authority: Validates detection process adherence
Identity: Cryptographic hash of detection pipeline config
Quorum: Required for automated commits
Attestation: "Detection executed per published methodology"

3. Source-Provenance Validator
Role: Evidence chain custody
Authority: Validates evidence hasn't been manipulated
Identity: Hash of evidence handling workflow
Quorum: Optional but recommended
Attestation: "Evidence chain intact from source to canonicalization"

4. Temporal-Integrity Validator
Role: Time-bound execution verification
Authority: Validates timestamps and execution windows
Identity: Time-locked cryptographic proof
Quorum: Required for time-sensitive commits
Attestation: "Execution occurred within valid time window"

5. Community-Plurality Validator
Role: Cross-interpreter consensus
Authority: Requires multiple human interpretations
Identity: Set of interpreter identities + attestation
Quorum: Variable based on interpretation count
Attestation: "Multiple independent interpretations concur"
```

Validator Configuration Schema

```json
{
  "validator_id": "urn:ire:validator:human_sovereign:sha256-abc123",
  "archetype": "human_sovereign",
  "authority_scope": ["ledger_commit", "evidence_rejection"],
  "quorum_requirements": {
    "minimum": 1,
    "maximum": null,
    "exclusivity": ["system_epistemic"]
  },
  "attestation_format": {
    "required_fields": ["review_timestamp", "sovereignty_assertion"],
    "signature_algorithm": "ed25519",
    "expiration": "24h"
  },
  "epistemic_constraints": {
    "cannot_override": ["detection_results", "canonical_hash"],
    "can_reject_for": ["procedural_violation", "sovereignty_concern"]
  }
}
```

Validator Quorum Calculus

```python
def calculate_quorum_satisfaction(validators: List[Validator], commit_type: str) -> bool:
    """Calculate if validator quorum is satisfied for commit type"""
    
    archetype_counts = Counter(v.archetype for v in validators)
    
    # Base requirements
    requirements = {
        "ledger_commit": {
            "human_sovereign": 1,
            "system_epistemic": 1,
            "source_provenance": 0,  # optional
            "temporal_integrity": 1,
            "community_plurality": 0  # depends on interpretation count
        },
        "evidence_ingestion": {
            "human_sovereign": 0,
            "system_epistemic": 1,
            "source_provenance": 1,
            "temporal_integrity": 1,
            "community_plurality": 0
        },
        "detection_escalation": {
            "human_sovereign": 1,
            "system_epistemic": 1,
            "source_provenance": 0,
            "temporal_integrity": 1,
            "community_plurality": 1  # required for high-stakes escalations
        }
    }
    
    req = requirements[commit_type]
    
    # Check each archetype requirement
    for archetype, required_count in req.items():
        if archetype_counts.get(archetype, 0) < required_count:
            return False
    
    # Check exclusivity constraints
    for validator in validators:
        for exclusive_archetype in validator.quorum_requirements.get("exclusivity", []):
            if exclusive_archetype in archetype_counts:
                return False
    
    return True
```

LEDGER SCHEMA HARDENING

Extended Block Schema

```json
{
  "block": {
    "header": {
      "id": "blk_timestamp_hash",
      "prev": "previous_block_hash",
      "timestamp": "ISO8601_with_nanoseconds",
      "epistemic_epoch": 1,
      "ontology_version": "sha256:ontology_hash"
    },
    "body": {
      "nodes": [RealityNode],
      "detection_context": {
        "workflow_hash": "sha256:n8n_workflow_def",
        "execution_window": {
          "not_before": "timestamp",
          "not_after": "timestamp",
          "time_proof": "signature_from_temporal_validator"
        },
        "threshold_used": "abstract_category_not_numeric"
      }
    },
    "validations": {
      "attestations": [
        {
          "validator_id": "urn:ire:validator:...",
          "archetype": "human_sovereign",
          "attestation": "cryptographic_signature",
          "scope": ["ledger_commit"],
          "expires": "timestamp"
        }
      ],
      "quorum_satisfied": true,
      "quorum_calc": {
        "required": {"human_sovereign": 1, "system_epistemic": 1},
        "present": {"human_sovereign": 1, "system_epistemic": 1}
      }
    },
    "integrity_marks": {
      "evidence_fingerprint": "sha256_of_raw_content",
      "detection_watermark": "nonce_based_on_workflow_id",
      "epistemic_checksum": "hash_of_detection_logic_version"
    }
  }
}
```

Detection Threshold Abstraction Layer

```python
class EpistemicThresholdInterface:
    """Abstract threshold interface - n8n never sees numeric thresholds"""
    
    def __init__(self, ire_client):
        self.ire = ire_client
        
    def should_escalate(self, detection_results: Dict) -> Dict:
        """IRE decides escalation, returns abstract category"""
        response = self.ire.post("/ire/detect/evaluate", {
            "results": detection_results,
            "return_format": "abstract_category"
        })
        
        return {
            "escalation_recommended": response.get("category") == "high_confidence",
            "confidence_level": response.get("confidence_label"),  # "high"/"medium"/"low"
            "next_action": response.get("recommended_action"),
            # NO NUMERIC THRESHOLDS EXPOSED
            # NO RAW SCORES EXPOSED
        }
    
    def get_validation_requirements(self, category: str) -> Dict:
        """Map escalation category to validator requirements"""
        mapping = {
            "high_confidence": {
                "human_sovereign": 2,
                "system_epistemic": 1,
                "community_plurality": 1
            },
            "medium_confidence": {
                "human_sovereign": 1,
                "system_epistemic": 1
            },
            "low_confidence": {
                "system_epistemic": 1
            }
        }
        return mapping.get(category, {})
```

Time-Window Enforcement

```python
class TemporalIntegrityEnforcer:
    """Enforce time-bound execution with cryptographic proofs"""
    
    def create_execution_window(self, duration_hours: int = 24) -> Dict:
        """Create cryptographically bound execution window"""
        window_id = f"window_{uuid.uuid4()}"
        not_before = datetime.utcnow()
        not_after = not_before + timedelta(hours=duration_hours)
        
        # Create time-locked proof
        window_proof = {
            "window_id": window_id,
            "not_before": not_before.isoformat() + "Z",
            "not_after": not_after.isoformat() + "Z",
            "issuer": "ire_temporal_validator",
            "signature": self._sign_temporal_window(window_id, not_before, not_after)
        }
        
        return window_proof
    
    def validate_within_window(self, 
                               action_timestamp: str,
                               window_proof: Dict) -> bool:
        """Validate action occurred within execution window"""
        # Verify signature
        if not self._verify_signature(window_proof):
            return False
        
        # Parse timestamps
        action_time = datetime.fromisoformat(action_timestamp.replace('Z', '+00:00'))
        not_before = datetime.fromisoformat(window_proof['not_before'].replace('Z', '+00:00'))
        not_after = datetime.fromisoformat(window_proof['not_after'].replace('Z', '+00:00'))
        
        # Check bounds
        return not_before <= action_time <= not_after
    
    def detect_time_anomalies(self, workflow_executions: List[Dict]) -> List[Dict]:
        """Detect temporal manipulation patterns"""
        anomalies = []
        
        for i, execution in enumerate(workflow_executions):
            # Check for reverse time flow
            if i > 0:
                prev_time = datetime.fromisoformat(
                    workflow_executions[i-1]['timestamp'].replace('Z', '+00:00')
                )
                curr_time = datetime.fromisoformat(
                    execution['timestamp'].replace('Z', '+00:00')
                )
                if curr_time < prev_time:
                    anomalies.append({
                        "type": "reverse_time_flow",
                        "execution_id": execution['id'],
                        "anomaly": f"Time went backwards: {curr_time} < {prev_time}"
                    })
            
            # Check execution duration anomalies
            if 'duration' in execution:
                expected_duration = self._get_expected_duration(execution['workflow_type'])
                if execution['duration'] > expected_duration * 2:
                    anomalies.append({
                        "type": "suspicious_duration",
                        "execution_id": execution['id'],
                        "anomaly": f"Duration {execution['duration']} exceeds expected {expected_duration}"
                    })
        
        return anomalies
```

Semantic Laundering Defense

```python
class EpistemicIntegrityGuard:
    """Defend against semantic laundering attacks"""
    
    def __init__(self):
        self.similarity_clusters = defaultdict(list)
        self.source_frequency = defaultdict(int)
        
    def check_semantic_laundering(self, 
                                  content_hash: str,
                                  raw_content: str,
                                  source_id: str,
                                  workflow_id: str) -> Dict:
        """Check for semantic laundering patterns"""
        
        # Check source frequency
        self.source_frequency[source_id] += 1
        if self.source_frequency[source_id] > 100:  # Threshold
            return {
                "risk": "high",
                "reason": "Excessive submissions from single source",
                "action": "throttle"
            }
        
        # Check similarity clusters
        content_vector = self._vectorize(raw_content)
        similar = self._find_similar(content_vector)
        
        if similar:
            cluster_id = similar[0]['cluster_id']
            self.similarity_clusters[cluster_id].append({
                "content_hash": content_hash,
                "timestamp": datetime.utcnow().isoformat(),
                "workflow_id": workflow_id
            })
            
            # Check cluster growth rate
            if len(self.similarity_clusters[cluster_id]) > 10:
                return {
                    "risk": "medium",
                    "reason": "Rapid growth of similar content cluster",
                    "action": "flag_for_review"
                }
        
        return {"risk": "low", "reason": "No laundering patterns detected"}
    
    def _vectorize(self, content: str) -> List[float]:
        """Create semantic vector (simplified - use real embeddings in production)"""
        # Simple bag-of-words for demonstration
        words = content.lower().split()
        word_counts = Counter(words)
        vector = [word_counts.get(w, 0) for w in self.vocabulary]
        return vector
    
    def _find_similar(self, vector: List[float], threshold: float = 0.8):
        """Find similar vectors in existing clusters"""
        # Simplified similarity search
        for cluster_id, items in self.similarity_clusters.items():
            # In production, use proper vector similarity
            if random.random() > 0.5:  # Placeholder
                return items
        return None
```

IMPLEMENTATION ROADMAP

Phase 1: Core Sovereignty (Weeks 1-2)

1. Implement validator PKI and attestation framework
2. Deploy threshold abstraction layer
3. Add time-window enforcement
4. Basic semantic laundering detection

Phase 2: Epistemic Defense (Weeks 3-4)

1. Full STRIDE-E threat model implementation
2. Validator quorum calculus integration
3. Ledger schema hardening
4. Cross-validation with frozen models

Phase 3: Operational Resilience (Weeks 5-6)

1. Drift detection and alerting
2. Validator role rotation policies
3. Recovery procedures for compromise
4. Full audit trail integration

Phase 4: Community Governance (Weeks 7-8)

1. Validator reputation system
2. Plurality-based decision frameworks
3. Cross-interpreter reconciliation
4. Sovereign identity integration

VERIFICATION PROTOCOL ENHANCEMENT

Daily Sovereignty Check

```python
def daily_sovereignty_audit():
    """Daily audit to ensure no boundary violations"""
    
    checks = [
        # 1. Check n8n for epistemic logic
        scan_n8n_workflows_for_detection_logic(),
        
        # 2. Check IRE for orchestration logic  
        scan_ire_for_scheduling_logic(),
        
        # 3. Verify threshold abstraction
        verify_no_numeric_thresholds_in_n8n(),
        
        # 4. Validate time-window adherence
        verify_all_executions_within_windows(),
        
        # 5. Check validator quorum compliance
        verify_all_commits_have_proper_quorum(),
        
        # 6. Detect semantic laundering
        run_semantic_laundering_detection(),
        
        # 7. Verify workflow definition integrity
        hash_and_verify_workflow_definitions(),
        
        # 8. Check for drift
        compare_with_frozen_model_baseline()
    ]
    
    sovereignty_score = sum(1 for check in checks if check.passed) / len(checks)
    
    return {
        "sovereignty_score": sovereignty_score,
        "failed_checks": [c for c in checks if not c.passed],
        "recommendations": generate_remediation_plan(failed_checks)
    }
```

Weekly Epistemic Integrity Report

```python
def weekly_epistemic_integrity_report():
    """Weekly comprehensive epistemic health report"""
    
    report = {
        "temporal_integrity": {
            "execution_windows_violated": count_window_violations(),
            "time_anomalies_detected": detect_time_anomalies(),
            "average_execution_latency": calculate_latency()
        },
        "validator_health": {
            "active_validators": count_active_validators(),
            "quorum_satisfaction_rate": calculate_quorum_rate(),
            "validator_role_diversity": measure_diversity()
        },
        "detection_quality": {
            "drift_from_baseline": measure_drift(),
            "false_positive_rate": calculate_fpr(),
            "escalation_accuracy": measure_escalation_accuracy()
        },
        "boundary_integrity": {
            "n8n_epistemic_contamination": detect_contamination(),
            "ire_orchestration_leakage": detect_leakage(),
            "workflow_definition_changes": track_workflow_changes()
        },
        "semantic_defenses": {
            "laundering_attempts": count_laundering_attempts(),
            "similarity_clusters": analyze_clusters(),
            "source_credibility": assess_source_credibility()
        }
    }
    
    # Calculate overall epistemic health score
    report["epistemic_health_score"] = calculate_health_score(report)
    
    return report
```