geo-infer-sec

name	geo-infer-sec
description	Security and threat detection for geospatial systems. Use when implementing spatial access control, anomaly detection on access patterns, geospatial threat assessment, security auditing, or spatial data anonymization.
prerequisites	{"required":["geo-infer-api"],"recommended":[]}
difficulty	intermediate
estimated_time	45min
examples_dir	../GEO-INFER-EXAMPLES/examples/

GEO-INFER-SEC

Instructions

Core Capabilities

• Access control: Role-based and spatial-boundary-based authorization (RBAC + SBAC)
• Threat detection: Anomaly detection on spatial access patterns, behavioral profiling
• Confidence scoring: Dynamic confidence computation (uses real calculated_confidence)
• Audit logging: Security event tracking with spatial context, chain of evidence
• Data protection: Spatial data anonymization, k-anonymity, differential privacy
• Integrity: Data integrity verification, tampering detection

Key Imports

from geo_infer_sec.core.integrated_security import IntegratedSecurityEngine
from geo_infer_sec.core.access_control import SpatialAccessController
from geo_infer_sec.core.threat_detection import ThreatAnalyzer
from geo_infer_sec.core.anonymization import SpatialAnonymizer

Examples

from geo_infer_sec.core.integrated_security import IntegratedSecurityEngine

engine = IntegratedSecurityEngine()
result = engine.assess_threat(
    request=api_request,
    user_context=user_profile,
    spatial_context=request_location
)
print(f"Confidence: {result.confidence_score}")  # Real computed value

Guidelines

• confidence_score is computed from calibrated constituent signals (base risk weights + correlation bonuses). The weights are heuristics and are explicitly documented here — they are not claimed to be empirically calibrated.

Integrations

• Integrates with API for endpoint security
• Test: uv run python -m pytest GEO-INFER-SEC/tests/ -v