geo-infer-sec
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.
Resources
9Install
npx skillscat add activeinferenceinstitute/geo-infer/geo-infer-sec Install via the SkillsCat registry.
We need to produce a 2-3 sentence plain-text summary, objective, factual, no marketing language, no superlatives, no calls to action. Must be natural prose, no bullet points, no headings, no markdown. At most 60 words. Provide only the summary text. We need to summarize: skill does security and threat detection for geospatial systems; solves problem of securing spatial data, controlling access, detecting anomalies, auditing, anonymizing; used when implementing spatial access control, anomaly detection, threat assessment, security auditing, spatial data anonymization.
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 SpatialAnonymizerExamples
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 valueGuidelines
confidence_scoreis 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
Install
npx skillscat add activeinferenceinstitute/geo-infer/geo-infer-sec Install via the SkillsCat registry.