Artemis Environmental Cyber-Risk gives executives and security leads a decision layer for environmental signals that can disrupt digital operations. The page translates ionospheric volatility, GNSS scintillation, HF absorption, over-the-horizon radar uncertainty, and precision timing drift into risk bands, affected services, and recommended action. Instead of treating space weather as a scientific feed, ClearGlassInc Artemis frames it as an operational dependency problem: which assets rely on timing, positioning, communications, or sensor confidence; what business process is exposed; and which fallback should be validated now. The system is designed for defensive use, public-feed provenance, and human-approved escalation, matching the ClearGlassInc pattern of calm command surfaces, evidence cards, and action-first CTAs. Phase 1 starts with deterministic thresholds and Python scoring. Later phases add telemetry fusion, client-specific dependency models, and executive briefings without removing operator governance.
The content follows the site-wide ClearGlassInc product pattern: a decisive hero, a clear operating problem, modular proof blocks, and direct CTAs into the relevant platform asset.
Ranks environmental cyber-risk by the systems that actually depend on positioning, communications, sensor confidence, and timing.
Turns raw public-feed signals into concise cards showing affected services, confidence, recommended fallback, and approval status.
Creates plain-language summaries that explain risk, business impact, and mitigation timing for leadership and incident teams.
A regional logistics operator sees elevated log N_F2 and GNSS scintillation. Artemis identifies dispatch, route validation, and timestamp reconciliation as exposed workflows, then recommends validating holdover timing and manual route confirmation before service degradation becomes customer-visible.
A security operations team receives a YELLOW band alert during solar activity. Artemis maps HF absorption risk to backup communications readiness, creates an evidence card for the duty lead, and holds client notification until an approved operator confirms the mitigation language.
event: space_weather.observation sources: [CSA alerts, NOAA SWPC, public ionospheric models] score: weighted(log_NF2, Kp, S4, D-region density, client dependency) actions: [monitor, validate_backup, activate_fallback] approval: human_required for external client escalation provenance: immutable lineage hash per observation
Start with a Phase 1 dependency review, Python threshold validation, and a pilot dashboard that shows where environmental disruption creates business risk.