Govern AI Across the Connected Vehicle Ecosystem
AI is embedded in automotive R&D, manufacturing, and connected services — from autonomous driving development to EV charging networks. Engineers use AI tools for design optimization, simulation analysis, and supplier collaboration, creating data paths that carry vehicle IP, telemetry data, and supplier pricing into model endpoints. Arbitex puts a compliance-grade governance layer in front of every AI call — inspecting, enforcing, and logging before any data reaches a model.
Capabilities
AI governance built for automotive and connected vehicle environments.
Connected Vehicle Telemetry DLP
GPS/OBD data, driver behavior patterns, vehicle diagnostics create privacy implications under CCPA/GDPR. AI-assisted fleet management and predictive maintenance tools process telemetry in prompts. Arbitex detects geolocation data and vehicle identification numbers before any data reaches model endpoints, and fleet-specific identifiers such as OBD-II diagnostic codes or driver scoring fields are covered by org DLP rules your team authors.
Autonomous Driving IP Protection
LiDAR point cloud datasets, perception model training data, path planning algorithms, and sensor fusion parameters are high-value IP. Engineers using AI tools for simulation analysis and model debugging can expose proprietary research. Arbitex detects autonomous driving dataset references, model architecture details, and sensor calibration data in AI prompts.
Supply Chain & Supplier Data Isolation
Powertrain specifications, battery chemistry formulas, pricing negotiations, and NDA-covered engineering data shared across supplier networks. AI-assisted procurement and supplier collaboration create exposure vectors. Arbitex enforces supplier data classification policies, detecting vendor pricing, contract terms, and proprietary engineering specifications.
UNECE R155 / WP.29 Cybersecurity Compliance
Automotive cybersecurity regulations require governance of AI tools accessing vehicle systems data. R155 mandates cybersecurity management systems for type approval. Arbitex maps enforcement actions to R155 requirements — AI interactions involving vehicle architecture, ECU configurations, and OTA update protocols are governed with compliance-mapped audit logging.
Air-Gap Outpost for R&D Environments
Connected vehicle labs, autonomous driving test facilities, and powertrain development centers operate in network-restricted environments. Arbitex Hybrid Outpost deploys the data plane inside your isolated R&D network. Policy bundles are delivered via signed configuration packages. R&D AI operates under full governance with zero cloud data exposure.
EV Charging & Energy Data Governance
Charging transaction data, grid interaction patterns, battery state-of-health metrics, and customer energy profiles flow through AI-assisted charging optimization and grid management tools. Arbitex detects charging session identifiers, energy consumption patterns, and customer billing data before reaching model endpoints.
How it works
Deploy at the AI gateway boundary
The Arbitex data plane installs in your automotive network using Docker Compose or Kubernetes — inside your R&D VPC, manufacturing operations environment, or connected services infrastructure. All AI traffic from engineering tools, manufacturing systems, and connected vehicle platforms routes through the gateway before reaching any model endpoint. Vehicle IP detection, supplier data isolation, and audit logging run entirely inside your environment.
Define automotive data policies — vehicle IP, telemetry, supplier
Your configured compliance bundle activates the relevant control sets. Vehicle telemetry pattern detection runs at Tier 1 — structural matching for VINs, OBD-II codes, GPS coordinates, and ECU identifiers. Tier 2 applies ML-based detection to identify autonomous driving research data, battery chemistry references, and supplier pricing in free-text queries. Contextual validation at Tier 3 resolves ambiguous detections. Enforcement actions — block, redact, or route-to-review — execute before any data reaches the model.
Every AI interaction audited with entity detection
The tamper-proof audit log accumulates a complete evidence trail for every AI interaction involving automotive data. Signed exports include compliance framework identifiers mapped to each enforcement action — ready for UNECE R155 type approval audits, WP.29 assessments, ISO 21434 reviews, and supplier compliance examinations. SIEM integrations deliver enforcement metrics for continuous monitoring.
Six frameworks. One policy layer.
Each compliance obligation maps to a specific Arbitex capability. All bundles are active simultaneously — no separate configuration per framework or vehicle program.
Type approval cybersecurity requirements for vehicle manufacturers. AI tool governance mapped to CSMS documentation requirements, threat assessment procedures, and risk management processes.
UN regulatory framework for automated driving systems. Data governance controls for AI tools processing autonomous driving data, sensor fusion outputs, and driving scenario datasets.
Personal data protection for EU connected vehicle operations. Driver PII, location tracking data, and in-vehicle behavior patterns detected and governed at the AI boundary.
Consumer privacy rights for connected vehicle data. Vehicle telemetry, charging behavior, and driver profiles governed under California Privacy Rights Act requirements.
Industrial control system security for automotive manufacturing environments. AI tools accessing production line data, SCADA systems, and manufacturing execution systems governed at the gateway boundary.
Road vehicles cybersecurity engineering standard. AI-assisted threat analysis, vulnerability management, and security validation workflows governed with compliance-mapped enforcement.
Related Resources
Ready to put governance in front of your automotive AI?
Talk to an Arbitex engineer about connected vehicle DLP, autonomous driving IP protection, supplier data isolation, and air-gap Outpost configuration for your R&D lab or manufacturing environment.