🛡️ AI Privacy & Data Governance Matrix

Investigative benchmark ranking data retention, training defaults, and leak vectors across top AI systems.

Threat Scenario Evaluator Live

Active Workload / Data Type

Evaluating leak risk for private repositories, API secrets, and infrastructure configurations. Zero-retention & training opt-out required.

Simulated Governance Overrides

Training Opt-Out Active

Ephemeral / Incognito Session

Enterprise Zero-Data-Retention (ZDR)

Local Execution Only (Airgapped)

Inspected Provider

Prompt Pipeline Data Flow (Anthropic Claude) Low Risk Vector

AI Provider Privacy & Governance Matrix Click row to inspect prompt pipeline & cited clauses

Rank & Provider	Simulated Score	Default Training	Retention SLA	Telemetry Sharing	Compliance Tier

Highest Rated for Consumer Privacy

DuckDuckGo AI Chat (98/100)

Lowest Rated for Consumer Privacy

Meta AI (34/100)

Threat Scenario Scoring and Pipeline Flow

Read the explanation

Every AI provider starts with a baseline privacy score based on its retention window, default model training, and third-party data telemetry. Selecting a workload scenario like Sensitive Code activates strict penalties, deducting twenty-five points for model training and further penalties for multi-day logging. When the user enables Training Opt-Out, fifteen points return, and the model training stage transitions from an active risk to a protected green state. Toggling zero data retention eliminates gateway logging penalties entirely, immediately updating the simulated audit rank in the comparison matrix.

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