Compliance · Data Protection
What sensitive data agents reached into — and who is accountable for it.
Your data-exposure surface: which sensitive paths were touched, who owns the agent that touched them, and where the privacy risk sits. Content scanning is the next step.
Bottom line
Agents touched 6 sensitive paths across 3 categories; 3 are high-severity (credentials/secrets). Most exposure attributes to mark@qpoint.io (4 opens); top path: ./.env. Prompt & response content is not yet inspected — today this is movement, not content.
6
sensitive paths touched
3 categories
3
high-severity exposures
credentials / secrets reached
3
owners attributed
who touched what
mark@qpoint.io
top exposure owner
4 opens
Sensitive access by category
opens · by severitySensitive access by user
who touched what — owner attributionHigh-severity exposure by agent
derived · Σ high-severity path opens per agentComputed from each agent's sensitive_files: how many high-severity secret/credential paths each one reached into. The asymmetry tag reads data_access — when writes are large relative to reads, data is being produced and moved, not just read in.
Top sensitive paths
5 paths · 6 distinct touchedPrompt & response content inspection
preview · not builtToday's signal is data movement — which sensitive paths were opened and how much was written or streamed. It does not yet read what is inside an agent's prompts and responses. Content scanning ships with the DLP engine.
Secrets in prompts
API keys / tokens pasted into context
not inspected today
PII in content
names, emails, customer records
not inspected today
Source code
proprietary code in prompts or responses
not inspected today
Lensed by seat via ?seat (compliance). Derived: credPathRows · exposureByAgent (Σ high-severity opens) · read/write asymmetry — from app/data/c16-fleet.ts (agents[].sensitive_files · data_access · dlp).