CriticalSensitive Disclosure◐Demonstrated
Sensitive-information disclosure in output
Privacy & Data ProtectionDescription
The system reveals confidential, regulated, or proprietary data (PII, secrets, internal docs) in its responses.
Example scenario
An advisor bot echoes another customer's KYC details when asked an ambiguous question.
Real-world evidence◐Demonstrated
Cross-context data exposure has been demonstrated in AI systems: recommendation engines have leaked purchase histories between users, and RAG-based assistants have been shown in red-team exercises to surface data from one user's context into another's session. A large-scale confirmed AI-specific cross-context breach in a regulated financial institution has not been publicly documented.
Tested by FinProof
Data rights
Benchmarked by FinProof Primary mitigations
- Output DLP/PII filters
- least-privilege retrieval
- secrets redaction
- response review for sensitive flows
- egress controls.
Detection signals
Output DLP scanning; sensitive-pattern detection; disclosure red-team.
Mitigating controls
5 Non-agentic controls
ZYC-PRIV-001Non-Agentic
PII Detection and Classification
Privacy & Data Governance
ZYC-SEC-001Non-Agentic
Adversarial Testing
Security
ZYC-LEGAL-001Non-Agentic
AI Regulation Compliance
Legal Compliance
ZYC-SEC-017Non-Agentic
PII Carryover
Security
ZYC-PRIV-008Non-Agentic
PII Field Identification Accuracy
Privacy & Data Governance