Solution / Digital Trust

IntegraPrivacy — AI Privacy Assurance

A controlled assurance platform for testing whether AI systems reproduce withheld personal or sensitive attributes without turning the assessment itself into a privacy risk.

Business context

What this capability solves

Organizations need evidence about AI memorization, reproduction and RAG leakage, but naive tests can confuse hallucination with memorization or expose additional personal information. IntegraPrivacy applies verified, privacy-preserving test design and evidence grading.

EBP Integra delivery principle

Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.

Deep-dive capabilities

Capability model

Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.

Identity & Consent

Verify test ownership and purpose before running person-specific assessment.

Seed / Key Separation

Provide only a minimal verified seed while keeping test attributes withheld and server-side.

Multi-Model Probing

Run recall, completion and linkage-style tests across distinct model publishers or model families.

Hallucination Controls

Use fictional-control identities and repeatable probes to reduce false interpretation.

Evidence Grading

Group findings by publisher/model and assign calibrated not-detected, weak, moderate or strong evidence levels.

Enterprise Assurance

Extend the method to RAG canaries, regression tests, deletion validation and AI DPIA evidence.

Reference architecture

How the capability fits together

Final topology, control placement and deployment model are validated during discovery and detailed design.

Verification Layer

Consent, identity proof and purpose limitation.

Test Controller

Seed/key separation, probe scheduling, test templates and control identities.

Model Routing

Approved model endpoints, data-retention settings and provider selection.

Evidence Layer

Redaction, grading, de-duplication, retention/expiry and export to governance workflows.

Controls & governance

No people-search mode
Data minimization and encrypted storage
Web browsing disabled for controlled memory tests
Redacted evidence excerpts
Time-bound report retention
Human interpretation before legal or risk conclusions

Priority use cases

Individual AI privacy check
Enterprise RAG leakage red team
AI DPIA evidence
Model regression after updates
Canary-data monitoring
Deletion/remediation validation

Key deliverables

Test plan and approved scope
Evidence report with confidence grading
Model/publisher comparison
DPIA or risk attachment
Remediation recommendations
Re-test schedule

Integration considerations

Model/API routing
Identity verification
GRC/DPIA workflow
Enterprise RAG test environment
Evidence repository
Ticketing and remediation

Implementation

Phased delivery

Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.

1. DefineConfirm purpose, test subject/system and evidence threshold.

2. VerifyProve authorization and establish withheld test keys.

3. ProbeRun controlled tests and hallucination controls.

4. InterpretGrade evidence, route actions and schedule re-test.

Outcome and KPI framework

False-positive control rateReproducibility of findingsEvidence agingRemediation closureRegression pass ratePrivacy test coverage