From b74e02ff0fdfb1edf690aec5c26d754c2f34ca8a Mon Sep 17 00:00:00 2001 From: Google AI Agent Date: Mon, 30 Mar 2026 23:35:12 +0000 Subject: [PATCH] feat: implement Phase 23 - Identity Auditor --- evolution/identity_auditor.py | 48 +++++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 evolution/identity_auditor.py diff --git a/evolution/identity_auditor.py b/evolution/identity_auditor.py new file mode 100644 index 00000000..ccba0e13 --- /dev/null +++ b/evolution/identity_auditor.py @@ -0,0 +1,48 @@ +"""Phase 23: Identity Health Auditor. + +Audits Timmy's decentralized identity for privacy leaks, correlation risks, and health. +""" + +import logging +import json +from typing import List, Dict, Any +from agent.gemini_adapter import GeminiAdapter + +logger = logging.getLogger(__name__) + +class IdentityAuditor: + def __init__(self): + self.adapter = GeminiAdapter() + + def audit_identity_health(self, did_inventory: List[Dict[str, Any]]) -> Dict[str, Any]: + """Performs a deep audit of Timmy's identity inventory.""" + logger.info("Performing deep identity health audit.") + + prompt = f""" +DID Inventory: +{json.dumps(did_inventory, indent=2)} + +Please perform a 'Deep Privacy Audit' of this identity inventory. +Identify correlation risks (where multiple DIDs can be linked to the same entity), potential metadata leaks, and revoked credential statuses. +Generate an 'Identity Sovereignty Score' and proposed 'Identity Rotation' strategies. + +Format the output as JSON: +{{ + "sovereignty_score": "...", + "audit_summary": "...", + "correlation_risks": [...], + "metadata_leaks": [...], + "rotation_strategies": [...], + "privacy_hardening_recommendations": "..." +}} +""" + result = self.adapter.generate( + model="gemini-3.1-pro-preview", + prompt=prompt, + system_instruction="You are Timmy's Identity Auditor. Your goal is to ensure Timmy's decentralized identity remains private, uncorrelatable, and truly sovereign.", + thinking=True, + response_mime_type="application/json" + ) + + audit_data = json.loads(result["text"]) + return audit_data