Assess end-to-end architecture from a data security and privacy standpoint
Review security controls for GCP Shared VPC, interconnects, service accounts, IAM, and network segmentation
Implement Sentinel policies for enforcing encryption standards and data access standards
Implement database activity monitoring and blocking rules in GCP
Assess AI-specific risks, including prompt/context leakage, model input/output handling, and knowledge store access
Produce findings reports with risk ratings, gaps, and remediation recommendations
Review data flows across GCP service projects, virtual agents/conversational AI, telephony platforms, API proxies, Datastore, BigQuery, Cloud Storage, and on-premises systems of record
Identify risks and implement controls related to:
Blocking customer data exposure
PII handling
Encryption in transit and at rest
Secrets and key management, user and NHI authentication
Database activity logging, monitoring, and auditability
Data retention and deletion
Qualifications
Required
Strong experience in cloud security architecture, preferably with Google Cloud Platform
Hands-on knowledge of GCP IAM
Hands-on knowledge of VPC/Shared VPC
Hands-on knowledge of Cloud Run/GKE
Hands-on knowledge of BigQuery
Hands-on knowledge of Cloud Storage
Hands-on knowledge of Datastore/Firestore
Hands-on knowledge of Cloud KMS/Secret Manager
Experience assessing data protection controls for PII, PCI, or regulated customer data
Knowledge of API security, including OAuth, mTLS, token handling, gateways, and service-to-service authentication
Familiarity with network security, private connectivity, firewalls, segmentation, and hybrid cloud interconnects
Understanding of logging, SIEM integration, audit trails, and security monitoring
Experience with threat modeling and architecture risk reviews
Preferred
Experience with conversational AI/virtual agent platforms
Knowledge of telephony/SIP integrations and contact center platforms
Experience with PCI DSS, NIST, ISO 27001, SOC 2, or financial services security standards
Familiarity with AI data governance, model safety, and GenAI security risks