Google Vertex AI Integration Overview
Areebi integrates with Google Vertex AI to bring enterprise governance to Gemini, PaLM 2, and custom models hosted on Google Cloud Platform. Organisations using GCP can deploy AI capabilities through Areebi's governed interface, ensuring every interaction is scanned by the DLP engine, logged for audit purposes, and subject to policy controls - all without sacrificing the performance or flexibility of Vertex AI's model offerings.
Vertex AI provides access to Google's most capable models alongside tools for fine-tuning, AutoML, and custom model deployment. Areebi adds the governance layer that GCP's native tooling lacks: organisation-specific DLP rules, user-identity-aware audit trails, workspace isolation between departments, and granular access policies managed through a centralised policy builder. This combination lets enterprises adopt Google's AI capabilities with the controls their security and compliance teams require.
The integration authenticates using GCP service account credentials, supporting both Workload Identity Federation and traditional key-based authentication. All traffic between Areebi and Vertex AI can be routed through GCP's private networking, and governance policies apply equally to Gemini, PaLM 2, custom fine-tuned models, and AutoML-generated models.
Governance for Vertex AI Workloads
Areebi's governance layer for Vertex AI addresses the three pillars of enterprise AI security: data protection, auditability, and policy enforcement. The DLP engine scans every prompt for PII, PHI, financial identifiers, and custom data patterns before the request reaches Vertex AI. Responses are similarly scanned to prevent model-generated content from surfacing sensitive information derived from training data.
Audit logging captures every Vertex AI interaction with full metadata: user identity, workspace context, GCP project, model endpoint, token consumption, and interaction content. Logs can be exported to Google Cloud Logging, BigQuery, or third-party SIEM platforms for unified security monitoring. For SOC 2 and HIPAA compliance, these records provide auditor-ready evidence of AI governance controls.
Policy enforcement through Areebi's policy builder allows administrators to control which user groups access which models and GCP projects, set token budgets and rate limits, and define acceptable use rules that are enforced automatically. Cost tracking aligns with GCP billing labels, enabling accurate chargeback reporting across departments without manual effort.
Governing Custom and AutoML Models
Organisations deploying custom fine-tuned models or AutoML-generated models on Vertex AI benefit from the same governance controls as foundation models. Areebi treats all Vertex AI endpoints uniformly - DLP scanning, audit logging, and policy enforcement apply regardless of whether the model is Gemini, a fine-tuned variant, or an AutoML classifier. This ensures governance scales with your AI adoption.
Compliance on Google Cloud
Google Cloud provides a robust compliance foundation with certifications including SOC 2, HIPAA, FedRAMP, and ISO 27001. Areebi builds on this foundation with AI-specific governance that GCP's general-purpose security tools do not address: prompt-level data inspection, AI usage audit trails, and model access policies tied to user identity.
For multi-cloud organisations, Areebi provides a consistent governance experience across Vertex AI, Azure OpenAI, and direct API integrations. Policies, DLP rules, and audit log formats are standardised, so compliance teams manage one set of controls regardless of which cloud provider hosts the models. Workspace isolation ensures business units operate in separate environments with tailored governance settings.
Explore the trust centre for security documentation, review pricing for GCP-aligned enterprise plans, or request a demo to see Vertex AI governance in your environment.