Illustrative Scenario: SOC 2 Compliance for AI in a Tier-2 Bank

Consider a tier-2 regional bank serving commercial and retail customers across multiple states with approximately 1,200 employees. A technology-forward strategy leads to significant AI adoption across the organization - trading desks using AI for market analysis and position sizing, advisory teams leveraging AI for portfolio recommendations and client communications, and operations teams deploying AI for fraud detection workflow augmentation and regulatory reporting assistance.

The problem typically becomes apparent during a routine OCC examination. Examiners flag the bank's lack of AI governance controls as a significant deficiency, noting that AI tools have access to customer financial data, trading algorithms, and advisory communications without adequate controls, monitoring, or audit capabilities.

Existing compliance infrastructure covers traditional IT systems, but AI usage has grown outside these established frameworks. There is no centralized inventory of AI tools in use, no DLP controls specific to AI interactions, and no audit trail showing what customer data has been processed by AI systems.

In this archetype, the bank needs to address AI governance across multiple overlapping compliance frameworks simultaneously:

• SOC 2 Type II: Annual SOC 2 audits are scheduled within weeks, and AI governance controls need to be operational and demonstrable before the audit window opens. SOC 2 requires evidence of access controls, monitoring, and incident response for all systems processing customer data - including AI.
• PCI-DSS: AI tools used by customer service and operations teams have potential access to cardholder data. PCI-DSS requires strict access controls and monitoring for any system touching payment card information.
• OCC requirements: OCC examination findings typically require a formal remediation plan with demonstrable progress within 90 days. The bank needs to show that AI governance controls are not just planned but implemented and operational.
• GLBA/Regulation SP: Customer financial data shared with AI tools falls under Gramm-Leach-Bliley Act protections, requiring the bank to demonstrate appropriate safeguards for all customer nonpublic personal information processed by AI.

An initial plan to build custom AI governance tooling on top of an existing security stack is typically scoped at 9 months with development, integration, and compliance consulting costs in the $400K-$500K range. With a SOC 2 audit weeks away, this timeline is unacceptable.

In this archetype, deployment is structured in phases over 4-6 weeks:

• Week 1: Core deployment and SSO integration. The Areebi golden image is deployed on the bank's Kubernetes infrastructure. SAML SSO is configured via an Okta instance with role-based access controls mapped to the bank's existing organizational structure. Okta integration enables automatic provisioning and deprovisioning tied to HR systems.
• Week 2: Policy configuration and DLP setup. SOC 2 compliance templates are activated and customized. DLP rules are configured to detect and block customer account numbers, SSNs, routing numbers, portfolio details, trading positions, and other sensitive financial data categories. PCI-DSS specific rules are layered on for customer service workspaces.
• Week 3: Workspace isolation and information barriers. Separate workspaces are created for trading, advisory, operations, customer service, and IT teams. Information barriers are configured to prevent data flow between trading and advisory workspaces, mirroring the bank's existing Chinese wall requirements. Each workspace receives function-specific AI model access and DLP policies.
• Weeks 4-5: Shadow AI discovery and remediation. The browser extension is deployed across all employees, designed to surface unapproved AI tools in active use. Users are progressively migrated to governed channels, and block rules are implemented for unauthorized tools.
• Week 6: Audit preparation and documentation. Areebi's compliance reporting engine generates SOC 2-aligned control documentation, including access control matrices, DLP policy specifications, audit log samples, and incident response procedures.

The deployment is designed to deliver measurable results across every compliance and operational dimension. The targets below are modelled against industry build-versus-buy economics and SOC 2 control-evidence benchmarks; they are not outcomes delivered to a paying customer today.

SOC 2 compliance design target. A SOC 2 Type II audit should complete with zero AI-related findings when Areebi's access controls, DLP policies, audit logs, and incident response procedures are deployed as designed and align to applicable trust service criteria. The modelled reduction in audit preparation time for AI-specific controls is more than 90% versus equivalent controls assembled across multiple tools.

Cost savings modelled. Deploying Areebi instead of building custom governance tooling is modelled to save more than $300,000 annually in development costs, maintenance overhead, and compliance consulting fees. The Areebi platform is designed to replace 4-5 separate tools for DLP, audit logging, access control, and compliance reporting.

Policy enforcement automated. Areebi's automated policy enforcement is designed to block 95%+ of policy violations without manual intervention. This includes blocking of customer financial data in AI prompts, enforcement of information barriers between trading and advisory, and prevention of unauthorized AI model access.

An OCC remediation plan should be submittable showing full implementation of AI governance controls within the 90-day window. Once our first banking design partners go public, real-world outcomes from this archetype will replace these design-target framings.