SAS Viya can act as an AI orchestration and governance layer that connects SAS analytics and applications to external AI/LLM capabilities—whether those models are hosted in a hyperscaler (e.g., Azure OpenAI, AWS Bedrock, Google Vertex AI) or deployed on-premises (e.g., customer-hosted open-source models, private inference endpoints). This gives customers the flexibility to select the right AI execution model based on data residency, regulatory constraints, cost, latency, and risk appetite, while still using SAS as the consistent control plane for integrating AI into business workflows, managing inputs/outputs, and operationalizing AI at scale across use cases.

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• Integrate SAS Viya with hyperscaler-managed AI services via secure REST/API connectivity, enabling rapid adoption of best-in-class models without moving core SAS workloads.
• Support on-prem/private AI inference by connecting SAS Viya to customer-hosted model endpoints, ensuring sensitive data remains within controlled environments.
• Use SAS Viya as the orchestration layer to standardize prompt/data pipelines, enforce governance/monitoring, and route workloads dynamically to cloud or on-prem models based on customer requirements.

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View a customer reference for SAS Anti-Money Laundering online.

View a customer reference for SAS Anti-Money Laundering online.

Latest whitepaper from SAS on how AI is transforming AML use cases.

SAS AML is delivered as a modern containerized, Kubernetes-based platform. This architecture provides strong scalability, resilience, and deployment flexibility but it also introduces additional operating complexity compared to traditional “single server” enterprise applications. For most customers, the hosting decision comes down to whether they want to retain full control of the environment (and operate the platform themselves), or outsource platform operations to reduce run-cost and delivery risk.

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1) Customer-Hosted: On-Premises or Customer Private Cloud

In this model, SAS AML is deployed into the customer’s own infrastructure environment (on-premises or customer-managed cloud). You retainfull control over the platform, security boundary, and operating model.

This option is commonly selected by organizations with strict requirements around:

• data residency and sovereignty
• internal security control and audit posture
• enterprise technology standards alignment

Because SAS AML is Kubernetes-based, this model assumes your teams can provide ongoing operational capability including environment management, patching, platform monitoring, and controlled upgrades.

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2) SureStep Managed Hosting: AWS or Azure

In this model, SAS AML is delivered through a SureStep-managed service hosted in AWS or Azure, significantly reducing the customer’s operational burden.

SureStep assumes responsibility for the platform and hosting layer, including:

• Kubernetes platform operations and lifecycle management
• monitoring and platform stability
• controlled release and upgrade management

This approach is typically selected by customers who want to:

• accelerate time-to-value and reduce implementation friction
• avoid building a Kubernetes operations function internally
• shift ongoing platform risk and overhead to a specialist delivery partner

The customer remains focused on business outcomes: AML configuration, investigative processes, governance, and compliance operations, rather than infrastructure management.

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SureStep Enhanced Support (Available for Both Models)

Regardless of hosting option, SureStep can provide Enhanced Support for SAS AML to ensure long-term reliability and operational success. Enhanced Support is designed to reduce delivery risk and stabilize “run operations” by providing expert coverage across both:

• application support and functional stability
• platform guidance (especially important given Kubernetes overhead)

This gives you flexibility: you can host SAS AML in the model that best fits your organizational strategy while still leveraging SureStep as a trusted partner to improve uptime, operational maturity, and sustainable delivery.

Based on your feedback for Inquiries, we've assessed the options and are able to confirm we can extend the Inqueries functionality to allow more integrations to the platform.

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With more discovery of your goals, we could evolve the SAS AML Inquiry object during impleentation from a passive tracking record into a case-native, SAR-aligned workflow implemented in SAS Visual Investigator, so FI-to-FI information requests become a governed investigation step with end-to-end traceability. The Inquiry would be launched directly from the Case, inherit the Case context (alerts, transactions, entities, investigative hypotheses), and guide investigators through a structured request builder with approved templates and required fields. This keeps the Inquiry process consistent, policy-aligned, and defensible, while reducing manual effort and eliminating "off-system" inquiry handling that weakens audit trails and slows SAR decisioning.

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On the back end, the solution would add an Inquiry process layer for assignment, SLAs, escalation, and evidence capture, all visible within the Case and linked to SAR preparation. Third-party responses (narrative text and attachments) would be captured as governed evidence objects, mapped back to the original questions, and completeness-scored to support investigator decisioning. As an enhancement, we can optionally introduce AI-assisted SAR drafting that produces a suggested narrative outline or draft language from the case and Inquiry evidence for investigator review and editing, while keeping the SAR narrative explicitly investigator-authored and fully controlled.

• Configure a Visual Investigator Inquiry workflow (states, approvals, SLAs, escalation, and case gating) embedded in the SAR case lifecycle.
• Implement a structured Inquiry builder with templates, mandatory data elements, and recipient routing to drive consistent FI-to-FI requests.
• Extend the case data model to link Inquiry to alerts/transactions/entities, ensuring complete lineage and regulator-ready evidence traceability.
• Enable response management: capture replies and attachments as evidence, map answers to questions, score completeness, and (optionally) generate an AI-assisted narrative draft for investigator edit/approval.

These are a sample of standard reports that come out of the box with SAS Anti-Money Laundering. The platform uses SAS Visual Analytics to view and author these reports.