---
name: pattern-ai-security-compliance-use-cases
title: Use Cases
description: This section provides real-world use cases demonstrating how to implement security and compliance for AI workloads on IBM Cloud.
last-updated: 2026-03-18
---

> ## Documentation Index
> The table of contents for this documentation set is at https://cloud.ibm.com/docs/pattern-ai-security-compliance?format=markdown
> The index for all IBM Cloud docs is at: https://cloud.ibm.com/docs/llms.txt
> Use these files to discover more information as needed.

# Use Cases
{: #use-cases}

This section provides real-world use cases demonstrating how to implement security and compliance for AI workloads on IBM Cloud.
{: shortdesc}

## Regulatory-Ready GenAI Assistants
{: #genai-assistants}

LLM-powered assistants with toxicity, IP, and PII filters, prompt-injection firewalls, and watsonx.governance monitoring. SCC WP secures the platform stack and provides evidence of compliance for audits.

### Key Features
{: #genai-features}

* Toxicity detection and content filtering
* Intellectual property and copyright protection
* PII redaction and data privacy controls
* Prompt injection and jailbreak defenses
* Continuous monitoring and audit trails

### Implementation
{: #genai-implementation}

Deploy watsonx.governance for input/output guardrails, integrate SCC Workload Protection for runtime security, and configure automated evidence collection for regulatory compliance.

## Healthcare Diagnostics (PHI)
{: #healthcare-diagnostics}

Protected PHI pipelines with encryption and TEEs, explainability and model factsheets for clinicians, runtime safeguards against data leakage, and audit trails for HIPAA and ISO 42001 alignment.

### Key Features
{: #healthcare-features}

* Protected Health Information (PHI) encryption at rest, in transit, and in use
* Trusted Execution Environments (TEEs) for sensitive data processing
* Model explainability and factsheets for clinical validation
* Data leakage prevention and egress controls
* HIPAA and ISO 42001 compliance evidence

### Implementation
{: #healthcare-implementation}

Enable confidential computing with IBM Hyper Protect or Intel SGX/TDX, deploy watsonx.governance for model documentation and explainability, and configure SCC Workload Protection for continuous compliance monitoring.

## Financial Crime and Fraud Detection
{: #financial-crime-detection}

High-throughput anomaly detection with hardened model APIs, rate limiting, and extraction defenses. SCC WP correlates risks across posture, identity, and runtime to prioritize remediation.

### Key Features
{: #financial-features}

* Real-time anomaly detection and fraud prevention
* Hardened model APIs with rate limiting
* Model extraction and inversion defenses
* Risk correlation across security domains
* Prioritized remediation workflows

### Implementation
{: #financial-implementation}

Deploy models with secured endpoints and rate limiting, implement watermarking and fingerprinting for model protection, and use SCC Workload Protection for unified risk visibility and response.

## Enterprise LLM Agents
{: #enterprise-llm-agents}

Tool-enabled agents constrained by policy with scoped credentials and action whitelisting. Guardrails prevent unsafe actions and limit blast radius with continuous red teaming.

### Key Features
{: #enterprise-agents-features}

* Policy-constrained tool access and action whitelisting
* Scoped credentials with least privilege access
* Guardrails for unsafe action prevention
* Blast radius limitation
* Continuous red teaming and security testing

### Implementation
{: #enterprise-agents-implementation}

Configure policy-as-code for agent actions, implement CIEM for credential management, deploy guardrails for action validation, and establish continuous red teaming processes.

## Government and Critical Infrastructure
{: #government-critical-infrastructure}

Zero-trust architectures with confidential computing for sensitive inference, Satellite for sovereign deployments, and continuous compliance with evidence generation.

### Key Features
{: #government-features}

* Zero-trust security architecture
* Confidential computing for sensitive workloads
* Sovereign deployment options with IBM Cloud Satellite
* Data residency and sovereignty controls
* Continuous compliance monitoring and evidence generation

### Implementation
{: #government-implementation}

Deploy IBM Cloud Satellite for sovereign infrastructure, enable confidential computing for sensitive inference workloads, implement zero-trust network segmentation, and configure automated compliance evidence collection.

## Getting Started
{: #getting-started}

To implement these use cases:

1. Run an AI security and compliance assessment
2. Stand up a pilot with policy-as-code and guardrails
3. Implement continuous assurance with automated evidence collection

## Next Steps
{: #next-steps}

* Review the [Overview](https://cloud.ibm.com/docs/pattern-ai-security-compliance?topic=pattern-ai-security-compliance-overview&format=markdown)
* Explore the [Architecture](https://cloud.ibm.com/docs/pattern-ai-security-compliance?topic=pattern-ai-security-compliance-architecture&format=markdown)
* Learn about [Key Features and Guardrails](https://cloud.ibm.com/docs/pattern-ai-security-compliance?topic=pattern-ai-security-compliance-features&format=markdown)