AI Workload Security Architecture on AWS

AI Workload Security Architecture mapped with the relevant AWS Security Services for a secure, compliant, and scalable AI/ML environment using SageMaker, Bedrock, and Amazon Q:

AI Workload Security Architecture

User Interaction Layer (Clients, Developers, Analysts)

[User/Developer/Analyst]

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[IAM Identity Center / IAM with MFA]

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[Amazon Q / Custom GenAI App / SageMaker Studio]

Security Controls:

• IAM roles & policies with least privilege
• IAM Identity Center (SSO) for centralized access
• MFA for all access to AI tools

Model Development & Training (SageMaker)

[S3: Training Data] <---> [SageMaker Notebook / Training Job] <--> [Private VPC + SageMaker Endpoints]

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[Macie - PII Detection] [KMS - Data Encryption at Rest]

[DataZone - Data Governance] [IAM - Scoped Role for Training]

Security Controls:

• Use VPC for training to isolate from the public internet
• Encrypt datasets & models with KMS
• Run PII detection on training data via Amazon Macie
• Use IAM permissions for scoped access to training data and artifacts

Model Hosting & Inference (Real-Time / Async / Batch)

[SageMaker / Bedrock Hosted Model Endpoints]

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[API Gateway + WAF + Lambda Authorizer (JWT/OAuth)]

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[Custom App / GenAI API Gateway]

Security Controls:

• Use WAF to block malicious requests (e.g., prompt injection, abuse)
• API Gateway integrates with Lambda Authorizer for dynamic token validation
• Model invocation logs sent to CloudWatch & CloudTrail

Monitoring & Logging

[CloudTrail - API Activity Logs]

[CloudWatch - Logs & Alarms]

[Amazon GuardDuty - Anomaly Detection]

[AWS Config - Drift Detection]

[Amazon Detective - Forensics]

Security Controls:

• CloudTrail to log all actions (e.g., model training, invocation)
• GuardDuty for unusual activity detection (e.g., excessive model usage)
• Detective for post-incident investigation
• CloudWatch Alarms for alerting on misuse or abuse of GenAI endpoints

Model Governance & Risk

[Amazon SageMaker Clarify - Bias/Explainability]

[Amazon A2I - Human-in-the-loop Reviews]

[Amazon Bedrock Guardrails - Content Filtering (Hate/PII/Violence)]

[AWS Audit Manager - Compliance Mapping]

[Custom Privacy Impact Assessment]

Governance Controls:

• Use Guardrails in Bedrock to block toxic/harmful content
• A2I integrates for review of high-risk inference results
• Audit Manager to generate evidence for frameworks like ISO 27001, PDPL, GDPR
• Maintain privacy risk register for GenAI use

Security Governance & Control Plane

[AWS Organizations + SCPs]

[AWS Config Rules]

[Service Control Policies (Limit AI usage in dev environments)]

[Centralized KMS]

[Security Hub - Control Summary]

Governance Features:

• SCPs enforce rules across AWS accounts (e.g., disallow public model endpoints)
• AWS Config ensures continuous compliance
• Security Hub aggregates findings from Macie, GuardDuty, and Inspector

CI/CD + MLOps Integration

[CodePipeline / GitHub Actions]

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[Model Training (SageMaker)]

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[Model Registry -> Approval -> Deploy]

Security Steps:

• Implement signed model artifacts
• Code scanning with Amazon Inspector
• IAM boundaries for pipelines (avoid privilege escalation)

Summary: Security Pillars Applied

Pillar Key AWS Services

Identity & Access IAM, IAM Identity Center, SCPs

Data Protection KMS, Macie, S3 Encryption, Secrets Manager

Network Security VPC, PrivateLink, Security Groups, WAF, Shield

Monitoring & Audit CloudTrail, CloudWatch, GuardDuty, Config, Detective

Governance & Risk Bedrock Guardrails, Audit Manager, Clarify, A2I

DevSecOps CodePipeline, Inspector, CI/CD with model validation