IAM role over-provisioning
SageMaker execution roles and Bedrock invocation roles often carry broader permissions than needed.
AI Security
AWS AI Security Services
Run Bedrock and SageMaker workloads with identity, network, and audit controls that scale.
Why AWS AI workloads need security
AWS offers the broadest AI portfolio, from Bedrock to SageMaker to custom silicon. That breadth also means the most knobs to misconfigure: IAM roles, VPC endpoints, S3 buckets, and SageMaker notebook permissions all need scrutiny.
MHCIS helps AWS customers secure their AI pipeline end to end, from data ingestion to model inference.
Common risks
Public S3 buckets
Training data, model artifacts, and inference outputs stored in S3 can be exposed by bucket policies.
Unprotected endpoints
SageMaker endpoints or Bedrock agent APIs without authentication are open to abuse.
Notebook and studio risks
SageMaker Studio notebooks can access production data and secrets without proper isolation.
Insufficient model logging
Model invocation, drift, and bias metrics may not be captured for security review.
What MHCIS does
AWS IAM and SCP design
Least-privilege roles, permission boundaries, and service control policies for AI accounts.
Network security
VPC endpoints, PrivateLink, security groups, and subnets for Bedrock and SageMaker traffic.
Data protection
S3 bucket hardening, KMS encryption, and Macie scanning for AI datasets.
SageMaker and Bedrock governance
Model registries, endpoints, agents, and pipelines with approval gates and audit logging.
Detection and response
CloudTrail, GuardDuty, and Security Lake coverage for AI service abuse and data exfiltration.
Checklist
AWS AI security essentials
- Scope SageMaker execution and Bedrock invocation roles to least privilege.
- Block public access on all S3 buckets holding training data or model artifacts.
- Encrypt data at rest with KMS and enforce TLS in transit.
- Use VPC endpoints for Bedrock and SageMaker where possible.
- Enable CloudTrail data events and GuardDuty for AI service APIs.
- Isolate SageMaker Studio users and notebooks from production data.
- Implement model approval workflows and endpoint monitoring.
Common questions
Straight answers to what regulated buyers ask first.
Related compliance
AI security work maps to the same compliance program we document on these pages.