AI-Security

OWASP Top 10 for LLM security is specifically designed for large language model applications and addresses the unique security challenges of generative AI.

Configure Amazon Bedrock Guardrails with appropriate content filtering policies to protect against harmful user inputs across multiple dimensions, including hate speech, insults, sexual content, and violence.

Amazon Bedrock Guardrails can be configured to filter out sensitive information in model outputs. 

Custom topic filters can be created to block specific categories of sensitive information. 

Guardrails can be applied to both model inputs and outputs to ensure comprehensive privacy protection. 

Configure Amazon Comprehend to analyze and filter user inputs before they reach foundation models, identifying potentially harmful content. Amazon Comprehend provides built-in PII detection capabilities that can identify over 25 types of sensitive information in text.

Amazon Comprehend can detect PII in real-time as part of AI processing pipelines. 

PII detection can be combined with redaction or entity replacement to protect sensitive information. 

Custom entity recognition models can be trained to identify organization-specific sensitive information. 

Amazon Macie can automatically discover, classify, and protect sensitive data stored in Amazon Simple Storage Service (Amazon S3). 

Design custom moderation workflows using Step Functions that orchestrate multiple safety checks in sequence or parallel.

Implement Lambda functions with specialized content moderation logic that goes beyond pre-built guardrails for organization-specific requirements.

Implement pattern matching and heuristic approaches to detect common jailbreak techniques targeting foundation models.

Develop post-processing Lambda functions that perform additional safety checks on model outputs before delivery to users.

Configure API Gateway request validators to perform initial validation of user inputs before they reach foundation models.

Configure JSON Schema validation in API Gateway to enforce structured outputs that conform to predefined safe patterns.

Implement real-time validation mechanisms using Lambda authorizers that can block harmful requests before they're processed.

Set up Amazon CloudWatch alarms to monitor and alert on patterns of blocked content to identify potential abuse.

Create comprehensive logging and auditing systems to track and analyze model outputs for safety compliance

Implement feedback loops that continuously improve content safety systems based on new patterns of harmful inputs and automated incident response workflows using Step Functions that trigger when safety violations are detected.

Set up knowledge bases with appropriate data sources and retrieval configurations to perform automatic fact-checking.

Implement confidence scoring mechanisms that assess the reliability of model outputs based on grounding evidence.

Different masking strategies (redaction, tokenization, pseudonymization) can be applied based on data sensitivity and use case requirements. 

Anonymization strategies

AWS Lambda functions can implement anonymization strategies, such as generalization, perturbation, or synthetic data generation. 

Differential privacy techniques can be applied to add statistical noise that protects individual privacy while maintaining aggregate utility. 

K-anonymity and other privacy-preserving techniques can be implemented for datasets used with foundation models. 

For healthcare applications dealing with protected health information (PHI), the most appropriate combination is Amazon Comprehend Medical for PHI detection and Amazon Bedrock Guardrails for implementing safeguards.

Use CloudTrail to log and monitor all create, read, update, and delete (CRUD) actions to Amazon Bedrock and Amazon S3

Amazon CloudWatch monitors applications, responds to performance changes, optimizes resource use, and provides insights into operational health.

Configure PrivateLink and VPC endpoints to enable secure, private connectivity to Amazon Bedrock and other AI services without exposing traffic to the public internet.

Set up VPC endpoints for Amazon Bedrock to keep all AI traffic within your VPC, enhancing security for sensitive workloads.

Configure interface VPC endpoints with security groups to control which resources within your VPC can access Amazon Bedrock services.

Implement fine-grained access control through IAM policies that restrict access to specific models, features, and operations in Amazon Bedrock.

Apply resource-based policies to knowledge bases and other Amazon Bedrock resources to control access based on identity, source, and other conditions.

Configure condition keys in IAM policies to enforce additional security requirements such as encryption, source VPC, or specific tags.

Implement Lake Formation to provide fine-grained access control for data used in AI training and inference, including column-level, row-level, and cell-level security.

Set up data catalogs in Lake Formation to track and control access to AI-related datasets across the organization.

Set up CloudWatch Logs Insights to analyze access logs and identify potential security issues or policy violations.

Configure CloudWatch to monitor and alert on suspicious data access patterns in AI applications.