Generative AI-Powered Threat Detection in Amazon Security Lake with Amazon Bedrock

Architecture Overview

Data Flow:

1. Amazon Security Lake aggregates OCSF-formatted logs in S3.
2. Lambda triggers on new data, preprocesses logs, and invokes Amazon Bedrock.
3. Bedrock’s Anthropic Claude 3 analyzes logs for novel threats.
4. Results stored in Amazon OpenSearch for visualization.

Step-by-Step Implementation

1. Configure Amazon Security Lake

2. Create Bedrock Access Policy

3. Lambda Function (Python 3.12)

Example Threat Detection Scenarios

1. Novel API Call Chain Detection

2. Data Exfiltration Pattern

Best Practices

• Cost Control:
  • Use Bedrock provisioned throughput for high-volume analysis
  • Filter Security Lake data to critical log types (CloudTrail, VPC Flow)
• Model Validation:

• Security:
  • Enable Bedrock guardrails for content filtering
  • Use IAM session tags for access control

Visualization in OpenSearch

• Threats by AWS service
• Risk score trends
• Geographic threat origins

Why This Approach is Unique

1. Beyond Signature-Based Detection: Bedrock identifies zero-day patterns missed by traditional SIEM rules.
2. OCSF Schema Awareness: Models trained on Security Lake’s standardized format improve accuracy.
3. AWS-Native: No third-party tools required – uses fully managed services.