The text discusses the increasing security threats faced by customers and the need to centralize and standardize security data. It introduces a novel approach using Amazon Security Lake and Amazon SageMaker for security analytics. The solution involves enabling Amazon Security Lake, processing log data, training an ML model, and deploying the model for real-time inference. The solution also includes setting up continuous monitoring and proactive Lambda function to consume new logs from Amazon Security Lake. The authors are Joe Morotti, Bishr Tabbaa, and Sriharsh Adari, who are Solutions Architects at Amazon Web Services (AWS).
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How AI Can Help Identify Cybersecurity Anomalies Using Amazon SageMaker
Customers are faced with increasing security threats and vulnerabilities across infrastructure and application resources as their digital footprint has expanded and the business impact of those digital assets has grown. A common cybersecurity challenge has been two-fold:
Consuming logs from digital resources that come in different formats and schemas and automating the analysis of threat findings based on those logs.
Whether logs are coming from Amazon Web Services (AWS), other cloud providers, on-premises, or edge devices, customers need to centralize and standardize security data.
Furthermore, the analytics for identifying security threats must be capable of scaling and evolving to meet a changing landscape of threat actors, security vectors, and digital assets.
A Novel Approach Using Amazon Security Lake and Amazon SageMaker
A novel approach to solve this complex security analytics scenario combines the ingestion and storage of security data using Amazon Security Lake and analyzing the security data with machine learning (ML) using Amazon SageMaker.
Amazon Security Lake is a purpose-built service that automatically centralizes an organization’s security data from cloud and on-premises sources into a purpose-built data lake stored in your AWS account. It automates the central management of security data, normalizes logs from integrated AWS services and third-party services, and manages the lifecycle of data with customizable retention and also automates storage tiering.
Amazon SageMaker is a fully managed service that enables customers to prepare data and build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows, including no-code offerings for business analysts. SageMaker supports two built-in anomaly detection algorithms: IP Insights and Random Cut Forest. You can also use SageMaker to create your own custom outlier detection model using algorithms sourced from multiple ML frameworks.
Practical Solution Overview
Enable Amazon Security Lake with AWS Organizations for AWS accounts, AWS Regions, and external IT environments.
Set up Security Lake sources from Amazon Virtual Private Cloud (Amazon VPC) Flow Logs and Amazon Route53 DNS logs to the Amazon Security Lake S3 bucket.
Process Amazon Security Lake log data using a SageMaker Processing job to engineer features. Use Amazon Athena to query structured OCSF log data from Amazon Simple Storage Service (Amazon S3) through AWS Glue tables managed by AWS LakeFormation.
Train a SageMaker ML model using a SageMaker Training job that consumes the processed Amazon Security Lake logs.
Deploy the trained ML model to a SageMaker inference endpoint.
Store new security logs in an S3 bucket and queue events in Amazon Simple Queue Service (Amazon SQS).
Subscribe an AWS Lambda function to the SQS queue.
Invoke the SageMaker inference endpoint using a Lambda function to classify security logs as anomalies in real time.
Prerequisites
To deploy the solution, you must first complete the following prerequisites:
- Enable Amazon Security Lake within your organization or a single account with both VPC Flow Logs and Route 53 resolver logs enabled.
- Ensure that the AWS Identity and Access Management (IAM) role used by SageMaker processing jobs and notebooks has been granted an IAM policy including the Amazon Security Lake subscriber query access permission for the managed Amazon Security lake database and tables managed by AWS Lake Formation.
- Ensure that the IAM role used by the Lambda function has been granted an IAM policy including the Amazon Security Lake subscriber data access permission.
Deploy the Solution
To set up the environment, complete the following steps:
Open the notebook 01_ipinsights/01-01.amazon-securitylake-sagemaker-ipinsights.ipy.
Implement the provided IAM policy and corresponding IAM trust policy for your SageMaker Studio Notebook instance to access all the necessary data in S3, Lake Formation, and Athena.
Create a Lambda function using the provided code and environment variables.
Conclusion
In conclusion, the novel approach using Amazon Security Lake and Amazon SageMaker provides a practical solution for identifying cybersecurity anomalies. By combining the capabilities of Amazon Security Lake and SageMaker, organizations can centralize and standardize security data and use machine learning to detect and classify security threats. This approach enables organizations to respond to security incidents in real time and proactively protect their digital assets. By deploying the solution in an end-to-end ML pipeline, organizations can continuously improve the model and enhance their security monitoring capabilities. This solution can redefine the way organizations approach cybersecurity and leverage AI to stay competitive in the rapidly evolving digital landscape.
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