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Blog March 7, 2024
6 min read

Invisible Threats, Visible Solutions: Integrating AWS Macie and Tiger Data Fabric for Ultimate Security

Data defenses are now fortified against potential breaches with the Tiger Data Fabric-AWS Macie integration, automating sensitive data discovery, evaluation, and protection in the data pipeline for enhanced security. Explore how to integrate AWS Macie into a data fabric.

Discovering and handling sensitive data in the data lake or analytics environment can be challenging. It involves overcoming technical complexities in data processing and dealing with the associated costs of resources and computing.  Identifying sensitive information at the entry point of the data pipeline, probably during data ingestion, can help overcome these challenges to some extent. This proactive approach allows organizations to fortify their defenses against potential breaches and unauthorized access.

According to AWS, Amazon Macie is “a data security service that uses machine learning (ML) and pattern matching to discover and help protect sensitive data”, such as personally identifiable information (PII), payment card data, and Amazon Web Services . At Tiger Analytics we’ve integrated these features into our pipelines within our proprietary Data Fabric solution called Tiger Data Fabric.

The Tiger Data Fabric is a self-service, low/no-code data management platform that facilitates seamless data integration, efficient data ingestion, robust data quality checks, data standardization, and effective data provisioning. Its user-centric, UI-driven approach demystifies data handling, enabling professionals with diverse technical proficiencies to interact with and manage their data resources effortlessly.

Leveraging Salient Features for Enhanced Security

The Tiger Data Fabric-AWS Macie integration offers a robust solution to enhance data security measures, including:

  • Data Discovery: The solution, with the help of Macie, discovers and locates sensitive data within the active data pipeline.
  • Data Protection: The design pattern isolates the sensitive data in a secure location with restricted access.
  • Customized Actions: The solution gives flexibility to design (customize) the actions to be taken when sensitive data is identified. For instance, the discovered sensitive data can be encrypted, redacted, pseudonymized, or even dropped from the pipeline with necessary approvals from the data owners.
  • Alerts and Notification: Data owners receive alerts when any sensitive data is detected, allowing them to take the required actions in response.

Tiger Data Fabric has many data engineering capabilities and has been enhanced recently to include sensitive data scans at the data ingestion step of the pipeline. Source data present on the S3 landing zone path is scanned for sensitive information and results are captured and stored at another path in the S3 bucket.

By integrating AWS Macie with the Tiger Data Fabric, we’re able to:

  • Automate the discovery of sensitive data.
  • Discover a variety of sensitive data types.
  • Evaluate and monitor data for security and access control.
  • Review and analyze findings.

For data engineers looking to integrate “sensitive data management” into their data pipelines , here’s a walkthrough of how we, at Tiger Analytics, implement these components for maximum value:

  • S3 Buckets store data in various stages of processing. A raw databucket for uploading objects for the data pipeline, a scanning bucket where objects are scanned for sensitive data, a manual review bucket which harbors objects where sensitive data was discovered, and a scanned data bucket for starting the next ingestion step of the data pipeline.
  • Lambda and Step Functions execute the critical tasks of running sensitive data scans and managing workflows. Step Functions coordinate Lambda functions to manage business logic and execute the steps mentioned below:
    • triggerMacieJob: This Lambda function creates a Macie-sensitive data discovery job on the designated S3 bucket during the scan stage..
    • pollWait: This Step Function waits for a specific state to be reached, ensuring the job runs smoothly.
    • checkJobStatus: This Lambda function checks the status of the Macie scan job.
    • isJobComplete: This Step function uses a Choice state to determine if the job has finished. If it has, it triggers additional steps to be executed.
    • waitForJobToComplete: This Step function employs a Choice state to wait for the job to complete and prevent the next action from running before the scan is finished.
    • UpdateCatalog: This Lambda function updates the catalog table in the backend Data Fabric database, and ensures that all job statuses are accurately reflected in the database.
  • A Macie scan job scans the specified S3 bucket for sensitive data. The process of creating the Macie job involves multiple steps, allowing us to choose data identifiers, either through custom configurations or standard options:
    • We create a one-time Macie job through the triggerMacieJob Lambda function.
    • We provide the complete S3 bucket path for sensitive data buckets to filter out the scan and avoid unnecessary scanning on other buckets.
    • While creating the job, Macie provides a provision to select data identifiers for sensitive data. In the AWS Data Fabric, we have automated the selection of custom identifiers for the scan, including CREDIT_CARD_NUMBER, DRIVERS_LICENSE, PHONE_NUMBER, USA_PASSPORT_NUMBER, and USA_SOCIAL_SECURITY_NUMBER.

      The findings can be seen on the AWS console and filtered based on S3 Buckets.  We employed Glue jobs to parse the results and route the data to the manual review bucket and raw buckets. The Macie job execution time is around 4-5 minutes. After scanning, if there are less than 1000 sensitive records, they are moved to the quarantine bucket.

  • The parsing of Macie results is handled by a Glue job, implemented as a Python script. This script is responsible for extracting and organizing information from the Macie scanned results bucket.
    • In the parser job, we retrieve the severity level (High, Medium, or Low) assigned by AWS Macie during the one-time job scan.
    • In the Macie scanning bucket, we created separate folders for each source system and data asset, registered through Tiger Data Fabric UI.

      For example: zdf-fmwrk-macie-scan-zn-us-east-2/data/src_sys_id=100/data_asset_id=100000/20231026115848

      The parser job checks for severity and the report in the specified path. If sensitive data is detected, it is moved to the quarantine bucket. We format this data into parquet and process it using Spark data frames.

    • If we peruse the parquet file, found below, sensitive data can be clearly seen as SSN and phone number columns.
    • In the quarantine bucket, the same file is being moved after finding the sensitive data.

      If there are no sensitive records, move the data to the raw zone from where data is further sent to the data lake.
  • Airflow operators come in handy for orchestrating the entire pipeline, whether we integrate native AWS security services with Amazon MWAA or implement custom airflow on EC2 or EKS.
    • GlueJobOperator: Executes all the Glue jobs pre and post-Macie scan.
    • StepFunctionStartExecutionOperator: Starts the execution of the Step Function.
    • StepFunctionExecutionSensor: Waits for the Step Function execution to be completed.
    • StepFunctionGetExecutionOutput Operator: Gets the output from the Step function.
  • IAM Policies grant the necessary permissions for the AWS Lambda functions to access AWS resources that are part of the application. Also, access to the Macie review bucket is managed using standard IAM policies and best practices.

Things to Keep in Mind for Effective Implementation

  • Based on our experience integrating AWS Macie with the Tiger Data Fabric, here are some points to keep in mind for an effective integration of AWS Macie. Macie’s primary objective is sensitive data discovery. It acts as a background process that keeps scanning the S3 buckets/objects. It generates reports that can be consumed by various users and accordingly, actions can be taken. But if the requirement is to string it with a pipeline and automate the action, based on the reports, then a custom process must be created.
  • Macie stops reporting the location of sensitive data after 1000 occurrences of the same detection type. However, this quota can be increased by requesting AWS. It is important to keep in mind that in our use case, where Macie scans are integrated into the pipeline, each job is dynamically created to scan the dataset. If the sensitive data occurrences per detection type exceed 1000, we move the entire file to the quarantine zone.
  • For certain data elements that Macie doesn’t consider sensitive data, custom data identifiers help a lot. It can be defined via regular expressions and its sensitivity can also be customized. Organizations with data that are deemed sensitive internally by their data governance team can use this feature.
  • Macie also provides an allow list—this helps in ignoring some of the data elements which by default Macie tag as sensitive data.’

The AWS Macie – Tiger Data Fabric integration seamlessly enhances automated data pipelines, addressing the challenges associated with unintended exposure of sensitive information in data lakes. By incorporating customizations such as employing regular expressions for data sensitivity and establishing suppression rules within the data fabrics they are working on, data engineers gain enhanced control and capabilities over managing and safeguarding sensitive data.

Armed with the provided insights, they can easily adapt the use cases and explanations to align with their unique workflows and specific requirements.

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