Intelligent Data Retention and Deletion Workflow with AI Integration

Intelligent Data Retention and Deletion Workflow

1. Data Classification

1.1 Identify Data Types

Utilize AI-driven tools such as IBM Watson to categorize data into sensitive, confidential, and public classifications.

1.2 Tagging and Metadata Enrichment

Implement Microsoft Azure Cognitive Services for automatic tagging of documents to enhance searchability and management.

2. Data Retention Policies

2.1 Policy Development

Leverage AI analytics to assess compliance requirements and establish data retention policies based on legal obligations.

2.2 Policy Implementation

Use Everlaw to automate the enforcement of retention policies across various data repositories.

3. Data Monitoring

3.1 Continuous Monitoring

Employ AI tools like Splunk to monitor data access and usage patterns, ensuring adherence to retention policies.

3.2 Anomaly Detection

Integrate Darktrace to identify unusual data access behaviors that may indicate compliance risks.

4. Data Deletion Process

4.1 Automated Deletion Triggers

Set up automated triggers using DocuWare to delete data that exceeds retention periods based on pre-defined criteria.

4.2 Approval Workflows

Utilize Zapier to create approval workflows for data deletion requests, ensuring necessary oversight.

5. Documentation and Reporting

5.1 Activity Logs

Maintain comprehensive logs of data retention and deletion activities using Loggly for auditing purposes.

5.2 Compliance Reporting

Generate compliance reports automatically through Tableau to provide insights into data management practices.

6. Review and Optimization

6.1 Periodic Review

Conduct regular reviews of retention policies and practices using AI analytics to ensure alignment with evolving legal standards.

6.2 Feedback Loop

Implement feedback mechanisms utilizing AI-driven surveys to gather insights from stakeholders on the effectiveness of the workflow.