AWS re:Invent 2025 - Leverage Graph Insights to Turbocharge Amazon Q for Business (IND209)

Leveraging Graph Insights to Turbocharge Amazon Q for Business

The Disruption of AI

• AI is rapidly disrupting how work is done, how value is built and delivered
• The ability to capture value is still catching up to the capabilities of AI models
• Amazon Q is an effective solution to help organizations leverage AI and deliver value to users

Securing AI-Powered Data Access

• Data security is a major roadblock for organizations trying to roll out AI
• Amazon Q is designed to be secure and private, respecting identities, roles, and permissions
• However, the speed and power of AI can lead to unintended data exposure if not properly controlled

The Complexity of Data Governance

• Data estates can be incredibly complex, with billions of files, millions of tables, and petabytes of data
• Different file types, sensitivity levels, regulations, and permission structures make data governance challenging
• Granular, file-level intelligence and control is required to enable safe AI-powered data access

Security AI's Graph-Based Approach

• Security AI scans data sources to extract thousands of metadata points, building a graph of connections
• This graph provides deep visibility into sensitive data, permissions, policies, and potential issues
• The graph-based approach enables:
  • Sensitive data classification and labeling
  • Removal of redundant, obsolete, or trivial (ROT) data
  • Preventing inappropriate data access through granular access controls
  • Validating and sanitizing new data before indexing by Amazon Q

Integrating with Amazon Q

• Security AI's solution acts as a parallel, companion solution to Amazon Q
  • Security AI handles data classification, labeling, and access control
  • Amazon Q then safely indexes the data, respecting the policies and permissions
• This integrated approach enables:
  • Preventing unintended data sharing through intelligent risk detection and remediation
  • Improving the quality of AI responses by reducing ROT data
  • Securing data and AI across the entire enterprise environment

Key Benefits

1. Prevent unintended data sharing and exposure through intelligent risk detection and automated remediation
2. Improve the quality of AI responses by reducing redundant, obsolete, and trivial (ROT) data
3. Secure data and AI across the entire enterprise environment, not just within AWS

Real-World Examples and Use Cases

• One large enterprise customer generates over 1 PB of unstructured, multi-structured log data per day
• Security AI's graph-based approach has helped customers:
  • Identify and remove redundant data, leading to significant ROI
  • Enforce granular, attribute-based access controls to prevent inappropriate data access
  • Validate and sanitize new data before indexing by Amazon Q, ensuring data quality and security

Conclusion

By integrating Security AI's graph-based data governance solution with Amazon Q, organizations can unlock the full potential of AI-powered data access while maintaining robust data security and privacy controls. This comprehensive approach addresses the complex challenges of modern data estates, enabling the safe and effective deployment of AI-driven business applications.