Data Governance & Privacy

Key concepts

1. 1
   Data catalog vs. data marketplace vs. data product
2. 2
   Fine-grained access: row, column, cell-level
3. 3
   Lineage and impact analysis
4. 4
   PII discovery and classification
5. 5
   AI governance: model cards, evaluation, watermarking

Key AWS services

• Amazon DataZone
• AWS Lake Formation
• AWS Glue Data Catalog
• Amazon Macie
• AWS Audit Manager

Learn more — curated resources

Hand-picked official docs, foundational papers, and the best community guides for going deeper on this topic.

• Amazon DataZone
• AWS Lake Formation
• OpenLineage — open standard
• NIST AI Risk Management Framework

Sessions on this topic

7 sessions from the Summit covered this topic. Each is a self-contained mini-lesson.

1. ANT301Advanced

   A practitioners guide to data for agentic AI

   In this session, gain the skills needed to deploy end-to-end agentic AI applications using your most valuable data. This session focuses on data management using processes like Model Context Protocol (MCP) and Retrieval Augmented Generation (RAG), and provides concepts that apply to other methods of customizing agentic AI applications. Discover best practice architectures using AWS database services like Amazon Aurora and OpenSearch Service, along with analytical, data processing and streaming experiences found in SageMaker Unified Studio. Learn data lake, governance, and data quality concepts and how Amazon Bedrock AgentCore and Bedrock Knowledge Bases, and other features tie solution components together.

2. ARC301Advanced

   Build an AI-ready data foundation

   An unparalleled level of interest in generative AI and agentic AI is driving organizations to rethink their data strategy. While there is a need for data foundation constructs such as data pipelines, data architectures, data stores and data governance to evolve, there are business elements that need to stay constant like cost-efficiency and effectively collaborating across data estates. In this session we will cover how building your data foundation on AWS provides the tools and the building blocks to balance both needs, and empower organizations to grow their data strategy for building AI-ready applications.

3. STP208Intermediate

   NextAI's LegalScout: A Data Foundation for Private Legal AI

   LegalScout helps Australian SME law firms turn Generative AI into a competitive advantage by securely leveraging their own client data and confidential matters to work smarter, not harder. Built with Australian lawyers on AWS using Amazon Bedrock for inference and Amazon S3Vectors for secure document searches, it automates repetitive work, streamlines workflows, and improves drafting, contract review, and research to boost productivity, reduce costs, and lift accuracy while maintaining strict privacy and compliance.

4. WPS203Intermediate

   Optimising Outpatient Waitlists with ML at Gold Coast Health

   Deploying ML in high-stakes environments demands enterprise readiness, governance, and continuous monitoring. In this session, you'll learn how Gold Coast Health moved from pilot to production with a predictive model identifying patients unlikely to attend procedures — achieving 33% precision, doubling the 15% manual baseline — while ensuring fairness across cohorts. The session covers real-world ML architecture on Amazon SageMaker Pipelines, production monitoring including data quality, pipeline health, and drift detection, plus navigating AI governance through bias analysis and impact assessment. Whether you're in healthcare, financial services, or any regulated industry, walk away with actionable patterns for deploying responsible ML at scale.

5. FSI207Intermediate

   From enterprise data mesh to AI with Amazon SageMaker Unified Studio

   From enterprise data mesh to AI with Amazon SageMaker Unified StudioFinancial institutions are unlocking enormous value with AI agents — from personalised customer experiences to better risk decision making. But to deliver on that promise, agents need data they can find, understand, and trust. This session shows how a data mesh architecture on Amazon SageMaker Unified Studio builds that foundation: discoverable data across lines of business, business context that grounds agent responses in real meaning, quality signals that build confidence in every answer, and governed access that keeps you compliant by design. We cover domain ownership, multi-account strategies, data contracts, business glossaries, data quality, and cross-domain governance — and demonstrate how this foundation empowers agentic AI that delivers trusted, accurate results at enterprise scale.

6. STP209Intermediate

   How Cartesian Turns AI Agents from SaaS Killer to SaaS Moat

   The agents invasion into the software market is a fact of life now. Agents are changing how we are consuming software, services and information. But just like any technological inflection point, theres a redistribution of power with and SaaS platforms are struggling to find their centre of gravity in this new world. In this talk we will explore how, Cartesian is helping platforms lean in to their strategic assets like access to customers and privacy and find their moat in the agentic age by distributing and monetizing 3rd party agents.

7. IDE101Foundational

   From principles to practice: Scaling AI responsibly

   Building AI applications that customers trust requires more than technical excellenceit demands a deliberate approach to managing risk across every stage of the AI lifecycle. As organizations scale their AI initiatives, the challenge of balancing innovation speed with responsible AI practices across dimensions like privacy, security, fairness, safety, and explainability becomes increasingly critical. Join our panelists for a 30-minute discussion where they will explore: Practical approaches to embedding responsible AI principles into AI application development without slowing down innovation, key considerations across privacy, security, fairness, safety, and explainability that organizations should prioritize, lessons learned from building AI applications that earn and maintain customer trust, and strategies for navigating the evolving responsible AI landscape and managing risk at scale. Whether you are a technical leader building AI solutions, a business decision-maker shaping your organization's AI strategy, or a practitioner looking to deepen your understanding of responsible AI, this session will provide actionable insights to help you build AI applications that are not only innovative but also trustworthy.

One sharp, contrarian insight per session — the things teams don't think of unprompted.

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