Accelerating AI Adoption with Trusted Data

As agencies face unprecedented data volumes and velocity, managing the speed and size of the data flow efficiently becomes a significant challenge, especially with the growing use of AI-based analytics. Over 80% of government employees use AI regularly, expanding the expectations and complexities in trusted data management.

While it can provide the rich insights needed to solve problems, extracting necessary actionable data in context often presents obstacles. In addition to the complexities posed by the flood of data, public expectations for AI-enabled government are growing.

Meanwhile, agencies at all levels of government are adopting hybrid cloud strategies, blending on-premises and public cloud environments. This approach provides the flexibility needed to manage and analyze data securely and effectively. However, it also introduces complexities that require sophisticated governance and security measures. With more than 700 AI use cases in development across federal agencies, ensuring robust cybersecurity and data capability is crucial.

Learning Objectives:

• Identify the ways AI can amplify the challenges agencies face, including potential new cybersecurity risks and data capability gaps
• Review the role of governance and security mandates that affect AI implementation and how the move to a zero trust architecture and improved access control management can assist in meeting those mandates
• Outline the many considerations that must be coordinated in working with vast amounts of data, such as multiple formats, locations, the devices generating the data (cloud, edge, intermittent, etc.)
• Delineate the complexities that arise in hybrid and multi-cloud environments and ways to address them