85% of respondents listed fighting fraud as a top five priority.

Nearly a third face significant resource limitations in fighting FWA.

Of those prioritizing AI security, three in five (60%) are leveraging established security vendors.

Most organizations (54%) report that edge AI complements their cloud AI strategy for a hybrid approach

33% of SOCs are using GenAI for Threat intelligence analysis.

57% of organizations view lack of trustworthiness as a major concern regarding AI adoption.

Of the 1,100 government fraud fighters surveyed, nearly all claimed their agencies were victims of AI-powered fraud schemes.

Of those prioritizing AI security, over two-thirds have acquired tools from their cloud providers.

Among those already using AI to confront FWA, nearly 40% cite better prioritization of fraud alerts and quicker identification of FWA as benefits.

LLMs see somewhat less adoption at the edge (47%).

Slightly over a quarter of those surveyed are using Generative AI (GenAI) for addressing FWA.

Risk management is planned for 73% of future edge AI implementations.

Current AI adoption rates for addressing FWA are relatively low, with about half of those surveyed using AI.

Respondents cited gaps in analytical skills (48%), technology (40%), and budgets (24%) among the limiting factors in fighting FWA.

Survey respondents estimated that approximately 16% of budgets could be saved by tackling fraud, waste, and abuse (FWA) in general.

Only 43% say AI has made a meaningful impact so far in improving threat intel sharing within their organization.

Large Language Models (LLMs) are widely deployed in the cloud (59%).

Security risks and data protection concerns represent the biggest Edge AI implementation challenge (42%).

97% expect to use GenAI within the next two years.

22% of organizations are actively in production with limited deployment of AI at the edge.