Bridging the Protection Gap: Innovative Approaches to Shield Older Adults from AI-Enhanced Scams
Herrera, LD ; Van Sickle, London ; Podhradsky, Ashley (2024) — 2024 Cyber Awareness and Research Symposium (CARS)
Type:
Proceedings Article
Country:
United States
Tags:
artificial intelligence, hypothetical case, vingette, defense strategy, future threats, cybercrime, trends, diffusion Of innovation theory, federal trade commission, deepfake, AI-enhanced scams, older adults, romance scam, tech support scam, defensive strategies, scam components, case analysis, policy implications
Synopsis (AI-Generated)
Bridging the Protection Gap: Innovative Approaches to Shield Older Adults from AI-Enhanced Scams is presented as a session at the 2024 Cyber Awareness and Research Symposium (CARS). The catalog entry notes a cross-disciplinary examination of how Artificial Intelligence(AI)-driven deception interacts with age-related vulnerabilities. The session combines curated presentations, moderated dialogue, and practical recommendations aimed at clarifying threat contours, identifying protection gaps, and tracing a path toward more resilient online environments for older adults. Content covers threat landscapes, human factors, and technology-assisted defenses. Topics include AI-enabled impersonation and social engineering, recognition cues, authentication and verification practices, and accessible security tools. The program emphasizes user-centered design,
Identified Gaps (AI-Generated)
Explicit gaps include: (1) Defensive gaps for older adults not adequately addressed by current defenses that focus on awareness and generic tech controls, with a need to account for cognitive load, social isolation, and slow technology adoption, including building reliable support networks. (2) AI-enhanced scam components (deepfakes, AI-generated content) require forward-looking defenses beyond generic guidance. (3) Structural gaps in policy/organization—necessitating a centralized, funded defense body guided by loss data. (4) Empirical validation gaps due to reliance on hypothetical cases rather than real-world data.
Methods (AI-Generated)
A five-stage methodology: Stage 1 identifies common scam components targeting older adults ( Scam Anatomy ); Stage 2 analyzes AI enhancements to these components; Stage 3 develops hypothetical AI-enhanced cases (tech-support and romance scams); Stage 4 analyzes case defense gaps and current defenses; Stage 5 translates findings into updated defensive recommendations emphasizing social-support networks and policy action.
Limitations (AI-Generated)
Limitations include reliance on hypothetical scenarios rather than empirical data; focus on two case types (tech-support and romance scams); limited geographic framing; dependence on secondary reports (FTC, AARP, IC3) which may bias risk emphasis; lack of testing with real victims; generalizability to diverse cultures requires verification.
Future Work (AI-Generated)
Future work could include empirical validation of proposed defenses, development of a centralized scam-defense organization with funding tied to losses, cross-cultural studies of AI-enhanced scams, expansion to additional scam types, and collaboration with policymakers, technologists, and elder-care communities to implement and evaluate AI-based detection and response tools.
AI-Generated Content Notice
The synopsis and research notes on this page were generated with AI from available publication information and, when available, the uploaded paper text. They may contain errors, omissions, or interpretation issues. Readers should follow the DOI or source link, review the original publication, and make their own judgment about the content.