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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)

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DOI: 10.1109/cars61786.2024.10778759 (opens in a new tab)

Type:   Proceedings Article
Country:   United States
Tags:   AI-Enabled Fraud, AI Enhanced Scams, Case Analysis, Cybercrime, Deepfake, Deepfakes, Defense Strategy, Defensive Strategies, Diffusion Of Innovation Theory, Federal Trade Commission, Future Threats, Hypothetical Case, Older Adults, Policy Implications, Prevention, Romance Scams, Scam Components, Support Networks, Tech Support Scam, Tech Support Scams, Trends, Vingette
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Synopsis

            
This paper uses two hypothetical cases - a technology-support scam and a romance scam - to examine how generative AI could increase the realism, speed, reach, and concealment of scams targeting older adults. Its five-stage analysis maps common scam components, considers AI enhancements, tests the hypothetical cases against existing defenses, and develops updated recommendations. The central proposal is a reliable social-support network that older adults can consult when synthetic text, images, audio, or video make independent verification difficult, supported by broader policy action. Because the cases are hypothetical and were not tested with victims, the paper identifies plausible defense gaps rather than measuring the prevalence or effectiveness of AI-enhanced scams.

          

        
                  

            
Identified Gaps

            
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

            
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

            
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

            
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.

          

              

    
    

        
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