## 7 Defensive Strategies Against Deepfake-Driven Social Engineering
### The Current Defensive Measures  
#### 7.1.1 Employee Training and Awareness Programs  
One of the primary defenses against deepfake-driven social engineering is extensive employee training and awareness programs [1]. Through our research, we observed that organizations recognize human vigilance as an important line of defense. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Findings** & **Challenges** \\ \hline
**Employee Training and Awareness Programs** & Organizations focus on training their employees so that they can identify phishing attempts and verify communications. Reporting mechanisms for suspicious content are established. & Potential gaps in defenses emerge as deepfake technology continuously advances. \\ \hline
**General Security Frameworks** & Reliance on security frameworks from major vendors like Microsoft. These provide basic protection but are not tailored specifically to deepfake detection. & Potential gaps in defenses emerge as deepfake technology continuously advances. \\ \hline
**AI- and Machine-Learning-Based Detection Tools** & AI and machine learning (e.g., GANs, CNNs) are used to detect visual and audio anomalies in videos/audios that might have been manipulated. Multimodal approaches combining visual and audio analysis will be considered in the future. & Companies have yet to implement specialized deepfake detection tools. \\ \hline
**Automated Alerts and Real-Time Monitoring** & Real-time monitoring systems can detect and respond to deepfake incidents quickly. & Specialized deepfake detections systems are not yet widely implemented. There are high computational costs and potential delays. \\ \hline
**Integration with Existing Cybersecurity Infrastructure** & Seamless integration with current cybersecurity measures (e.g., firewalls, intrusion detection systems) is crucial. & Difficulty embedding advanced deepfake detection within current frameworks. \\ \hline
**Privacy Policies** & Comprehensive privacy policies help protect sensitive information from deepfake attacks. & The existing policies may not fully address future advancements in deepfake technology. \\ \hline
**Legal Regulations and Compliance** & Adherence to legal regulations (e.g., the GDPR) and industry standards is essential. Continuous monitoring and adaptation are required. & Keeping up with new laws related to digital impersonation and cybercrime. \\ \hline
**Incident Response Plans** & Robust incident response plans are in place, but specific plans for deepfake-related incidents are underdeveloped. & The need for more detailed and specific response strategies for deepfake attacks. \\ \hline
**Real-Time Monitoring Systems** & There is set to be further focus on real-time systems capable of detecting deepfake content at all times in the future. & Technologies are still in the early stages and not yet widely adopted. \\ \hline
**Collaboration with Cybersecurity Experts and Policymakers** & Partnerships with cybersecurity firms provide access to advanced detection technologies and expert knowledge. & Requires investment and coordination with external entities. \\ \hline
**Zero Trust Deepfake Framework** & The Zero Trust framework can be adopted, including principles such as continuous verification, micro-segmentation, least privilege access, and advanced authentication methods. & This framework is still under development. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overview of defensive strategies. * **Phishing avoidance:** From the interviews with Company A and Company B, training focuses on educating employees to identify phishing attempts, including those potentially using deepfake technologies. For example, from the interview with Company A, we learned that it could foresee a future where deepfake recognition training would be integrated into its annual training modules.