## 9 Lessons Learned and Recommendations  
### Anticipating the Deepfake Threat  
As we were able to confirm through our interviews, many organizations now see deepfakes as a niche or low-probability threat, despite the fact that they have garnered a lot of attention in the media and cybersecurity circles. Our study's interviewees reported that although they are aware of the possible dangers, the majority have not personally experienced fraud or impersonation connected to deepfakes. Companies have adopted a \"wait and see\" mentality, concentrating on more urgent cybersecurity issues like ransomware, insider threats, or conventional phishing attacks through well-established protocols and controls, as a result of this sense of relative safety and the comparatively low sophistication of many _currently circulating_ deepfakes. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Phase** & **Purpose** & **Key Actions** & **Desired Outcome** \\ \hline \multirow{4}{*}{**Policies**} & Create guidelines for the acceptable use of generative AI and ensure accountability. & Define acceptable AI use and ensure alignment with legal/ compliance requirements; Establish secure communication channels for sensitive requests and reports. & Clear organizational rules and responsibilities that allow for an environment of ethical AI usage and compliance. \\ \hline \multirow{4}{*}{**Readiness**} & Build governance, infrastructure, and “preparedness” to face deepfake risks. & Conduct risk assessments; Allocate resources for detection tools and training; Form governance committees for oversight. & An organization equipped with the necessary tools, structures, and expertise to address deepfake threats effectively. \\ \hline \multirow{4}{*}{**Education**} & Promote a security-first culture and enable staff to identify and report deepfakes. & Offer awareness campaigns with real-world examples; Provide specialized training for high-risk roles; Regularly update training to address new tactics. & A knowledgeable workforce capable of quickly recognizing and responding to potential deepfakes. \\ \hline \multirow{4}{*}{**Detection**} & Deploy and integrate specialized tools to identify deepfakes in real time or near-real time. & Use AI-driven algorithms for anomaly detection; Partner with vendors for advanced detection; Apply digital forensics to validate authenticity. & The timely and accurate identification of suspicious or manipulated media before it causes harm. \\ \hline \multirow{4}{*}{**Incident Response**} & Contain and manage deepfake-driven incidents to minimize damage. & * Verify authenticity and freeze actions if suspicious; Coordinate internal and external communications. & A fast and coordinated response that limits the impact of deepfake attacks and maintains stakeholder confidence. \\ \hline \multirow{4}{*}{**Continuous Improvement**} & Refine defenses through ongoing learning, partnerships, and updates. & Review incidents and feedback loops; Leverage threat intelligence and expertise; Update detection technologies regularly. & Evolving defenses that remain effective against emerging threats and tactics. \\ \hline \multirow{4}{*}{**Testing**} & Validate the effectiveness of policies, tools, and training through simulations. & Conduct simulated deepfake attacks; Run table-top exercises to assess readiness. & A proactive improvement cycle that ensures all components of the PREDICT model stay current and robust. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overview of the PREDICT model. However, the overall direction of AI's development shows that people might not stay satisfied for long. As described by [PERSON] et al. in their paper [31], artificial intelligence is in an extraordinary period of accelerated growth. They found that the number of AI researchers and academic papers on AI is increasing very fast, which means knowledge about AI is being created and shared faster than before.