## 8 PREDICT: A Cyclical Framework for Defending Against Deepfake-Driven Social Engineering Threats  
In this section, we introduce _PREDICT_, a theoretical framework, shown in Figure 2, designed to guide organizations in proactively defending against the multifaceted challenges posed by deepfake-driven social engineering attacks. PREDICT is not just a high-levelconcept; it outlines a structured, cyclical process. This process integrates organizational policies, technical tools, and iterative improvements to address the evolving threat landscape. Below, we elaborate on each phase of PREDICT in greater technical and procedural depth, clarifying the rationale and mechanisms of the model. **Policies. ** Rather than only providing a set of guidelines, this phase also establishes the legal, ethical, and accountability structures necessary for AI-driven media within an organization. At the technical level, _Policies_ may include setting baseline requirements for content verification tools, cryptographic signing of official digital communications, or the mandated use of secure channels for the transfer of sensitive media. These requirements create a defensive perimeter where employees, contractors, and stakeholders are formally guided in how to handle AI-generated content and are held accountable if they fail to adhere to the best practices or engage in its misuse. **Readiness. ** This phase encompasses the governance models and risk assessment methodologies that ensure an organization can rapidly adopt new detection and forensic tools. From a technical standpoint, _Readiness_ includes establishing or licensing specialized AI infrastructure capable of analyzing media in real time. Additionally, it involves creating cross-functional committees (e.g., IT security, legal, and compliance) to regularly review emerging deepfake capabilities and vulnerabilities. In this way, the organization formalizes a chain of responsibility for quick resource allocation when potential threats arise. **Education. ** In addition to general security awareness, _Education_ entails detailed workshops and general education on how different deepfake models work. This includes identifying common artifacts or anomalies produced by these models, such as subtle facial distortions or mismatched lighting. This technical grounding helps employees, especially those in high-risk roles, recognize suspicious media more easily. Furthermore, practical exercises--such as exposing staff to a curated set of deepfake examples--build an intuitive, experience-based capability to detect manipulation. Integrating these educational programs into broader security training ensures that both technical and non-technical personnel remain vigilant against ever-evolving tactics. **Detection. ** While detection capabilities remain an active research area, _Detection_ in PREDICT emphasizes integrating cutting-edge techniques directly into existing workflows. This includes machine-learning-based classifiers that analyze audio, video, or image streams for signs of tampering, as well as rule-based systems that cross-reference metadata inconsistencies (e.g., timestamps, camera signatures, or voiceprint mismatches). Organizations might employ vendor solutions for advanced detection or even develop in-house detection pipelines using open-source libraries. Additionally, digital watermarking and content fingerprinting methods can be incorporated to confirm authenticity. By weaving these tools into communication platforms, suspicious content can be flagged in near-real time, allowing security teams to respond quickly. **Incident Response. ** Though incident response is often described at a high level, _Incident Response_ in PREDICT uses forensic validation and decision-making protocols tailored to deepfake incidents. For example, once a piece of suspicious media is detected, an automated workflow might (1) quarantine the content, (2) run it through multiple AI-based deepfake detection models, and (3) generate a confidence score for authenticity.