# High-Risk AI Systems--Lie Detection Application
## 5 Threats and Countermeasures
### Proposed Framework  
While this paper has predominantly offered a state-of-the-art survey of AI-based lie detection in the context of the EU AI Act, we also propose a conceptual framework to guide future methodologies and research experiments in this field. Specifically, we recommend a three-layered approach:  
Multimodal Data Acquisition Layer: Integrate advanced techniques such as micro-expression analysis, vocal cues, and behavioral biometrics into a unified and synchronized data stream. This integration relies on cutting-edge sensor technologies, including modern high-speed cameras capable of capturing transient micro-movements, multi-microphone arrays for precise vocal signal analysis, and wearable inertial sensors for behavioral tracking [67]. These tools are meant to bring data collection precision to higher levels, with low intrusiveness, allowing for scalable deployment in many different settings, from security screening to corporate recruitment procedures. Hybrid Deep Learning Layer: This layer emphasizes combining sophisticated models such as attention-based transformers and spatiotemporal convolutional networks for the seamless fusion of multimodal inputs. By using the cross-modal attention mechanisms, these networks skillfully assign weights to features extracted from different streams of data, ensuring that the model focuses on the most relevant cues in various contexts, including the detection of stress-related changes in vocal pitch together with subtle micro-facial muscle contractions. Such an integration approach serves to effectively encode the complex and often intricate, non-linear inter-modality relationships that are typically beyond the capacity of traditional methods to capture [68]. This specific layer holds the key to bringing about improvement in the different models for adaptability, as this layer will allow them to dynamically redefine their parameters effectively. This happens in direct relation to the diverse contextual requirements likely to emerge in different instances. Such instances include processes involved in security screening, therapeutic interventions specifically aimed at treatment, or comprehensive and detailed forensic investigations seeking the discovery of critical information. Regulatory Compliance and Ethical Governance Layer: To guarantee the ethical implementation of artificial intelligence, this tier integrates rigorous privacy-preserving strategies, including advanced encryption techniques, immediate anonymization of confidential information, and explainable AI methodologies that yield clear and comprehensible results. The integration of both regulatory and ethical oversight is held in high regard in the industry. This, in essence, actively encourages interdisciplinary cooperation, where AI experts and legal scholars unite to work in harmony. With this particular approach, it massively empowers compliance with and adherence to the many provisions under the EU AI Act, specifically those AI applications that indeed fall under the category of being high risk. Furthermore, a very favorable enhancement would be the integration of automated audit trails with real-time compliance monitoring systems to increase the ability to guarantee accountability among the parties and to enhance the overall integrity of the operation. This strategic plan sufficiently addresses all concerns regarding bias and the potential for abuse in different decision-making processes [69]. Figure 4 below illustrates the proposed three-layered conceptual framework for AI-based lie detection systems, integrating advanced technologies with ethical and regulatory safeguards. The first layer emphasizes the acquisition of diverse data through multimodal techniques such as micro-expression analysis, vocal cues, and behavioral biometrics, leveraging state-of-the-art sensors to ensure precision and applicability across various scenarios.