This paper presents preliminary results for a comprehensive framework for integrating ad-hoc privacy preferences into information systems. The framework is structured into three layers: Preference Identification, Semantic Representation, and Integration and Enforcement. The Preference Identification Layer analyzes user interactions, feedback, and predefined settings to identify privacy preferences and generate detailed user profiles. The Semantic Representation Layer standardizes these preferences into machine-readable formats using ontologies, XML, and JSON schemas, as well as Semantic Web technologies like RDF and OWL, facilitating interoperability across systems. The Integration and Enforcement Layer embeds and enforces these preferences within application settings, ensuring consistent application and compliance with regulatory standards through policy enforcement mechanisms, context-aware systems, and real-time monitoring tools. This framework enhances user control over personal data and aligns with global privacy regulations such as GDPR and CCPA, addressing challenges like dynamic preference management and system integration complexities. Future research should focus on scalability across different domains and the integration of advanced technologies like machine learning to further refine and validate the framework. This paper contributes to the development of privacy-aware information systems that prioritize user autonomy and ethical design, facilitate trust and compliance in the digital landscape.