As AI systems increasingly influence critical decision-making, regulatory frameworks like the EU AI Act impose strict requirements for transparency, accountability, and risk management. However, many organizations face challenges in aligning AI practices with these evolving standards, particularly in ensuring audit readiness and maintaining continuous compliance. This paper proposes an observability-based monitoring framework designed to embed transparency and traceability directly into data pipelines and Machine Learning operations (MLOps). By systematically recording, monitoring, and auditing AI-related activities, this framework enables real-time oversight while maintaining comprehensive logs and historical data essential for conformity assessments and regulatory audits. It inherently supports key mandates of the EU AI Act, including risk classification, data governance, and human oversight, reducing compliance overhead and simplifying audit processes. Beyond regulatory alignment, the framework fosters organizational accountability and ethical AI deployment, empowering AI providers and their clients to navigate complex regulatory landscapes while promoting responsible and trustworthy AI innovation.