Data gathering and preprocessing are important steps in the data science pipeline that are often missed. Their role is crucial in ensuring data reliability, security, and compliance, particularly in security-sensitive domains such as cybersecurity, fraud detection, and surveillance. This paper highlights the impact of inadequate data handling on model performance, security vulnerabilities, and regulatory risks. We show through case studies and best practices how bad preparation can cause models that are biased, attacks that are not based on facts, and privacy breaches. We suggest a framework that includes privacy-preserving and security-by-design methods in data processes. This framework provides useful methods and tools to deal with modern problems.