Machine learning (ML) pipelines are increasingly being managed like software systems, incorporating security measures to protect against vulnerabilities such as poisoned training data, backdoored models, and dependency exploits. ML teams can utilize existing tools like Python, pip, and CircleCI to integrate security checks directly into their CI/CD workflows without overhauling their current setup. This approach includes secret scanning with tools like Gitleaks to catch hardcoded secrets, dependency auditing with pip-audit to identify vulnerable packages, and model hash validation to ensure consistency and integrity of trained models. These practices help mitigate risks associated with ML systems, such as data poisoning and model drift, by catching potential issues early in the development pipeline. The tutorial emphasizes that enhancing ML security does not require rebuilding tools from scratch but rather integrating lightweight, automated checks into existing processes to ensure the trustworthiness of ML models and data.