Cybersecurity remains one of the most pressing challenges in the digital era, as organizations grapple with increasingly complex threats that exploit vulnerabilities across networks, devices, and data flows. Traditional detection mechanisms, though effective against known attack patterns, often lack adaptability when faced with novel or evolving intrusions. To address these gaps, the integration of machine learning into cybersecurity has gained traction, offering predictive and adaptive capabilities that strengthen resilience. However, widespread deployment of machine learning tools has been hindered by concerns regarding opacity, interpretability, and user trust. Black-box algorithms, while powerful, can obscure decision-making processes, leading to uncertainty and resistance among security professionals and stakeholders. Interpretable machine learning provides a potential resolution by combining predictive accuracy with transparency. By embedding explainability into algorithms, it becomes possible to not only detect anomalous behaviors but also articulate the reasoning behind flagged events. This fosters accountability and enables human–machine collaboration, an essential component in high-stakes cybersecurity contexts. Moreover, interpretable models enhance the credibility of data science pipelines by ensuring that outputs are auditable, fair, and aligned with organizational policies. When integrated with advanced computer algorithms, these approaches deliver layered defenses capable of addressing both technical threats and governance concerns. This article explores how interpretable machine learning and algorithmic transparency can reshape cybersecurity strategies, reinforce trust in data-driven systems, and create robust pipelines for real-time threat detection. By bridging technical innovation with human trust, it outlines a path toward sustainable, trustworthy, and resilient cybersecurity ecosystems.