As artificial intelligence plays an increasingly critical role in modern security architectures, algorithmic transparency has emerged as a foundational requirement for organizations committed to building responsible, resilient, and trustworthy AI systems. This session introduces a structured framework centered around four pillars—explainability, accountability, bias mitigation, and auditability—and explores how these principles enable organizations to gain visibility into AI-driven decisions, improve threat detection accuracy, and meet evolving regulatory demands such as GDPR and the EU AI Act. Drawing on real-world case studies and enterprise benchmarks, the session outlines actionable strategies for embedding transparency into the AI lifecycle, including interpretable model design, stakeholder-specific explanation models, and auditable decision pathways. Attendees will gain practical insights into balancing model performance with traceability, ensuring regulatory alignment, and building governance-ready AI systems that deliver both technical excellence and ethical assurance.