Token leakage in AI systems occurs when sensitive information is inadvertently disclosed through LLM interactions. This can include API keys, system instructions, environment variables, training data, or proprietary prompts used in AI applications. Unlike traditional token leakage in software systems, AI-powered applications amplify this risk due to their conversational nature and complex prompt-response dynamics. Real-world incidents demonstrate the amplified risk, such as Mercedes-Benz's GitHub token exposure compromising automotive software repositories, while Microsoft AI researchers accidentally leaked 38 terabytes of private data through misconfigured storage tokens. As AI usage patterns shift from isolated prompts to full-session workflows and multi-agent systems, the attack surface expands significantly. Token leakage becomes especially critical for teams deploying LLMs in customer-facing tools, autonomous agents, or integrations with sensitive backend infrastructure. To prevent token leakage, teams need targeted interventions across system prompts, token handling, model outputs, and conversational behavior. This requires a proactive layered approach to prevention, monitoring, and governance. Teams should separate prompt logic from output, enforce pre-completion filtering, version and test tokenizers as critical system dependencies, apply guardrail metrics to automate output risk evaluation, log multi-turn sessions to detect context drift and emerging risks, and operationalize token safety with Galileo's modular platform. By taking a proactive and structured approach, teams can mitigate the serious security and compliance challenge of token leakage in AI systems.