RSAC 2026 in San Francisco will feature a push against traditional data loss prevention (DLP) methods. The article argues that policy-based DLP fails because it relies on static rules that cannot adapt to the complexity and scale of modern data environments. Such policies only guard against known threats, leaving organizations vulnerable to new forms of data exfiltration. The problems of false positives and maintenance costs stem from this flawed approach rather than execution issues. ORION offers a different solution by using context-aware AI agents instead of rigid policies. These agents can detect both current and past data breaches right from day one of implementation. The platform claims to improve incident detection rates significantly across various DLP use cases, including endpoints, cloud services, and email. ORION’s strength lies in its ability to learn organizational data movement patterns, which allows it to refine its analysis and improve over time. The system gathers a comprehensive range of contextual information necessary for identifying potential data loss. This includes data classification, mapping data movement within the organization, and integrating with identity and HR platforms for employee data. ORION also collects environmental signals and external relations data to assess risk accurately. While it allows for some policy use for compliance, the focus remains on context-driven insights to prevent data loss effectively.