The Challenge of Non Deterministic AI
The rapid adoption of AI agents and applications presents a unique security challenge. Unlike traditional deterministic software, AI systems behave unpredictably. This non determinism breaks the security contract enterprises rely on, making it difficult to secure AI applications using conventional methods. The pressure to deploy quickly often leaves security teams struggling to keep up with the expanding blast radius of potential threats.
A Unified Framework for Context and Control
According to Niv Braun, CEO of Noma Security, an effective AI security strategy must be built on two pillars: a holistic framework that can absorb fast moving technologies like the Model Context Protocol (MCP), and deep contextualization that connects posture management, access controls, and runtime monitoring into a single, unified signal. Without visibility into runtime behavior, it is impossible to provide accurate recommendations for configuration and access control for AI agents. A unified platform that integrates these elements is essential for distinguishing legitimate agent actions from those that represent real risk.
Impact and Scope
This approach addresses the growing need for secure by design capabilities in the AI ecosystem. By connecting posture management with real time runtime monitoring, organizations can better understand and control the actions of autonomous agents. This context aware security model is critical as enterprises race to adopt AI while managing the associated risks. Early partnerships between AI providers and security vendors can help embed these principles into the development lifecycle, reducing the gap between innovation and security.
Source: Healthcareinfosecurity
