The AI Security Challenge
Organizations have rapidly adopted AI tools without formal security planning, creating a critical visibility gap. AI agents, chatbots, and embedded models now operate across enterprise environments with access to customer data, employee information, and code repositories. Most security teams cannot answer the fundamental question of what their AI systems can actually access. Traditional security tools only address fragments of the AI security challenge—some focus on discovery, others on monitoring or governance—but AI security requires a unified approach that understands the full data context across all AI touchpoints.
The Three-Pillar Framework
Effective AI security requires three integrated components working together. First, visibility and posture management discovers all AI assets including shadow AI, scanning code repositories and assessing what each agent can access. Second, runtime protection provides real-time monitoring and guardrails that understand data context and can block dangerous actions like unauthorized data exfiltration or policy violations. Third, governance ensures compliance with evolving AI regulations through third-party risk management, audit-ready reporting, and supply chain AI oversight. Missing any pillar creates security gaps that leave organizations vulnerable as AI adoption accelerates.
