In traditional environments, code is predictable. In AI, the model’s behavior is non-deterministic. Attackers don’t just look for software bugs; they exploit the “logic” of the model through prompt injection, data poisoning, or model evasion. Because AI systems are often integrated directly into corporate databases to provide answers, a compromised model acts as a privileged gateway to sensitive internal information.

Employees often use unauthorized AI tools (Shadow AI) to process company data, leading to accidental data leakage. Furthermore, when authorized AI agents are connected via APIs to internal systems (like Slack, Jira, or Customer Databases), they create new automated attack paths. If the AI isn’t properly “sandboxed,” an attacker can trick the AI into executing malicious commands or exfiltrating data from those connected systems.

Bad actors use “Jailbreaking” techniques to bypass safety filters, forcing the AI to reveal its underlying system prompts or training data. They may also target the AI Supply Chain, compromising the open-source libraries or pre-trained models that developers download. Once an attacker controls the input or the model’s environment, they can move laterally from the AI interface into the core cloud infrastructure.

XM Cyber extends its attack path modeling to the AI attack surface, identifying the hidden links between your models, MCP servers, agentic agents and your critical assets. The platform provides: 1. AI Attack Surface Discovery: Automatically maps all AI services and “Shadow AI” instances across your hybrid cloud. 2. AI Exposure and Attack Path Validation: Tests if a prompt injection or a compromised AI API key can actually lead to a breach of your “crown jewel” databases. 3. Enforce AI Security and Compliance Policies: Identifies where sensitive PII (Personally Identifiable Information) is being fed into training sets or RAG (Retrieval-Augmented Generation) systems without proper access controls.