Disrupt With AI
   Defend Against Risk

In this stage, organizations recognize the need for visibility into AI-enabled interactions as more SaaS applications integrate AI.

The primary focus is on evaluating the security and risk of these applications. This is a crucial step to understand what AI tools are being used and to begin assessing their potential risks.

This is the stage where many organizations currently find themselves. They are standardizing on a single genAI tool (e.g., Microsoft Copilot, Google Gemini) to establish a proper risk posture and create uniform policies. The goal is to mitigate the expanding risk surface by centralizing AI usage and control.

Moving beyond off-the-shelf tools, organizations in this stage build their own AI applications on local or cloud platforms. This expands the risk surface even further.

Key challenges include ensuring the application itself is secure and not vulnerable to exploits, as well as verifying that the data used to train the model is not sensitive.

This stage involves the deployment of autonomous AI agents that act as “10X users” with access to various applications and data within the organization’s environment. This phase introduces new challenges related to AI access and data privileges, as these agents operate independently and can interact with sensitive systems and information