The expansion of generative AI and new data security risks
The increasingly widespread adoption of generative artificial intelligence (GenAI) is creating a new landscape of data security risks. Organizations are facing an unprecedented attack surface, where uncontrolled use of AI tools, both approved and unauthorized (shadow AI), can expose sensitive data such as intellectual property, customer information, and credentials through prompts or responses. This phenomenon requires an innovative approach to data security, capable of adapting to the new challenges posed by AI.
The gaps in traditional governance and security measures
Traditional data security and governance solutions are not designed to address the challenges posed by AI. These measures lack visibility into the data accessible and exposed by AI systems, creating growing gaps in compliance and governance. Organizations struggle to monitor how third-party AI systems process, share, and store regulated data, increasing the risk of accidental breaches and continuous data exposure.
The risk of data exposure with generative AI
Generative AI increases data exposure when governance is weak. These systems can access, combine, and bring to light sensitive data on a large scale, including data that users would not normally find on their own. Without visibility into AI activity, these risks grow rapidly. Common risk areas include:
- Unauthorized AI tools that store prompts or uploaded data outside the organization's control
- Users entering sensitive data into public or consumer AI platforms
- High-volume AI activities, such as bulk retrieval, summarization, or cross-dataset analysis
The need for a unified solution for AI data security
To address these challenges, it is essential to adopt a unified solution for AI data security. This must provide visibility into data usage in both approved and shadow AI tools, unifying monitoring, data controls, and insights into AI activity. Such a solution helps reduce data exposure and enable secure and scalable AI adoption.
The differences between Data Security for AI and traditional solutions
Data Security for AI focuses on how users interact with AI systems and how these systems access, process, and expose data in real time. In contrast, traditional security measures are based on static rules and do not provide visibility into AI-driven data usage. Key differences include:
- Real-time monitoring of prompts, uploads, and responses involving sensitive data
- Unified visibility into both approved and shadow AI tools
- Consistent data protection controls across all AI interactions
The impact of the EU AI Regulation
The EU AI Regulation introduces new prescriptions on how organizations develop and use AI systems, especially those handling sensitive or regulated data. Organizations must ensure that AI security controls are integrated with existing compliance frameworks and that detailed records of AI activities are maintained for audit and investigation purposes.
How can AI data security support innovation?
AI data security does not necessarily slow down innovation. On the contrary, by providing visibility and controls over AI tools, organizations can adopt new technologies in a secure and responsible manner. This allows security teams to identify and mitigate risks without limiting access to tools that can improve productivity and efficiency.
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