
This is the third part of a series on SentinelOne’s Prompt Security solution. In my previous articles, I wrote about risks related to employees and developers. This time, we’re focusing on AI applications built in-house by organizations—I’m referring here to things like chatbots designed for customer service, as well as assistants powered by the organization’s own data.
A different risk profile than Shadow AI
When a company builds its own language model-based application—such as a chatbot, a semantic search engine, or an internal assistant—the risk is no longer limited to what an employee pastes into a public tool. That risk also extends to what an outsider might enter into the application that the company itself makes available to customers or its team.
This is an entirely different category of threats, requiring a different approach to security.
Four key risk areas in our own applications:
- Prompt injection and jailbreak – a maliciously crafted query can cause the model to act in a way contrary to the app developer's intent or to disclose information that should remain hidden
- Denial of wallet—massive, automated queries that generate significant, real-world AI infrastructure costs without any business value
- Remote Code Execution (RCE) and Other Attacks Exploiting AI Application Integrations with Backend Systems
- Data leaks when integrating applications with LLM models or vector databases—without proper filtering, sensitive data can end up where it shouldn't.
Add to that the reputational risk: an AI application without content moderation can generate a response that is inappropriate, harmful, or simply inconsistent with company policy—and your customer sees this directly.
≫ How does Prompt Security respond to this?
The Prompt Security platform includes:
- AI Risk Management – protection against prompt injection, jailbreaking, denial-of-wallet attacks, RCE, and other threats specific to LLM applications
- Preventing Data Leaks – filtering and masking sensitive data when integrating applications with language models or vector databases while ensuring compliance with regulations
- Content moderation – preventing the generation of content that is inappropriate, harmful, or in violation of company policy before it reaches the end user
It is important to note that all of these security measures operate without affecting the user experience, meaning that the protection is invisible to the end user.
For organizations with high requirements, an on-premises option is also available
For companies with stringent data requirements—such as those in the financial sector, the public sector, and critical infrastructure—Prompt Security offers on-premises deployment via Kubernetes. This provides full control over infrastructure and data while ensuring compliance with internal security policies, without the need to transfer data outside the organization.
What's next?
In the next installment of Sentinel One’s Prompt Security series, I will discuss the security aspects of AI agents and the MCP protocol as a new front in today’s AI security landscape.
Are you building your own AI app and wondering how to secure it?
Contact us, and we'll help you choose the right scope for implementing Prompt Security within your organization's architecture.
Schedule a demo: https://outlook.office.com/book/KonsultacjaPromptSecurity@ISCG.onmicrosoft.com/s/Lc5jNKTyHUSx5as_9eqXOw2?ismsaljsauthenabled


