Prompt Security

Developer integrates Prompt Security SDK into the chatbot's backend architecture.

SDK intercepts incoming user prompts before they are sent to the foundational model (e.g., OpenAI).

The Prompt Security engine analyzes the prompt against known attack signatures and behavioral models in <50ms.

If classified as malicious, the request is blocked and a safe, canned response is returned to the user.

IT deploys the Prompt Security browser extension to all employee endpoints.

An employee attempts to paste proprietary code or API keys into the public ChatGPT interface.

The extension detects the sensitive syntax and patterns locally before the form is submitted.

The prompt is either blocked with a warning to the employee or sensitive variables are redacted dynamically.

Administrator configures network-level integrations or deploys endpoint agents.

The system passively monitors outbound traffic, matching against a database of thousands of GenAI applications.

The central dashboard aggregates usage metrics, risk scores, and data volumes per user and application.

Administrators create access policies to outright block high-risk tools while approving enterprise-ready alternatives.

Security team integrates Prompt Security with the enterprise M365 tenant.

The platform continuously monitors Copilot queries and the context retrieved from SharePoint/OneDrive.

Data access governance rules are applied to identify if highly sensitive documents are being inappropriately summarized.

The transaction is flagged or blocked, and compliance reports are generated for auditors.

Connect the Prompt Security API middleware between the RAG retrieval mechanism and the LLM generation step.

Scan the initial user query for malicious intent or prompt injection attempts.

Scan the retrieved vector database context for unexpected sensitive data leakage.

Scan the final LLM output to prevent hallucinations or malicious payload delivery back to the user.