AI Data Leak Prevention for Organisations
AI data leak prevention is becoming a critical requirement for organisations using tools like ChatGPT in daily work. Teams move fast, copy content between systems, and rely on AI for summarisation, rewriting, and analysis. This is where accidental disclosures can occur.
Trust-Prompt adds a local, preventive precheck layer directly in the browser. It reviews user input before content is sent to an AI system and warns or blocks when risk signals appear.
Who Trust-Prompt Is Designed For in AI Data Leak Prevention
Trust-Prompt is designed for organisations that operate under data protection, compliance, or regulatory constraints. It supports teams that use public AI tools but need stronger safeguards during everyday work.
- Compliance and Risk teams
- Data Protection Officers (DPOs)
- Security-aware Operations, Support, Product, and Marketing teams
- SMEs without enterprise-scale DLP infrastructure
Trust-Prompt is not a replacement for enterprise DLP or SIEM platforms. It complements existing controls by reducing risk earlier in the workflow.
Common Enterprise Use Cases
Most AI-related incidents are accidental. They happen during routine tasks, often under time pressure.
- Pasting support tickets or CRM entries into ChatGPT
- Rewriting internal documentation for external communication
- Drafting emails or policies using real customer examples
- Copying content from documents with internal labels or reference numbers
A detailed explanation of how the precheck works is available here: How Trust-Prompt works .
How Trust-Prompt Differs From Similar Tools
Several browser extensions attempt to detect sensitive content in AI prompts. Trust-Prompt focuses on one clear principle: prevention before sending.
- Local-first: all checks run in the browser
- Privacy-by-design: no prompt content is stored or transmitted
- Transparent rules: users see why a warning or block appears
- No black-box AI decisions: predictable and explainable behaviour
Scalability and Multi-AI Support
The Basic version focuses on ChatGPT to ensure reliable interception and a clear user experience. Future versions extend support to additional AI platforms.
To reduce false positives, Trust-Prompt relies on:
- Rule-based detection instead of opaque classifiers
- Clear thresholds and human-readable explanations
- Policy controls for Pro and Enterprise environments
Enterprise Policy Alignment
Enterprise environments require alignment with internal policies and regulatory frameworks. Trust-Prompt is designed to evolve toward policy-based configuration while maintaining a privacy-first approach.
A practical example of why timing matters in AI data protection is discussed in our analysis: ChatGPT data leak: what the U.S. government case reveals .
Measuring Value and Risk Reduction
Trust-Prompt does not promise zero risk. It reduces exposure where many incidents start: the moment users paste or type sensitive content into AI tools.
- Number of warnings and blocks triggered
- Recurring risk patterns in daily workflows (without storing content)
- Improved user awareness over time
- Support for compliance reviews and internal reporting
Helpful References
A Preventive Layer for Responsible AI Use
AI governance cannot rely only on detection after incidents occur. Organisations need safeguards that act before sensitive information leaves their environment. A local precheck layer closes this gap without adding heavy infrastructure.
For enterprise discussions or roadmap updates, contact us here: Contact Trust-Prompt .