PrismSek1 in 5
AI prompts carry sensitive data before controls
AI
AI Security
Let your company use AI without your data training someone else's model.
The problem
Employees paste customer records into chatbots. Copilots read repositories nobody scoped. RAG pipelines happily index the HR share. AI adoption is not waiting for security review, so security has to move to where the prompts are.
How it works
What it covers
Data types
Prompts & completionsEmbeddings & vector storesFine-tuning datasetsRAG source documentsAgent tool calls
Detection
- Inline prompt and completion classification
- Shadow-AI discovery from OAuth, traffic, and endpoint telemetry
- Vector-store content scanning
- Training-set audits against regulated data types
Actions
- Real-time prompt redaction with user coaching
- Allow/deny policies per AI tool, team, and data type
- Pre-indexing filters for RAG pipelines
- AI usage and risk reporting for governance reviews
AI coverage
- ChatGPT, Copilot, Gemini, Claude, and custom internal LLM apps
- Vector databases and embedding pipelines
- Agent frameworks and tool-use flows
Deployment
SaaS multi-tenantDedicated single-tenantPrivate-cloud data plane
Regulations
GDPRHIPAAIndia DPDPEU AI Act readiness
83%
of orgs cannot see how GenAI is used today
0
sensitive records reaching external models after inline redaction
Common questions
Does this mean blocking ChatGPT?
Usually the opposite. Redaction and coaching let you sanction tools instead of banning them, because the sensitive spans never leave.
Can it see internal LLM apps, not just SaaS chatbots?
Yes. A lightweight SDK and gateway integration covers internal apps, RAG services, and agents with the same policies.
How does this relate to the EU AI Act?
PrismSek documents what data enters which models, which is the evidence base most AI governance frameworks, including the AI Act, ask for.
Solutions this powers
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