ChatGPT Data Security & GenAI Data Security for Enterprises

Define policies once; the MCP Server enforces them across Microsoft Copilot (Word, Excel, Teams, etc.), enterprise ChatGPT, internal LLM/RAG apps, and backend LLM API workflows.

Examples: Block board/M&A docs from external LLMs; allow wiki snippets but strip customer names; route CUI/ITAR only to approved internal LLMs.

All the easy stuff. Also the hard stuff. The really hard stuff.

Complex unstructured content: IP/product docs, legal deals (NDAs, MSAs), executive/financial materials, customer support logs, regulated data (CUI/ITAR, clinical trials).

No. Classification runs inside your tenant. We never train global models on your data. You control logs, retention, and deletion—data removed when you uninstall.

Native tools are platform-specific, rely on manual labels/regex, and lack unified visibility. Secuvy adds cross-LLM classification, org-tuned unsupervised models, and global risk analytics (e.g., top risky prompts). Complements existing controls with a true LLM guardrail layer.

No. You can match speed of rollout to accomodate the needs of your organization. Start monitor-only, then targeted user groups for critical data, then warn/redact for the rest.

Example: User pastes board deck → Secuvy replies, “Contains Board-Restricted info—here’s a safe summary instead.” Users learn fast and keep productivity high with real protection.

Secuvy is built for teams that need LLM security and governance, not just generic DLP:

• CISOs / Security leaders who need guardrails for ChatGPT and internal LLMs
• Privacy, legal, and compliance teams that worry about regulated data landing in external models
• Data / AI platform teams rolling out corporate ChatGPT, RAG apps, and LLM APIs at scale
• Mid-market and enterprise orgs with lots of unstructured content (docs, decks, tickets, transcripts, wikis, etc.)

If your users are pasting sensitive docs into LLMs faster than you can write regex rules, you’re the target audience.

No.

Secuvy is LLM-agnostic and plugs in as a policy and classification layer, not a replacement for your LLMs.

• Keep using ChatGPT Enterprise, internal LLMs, RAG apps, and LLM APIs
• Add the Secuvy MCP Server to evaluate prompts and documents before they leave your environment
• Apply consistent policies across different tools instead of re-implementing rules N times

You keep your LLM stack; Secuvy standardizes classification, policy, and visibility across it.

Secuvy uses organization-specific, unsupervised classification:

• You point it at a sample of your real content (contracts, board decks, strategy docs, tickets, etc.)
• It learns how your org actually writes and structures information, including domain-specific terms
• You can optionally define or tweak labels and classes (e.g., Board-Restricted, CUI/ITAR, M&A, Customer PII)

Instead of spending weeks building regexes and keyword lists, you get a working classification model in hours, then refine it based on real results.

Secuvy is designed to stay out of the way:

• Classification and policy checks are optimized to run in milliseconds to low seconds, even on large docs
• You can choose where to apply heavy analysis (e.g., only for certain apps, doc types, or sensitivity levels)
• For some workflows, Secuvy can pre-classify common repositories (like SharePoint or internal wikis) to reduce runtime work

The typical user experience:

“My Copilot/ChatGPT still feels fast, I just get warned (or blocked) when I try to send something truly sensitive.”

Typical path:

• Free trial / early access – connect a small corpus, test classifications, and simulate policies.
• Pilot – enable monitor-only + targeted blocking for one or two business units.
• Refinement – tune classes and finalize policies.
• Scale out – extend to more teams and connect to SIEM/DLP.

Because policies are defined once and reused, expanding coverage is mostly about turning on more surfaces, not re-writing rules.

Admins typically:

• Review LLM usage and risk dashboards (top blocked prompts, risky teams, sensitive classes)
• Fine-tune policies and classes based on business feedback
• Handle exceptions (e.g., temporarily allow a highly trusted team or project)
• Export logs and reports for security, legal, and compliance reviews

You’re not constantly editing regexes—most work is policy decisions and tuning, not low-level rule maintenance.

Yes, and that’s the recommended path.

• Start with monitor-only: classify everything, block nothing
• Share early insights with stakeholders: “Here’s what people are putting into ChatGPT Enterprise today.”
• Agree on “never allowed” categories (Board-Restricted, CUI/ITAR, M&A, etc.)
• Enable targeted blocking + warnings once everyone is aligned

This avoids surprise friction and builds trust that Secuvy is a safety net, not a productivity killer.