Enterprise AI Security
Enterprise AI Security: a practical guide for regulated organizations
Employees in legal, fintech and healthcare are already using LLMs at work. This guide covers the threat model, the essential controls and a rollout plan you can execute in weeks — not quarters.
The threat model
Enterprise AI security is not a model problem — it is a data-flow problem. Sensitive data leaves the organization the moment an employee pastes it into ChatGPT, Claude or an internal agent. Once inside a provider's logs or training pipeline, it is out of your control.
- Prompt leakage — PII, contracts and secrets pasted into public LLMs.
- Agent-to-agent handoffs — one agent forwards sensitive context to another without policy checks.
- Embedding drift — sensitive strings preserved in vector databases far longer than expected.
- Third-party plugins — tool calls that exfiltrate data outside your perimeter.
Essential controls
- Detection — hybrid regex + NLP models to identify PII, financial data and contracts in every prompt.
- Policy enforcement — tokenize, mask or block based on role, org and provider.
- Reversible tokenization — AES-256 vault so the business can still function.
- Multi-provider proxy — one integration for OpenAI, Anthropic and Gemini.
- Audit and evidence — immutable logs mapped to GDPR and the EU AI Act.
A 4-week rollout
- Week 1 — Run an AI Risk Assessment to map current AI usage across teams.
- Week 2 — Deploy Privaro as a proxy for one high-risk workflow (e.g. contract review).
- Week 3 — Roll out policies per role and enable DPO reporting.
- Week 4 — Extend to all providers, enable blockchain certification for regulated flows.
Where Privaro fits
Privaro is the enterprise AI security layer that sits between your applications and any LLM. It applies your policies before data reaches the model and records every decision for audit.