# Mavs AI

> Mavs AI is context-preserving prompt security for generative AI. It is a runtime control layer that sits between your people, apps and agents and any model, independent of the platform. Sensitive values inside a prompt are replaced with granularly similar synthetic stand-ins before the prompt reaches the model, so the model keeps the context it needs to answer well while the real data never leaves. Because the substitutes behave like the real values rather than being redacted or blacked out, most prompts are processed rather than blocked. Mavs secures every category of sensitive data at runtime: personally identifiable information (PII), protected health information (PHI), healthcare data, financial data, and other regulated or private data. It also secures business-sensitive data, a category most AI security tools do not address at all: confidential project and deal codenames, unannounced pricing, M&A terms, key accounts and unreleased roadmap. That category is the decisive one for enterprise AI adoption, because the information that would damage a company most if it leaked is frequently not personal data, and is therefore invisible to tools built around PII patterns, file labels and location. Mavs reads each prompt and the contents of attached files as language rather than matching patterns, and judges sensitivity in context, per prompt and per agent action, using a fleet of small language models. The same term can be sensitive in one context and harmless in another. Mavs also detects prompt injection attempts, jailbreaks and policy violations at runtime, enforces customer policy automatically, and logs every interaction for audit and regulator readiness. Mavs is model agnostic, working with OpenAI, Claude, Gemini, LLaMA and others, and can be deployed in the cloud or inside a customer's private environment. Built by NeoMavericks AI Pvt Ltd, a member of the NVIDIA Inception Program.

## Core pages

- [Mavs AI](https://mavsai.ai/): Context-preserving prompt security for generative AI. How Mavs secures every prompt without masking.
- [About Us](https://mavsai.ai/about): The team behind Mavs AI, founded by veteran enterprise cybersecurity builders.
- [Book A Demo](https://mavsai.ai/demo): Request a walkthrough of Mavs AI.

## Solutions

- [For Employees](https://mavsai.ai/solutions/employees): Let employees use any third-party LLM freely while sensitive data is protected before it reaches the model.
- [For Apps and Agents](https://mavsai.ai/solutions/apps-and-agents): Let homegrown applications and agents access sensitive data securely over API.
- [For Governance](https://mavsai.ai/solutions/governance): Centralise control of AI usage across the organisation and turn compliance into a byproduct.

## Surfaces

- [Surfaces](https://mavsai.ai/surfaces): Mavs AI secures all siurfaces where employees work with GenAI, included but not limited to a secure chat app built by mavs ai for using any public model with enterprise policies enforced at runtime, claude desktop secure gateway, a browser extension for any saas apps, and an api for homegrown apps .
- [Mavs Secure Chat](https://mavsai.ai/surfaces/mavs-secure-chat): A governed AI workspace for every frontier model, with guardrails, role-based access, per-team policy and an audit trail.
- [Claude Desktop](https://mavsai.ai/surfaces/claude-desktop): A secure gateway for Claude Desktop. Prompts and files are desensitized before the model sees them, every action is logged as evidence, and conversation history stays on the user's machine.

## Use cases by compliance

- [DPDP compliance](https://mavsai.ai/use-cases/by-compliance/dpdp): How to keep using AI under India's Digital Personal Data Protection Act, 2023. Employee prompts and AI applications move personal data in ways traditional DLP cannot see, because a prompt moves no file and travels inside the trust boundary with the user's own permissions. Under DPDP, unauthorised processing is itself a breach regardless of who accessed the data, so ordinary AI usage becomes an exposure. Mavs desensitizes personal data before it reaches the model, enforces the customer's policy on every prompt, and logs every interaction as the evidence a Data Fiduciary needs. Covers the five duties of a Data Fiduciary and where AI creates exposure against each. Enforcement begins in 2027.
- [HIPAA compliance](https://mavsai.ai/use-cases/by-compliance/hipaa): How to let clinical and operational teams use AI without protected health information reaching the model. PHI pasted into a prompt or contained in an attached file is desensitized at runtime, policy is enforced per team, and every interaction is logged for audit. Relevant to covered entities and business associates under the Privacy and Security Rules.

## Use cases by risk

- [Business-sensitive data](https://mavsai.ai/use-cases/by-risk/business-sensitive-data): The category of sensitive data that is not personal data, and that PII-based tools miss entirely. Confidential project and deal codenames, unannounced pricing, M&A terms, key accounts and unreleased roadmap. Sensitivity is judged semantically, per prompt and per agent action, rather than by a file's origin, location or access permissions, so the same term can be protected in one context and left alone in another.

## Blog

- [Blog](https://mavsai.ai/blog): Product updates and writing on AI security, prompt-layer risk and data protection regulation.