Knostic provides an AI security platform designed to govern and protect enterprise users, data, and AI tools. The platform offers visibility into unsanctioned AI usage, controls data access for AI assistants, and defends applications against malicious prompts. It secures the entire AI ecosystem, including coding assistants, autonomous agents, and MCP servers, ensuring data governance and compliance.
Funding
$3.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Large language models (LLMs) in enterprise settings often provide overly broad access to sensitive data, leading to potential data leakage and compliance violations. Existing access control mechanisms are insufficient to prevent users from accessing information beyond their need-to-know. This oversharing introduces significant security and privacy risks for organizations adopting AI technologies.
Solution
Knostic provides need-to-know based access controls for LLMs, enabling organizations to manage user permissions and prevent data oversharing. The platform automates the analysis of user roles and permissions to determine appropriate access levels. By enforcing granular access controls, Knostic ensures that users only receive the information they require, mitigating the risk of data exposure. The solution integrates with existing enterprise AI search tools and chatbots to provide personalized and secure access to institutional knowledge.
Target Audience
Knostic targets enterprises utilizing LLMs for internal knowledge sharing and AI-powered search, particularly those in regulated industries with strict data security and compliance requirements.
Features
- Automated analysis of user roles and permissions to determine need-to-know levels
- Granular access controls that restrict LLM responses based on user context
- Integration with enterprise AI search tools and chatbots
- Guided user flows that provide users with relevant information while preventing oversharing
- Ongoing analysis and remediation guidance to address improperly secured content and over-permissioned users
- Adoption readiness review to identify and mitigate residual risks before LLM rollout