AI Security
Knostic Platform
Detects AI knowledge oversharing and enforces need-to-know access controls for enterprise copilots, LLMs, and AI agents.
Knostic Platform Overview
Knostic gives enterprises visibility and control over how copilots, large language models (LLMs), and AI agents access and expose internal knowledge. The platform centers on a need-to-know model: rather than only filtering prompts, it detects when an AI system surfaces or infers information that a given user is not authorized to see. It treats this failure mode, known as knowledge oversharing, as an access-control problem rather than content moderation.
The platform builds role-based access policies and data labels, then runs simulation tests across user personas before an assistant goes live, probing many prompt patterns per persona to expose inference-based oversharing and anomalous knowledge access. A Shadow AI module finds unsanctioned tools, and the Kirin component extends the same controls to coding assistants and Model Context Protocol (MCP) servers. It then monitors continuously, flagging policy drift and new exposure as permissions and content change.
Knostic was named a finalist in the 2025 RSA Conference Innovation Sandbox and appears in the Cloud Security Alliance Agentic AI Security Innovator Market Map. Backed by Seedcamp and DNX Ventures, the company serves security, governance, and engineering teams in regulated sectors including finance, healthcare, and government.
Key Capabilities
mapped to solution categoriesEnforces IAM-style policies on LLM API access, controlling which users and applications can invoke which models and data sources, with audit logging.
Enforces document-level access at retrieval time so a user receives only context they are authorized to see, filtering before the vector search, after retrieval, or both.
Intercepts prompts and completions to prevent sensitive data (PII, credentials, internal IP), from being transmitted to external LLM services or returned in model responses.
Evaluates model outputs against content policy, data classification rules, and format expectations before delivery to end users, blocking responses containing sensitive data or policy violations.
Detects sensitive or regulated data in AI training, fine-tuning, or third-party LLM flows without appropriate controls, such as unencrypted PII in inputs or PHI sent to external APIs.
Analyzes AI runtime behavior to surface prompt injection, anomalous data access, and model extraction as posture findings, exporting scores and telemetry to SIEM and SOAR rather than blocking inline.
Assesses the identities and service accounts that AI models, pipelines, and agents use, flagging over-permissioned non-human identities and access paths that violate least privilege. Reports identity risk as a posture finding, distinct from enforcing access policies at the model API at runtime.
Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.
Integrations
compatible toolsImplementation & support
Info last updated on June 26, 2026