AI Security
WitnessAI Platform
Discovers shadow AI, blocks prompt injection and data leakage at runtime, and enforces identity-based governance across enterprise AI apps and agents.
WitnessAI Platform Overview
WitnessAI is an AI security and governance platform that gives enterprises visibility and control over how employees and AI agents use generative AI. It runs as a network-level layer between users and AI applications, applying intent-based machine learning that classifies the meaning of each prompt and response rather than matching keywords. This lets security teams discover unsanctioned AI use, block AI-specific attacks, and enforce access policies without deploying endpoint agents.
The platform is organized into four functions. Observe catalogs AI applications, agents, and Model Context Protocol (MCP) servers across the environment, flagging shadow AI and scoring interaction risk in real time. Protect inspects traffic bidirectionally, blocking prompt injection and jailbreaks before they reach a model and tokenizing or filtering sensitive and harmful content in responses. Control routes prompts to approved models, enforces identity-based policies that map every action to a named user, and writes detailed audit trails. An Attack function red-teams models before production.
WitnessAI holds SOC 2 Type I and Type II attestations and runs in single-tenant, customer-encrypted environments with multi-region data residency. Founded in 2023 and based in Mountain View, California, the company is backed by Google Ventures, Ballistic Ventures, and Forgepoint Capital, and counts enterprises in aviation and payments among its customers. It was named a Fortune Cyber60 honoree and an SC Awards Excellence finalist.
Key Capabilities
mapped to solution categoriesDetects and blocks adversarial inputs designed to override system prompts, extract training data, or redirect model behavior. Detection approaches include pattern matching, input semantic analysis, and secondary model classification.
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.
Intercepts prompts and completions to prevent sensitive data (PII, credentials, internal IP), from being transmitted to external LLM services or returned in model responses.
Enforces IAM-style policies on LLM API access, controlling which users and applications can invoke which models and data sources, with audit logging.
Records prompts, completions, and metadata for all AI interactions with tamper-resistant storage, supporting compliance, forensics, and policy investigation.
Continuously stress-tests the product's own guardrails and filters against jailbreaks, prompt-injection payloads, and data-extraction attempts, then re-tightens policies after model or prompt changes. A self-validation loop within the runtime protection layer, distinct from the standalone AI Red Teaming discipline that tests AI systems end to end.
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.
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.
Maps the AI inventory and controls to EU AI Act risk classification, ISO/IEC 42001, and NIST AI RMF, generating auditable evidence for each framework.
Autonomously plans and executes multi-step adversarial campaigns against AI systems, emulating real attacker workflows across reconnaissance, exploitation, and escalation rather than running a fixed checklist of tests.
Compliance
certificationsIntegrations
compatible toolsImplementation & support
Info last updated on June 26, 2026