WitnessAI Platform

Detects 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.

Discovers, governs and allowlists the Model Context Protocol servers and tools that AI agents are permitted to invoke.

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.

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.