Monitors the LLM layer in production and blocks abuse at runtime. Every decision is traced to produce proof of compliance.
Direct and indirect injections (via RAG, documents, tools) caught and blocked at runtime, with no change to application code.
Filtering of dangerous outputs, masking of personal data and blocking of unauthorized actions.
Production monitoring of drift: hallucinations, misinformation and unexpected behavior.
ASR (Attack Success Rate) measured on your real system, to objectify exposure.
Time-stamped audit trail sealed in SHA-256, aligned with articles 9 to 15 of the AI Act.
Correlation with the continuously updated LLM vulnerability database.
Hook-up to your production models and agents
Analysis of LLM inputs and outputs at runtime
Abuse blocking and attack-rate measurement
Exposure report and time-stamped audit trail
Semantic analysis of streamed LLM inputs and outputs (SSE), with no change to application code.
Detection of hidden instructions in documents, RAG sources, pages and called tools.
Blocking of dangerous actions triggered by an agent: tool calls, exfiltration, alarm masking.
Quantification of the attack rate (ASR) on the real environment rather than a theoretical bench.
Detection of unfounded answers and unintended implicit contractual commitments.
Time-stamped SHA-256 chaining for enforceable proof, usable in the event of an audit.
Quantifying the attack rate before putting a new model or agent into production.
Continuous detection and blocking of abuse on already-deployed LLM systems.
Building the audit trail required by articles 9 to 15 of the AI Act.
Submit a system, receive an exposure report with the measured attack rate.