LLM semantic recognition
Designed to detect ambiguous semantics, metaphorical language, and progressive attack intent beyond keyword or regular-expression matching.
Codiris
Your LLM security guardian.
Codiris secures LLM usage with semantic recognition, prompt assessment, output guardrails, and full-scenario AI data collection.
Highlight Features
Semantic LLM defense from prompt to output.
The source design positions Codiris as an LLM Security Guardian with semantic recognition, LLM risk assessment, prompt assessment, output guardrails, runtime monitoring, and AI data collection.
For review
LLM Risk Assessment
Semantic intent
Prompt control
Output guard
LLM risk assessment
Evaluates model interactions as security-relevant events rather than generic application traffic.
Prompt assessment
Assesses instructions, intent, and context before they become operational risk.
Output Guard
Applies policy attention to model responses and generated content.
Codiris secures LLM usage with semantic recognition, prompt assessment, output guardrails, and full-scenario AI data collection.
Source modules describe asset inventory and behavior audit for fragmented AI usage, opaque paths, and unclear responsibility boundaries.
Full-Scenario Data Collection
Collect LLM security evidence across access models.
Codiris organizes public LLM usage, API calls, enterprise AI services, and agent activity into an auditable security surface.
Multimodal non-intrusive access
Supports endpoint bypass monitoring, proxy control, and asynchronous integration for varied LLM operating paths.
Deep semantic reconstruction
Reconstructs prompt content, model reasoning parameters, and multi-round conversation context for review.
AI Visibility and Auditing
Expose LLM paths that would otherwise stay opaque.
Source modules describe asset inventory and behavior audit for fragmented AI usage, opaque paths, and unclear responsibility boundaries.
LLM asset inventory
Builds a working register of models, applications, access paths, prompts, and generated-output surfaces.
Conversation audit trail
Links prompt, response, model context, and user session for security review.
AI Risk and Compliance Monitoring
Govern generated content and model interactions.
Codiris connects prompt assessment, output guardrails, runtime monitoring, and compliance workflows so LLM security can be reviewed as an operating process.
Runtime monitoring
Monitors model interaction behavior while requests and responses remain operationally actionable.
Compliance workflow connection
Routes risk signals into review, investigation, and evidence-management processes.
Industry Solutions
LLM security scenarios for regulated operations.
Codiris source material repeats oil and energy, aviation, and transportation and shipping as scenario contexts. They remain scenario directions rather than deployment proof.
Oil and Energy
LLM guardrails for industrial knowledge workflows and safety-sensitive operations.
Aviation
Assessment and monitoring patterns for LLM use in regulated aviation contexts.
Transportation and Shipping
Security review patterns for distributed logistics, route, and operations-assistance use cases.