Securyze LLM Investigator - AI red teaming
Understand the whole LLM product in one picture.
A customer-controlled AI red-team sandbox, in your preferred GEO, that will attack the model, validating what actually matters, mapping the result to MITRE ATLAS and OWASP LLM, delivering one complete security assessment.
LLM Investigator provides a structured environment for authorized AI red teaming and LLM security testing. It helps security teams evaluate how private models, enterprise GenAI platforms and AI-enabled applications behave when exposed to adversarial techniques.
Testing can cover prompt injection, jailbreaks, encoding and evasion techniques, sensitive information exposure, unsafe model behaviour and weaknesses in the surrounding AI application or security controls.
The objective is not simply to generate attacks, but to understand how the complete AI system responds under realistic adversarial conditions.
Attack the AI system you actually operate — not just the model in isolation.
AI red teaming can generate large volumes of observations, but not every unusual response represents a meaningful security finding.
LLM Investigator combines attack execution with investigation, reproduction and validation. Potential findings are reviewed, correlated and prioritized to separate meaningful security issues from noise and non-reproducible behaviour.
Validated findings can then be mapped to recognized AI security frameworks such as MITRE ATLAS and the OWASP Top 10 for LLM and GenAI applications, providing a clearer connection between observed behaviour, known attack techniques and security risk.
The result is a more useful assessment for red teams, security engineers, architects and CISOs:
Attack. Investigate. Validate. Map. Explain.
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LLM Investigator turns AI red-team activity into a structured security investigation case with traceable evidence from testing through to the final finding.
An assessment can include executive and technical reports, prioritized findings, normalized tool results, authorized raw evidence, execution information, provenance, forensic information and framework mappings.
This provides security teams with more than a collection of scanner outputs. It creates a documented evidence trail that can support technical remediation, security review, risk decisions, governance and audit activities.
Sensitive prompts, model responses and investigation evidence can remain within a customer-controlled assessment environment, making the approach suitable for private models, enterprise AI platforms and business-critical GenAI applications.
From adversarial testing to evidence that security teams can act on.
