Stress-tests your AI before your customers do.
Points the explainability engine outward: adversarially probes a customer’s own models or agents — prompt injection, jailbreaks, bias, unsafe tool calls — and hands back a plain-language report of what broke and why, not just a pass/fail score. The EU AI Act now legally requires this kind of pre-deployment safety validation.
Actively attempts prompt injection, jailbreaks, bias elicitation, and unsafe tool calls against a customer’s own AI.
Explains what broke and why for every successful probe, not just a numeric pass/fail score.
Built around the pre-deployment safety validation now legally required under the EU AI Act.
Planned design — this pipeline does not exist yet.
Runs a structured set of adversarial attempts — prompt injection, jailbreaks, bias elicitation, unsafe tool calls — against a customer's model or agent.
For every probe that succeeds, explains why it worked in plain language, not just that it worked.
Generates a report suited to the EU AI Act's pre-deployment safety validation requirement, not just an internal red-team log.
Each cell represents one probe category run against one target surface. This shows the planned run order — no probe library exists yet, so no cell has ever actually been filled.
The reasoning approach this concept is designed around — zoomed in on how a single probe would get scored.
Both outcomes shown above are illustrative placeholders — no probe library exists, so no verdict has ever actually been produced.
Same explainability core as Decision Intelligence and LegacySense, pointed at a customer’s own AI system as the subject rather than an internal model.
The differentiation from a generic red-teaming tool is the plain-language explanation attached to every successful probe.
Explicitly tied to the EU AI Act’s pre-deployment safety validation mandate, not a speculative compliance angle.
Like AgentSense, ClinicalSense, and LegacySense, this depends on Decision Intelligence’s core engine existing first.
Nothing below is built. This describes the intended design and its dependencies.
Rows are attack categories, columns are target surfaces. Shading marks planned build priority — it is not a test result, since nothing here has been built or run yet.
Prompt injection, jailbreaks, bias, unsafe tool calls — actively probed, not assumed absent.
Every successful probe gets a plain-language ‘why it worked,’ not just a score.
Built around an actual legal pre-deployment validation requirement.