Where this offering fits
Paladin identifies DeepGaze as a big-data analytics platform. A useful evaluation starts with the datasets, questions and authorised analysts, rather than treating the name as a generic malware product. Keep analytics outputs connected to source evidence so a reviewer can check what supports a conclusion.
Architecture and rollout decisions
Define a bounded dataset with approved handling and establish the questions the platform must help answer. Agree data preparation, access roles and the method for reviewing results. Use synthetic or properly authorised material for demonstrations. For UAE organisations, the source owner should approve any processing location and external access before real records enter an evaluation. Record limitations, uncertain matches and the steps a human analyst must complete before an output is acted on.
An evaluation scenario for this offering
Provide a small authorised dataset with known relationships and deliberately ambiguous examples. Ask analysts to answer defined questions and show the source records supporting each conclusion. Review a misleading correlation as well as a correct result. The evaluation should preserve the difference between a discovered pattern and an established fact. Keep the data synthetic or expressly approved, and document access and deletion so an exploratory analytics test does not become ungoverned production evidence collection.
Evidence to request for this scope
- Request the current DeepGaze capability and connector documentation for the selected use case.
- Compare outputs with known test answers and retain traceability to the underlying records.
- Define retention, export access and the process for correcting a misleading analytical result.