Omniculus — Vision
Domain: omniculus.com Tagline (working): See the whole picture. Decide with a human.
What Omniculus is
Omniculus is a defensive security analytics research platform. It ingests security telemetry, fuses it into a knowledge graph, detects dangerous activity, and supports a human analyst's decisions. The name reads as omni (all) + oculus (eye) — a wide field of view over your own environment, in service of defending it.
What Omniculus is not
This boundary is the product, not a disclaimer. It is deliberately chosen and should survive every future design decision:
- Not a mass-surveillance system. It runs on data you are authorized to use: your own lab/network telemetry, public threat-intel feeds, and synthetic data. It is not for monitoring people you have no authority over.
- Not an autonomous actuator. It recommends and explains; a human decides and acts. There is intentionally no response-execution component in the codebase. Consequential alerts route to a person.
- Not a production weapon. It is developed and validated against synthetic data and lab replicas / digital twins — never pointed at infrastructure the operator does not own and is not authorized to test.
Why it exists
To study, in a safe lab setting, two hard questions:
- How do you make a detection system more vigilant over time without making it a black box — i.e. learning that stays explainable and auditable?
- How little can you collect and still answer the security question — can strong defensive analytics be built with data minimization rather than maximal collection?
Operating principles
| Principle | What it means in the code |
|---|---|
| Human-in-the-loop | investigate/ ranks and routes; no module executes a response. |
| Synthetic-first | ingest/synthetic.py is the default data source; real connectors are opt-in and authorization-gated. |
| Explainable detections | Every Alert carries the rule that fired and its evidence events. |
| Auditable learning | feedback/ logs every threshold change with the precision that justified it. |
| Data minimization | A design goal and research question, not an afterthought. |
Audience
Security researchers, blue teams, and students building intuition about detection engineering, data fusion, and accountable decision support.