Security analytics · Digital services · 2025
Absher Insight AI
Explainable behavioural-risk prototype
Scores access events against explainable behavioural rules and surfaces unusual activity on an operations dashboard — built entirely on synthetic data.
- Role
- AI security solution contributor
- Status
- Hackathon prototype
- Evidence
- Public repository
- Year
- 2025
Evidence
What a reviewer can check today — and what is not claimed.
- Status
- Built during the Absher × Tuwaiq hackathon. An independent prototype on synthetic data — not affiliated with Absher or any government entity, and not a deployed security system.
- Evidence
- Public repository on GitHub(opens in a new tab)
FastAPI service, rules engine, dashboard, an API test and a plain account of what the numbers mean.
- Dashboard screenshots in the README(opens in a new tab)
Captured from the committed dashboard — every event shown is synthetic.
- Not claimed
- No production deployment, no government affiliation and no detection-accuracy figures are claimed.
- Stack
- Python
- FastAPI
- Pydantic
- scikit-learn
- pandas
- NumPy
Context
Built during the Absher Tuwaiq Hackathon as an independent demonstration of proactive, explainable risk monitoring.
The problem
Account-takeover signals in digital services are behavioural: the right credentials used from an unusual place, at an unusual hour, at an unusual rate. Monitoring usually reacts after an incident, and analysts also need to know why an event was flagged.
Constraints
- No real citizen data — every event is synthetic or simulated
- Every flag has to explain itself
- A hackathon timeline
Solution
A FastAPI service scores each access event with rules that state their reasoning — unusual location, late-night access, high action volume — and keeps live statistics that feed an operations dashboard. A simulator keeps the dashboard populated for demonstration.
My responsibility
AI security solution contributor
- Contributed to the proactive, behaviour-based security concept
- Helped shape the behavioural risk scenarios
- Supported the dashboard and decision-support design
- Kept the work privacy-first by building on synthetic data
Architecture & workflow
- 01 · Input
Access event
- 02 · Processing
Explainable rules
- 03 · Intelligence
Risk flag with reasons
- 04 · Output
Operations dashboard
How it works
- A /predict endpoint receives service type, location, login time and action count
- Rules check for unusual location, late-night access and high action volume
- Each response returns the flag, a rule-assigned score and the reasons behind it
- In-memory statistics expose totals, top services and a recent timeline
- Joblib and scikit-learn utilities support anomaly-model experiments
Decisions
Proactive over reactive
Designed around spotting risky behaviour early, rather than responding after something has already gone wrong.
Explainable rules before a black box
The live path uses rules that return the reasons behind each flag. Model utilities stay experimental, and the rule-assigned score is never presented as a calibrated probability.
Privacy by design, on synthetic data
Everything runs on synthetic behaviour, so the idea can be tested and demonstrated without touching real personal data.
Verified outcome
A runnable prototype: an endpoint that returns explainable rule-based flags, live statistics, a background simulator and a static dashboard, plus an API test for the response schema. No real-world detection performance is claimed.
Limitations
- Synthetic data only — there is no production deployment and no affiliation with any government service.
- Rules, not a trained detector: the model artifacts support experiments only.
- State lives in memory and resets on restart; there is no persistent event store.
- Tests cover the response schema, not detection quality.
What would come next
- Replay realistic benchmark data to measure false-positive rates
- Persist events and capture analyst feedback on each flag
- Compare a trained anomaly model against the rule baseline
The idea, sketched
True innovation does not wait for the perfect moment. It creates it.
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