ENGINEERING CASE STUDY / 04
CyberShield AI.
Multiple threat signals, one explicit policy decision. An exploration of intelligent Zero Trust security.
PROJECT SCOPEEducational prototype. No independently verified detection accuracy is claimed.
01 / CONTEXT
The problem worth solving.
Email, network, behavior, and document signals describe different aspects of risk. A shared policy layer makes them actionable.
Engineering objectives
- Combine complementary detectors.
- Explain the path from signal to decision.
- Present events through a dashboard.
02 / SYSTEM DESIGN
Follow the flow.
Implementation details
- TF-IDF and logistic regression classify phishing.
- Isolation Forest and K-Means explore anomalies.
- Random Forest evaluates document structure.
03 / TRADEOFFS
The reasoning behind the code.
Separate detection and policy
Model outputs feed explicit access rules.
Reproducible demonstration
Synthetic datasets and simulated events drive the prototype.
Engineering challenges
Synthetic examples demonstrate integration but cannot establish real-world detection quality.
Security & performance boundaries
Untrusted document processing would need deployment isolation and further validation.
04 / EVIDENCE
What’s there. What’s next.
Four model pipelines, weighted risk scoring, and a monitoring interface are documented.
Explore the source, documentation, and tests to inspect the implementation and its current boundaries.
Read the repository