Anomaly Detection in Access Logs with AI

The Core Idea

Access-control systems generate rich event logs—entries, denials, and door states.

AI Surfaces Anomalies

AI surfaces anomalies: unusual after-hours access, repeated failed attempts, tailgating indicators, and atypical movement sequences across zones.

live demo · related simulation● LIVE

Feature Engineering for Insights

Feature engineering aggregates temporal patterns, peer group baselines, and role-specific norms.

Frequently asked questions

What does AI do to detect anomalies in access logs?

Outputs integrate with video for human verification and create actionable tickets with audit trails. Privacy-by-design constrains scope and retention.

How does the system aggregate temporal patterns and peer baselines?

Aggregate temporal patterns, peer baselines, and role norms. Detect after‑hours anomalies, repeated failures, tailgating, and atypical cross‑zone sequences.

How does graph analytics help identify suspicious activity?

Use graph analytics to reveal suspicious linkages; score events with risk and context. Link to video for verification; open tickets with audit trails.

How does the system manage data privacy during analysis?

Constrain analysis scope and retention; enforce purpose‑limited queries and role‑based access.

▶ Try it live

Everything above runs in your browser — open Force-Directed Graph and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Force-Directed Graph simulation