Intelligent Caching.
Caching ABIS responses reduces latency and API costs. But cache behavioral data incorrectly, and you'll miss real-time threats. Balance freshness with performance.
Behavioral intelligence is inherently time-sensitive—a user's risk profile can change within minutes. Caching must balance two competing forces: performance optimization (reduce API calls) and data freshness (detect emerging threats).
Critical decisions: what to cache (user baselines vs real-time scores), cache duration (TTL based on risk level), cache key design (user+session+action composite keys), and invalidation triggers (manual invalidation after high-risk events).
Advanced patterns include: write-through caching (update cache after ABIS calls), probabilistic early expiration (refresh before TTL expires), and cache warming (pre-populate cache for known users during off-peak hours).
USER BASELINE CACHE
Cache user behavioral patterns and historical risk profiles. These change slowly and are expensive to recalculate.
TTL: 24 HOURSDEVICE FINGERPRINT CACHE
Cache device reputation scores. Devices rarely change characteristics within a session.
TTL: 12 HOURSREAL-TIME RISK CACHE
Short-lived cache of recent risk assessments. Valid only within same session for identical actions.
TTL: 5 MINUTESGEO/IP REPUTATION
Cache IP address reputation and geographic risk factors. These change infrequently.
TTL: 6 HOURS