Intermediate
Deploying AI IDS/IPS
Deploy AI-powered intrusion detection and prevention in production networks with proper architecture, performance, and security operations integration.
Deployment Modes
| Mode | Placement | Action | Risk |
|---|---|---|---|
| Passive/TAP | Mirror port or network TAP | Alert only | None (no traffic impact) |
| Inline IDS | In traffic path | Alert only | Low (fail-open) |
| Inline IPS | In traffic path | Block + alert | Medium (false positive blocking) |
Deployment strategy: Start in passive mode to validate detection accuracy. Move to inline IDS once false positive rates are acceptable. Only enable IPS (blocking) for high-confidence detections with proven accuracy.
SIEM and SOC Integration
- SIEM forwarding: Send alerts to Splunk, Elastic SIEM, or Microsoft Sentinel via syslog/API
- SOAR playbooks: Trigger automated investigation and response workflows
- Threat intelligence: Enrich detections with IOC feeds and reputation data
- Case management: Create and track investigation cases from AI detections
Performance Requirements
- Throughput: Must process traffic at line rate (1G, 10G, 100G depending on deployment point)
- Latency: Inline IPS adds microseconds to milliseconds of latency; must be within SLA
- Availability: Fail-open capability to prevent IDS/IPS from becoming a single point of failure
- Scalability: Horizontal scaling for high-traffic environments
Production tip: Deploy AI IDS sensors at network boundaries (perimeter, DMZ), internal segmentation points, and cloud VPC flow log analysis. This layered approach provides defense in depth without requiring every link to be monitored.
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