Advanced
Automated Firewall Policies
Generate and maintain firewall policies automatically using ML-driven traffic analysis, microsegmentation, and intent-based networking principles.
ML-Driven Policy Generation
- Traffic learning: Monitor all traffic flows for 30–90 days to establish application communication patterns
- Flow clustering: Group related flows by application, service, and business function
- Rule generation: Create least-privilege allow rules for each legitimate flow group
- Default deny: Block everything not explicitly allowed
- Validation: Test generated rules against historical traffic to ensure no legitimate flows are blocked
Microsegmentation
AI enables granular microsegmentation at scale:
- Application discovery: ML identifies applications from traffic patterns without manual inventory
- Dependency mapping: Automatically map which applications communicate with which
- Policy generation: Create segment-to-segment rules based on discovered dependencies
- Drift detection: Alert when new flows appear that don't match existing policies
Zero trust alignment: AI-generated microsegmentation policies implement zero trust principles automatically. Instead of trusting everything inside the network perimeter, each application gets exactly the access it needs — nothing more.
Intent-Based Policies
Express security requirements as high-level intents, let AI translate to firewall rules:
| Intent | AI Translation |
|---|---|
| "Web servers can reach database servers on port 3306" | Allow TCP 3306 from web-tier subnet to db-tier subnet |
| "No lateral movement between workstations" | Deny all between endpoint VLANs, allow only to servers |
| "PCI segment isolated from general network" | Strict ACLs between PCI zone and all other zones |
Implementation approach: Start with one application environment (e.g., a web application stack). Run traffic learning in observation mode, generate policies, validate against logs, then enforce. Expand to additional applications iteratively.