Automated moderation handles the bulk of cases. Pattern matching catches obvious violations — anything containing "admin," substring matches for brand names, regex for leet speak variations. Good automation catches 95% of issues before human review.
Human review triggers should be explicit. What makes a username get flagged for manual review? High-value terms? Similarity to existing verified accounts? User reports? Define these triggers so your trust and safety team knows what to expect.
Escalation paths matter for disputes. First-tier support can handle simple username changes. Trademark claims need legal review. Celebrity impersonation might need identity verification. Build tiered escalation that matches complexity to expertise.
Monitoring catches issues after registration. Users change usernames. Previously allowed terms become problematic. Regular audits of username patterns help catch new abuse vectors before they spread.