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27/06/2026 2:02 pm
TL;DR: Custom detection rules in email security degrade over time, creating blind spots, false positives, and growing maintenance overhead for security teams, according to Abnormal AI. The shift to behavioral detection is less about replacing analysts than reducing brittle rule labor that scales poorly across modern threat volume.
NHIMG editorial — based on content published by Abnormal AI: Key Insights on behavioural email security and the limits of custom detection rules
By the numbers:
- Only 38% have automated certificate lifecycle management in place.
- 61% rely on spreadsheets or manual tracking for machine identity management.
Questions worth separating out
Q: How should security teams reduce dependence on custom detection rules?
A: Security teams should reduce custom rule dependence by using controls that learn normal behaviour, surface contextual evidence, and support repeatable triage.
Q: When do custom detection rules become a governance problem?
A: Custom detection rules become a governance problem when they outlive the conditions they were written for and require continuous tuning to stay useful.
Q: What should teams measure to know if behavioural detection is working?
A: Teams should measure whether the system reduces false positives, shortens investigation time, and still explains its decisions clearly.
Practitioner guidance
- Inventory rule dependencies and ownership Document every custom detection rule, its business purpose, and the person or team responsible for tuning it.
- Set expiry dates for handwritten logic Treat rule creation as a temporary response, not a permanent control.
- Shift to behaviour-linked evidence Prefer detections that surface contextual evidence such as sender history, reply-chain anomalies, and unusual request timing.
What's in the full article
Abnormal AI's full article covers the operational detail this post intentionally leaves for the source:
- How the behavioural model maps normal communication patterns for employees and vendors.
- How contextual evidence is surfaced for analyst triage instead of hidden inside detection logic.
- How removing rule dependencies changes onboarding, response workflow, and rule maintenance overhead.
- How the platform frames explainability for security teams that need to audit detections quickly.
👉 Read Abnormal AI's analysis of why custom email detection rules no longer scale →
Behavioral email security: what it means for SOC and IAM teams?
Explore further
27/06/2026 3:37 pm
Custom detection logic is a governance liability once the threat pattern changes faster than the control can be maintained. Rules are only as durable as the assumptions they encode, and email threats mutate continuously. The consequence is not just false positives, but a control estate that quietly drifts out of alignment with real attacker behaviour. Practitioners should treat rule-heavy detection as a lifecycle problem, not a one-time design choice.
A few things that frame the scale:
- Only 38% have automated certificate lifecycle management in place, according to The Critical Gaps in Machine Identity Management report.
- 59% of companies face greater difficulties auditing machine identities, primarily due to lack of clear ownership and limited visibility.
A question worth separating out:
Q: How can organisations keep email detection resilient as threats change?
A: Organisations can keep email detection resilient by reviewing control drift, limiting bespoke logic, and preferring context-aware detections that adapt as message patterns change. Resilience comes from controls that learn with the environment rather than from accumulating more one-off rules.
👉 Read our full editorial: Behavioral email security exposes the limits of custom detection rules
