Notifications
Clear all
Topic starter
27/06/2026 1:47 pm
TL;DR: Reactive security models leave teams chasing alerts while attackers exploit identity, trust, and behavioural patterns faster than defenders can respond, according to Abnormal AI. The deeper issue is not tooling alone but whether security programmes can turn context, collaboration, and curiosity into repeatable decision-making before incidents escalate.
NHIMG editorial — based on content published by Abnormal AI: proactive security culture, AI-assisted defence, and the limits of reactive security
Questions worth separating out
Q: How should security teams reduce alert fatigue without missing real identity risk?
A: They should tie alerts to business context, ownership, and likely impact before escalation.
Q: Why do identity and trust attacks overwhelm reactive security programmes?
A: Because reactive programmes assume defenders have enough time to detect, investigate, and respond before compromise spreads.
Q: How can organisations tell whether their threat modelling is actually improving security?
A: Look for models that change when the business changes.
Practitioner guidance
- Embed business context into identity alert triage Map identity events to the applications, processes, and data they affect so analysts can separate meaningful risk from background noise.
- Rebuild threat models as living documents Refresh threat models whenever cloud services, access paths, or collaboration patterns change.
- Align SOC workflows with IAM ownership Define who can validate risky identity behaviour, who can revoke access, and who can approve exceptions before an incident happens.
What's in the full article
Abnormal AI's full article covers the operational detail this post intentionally leaves for the source:
- The article expands on the culture shift from reactive alert chasing to proactive security operations.
- It discusses how context, collaboration, and curiosity work together inside security teams.
- It outlines adaptive playbooks and risk-based prioritisation as operational enablers for faster response.
- It frames AI as an assistive layer for anomaly detection rather than a replacement for human judgement.
👉 Read Abnormal AI's analysis of proactive security culture and AI-assisted defence →
Proactive security culture: what IAM and SOC teams need to change?
Explore further
27/06/2026 3:15 pm
Reactive security is an identity governance problem before it is a tooling problem. The article describes alert fatigue, but the deeper failure is that programmes are still organised around post-event review instead of pre-event control of identity, trust, and behavioural risk. That makes the operating model too slow for modern attack paths, especially where identity is the entry point. Practitioners should read this as a governance failure mode, not just an analyst workload issue.
A few things that frame the scale:
- 90% of IT leaders say properly managing NHIs is essential for a successful zero-trust implementation, according to Ultimate Guide to NHIs.
- Only 5.7% of organisations have full visibility into their service accounts, which shows how weak identity oversight still is in practice.
A question worth separating out:
Q: Who should retain decision authority when AI is used in security operations?
A: Humans should retain authority over interpretation, escalation, and containment decisions. AI can help by finding weak signals in large data sets and ranking likely risk, but it cannot own the business context or ethical judgement required to act safely. Clear accountability prevents automation from becoming an excuse for weak governance.
👉 Read our full editorial: Proactive security culture is the real gap in modern cyber defense
