Topic starter
27/06/2026 2:01 pm
TL;DR: AI-era security is increasingly judged on detection of compromised accounts and socially engineered abuse, not just platform breadth, while Abnormal AI says it was named to the Forbes 2025 Cloud 100 for the third year in a row and entered the top 20 for the first time, citing 3,200-plus customers and 25% of the Fortune 500 as evidence of growth.
Editorial analysis by NHI Mgmt Group, based on content published by Abnormal AI: “Abnormal AI Secures Spot on Forbes 2025 Cloud 100 for Third Year Running”.
By the numbers:
- Abnormal AI says it was named to the Forbes 2025 Cloud 100 for the third year in a row and entered the top 20 for the first time.
- Abnormal AI says it is trusted by more than 3,200 organizations.
- Abnormal AI says it is used by 25% of the Fortune 500.
Key questions
Q: How should security teams detect identity compromise after authentication?
A: They should monitor what each identity actually does after login, including privilege use, command patterns, unusual data access, and cross-system movement.
Q: Why do socially engineered attacks remain hard to stop in cloud environments?
A: They succeed by exploiting trust relationships that already exist between users, mailboxes, and connected applications.
Q: What are the signs that human-behavior security controls are too narrow?
A: A narrow programme usually sees phishing or login anomalies but misses what happens next inside SaaS workflows.
Practitioner guidance
- Prioritise post-authentication detection Assess whether your controls detect suspicious action after a user has already authenticated, not just failed logins or blocked messages.
- Correlate email and SaaS activity Join mailbox telemetry with connected-application events so account abuse can be traced across business workflows instead of handled as isolated alerts.
- Map high-value user journeys Identify the user journeys most likely to be abused through social engineering, then tune behavioural baselines and response playbooks around those paths.
Bottom line: The article’s real security signal is not the Cloud 100 ranking itself, but the growing importance of detecting compromised human identities and socially engineered abuse across connected applications.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 4 days ago by NHI Mgmt Group
27/06/2026 3:36 pm
Human behavior security is becoming an identity control, not just a threat-detection category. The article’s framing matters because the real risk sits inside the human identity boundary, where compromised accounts and normal-looking actions overlap. That shifts the discussion from message filtering to identity governance across email and connected applications. Practitioners should treat behavior analytics as part of the identity plane, not an adjacent security add-on.
A question worth separating out:
Q: How should IAM and SOC teams decide where to focus behaviour analytics first?
A: Start with the user journeys that combine high business value, frequent external contact, and multiple connected applications. Those paths create the highest payoff for behaviour analytics because attackers can pivot from initial deception into trusted workflows quickly, and the business impact is usually immediate.
👉 Read our full editorial: AI-native human behavior security and Cloud 100 recognition
This post was modified 4 days ago by NHI Mgmt Group