Identity-based attacks are now the #1 vector for enterprise breaches. According to Verizon's 2025 Data Breach Investigations Report, over 80% of breaches involve compromised credentials — making identity threat detection (ITDR) one of the most critical capabilities a security team can invest in.
Identity Threat Detection and Response (ITDR) is the practice of detecting attacks that exploit compromised user credentials, service accounts, or privilege escalation paths — rather than malware or network intrusion techniques. Modern attackers increasingly favor identity attacks because they're harder to detect with traditional endpoint and network tools.
Traditional SIEM rules for identity threats are brittle — they fire on obvious anomalies (new country login) but miss sophisticated attacks that stay within "normal" parameters. AI-based ITDR systems work differently:
AI builds a behavioral profile for every user, service account, and device — capturing normal login times, typical locations, usual application access patterns, and average data volumes. Deviations trigger investigation, not just rule matches.
A single anomalous event (a login from a new IP) might be legitimate travel. But a new IP login followed by an MFA push-accept, followed by an unusual S3 download, followed by a Salesforce bulk export — that's a credential compromise chain. AI correlates these cross-source signals in real time.
If a user authenticates from London at 9am and from Singapore at 11am, that's physically impossible. AI identity threat detection flags this immediately, regardless of whether it matches a predefined rule.
Key Insight: Identity threats rarely look like traditional attacks. They use legitimate credentials, generate normal-looking event logs, and stay below threshold-based alert rules. Only behavioral AI can reliably catch them.
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