Overview
Identity Threat Detection identifies and responds to attacks targeting user identities, credentials, and access privileges. As attackers increasingly bypass perimeter defenses by compromising legitimate identities, detecting malicious use of valid credentials has become critical. Identity-based attacks—including credential stuffing, session hijacking, privilege escalation, and insider threats—often blend with normal user activity, requiring sophisticated behavioral analysis to detect. Effective identity threat detection correlates signals across authentication events, access patterns, and directory changes to identify anomalies that indicate compromise. Good looks like detecting compromised credentials within minutes of first malicious use, automatically containing threats before lateral movement, and maintaining visibility across hybrid identity environments.
Architecture & Reference Patterns
Pattern 1: Identity-Centric SIEM Integration
Feed identity events (authentication logs, directory changes, privilege modifications) into SIEM with identity-specific detection rules. Enrich events with identity context (user risk score, role, access history) to improve detection accuracy. This pattern extends existing security operations investment with identity-aware detection.
IdP Logs → Log Collector → SIEM → Identity Detection Rules → Alert
↓ ↓
Directory Logs Identity Context Enrichment
↓ ↓
MFA Events User Risk Scoring
Pattern 2: Dedicated ITDR Platform
Deploy a purpose-built Identity Threat Detection and Response (ITDR) platform that specializes in identity attack patterns. These platforms understand identity-specific attack chains (Kerberoasting, Golden Ticket, Pass-the-Hash) and provide pre-built detections, behavioral baselines, and automated response actions. Recommended for organizations with complex AD environments or sophisticated threat actors.
Pattern 3: Zero Trust Continuous Evaluation
Implement continuous access evaluation using CAEP (Continuous Access Evaluation Profile) to revoke sessions in real-time when threats are detected. Instead of detecting and alerting, this pattern integrates detection directly into access decisions—when a threat signal fires, active sessions are immediately invalidated.
User Session Active → Threat Signal Detected → CAEP Event Published →
Applications Receive Signal → Sessions Terminated → User Re-authenticates
Key Decisions
| Decision | Options | Recommendation | Notes / Gotchas |
|---|---|---|---|
| Detection platform approach | SIEM-based, Dedicated ITDR, Hybrid | Hybrid | SIEM for breadth; ITDR for identity-specific depth |
| Behavioral baseline scope | Per-user, Per-role, Per-department, Global | Per-user + Per-role | Balance accuracy (per-user) with cold-start problem (per-role fallback) |
| Response automation level | Alert only, Semi-automated, Fully automated | Semi-automated | Full automation for high-confidence; human review for ambiguous |
| On-prem AD coverage | Agent-based, Agentless (DC logs), Hybrid | Hybrid | Agents for real-time; DC logs for complete audit trail |
| Cloud identity coverage | Native cloud logs, CASB integration, Direct API | Direct API to IdP | Native logs often lack context; direct integration provides richer signals |
| Threat intelligence integration | Commercial feeds, Open source, Internal only | Commercial + Internal | Commercial for breadth; internal IOCs for targeted threats |
Implementation Approach
Phase 0: Discovery
Inputs: Current security monitoring capabilities, identity infrastructure inventory, incident history, threat model Outputs: Identity threat detection gap analysis, current detection coverage map, baseline metrics (MTTD, MTTR), log source inventory with quality assessment
Phase 1: Design
Inputs: Gap analysis, detection requirements, response workflow requirements, integration constraints Outputs: Detection architecture design, use case prioritization matrix (by attack technique and likelihood), integration specifications, response playbook framework, success metrics definition
Phase 2: Build & Integrate
Inputs: Architecture design, selected tools, integration specifications Outputs: Detection platform deployed, log sources connected and validated, detection rules implemented (prioritized use cases), SIEM/SOAR integration completed, initial baselines established
Phase 3: Rollout
Inputs: Built platform, detection rules, response playbooks Outputs: Detection rules tuned (false positive reduction), SOC analysts trained, response playbooks validated through tabletop exercises, escalation procedures documented, go-live for production monitoring
Phase 4: Operate
Inputs: Production detection platform, monitoring procedures, threat intelligence feeds Outputs: 24/7 monitoring operations, weekly detection tuning, monthly threat hunting exercises, quarterly detection coverage reviews, continuous rule updates based on emerging threats
Deliverables
- Identity threat model documenting attack vectors and detection opportunities
- Detection architecture with component integration diagram
- Use case catalog with detection logic, severity, and response actions
- Behavioral analytics baseline methodology
- SOC playbooks for identity incident investigation
- Detection rule tuning guide with threshold adjustment procedures
- Threat hunting procedures for proactive identity threat discovery
- Metrics dashboard for detection program effectiveness
Risks & Failure Modes
| Risk | Likelihood | Impact | Early Signals | Mitigation |
|---|---|---|---|---|
| Alert fatigue from high false positive rates | H | H | SOC ignoring alerts, missed true positives, analyst burnout | Continuous tuning, risk-based prioritization, ML-based scoring |
| Gaps in log collection create blind spots | M | H | Attacks detected late or not at all, incomplete investigations | Log source inventory, collection monitoring, gap analysis |
| Sophisticated attackers evade behavioral detection | M | H | Breaches despite detection investment, "low and slow" attacks | Multiple detection approaches, threat hunting, red team exercises |
| Slow response due to manual processes | M | H | Long dwell time after detection, damage before containment | SOAR integration, pre-approved automated responses |
| Detection rules not updated for new attack techniques | M | M | Emerging attacks go undetected | Threat intel integration, regular rule reviews, vendor updates |
KPIs / Outcomes
- Mean time to detect (MTTD) identity threats (target: less than 15 minutes for known patterns)
- Mean time to respond (MTTR) to confirmed incidents (target: less than 1 hour)
- Detection coverage against MITRE ATT&CK identity techniques (target: greater than 80%)
- False positive rate per detection rule (target: less than 10%)
- Percentage of identity events with behavioral baseline (target: greater than 95%)
- Threat hunting findings per quarter (indicates detection gap discovery)
- Alert-to-incident ratio (efficiency measure)