Identity Threat Detection in Identity Management

This curriculum spans the design and operationalization of an enterprise identity threat detection program, comparable in scope to a multi-phase advisory engagement that integrates risk assessment, data architecture, behavioral analytics, detection engineering, and response automation across hybrid environments.

Module 1: Foundational Identity Threat Landscape and Risk Assessment

• Conducting a privilege audit across hybrid identity stores to identify overprovisioned accounts in Active Directory and Azure AD.
• Selecting identity telemetry sources based on signal reliability, including event logs, sign-in logs, and privileged access workstations.
• Mapping identity attack paths using MITRE ATT&CK framework to prioritize detection coverage for T1078 (Valid Accounts) and T1098 (Account Manipulation).
• Establishing risk thresholds for anomalous behavior, such as logins from high-risk countries or impossible travel, based on organizational travel patterns.
• Integrating threat intelligence feeds to enrich identity events with known malicious IPs, domains, and user agent anomalies.
• Defining scope for identity threat detection by excluding service accounts with static, non-interactive usage patterns from behavioral baselines.

Module 2: Identity Data Collection and Log Aggregation Architecture

• Configuring Windows Event Forwarding to centralize critical identity events (e.g., 4624, 4625, 4768) from domain controllers at scale.
• Normalizing identity log schemas across cloud and on-premises systems to enable correlation in a SIEM or data lake.
• Implementing log sampling strategies for high-volume identity sources to balance cost and detection efficacy.
• Deploying lightweight agents or APIs to collect identity data from SaaS applications without native logging integration.
• Enabling Azure AD audit and sign-in logs with appropriate retention policies aligned with incident response requirements.
• Securing log transmission channels using mutual TLS and ensuring integrity with hashing mechanisms like SHA-256.

Module 3: Behavioral Analytics and Anomaly Detection Models

• Establishing baseline login patterns for individual users and groups by analyzing time, location, and device frequency over a 30-day period.
• Tuning machine learning models to reduce false positives in peer-group analysis for global organizations with regional access norms.
• Implementing risk-based session controls using conditional access policies triggered by anomaly scores from identity protection systems.
• Handling model drift by retraining behavioral baselines quarterly or after major organizational changes like mergers.
• Excluding automated processes from behavioral models by tagging service accounts and non-human identities in the directory.
• Validating anomaly detection efficacy through red team exercises that simulate credential theft and lateral movement.

Module 4: Detection Engineering for Identity-Specific Threats

• Writing Sigma rules to detect Kerberoasting by identifying repeated service ticket requests with RC4 encryption.
• Creating correlation rules to flag Golden Ticket attacks via abnormal PAC validation patterns in domain controller logs.
• Developing detection logic for password spray attacks by aggregating failed logins across multiple accounts from a single source IP.
• Monitoring for abnormal consent grant activity in OAuth applications to detect malicious app registration exploitation.
• Implementing alerts for unexpected changes to high-privilege groups such as Domain Admins or Enterprise Admins.
• Correlating authentication failures with subsequent successes to detect brute-force and credential stuffing sequences.

Module 5: Privileged Access and PAM Integration

• Integrating Privileged Access Management (PAM) solutions with SIEM to monitor just-in-time (JIT) elevation requests and approvals.
• Enforcing time-bound access for privileged roles and auditing session recordings from PAM-managed systems.
• Mapping privileged roles across cloud platforms (e.g., AWS IAM, Azure RBAC) to detect privilege creep over time.
• Configuring break-glass account monitoring with multi-factor alerting and immediate revocation workflows.
• Validating that privileged sessions are brokered through jump hosts or PAM gateways to ensure full auditability.
• Enforcing credential rotation after privileged session termination using automated vaulting mechanisms.

Module 6: Incident Triage, Forensics, and Response Automation

• Developing runbooks for identity incident types, including account takeover, pass-the-hash, and token theft.
• Preserving forensic artifacts such as Kerberos tickets, LSASS memory dumps, and authentication timestamps during investigations.
• Automating account disablement and MFA reset via SOAR playbooks upon confirmation of credential compromise.
• Coordinating identity revocation across on-premises and cloud directories during cross-realm compromise scenarios.
• Reconstructing attack timelines using correlated identity events across endpoints, directories, and cloud services.
• Engaging HR and legal teams for insider threat cases involving identity misuse by terminated or disgruntled employees.

Module 7: Governance, Compliance, and Continuous Improvement

• Conducting quarterly access reviews for privileged and cross-system roles to enforce least privilege.
• Aligning identity threat detection controls with compliance frameworks such as NIST 800-63, ISO 27001, and SOC 2.
• Measuring detection efficacy using metrics like mean time to detect (MTTD) and false positive rates per rule.
• Managing rule lifecycle by deprecating stale detections and documenting changes in a centralized repository.
• Coordinating cross-functional tabletop exercises involving IAM, SOC, and IT operations to validate response readiness.
• Updating detection logic in response to new identity threats identified in industry threat reports or internal post-mortems.