Microsoft Sentinel's UEBA Behaviors Layer: A Strategic Shift in Multi-Cloud Security Operations

Security operations centers face escalating challenges in multi-cloud environments, where fragmented logs from AWS, GCP, firewalls, and identity systems create investigative bottlenecks. Microsoft Sentinel's newly launched UEBA Behaviors layer addresses this by applying generative AI to convert low-level telemetry into human-readable behavioral narratives. This capability answers the critical question: "Who did what to whom, and why does it matter?"

Beyond Alerts and Anomalies

Unlike traditional SIEM alerts (which signal threats) or anomaly detection (which flags irregularities), behaviors establish a neutral descriptive layer. They translate clusters of raw events—like AWS CloudTrail logs or Palo Alto firewall data—into contextualized security stories mapped to MITRE ATT&CK tactics. Each behavior includes entity tagging, natural-language explanations, and traceability to source logs.

Strategic Differentiation in Cloud Security

This positions Microsoft Sentinel uniquely against cloud-agnostic SIEM solutions:

• Cross-Platform Normalization: Processes data from AWS, GCP, CyberArk, and Palo Alto through consistent behavioral models
• AI-Powered Abstraction: Generative AI handles behavior logic, entity mapping, and MITRE taxonomy alignment
• Multi-Cloud Efficiency: Reduces time spent schema-hopping between cloud providers' native logging formats

Enterprise validation comes from early adopters like BlueVoyant: "These behaviors immediately highlight suspicious activities across multi-cloud environments, replacing hours of log sifting with contextual narratives."

Operational Mechanics

Two pattern-detection methodologies drive the system:

1. Aggregation Behaviors Detects volume-based patterns (e.g., "User accessed 50+ AWS S3 buckets in 60 minutes")
2. Sequencing Behaviors Identifies chained actions (e.g., "AWS access key creation → authentication from new region → privileged API calls")

Behaviors generate near-real-time as Log Analytics records, incurring standard ingestion costs. This architectural choice enables integration with existing Sentinel workflows.

Transformative Use Cases

Persona	Impact	Example
SOC Analysts	80% faster incident investigation	Querying BehaviorEntities for [email protected] instead of parsing raw CloudTrail
Threat Hunters	Proactive MITRE-based hunting	Detecting credential hopping across GCP and AWS via pre-mapped TTPs
Detection Engineers	Simplified rule creation	Correlating "Access Key Creation" behaviors with privilege escalation signals

Implementation Considerations

• Coverage: Currently supports AWS CloudTrail, GCP Audit Logs, and CommonSecurityLog (Palo Alto/CyberArk)
• Workspace Strategy: Single-workspace deployment per tenant recommended
• Pricing: Behaviors consume Log Analytics ingestion capacity at standard rates
• Migration Path: Coexists with Defender XDR behaviors; queries display combined results

Strategic Recommendations

1. Prioritize enabling behaviors for AWS/GCP environments lacking native UEBA context
2. Rebuild high-noise detections using behavior-based KQL (reducing false positives)
3. Monitor ingestion volume when activating aggregation behaviors

The Behaviors layer represents a fundamental shift from log-centric to behavior-centric security operations. By abstracting multi-cloud complexity into actionable narratives, it enables SOC teams to focus on strategic threat response rather than data wrangling.

Next Steps:

• Review Microsoft Sentinel documentation for prerequisites
• Enable via UEBA settings in your Sentinel workspace
• Begin querying BehaviorInfo and BehaviorEntities tables