Analyzing Office365 Audit Logs For Compromise

---
name: analyzing-office365-audit-logs-for-compromise
description: Use when parse Office 365 Unified Audit Logs via Microsoft Graph API
  to detect email forwarding rule creation, inbox delegation, suspicious OAuth app
  grants, and other indicators of account compromise. Use when working with analyzing
  office365 audit logs for compromise.
domain: cybersecurity
subdomain: cloud-security
tags:
- Office365
- Microsoft-Graph
- audit-logs
- email-compromise
- inbox-rules
- OAuth
- BEC
version: '1.0'
author: oyi77
license: Apache-2.0
nist_csf:
- PR.IR-01
- ID.AM-08
- GV.SC-06
- DE.CM-01
category: cybersecurity
---


# Analyzing Office 365 Audit Logs for Compromise

## Overview

Business Email Compromise (BEC) attacks often leave traces in Office 365 audit logs: suspicious inbox rule creation, email forwarding to external addresses, mailbox delegation changes, and unauthorized OAuth application consent grants. This skill uses the Microsoft Graph API to query the Unified Audit Log, enumerate inbox rules across mailboxes, detect forwarding configurations, and identify compromised account indicators.


## When to Use
**Trigger phrases:**
- "analyzing office365 audit logs for compromise"
- "Parse Office 365 Unified Audit Logs via Microsoft Graph API to detect email forw"


- When investigating security incidents that require analyzing office365 audit logs for compromise
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques

## Prerequisites

- Azure AD app registration with `AuditLog.Read.All`, `MailboxSettings.Read`, `Mail.Read` (application permissions)
- Python 3.9+ with `msal`, `requests`
- Client secret or certificate for authentication
- Global Reader or Security Reader role

## Steps

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. Authenticate to Microsoft Graph using MSAL client credentials flow
2. Query Unified Audit Log for suspicious operations (Set-Mailbox, New-InboxRule)
3. Enumerate inbox rules across mailboxes and flag forwarding rules
4. Detect mailbox delegation changes (Add-MailboxPermission)
5. Identify OAuth consent grants to suspicious applications
6. Check for suspicious sign-in patterns from audit logs
7. Generate compromise indicator report with timeline

## Expected Output

- JSON report listing forwarding rules, delegation changes, OAuth grants, and suspicious audit events with risk scores
- Timeline of compromise indicators with affected mailboxes
## When NOT to Use

- You need to perform the attack, not analyze it (use performing-* skills)
- Task is about detection, not analysis (use detecting-* skills)
- You need to implement controls (use implementing-* skills)
- Task is about threat hunting, not post-incident analysis (use hunting-* skills)
- You don't have access to the artifacts/logs to analyze
- Task requires real-time monitoring (use SOC tools)


## Red Flags

- Performing actions without explicit written authorization from the asset owner
- Testing against production systems without a defined scope and rules of engagement
- Treating compliance checklists as security guarantees rather than minimum baselines
- Failing to document exceptions and risk acceptance decisions
- Relying on point-in-time audits instead of continuous monitoring

## Process

1. **Scope** — Define research questions, identify data sources, set time boundaries
1. **Gather** — Collect data from primary sources, APIs, and public records
1. **Synthesize** — Analyze findings, identify patterns, produce actionable report

## Verification

- All steps executed successfully against a test environment before production use
- Output documented with screenshots or logs demonstrating expected behavior
- Vulnerabilities reproduced with proof-of-concept and impact analysis
- False positives filtered out through manual verification
- Fix recommendations include code-level remediation guidance

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |