Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.
Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.
When investigating alerts about unusual cloud API activity from unfamiliar locations
When building detection rules for credential theft and abuse across cloud environments
When responding to notifications from cloud providers about exposed credentials
When monitoring for credential stuffing or brute force attacks against cloud identities
When assessing the scope of a credential compromise after initial detection
Do not use for preventing credential compromise (use MFA, credential rotation, and secrets management), for detecting application-level credential theft (use application security monitoring), or for endpoint credential harvesting detection (use EDR tools).
Prerequisites
AWS GuardDuty enabled across all accounts and regions
Azure Defender for Identity and Entra ID Protection configured
GCP Security Command Center with Event Threat Detection enabled
CloudTrail, Azure Activity Log, and GCP Audit Log centralized for analysis
SIEM integration for cross-cloud correlation of credential abuse indicators
Threat intelligence feeds for known malicious IP ranges
Workflow
Step 1: Detect Credential Compromise Indicators in AWS
Monitor GuardDuty findings and CloudTrail anomalies that indicate credential abuse.
# Detect impossible travel (same user from geographically distant IPs within short time)
date
"24 hours ago"
date
'
fields @timestamp, userIdentity.arn, sourceIPAddress, eventName
| filter userIdentity.type = "IAMUser"
| stats earliest(@timestamp) as first_seen, latest(@timestamp) as last_seen,
count_distinct(sourceIPAddress) as unique_ips by userIdentity.arn
| filter unique_ips > 3
'
Step 2: Detect Credential Abuse in Azure
Monitor Entra ID sign-in logs and Defender for Identity alerts for compromised credentials.
# Check for risky sign-ins
az rest --method GET \
--url "https://graph.microsoft.com/v1.0/auditLogs/signIns?\$filter=riskLevelDuringSignIn ne 'none' and createdDateTime ge 2026-02-16T00:00:00Z&\$top=50" \
--query "value[*].{User:userPrincipalName,Risk:riskLevelDuringSignIn,IP:ipAddress,Location:location.city,App:appDisplayName,Status:status.errorCode}" \
-o table
# Check for sign-ins from anonymous or Tor IPs
az rest --method GET \
--url "https://graph.microsoft.com/v1.0/auditLogs/signIns?\$filter=riskEventTypes_v2/any(r:r eq 'anonymizedIPAddress') and createdDateTime ge 2026-02-22T00:00:00Z" \
--query "value[*].{User:userPrincipalName,IP:ipAddress,Location:location.city}" \
-o table
# List users flagged as compromised by Identity Protection
az rest --method GET \
--url "https://graph.microsoft.com/v1.0/identityProtection/riskyUsers?\$filter=riskLevel eq 'high'" \
--query "value[*].{User:userPrincipalName,RiskLevel:riskLevel,RiskState:riskState,LastDetected:riskLastUpdatedDateTime}" \
-o table
# Check for suspicious application consent grants
az rest --method GET \
--url "https://graph.microsoft.com/v1.0/auditLogs/directoryAudits?\$filter=activityDisplayName eq 'Consent to application' and activityDateTime ge 2026-02-16T00:00:00Z" \
--query "value[*].{Activity:activityDisplayName,User:initiatedBy.user.userPrincipalName,App:targetResources[0].displayName}" \
-o table
Step 3: Detect Credential Abuse in GCP
Query GCP audit logs and SCC findings for credential compromise indicators.
# Check SCC Event Threat Detection findings
gcloud scc findings list ORG_ID \
--filter="state=\"ACTIVE\" AND (category=\"ANOMALOUS_CALLER_LOCATION\" OR category=\"SUSPICIOUS_LOGIN\" OR category=\"CREDENTIAL_ACCESS\")" \
--format="table(finding.category, finding.severity, finding.resourceName, finding.eventTime)"# Query audit logs for service account key usage from unusual IPs
gcloud logging read'
protoPayload.authenticationInfo.principalEmail:*@*.iam.gserviceaccount.com
AND protoPayload.requestMetadata.callerIp!=("10." OR "172." OR "192.168.")
AND timestamp>="2026-02-22T00:00:00Z"
' --limit=100 --format="table(timestamp, protoPayload.authenticationInfo.principalEmail, protoPayload.requestMetadata.callerIp, protoPayload.methodName)"# Detect API calls from Tor exit nodes
gcloud logging read'
protoPayload.requestMetadata.callerIp:("185." OR "198." OR "45.")
AND protoPayload.authenticationInfo.principalEmail:*@company.com
AND timestamp>="2026-02-22T00:00:00Z"
' --limit=50 --format=json
# Check for new service account keys created (persistence indicator)
gcloud logging read'
protoPayload.methodName="google.iam.admin.v1.CreateServiceAccountKey"
AND timestamp>="2026-02-16T00:00:00Z"
' --format="table(timestamp, protoPayload.authenticationInfo.principalEmail, protoPayload.request.name)"
Step 4: Build Cross-Cloud Correlation Rules
Create SIEM rules that correlate credential abuse indicators across cloud providers.
# siem_correlation.py - Cross-cloud credential abuse detectionimport json
from datetime import datetime, timedelta
defdetect_impossible_travel(events):
"""Detect same identity used from distant locations in short timeframe."""
user_events = {}
for event in events:
user = event.get('principal', '')
ip = event.get('source_ip', '')
ts = event.get('timestamp', '')
cloud = event.get('cloud_provider', '')
key = f"{user}_{cloud}"if key notin user_events:
user_events[key] = []
user_events[key].append({'ip': ip, 'timestamp': ts, 'cloud': cloud})
alerts = []
for user_key, accesses in user_events.items():
accesses.sort(key=lambda x: x['timestamp'])
for i inrange(1, len(accesses)):
time_diff = (datetime.fromisoformat(accesses[i]['timestamp']) -
datetime.fromisoformat(accesses[i-1]['timestamp']))
if time_diff < timedelta(hours=1) and accesses[i]['ip'] != accesses[i-1]['ip']:
alerts.append({
'type': 'IMPOSSIBLE_TRAVEL',
'user': user_key,
'ip_1': accesses[i-1]['ip'],
'ip_2': accesses[i]['ip'],
'time_gap_minutes': time_diff.total_seconds() / 60,
'severity': 'HIGH'
})
return alerts
defdetect_credential_stuffing(events, threshold=10):
"""Detect multiple failed logins followed by success."""
user_attempts = {}
for event in events:
user = event.get('principal', '')
success = event.get('success', False)
key = user
if key notin user_attempts:
user_attempts[key] = {'failures': 0, 'success_after_failures': False}
ifnot success:
user_attempts[key]['failures'] += 1elif user_attempts[key]['failures'] >= threshold:
user_attempts[key]['success_after_failures'] = Truereturn [{'user': u, 'failures': d['failures'], 'severity': 'CRITICAL'}
for u, d in user_attempts.items() if d['success_after_failures']]
Step 5: Respond to Confirmed Credential Compromise
Execute containment actions when credential compromise is confirmed.
# AWS: Deactivate access key immediately
aws iam update-access-key --user-name COMPROMISED_USER \
--access-key-id AKIA_COMPROMISED --status Inactive
# AWS: Invalidate temporary role credentials by updating role trust policy
aws iam update-assume-role-policy --role-name COMPROMISED_ROLE \
--policy-document '{"Version":"2012-10-17","Statement":[{"Effect":"Deny","Principal":"*","Action":"sts:AssumeRole"}]}'# AWS: Revoke all sessions for an IAM user
aws iam put-user-policy --user-name COMPROMISED_USER \
--policy-name RevokeOldSessions \
--policy-document '{
"Version":"2012-10-17",
"Statement":[{
"Effect":"Deny",
"Action":"*",
"Resource":"*",
"Condition":{"DateLessThan":{"aws:TokenIssueTime":"2026-02-23T10:00:00Z"}}
}]
}'# Azure: Revoke all sign-in sessions
az rest --method POST \
--url "https://graph.microsoft.com/v1.0/users/COMPROMISED_USER_ID/revokeSignInSessions"# Azure: Force password reset
az ad user update --id COMPROMISED_USER_ID --force-change-password-next-sign-in true# GCP: Disable service account
gcloud iam service-accounts disable COMPROMISED_SA_EMAIL
# GCP: Delete service account keys
gcloud iam service-accounts keys delete KEY_ID --iam-account=COMPROMISED_SA_EMAIL
Key Concepts
Term
Definition
Impossible Travel
Detection of the same credential being used from geographically distant locations within a time period that makes physical travel impossible
Credential Stuffing
Attack using stolen username/password combinations from data breaches to attempt login across multiple cloud services
Instance Credential Exfiltration
GuardDuty finding indicating EC2 instance role credentials are being used from outside the expected AWS network
Anomalous Behavior
Machine learning-based detection of API call patterns that deviate significantly from the established baseline for a principal
Session Revocation
Invalidating all active authentication sessions for a compromised principal to force re-authentication with new credentials
Persistence Indicator
Attacker actions designed to maintain access after initial compromise, such as creating new access keys or service account keys
Tools & Systems
AWS GuardDuty: ML-based threat detection with specific finding types for credential compromise and unauthorized access
Microsoft Entra ID Protection: Identity risk detection for sign-in anomalies, compromised credentials, and risky user behavior
GCP Event Threat Detection: SCC component detecting anomalous API usage and credential abuse in GCP environments
CloudTrail / Activity Log / Audit Log: API audit logs providing the raw data for credential compromise investigation
SIEM (Splunk, Elastic, Sentinel): Centralized platform for cross-cloud correlation of credential abuse indicators
Common Scenarios
Scenario: Detecting an Access Key Compromised via Phishing
Context: A developer receives a phishing email that harvests their AWS console credentials. The attacker logs in from a foreign IP, creates a new access key, and begins enumerating the account.
Approach:
GuardDuty triggers UnauthorizedAccess:IAMUser/ConsoleLoginSuccess.B for login from unusual country
SOC reviews the finding and correlates with phishing reports from the email security team
Query CloudTrail for all actions by the compromised user from the attacker's IP
Discover the attacker created new access keys and ran IAM enumeration commands
Immediately deactivate all access keys for the user and revoke active sessions
Force password reset and re-enroll MFA
Check for persistence: new IAM users, roles, Lambda functions, or EC2 instances created
Remove any persistence artifacts and document the incident timeline
Pitfalls: Simply changing the password does not invalidate existing access keys or active sessions. All access keys must be rotated and temporary credentials revoked by adding a deny-all policy for tokens issued before the compromise was detected. Attackers may create new IAM users or roles for persistence before the initial credential is revoked.
Output Format
Cloud Credential Compromise Detection Report
===============================================
Detection Date: 2026-02-23
Scope: Multi-cloud (AWS, Azure, GCP)
Period: 2026-02-16 to 2026-02-23
ACTIVE COMPROMISE INDICATORS:
[CRED-001] AWS Console Login from Unusual Location
User: developer@company.com
Source IP: 185.x.x.x (Russia)
Normal Location: US-East
GuardDuty Finding: UnauthorizedAccess:IAMUser/ConsoleLoginSuccess.B
Severity: HIGH
Status: Credential deactivated
[CRED-002] Azure Impossible Travel Detection
User: admin@company.onmicrosoft.com
Location 1: New York, US (09:00 UTC)
Location 2: Beijing, CN (09:15 UTC)
Risk Level: HIGH
Status: Sessions revoked, under investigation
DETECTION METRICS (Last 7 Days):
Impossible travel detections: 5
Anomalous API activity alerts: 12
Failed login attempts > threshold: 3
New credentials from unusual IPs: 2
Total compromises confirmed: 2
CONTAINMENT ACTIONS TAKEN:
AWS access keys deactivated: 3
Azure sessions revoked: 2
GCP service accounts disabled: 1
Passwords force-reset: 4
MFA re-enrolled: 4
