automationSIEMsecurity

Automated Detection of Credential Leaks: Using Email Logs and Threat Feeds

UUnknown

2026-02-18

10 min read

Wire threat-feed APIs and email telemetry into SIEM/SOAR to auto-flag leaked credentials and trigger containment playbooks.

Hook: Stop the slow bleed—automate credential-leak detection before attackers use them

Every security team I meet can quote a nightmare scenario: an employee’s credentials show up in a breach feed, but that signal sits in a ticket queue for days while attackers mount credential-stuffing or takeover campaigns. In 2026, with social platforms like LinkedIn and Facebook facing waves of password-reset and account-takeover attacks, that delay is unaffordable. The high-value fix is not just better threat feeds or more logs—it's wiring those signals together into automated credential-leak detection and workflows that flag a leaked credential and trigger containment actions instantly.

Executive summary (most important first)

Automated credential-leak detection combines three realtime data streams: threat-feed API hits (breach and credential lists), email-security telemetry (authentication failures, password-reset email volume, outbound mail anomalies), and identity & access logs (login attempts, OAuth token changes). When these signals are enriched and correlated in a SIEM/SOAR, pre-approved containment playbooks can run automatically: force password resets, revoke sessions and tokens, isolate mailboxes, block suspicious IP ranges, and notify affected users and security operations teams.

Later in this article you'll get:

Concrete integration patterns (webhooks, polling, STIX/TAXII, MISP)
Sample SIEM/SPL/KQL queries and enrichment schemas
Actionable playbook templates and automation-safe guardrails
Compliance and privacy controls for handling leaked credentials

Why this matters in 2026

Late 2025 and early 2026 saw renewed waves of credential-based attacks across social platforms and enterprises, demonstrating that even as passkeys gain traction, credential leaks remain a primary attack vector. Threat feeds got richer (higher fidelity breach contexts, hashed credential lists, and STIX2 bundles), and SIEM/SOAR platforms have matured with native API integrations and event-driven webhooks. That convergence makes automated containment both practical and necessary.

Trends shaping automation

Richer threat feeds: Providers now deliver contextual breach metadata (source, exposure date, password hash type) and standardized STIX/TAXII bundles.
Real-time email telemetry: Modern MX/MTA providers, secure mail gateways and cloud suites publish webhooks for auth events, Dmarc/DMARC failures and outbound anomaly alerts.
SOAR-first playbooks: Automation platforms emphasize low-latency API playbooks and “safe mode” actions to reduce false-positive impact.
Compliance guardrails: Privacy frameworks require careful PII handling when storing breached credential data—especially hashed passwords or login timestamps.

Architecture: How the pieces fit

At a high level, build three layers:

Ingestion & normalization — threat feed APIs and email telemetry feed into your SIEM or message bus (Kafka, SQS).
Enrichment & correlation — device context (HR/IdP), login telemetry, and device context enrich feed hits; correlation engine computes a threat score.
Containment & playbooks — SOAR or automation runner executes pre-approved actions based on score and policy.

Data sources and examples

Threat feeds: breach lists (hashed/email/password), credential stuffing lists, paste sites (via APIs from providers like Recorded Future, HaveIBeenPwned, MISP integrations, VirusTotal). Use STIX/TAXII where offered.
Email telemetry: authentication failures, password-reset email counts, suspicious outbound spikes, DMARC aggregate alerts, mailbox forwarding rules created, and SMTP relay logs.
Identity & access logs: IdP (Microsoft Entra ID/Okta) sign-in logs, device signals, geolocation and impossible-travel flags.
Network & endpoint: IP requests, firewall blocks, endpoint EDR signals that indicate lateral movement attempts.

Integration patterns: webhooks, polling, and STIX/TAXII

Choose integrations that match vendor capabilities and your latency needs.

1. Webhooks (preferred for low latency)

Subscribe to provider webhooks for breach hits and email-security events. Forward webhook payloads into a validation layer that performs schema checks and deduplication.

{
  "source": "breach-feed.example.com",
  "type": "credential-leak",
  "indicator": "alice@example.com",
  "password_hash": "sha1:5baa61e4...",
  "exposure_date": "2025-12-31T12:00:00Z",
  "confidence": 0.8
}

Validation should check signature headers, TLS client certs, and HMAC signatures to ensure authenticity.

2. Polling APIs

For feeds that rate-limit or lack webhooks, poll periodically. Cache ETags, respect rate limits, and use incremental sync tokens where available.

3. STIX/TAXII and MISP

Use STIX2 bundles for richer context and MISP for sharing internal findings. STIX objects can carry attributes like password exposure metadata; ingestion pipelines should map STIX fields into your SIEM normalized schema.

Normalization & enrichment: map to a canonical schema

Create a canonical event schema so a leaked credential event looks the same whether it originated from HaveIBeenPwned, a paste site, or your email gateway.

Canonical fields:
- indicator_type: "email"
- indicator_value: "alice@example.com"
- exposure_type: "password-leak"
- password_hash: "sha1:..."
- source: "provider-name"
- exposure_timestamp: "..."
- confidence_score: 0.0-1.0
- raw_payload: {...}

Enrich with:

HR attributes (employee status, role, manager)
IdP last-login timestamp, MFA status
Recent password-reset emails from your MTA (count in last 24h)
Active OAuth sessions and refresh token timestamps

Correlation & scoring: when does a leak become a compromise?

Not every breached email requires immediate account suspension. Use a scoring model that combines evidence sources into a numeric risk score. Example weighted model:

Threat feed confidence: 0.3
Recent failed auth attempts on IdP (last 24h): 0.25
High volume of password-reset emails or suspicious outbound: 0.2
New forwarding rule or mailbox rule created: 0.15
Device/geo anomalies: 0.1

Policy thresholds:

Score >= 0.85: automatic containment (force password reset, revoke tokens, isolate mailbox)
Score 0.6-0.85: create high-priority incident for SOC review and temporary session revocation
Score < 0.6: monitor and send user-aware guidance (MFA check/recovery prompts)

Playbooks: automated containment actions and safe guards

Design playbooks with reversible and non-disruptive defaults. Always apply an escalation path for business-critical users (executives, system accounts).

High-confidence automation playbook (score ≥ 0.85)

Validate breach hit signature and dedupe event.
Enrich with IdP and HR data.
Revoke all active sessions via IdP API (e.g., Microsoft Graph revokeSignInSessions or Okta revoke refresh tokens).
Force password reset (set temporary strong random password and require reset on next login).
Revoke OAuth app consents and rotate service keys if service accounts involved.
Place mailbox in quarantine mode (mail gateway API) to prevent outbound phishing.
Create a SOAR incident with full context and send secure notification to the user and manager.

Mid-confidence playbook (0.6–0.85)

Temporarily disable new session tokens but keep existing sessions alive for SOC review.
Require MFA revalidation on next login.
Increase monitoring for 72 hours (accelerated telemetry retention).
Notify user with step-by-step remediation guidance.

Low-confidence playbook (<0.6)

Send a user security advisory (do not force actions).
Continue enrichment and monitor for escalation signals.

Sample SIEM rules and queries

Splunk SPL example

index=threat_feeds indicator_type=email indicator_value=*@example.com
| stats latest(exposure_timestamp) as last_seen by indicator_value
| join type=left indicator_value [search index=okta_logs event=auth_fail OR event=password_reset | stats count by user]
| eval score = (if(isnull(count),0,count/10) + (if(isnull(confidence_score),0,confidence_score)))
| where score >= 0.85
| `trigger_SOAR_playbook(user,score)`

Elastic KQL example

event.dataset:threat_feed and threat.indicator.type:email and threat.indicator.value:"*@example.com"
| join with [authentication-logs] on user
| where (threat.confidence >= 0.8 and authentication.failures > 5)

API integration patterns and sample automations

Automation often runs in Python or Node and calls IdP, MTA, firewall, and SOAR APIs. Key patterns:

Use OAuth2 client credentials for service-to-service calls and restrict scopes to least privilege.
Implement idempotency tokens for containment actions to avoid duplicate resets.
Log every automated action in an immutable audit trail for compliance.

Minimal Python pseudo-code: breach → contain

def handle_breach_event(event):
    user = event['indicator']
    enrich = enrich_user(user)
    score = compute_score(event, enrich)
    if score >= 0.85:
        revoke_sessions(user)
        force_password_reset(user)
        quarantine_mailbox(user)
        create_soar_incident(user, event, score)
    elif score >= 0.6:
        require_mfa_reauth(user)
        create_soar_incident(user, event, score)
    else:
        notify_user_advisory(user)

Dealing with false positives and business impact

Automated containment can disrupt work. Use these safeguards:

Exemptions: allow HR to mark critical users as “manual review” and define SLA for SOC actions.
Cooling windows: if a similar automated action ran in the last 24h, escalate to human review rather than repeat.
Approval chains: for high-impact actions (disable MFA or revoke enterprise API keys), require a fast multi-person approval via SOAR before execution.
Rollback playbooks: have automated rollback actions (re-enable sessions, reinstate mailbox) tied to incident closure criteria.

Privacy, compliance and safe handling of leaked credentials

Storing or processing breached credentials is sensitive. Best practices:

Avoid storing raw passwords. If you must, encrypt at rest and apply strict access controls. Prefer storing hashes or indicators only.
Apply data minimization: retain breach metadata (date, source) but not the full payload beyond what’s needed for remediation.
Follow regional regulations (GDPR, CCPA, Schrems II contexts) when transferring PII to third-party enrichment vendors.
Document the legal basis for processing (legitimate interest for security) and maintain DPIAs for large-scale leak processing.

Operationalizing: runbooks, SLAs and SOC integration

To move from prototype to production, create:

Runbooks for each playbook with clear rollback steps and communications templates for users and execs. See postmortem templates and incident comms for incident comms patterns.
SLAs for automated and manual responses—e.g., auto-contain within 5 minutes of high-confidence signal; SOC review within 30 minutes for mid-confidence.
Training for SOC analysts on interpreting feed metadata and mitigation actions.

Real-world example: how a mid-size company stopped a takeover campaign

Case study (anonymized): In January 2026 a mid-size SaaS company integrated a commercial breach feed with their Splunk instance and Microsoft Entra sign-in logs. An employee email appeared in a high-confidence leaked dataset. The SIEM correlated the feed hit with a spike in password-reset emails and three failed sign-in attempts from two countries. The SOAR playbook immediately revoked the employee’s sessions, forced a password reset, quarantined the mailbox, and opened a SOC incident. The attacker’s automated credential-stuffing run failed after revoked sessions and blocked IP lists were applied. SOC analysts confirmed the event and restored access after a secure re-enrollment. The containment reduced potential lateral movement and prevented a likely data exfiltration event.

Advanced strategies and futureproofing (2026+)

Threat feed fusion: combine multiple feeds and use ensemble scoring to reduce false positives—weight newer breaches higher.
Behavioral baselines: use ML models to establish normal email and login behaviors per account for better anomaly detection.
Passkey transition: instrument automated detection to pivot containment policies as organizations shift to passkeys—still watch for account recovery abuse.
Cross-organization indicators: share anonymized indicators via MISP/STIX to warn partners of large-scale leaks affecting supply chains.

Checklist: launch an automated credential-leak workflow in 8 weeks

Inventory feeds and capabilities (webhook vs polling, STIX support).
Design canonical schema and enrichment sources (HR, IdP, MTA).
Build ingestion layer with signature validation and dedupe logic.
Implement scoring model and map to containment playbooks.
Develop SOAR playbooks with idempotency, approval gates and rollback.
Set up audit logging and governance review for PII handling.
Run tabletop exercises and tune thresholds based on false-positive rates.
Go live in staged fashion: monitor, then enable mid-confidence actions, then high-confidence automation.

Quick principle: detect early, enrich before you act, automate at scale—but never without human-safe guardrails.

Actionable takeaways

Don't treat breach feed hits as binary—enrich and correlate with email security telemetry to raise your confidence level before acting.
Use webhooks + STIX/TAXII where available for low-latency ingestion and richer context.
Design a scoring model and map it to clear automated playbooks with rollback and exemption controls.
Protect privacy: minimize storage of raw breached credentials and document legal basis for processing.
Test with tabletop exercises and adjust thresholds to balance security and business continuity.

Next steps (call-to-action)

If your team is still triaging breach feed alerts manually, start with a 2-week pilot: connect one threat feed, ingest IdP sign-in logs, and implement a simple SOAR playbook that forces MFA revalidation for mid-confidence hits. Need help mapping feeds to your SIEM or writing safe playbooks? Contact our integrations team at webmails.live for a workshop and a reusable automation template tailored to your environment.

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