DE.AE-03: Information is correlated from multiple sources

To meet the de.ae-03: information is correlated from multiple sources requirement, you need a repeatable detection workflow that collects security-relevant signals from more than one system, normalizes them, and correlates them into alerts that drive triage and response. Operationalize it by defining required data sources, correlation rules/use cases, ownership, and evidence of ongoing tuning and review. ¹

Key takeaways:

• Correlation means linking related events across systems (identity, endpoint, network, cloud, email, apps) into actionable detections, not just centralizing logs.
• You need defined minimum sources + documented correlation logic (use cases, rules, or analytics) tied to threats and business impact.
• Auditable operation requires run evidence: data onboarding status, alert examples, tuning history, exceptions, and management review notes.

DE.AE-03 sits in the NIST CSF 2.0 Detect function and asks for a concrete capability: you should be able to take signals that are weak in isolation and combine them to raise confidence, reduce false positives, and speed up investigation. The compliance trap is treating this as “we have a SIEM” or “logs go to a bucket.” Examiners and customers typically look for proof that correlation exists as a working practice: defined sources, implemented logic, repeatable triage, and a feedback loop that improves detection quality over time.

This requirement applies whether you run a formal SOC or a small security team with outsourced monitoring. The operational goal is the same: correlate identity events with endpoint activity, network flows, cloud control-plane logs, and application telemetry so you can detect behaviors like impossible travel, credential misuse, lateral movement, data staging, and suspicious admin actions with context.

Use this page as a build guide: minimum correlation scope, step-by-step implementation, the evidence to retain, and the questions you will get in audits and due diligence. ²

Regulatory text

Requirement (excerpt): “Information is correlated from multiple sources.” ³

Operator interpretation: You must (1) ingest relevant security telemetry from multiple systems, (2) normalize/align it so it can be compared, and (3) correlate it into detections that support analysis. Correlation can be rule-based (use cases), analytic (behavioral), or manual-supported (investigation playbooks), but it must be defined, repeatable, and demonstrably used in operations. ¹

Plain-English interpretation (what “correlated” means in practice)

Correlation is the act of connecting related events that happen in different places so an analyst gets one coherent story. Examples:

• A sign-in from a new country (IdP) + a new endpoint process spawning PowerShell (EDR) + a spike in file reads (server/app) becomes one prioritized incident.
• A cloud admin role granted (cloud audit log) + MFA disabled (IdP) + new access key created (cloud) becomes one detection with a clear investigation path.

Centralized logging is necessary, but not sufficient. If you cannot point to detection content that explicitly references multiple sources, you are not meeting DE.AE-03 in an auditable way. ¹

Who it applies to

Entity scope

• Critical infrastructure operators, service organizations, and any organization running a cybersecurity program aligned to NIST CSF 2.0. ¹

Operational contexts where auditors expect correlation

• You operate a SOC (internal or outsourced).
• You claim 24x7 monitoring, MDR, SIEM, or “centralized logging.”
• You support regulated customers who ask for detection capabilities in security questionnaires.
• You have hybrid environments (cloud + SaaS + on-prem) where single-source monitoring misses attack paths.

Third-party angle (where CCO/GRC sees friction) Correlation often depends on third parties (MDR, SIEM vendor, cloud provider logs, SaaS audit logs). You remain accountable for defining minimum sources, ensuring data availability, and retaining evidence that correlation use cases actually run.

What you actually need to do (step-by-step)

Step 1: Define the “minimum viable correlation set” (MVCS)

Document the minimum set of sources you will correlate for production coverage. Keep it tight and defensible:

• Identity provider / SSO (auth, MFA, conditional access)
• Endpoint detection (process, command line, malware/behavior)
• Network or secure web gateway (DNS/proxy/firewall) or cloud network telemetry
• Cloud control-plane audit logs (if you use IaaS/PaaS)
• Email security (if email is a primary ingress path) Pick what matches your environment, then explicitly state exclusions and compensating controls. ¹

Artifact: “DE.AE-03 Correlation Scope” one-pager with sources, owners, and ingestion method.

Step 2: Make data correlation-ready (normalization + time + identity alignment)

Correlation fails when data is incomparable. Implement and document:

• Time synchronization strategy (consistent timestamps, time zones)
• Common identifiers mapping (user, device, IP, asset ID, cloud account, tenant)
• Normalization approach (field mapping to a schema your tools support)
• Data quality checks (missing fields, parsing failures, dropped events) If you outsource monitoring, require these in the statement of work and review them. ¹

Evidence: parsing/ingestion health dashboards, field-mapping documentation, sample raw-to-normalized event record.

Step 3: Build correlation use cases that explicitly require multiple sources

Create a short backlog of correlation detections where each use case lists:

• Goal (threat behavior)
• Required sources (at least two)
• Correlation logic (rule/analytic description)
• Threshold/conditions (qualitative if needed)
• Triage steps and expected enrichment

Examples of auditable multi-source use cases

• Suspicious login + endpoint execution of credential tooling
• Privilege escalation in cloud + new network egress pattern
• MFA reset + mailbox forwarding rule creation + unusual download activity

Artifact: “Detection Use Case Register” with version history and owner. ¹

Step 4: Operationalize triage so correlation outcomes drive action

Correlation has to feed operations:

• Route correlated alerts to a ticket queue with severity guidance.
• Require analysts to record whether correlation improved confidence and what data was missing.
• Tie triage outcomes to incident categories and response playbooks.

This is where many programs fail audits: they have detections, but no consistent record that alerts were reviewed and dispositioned. ¹

Evidence: tickets, case notes, alert screenshots/exports showing multiple source events, closure codes.

Step 5: Establish a tuning and exception process

You need proof the control “breathes”:

• Track false positives/false negatives and root cause (bad parsing, missing source, weak rule).
• Approve changes (who can tune detections).
• Record exceptions (source unavailable, license limitation) with compensating monitoring.

A lightweight monthly or quarterly review cadence is enough if you retain minutes and actions. The key is repeatability and traceability. ¹

Artifacts: tuning log, exception register, remediation tickets with due dates, management review notes.

Step 6: Create a concise evidence bundle per review cycle

Package the above into a ready-to-hand “audit folder”:

• Current source inventory and ingestion status
• Top correlation use cases and last-updated dates
• Samples of correlated alerts and completed investigations
• Metrics you track (see below)
• Exceptions and remediation status ¹

If you use Daydream to manage your control library and evidence bundles, DE.AE-03 becomes easier to defend because you can tie “what we said we do” to “what ran” and “what changed,” with a single owner view.

Required evidence and artifacts to retain (operator checklist)

Evidence item	What an auditor is validating	Owner
Correlation scope (minimum sources)	“Multiple sources” is defined, not implied	Security/GRC
Log source onboarding records	Sources are actually connected and producing data	Security Engineering
Normalization/field mapping notes	Events can be correlated consistently	Security Engineering
Detection Use Case Register	Correlation logic exists and is maintained	Detection Engineering/SOC
Sample correlated alerts + case notes	Correlation drives investigation outcomes	SOC/MDR
Tuning & change log	Control is governed and improved over time	SOC Lead
Exception register	Known gaps are tracked with compensating controls	GRC

(Framework basis: NIST CSF 2.0)

Common exam/audit questions and hangups

1. “Which sources do you correlate today?”
   Hangup: vague answers like “many logs.” Fix: list the minimum viable correlation set and show ingestion health.

2. “Show me an alert that includes more than one source.”
   Hangup: alerts only show one product’s telemetry. Fix: retain 2–3 sanitized examples where the case timeline includes identity + endpoint, or cloud + identity.

3. “How do you know correlation is working?”
   Hangup: no metrics. Fix: track operational indicators such as number of correlated alerts reviewed, ingestion failures, and tuning actions taken. ¹

4. “What happens when a source goes dark?”
   Hangup: no exception process. Fix: documented exception with risk acceptance and compensating monitoring.

Frequent implementation mistakes (and how to avoid them)

• Mistake: Confusing aggregation with correlation.
  Avoidance: require every “high priority” detection to cite multiple sources or enriched context and prove it with examples.

• Mistake: No identity stitching.
  Avoidance: implement consistent user/device identifiers and document mapping rules (UPN vs email vs SID; hostnames vs device IDs).

• Mistake: Correlation rules exist but aren’t owned.
  Avoidance: assign a named owner for the use case register and require periodic review sign-off. ¹

• Mistake: Outsourcing to MDR without verifying data onboarding.
  Avoidance: include onboarding verification and monthly reporting requirements in the MDR contract and keep the reports.

Enforcement context and risk implications

No public enforcement cases were provided in the source set for this requirement, so you should treat DE.AE-03 as a defensibility and negligence risk reducer, not a check-the-box control. Weak correlation increases dwell time and investigation cost because analysts chase isolated alerts without context. Strong correlation also improves your ability to explain “what happened” to regulators, customers, and insurers using a coherent event timeline. ¹

Practical 30/60/90-day execution plan

First 30 days (foundation)

• Assign an executive owner (accountable) and an operational owner (responsible) for DE.AE-03 outcomes. ¹
• Publish the minimum viable correlation set and validate which sources are already ingested.
• Stand up an evidence bundle structure (folders or GRC tool) and start saving onboarding proofs and sample alerts.

Days 31–60 (implement correlation use cases)

• Build the Detection Use Case Register with prioritized multi-source detections.
• Implement normalization and identifier mapping for the highest-value sources first.
• Run tabletop-style triage on a few correlated alerts to confirm workflows, ticketing, and escalation paths.

Days 61–90 (make it auditable and repeatable)

• Establish a tuning/change workflow with approvals and a simple tuning log. ¹
• Produce a management review note that summarizes correlation coverage, exceptions, and next actions.
• Test your audit story: pick one correlation use case and walk from raw events to correlated alert to closed ticket, using retained evidence.

Frequently Asked Questions

Do we need a SIEM to satisfy de.ae-03: information is correlated from multiple sources requirement?

No. You need demonstrable correlation across multiple sources; a SIEM is one common way to do it. If you use an MDR portal, EDR + identity correlation, or a data lake with detection rules, retain evidence that multi-source correlation exists and drives triage. ¹

What counts as “multiple sources”?

Two independent telemetry sources can qualify if the correlation is meaningful (for example, identity + endpoint). Auditors respond well to a documented minimum set of sources and examples where a single case timeline includes events from more than one system. ¹

Can we meet DE.AE-03 if some logs aren’t available from a SaaS third party?

Yes, if you document the gap, assess the risk, and implement compensating monitoring (for example, stronger identity controls or alternative telemetry). Keep the exception record and show that leadership reviewed and accepted the residual risk. ¹

How do we prove correlation is happening without sharing sensitive logs with auditors or customers?

Keep sanitized screenshots/exports that show the alert timeline referencing multiple sources, plus the use case description and the ticket disposition. Redact identifiers, but preserve enough structure to demonstrate correlation logic and analyst action. ¹

What metrics should we track for correlation without inventing vanity numbers?

Track operational indicators you can evidence: source ingestion health, count of correlated alerts reviewed, number of tuned rules/changes, and open exceptions. Use trends and narratives rather than unsupported benchmarks. ¹

Our SOC is outsourced. Who “owns” DE.AE-03?

You own it. The third party can operate detection content, but you must define required sources, verify onboarding, review performance, and retain evidence bundles that demonstrate correlation and tuning over time. ¹

Footnotes

1. NIST CSF 2.0

2. NIST CSF 2.0; NIST CSWP 29

3. NIST CSWP 29; NIST CSF 1.1 to 2.0 Core Transition Changes