SI-4(13): Analyze Traffic and Event Patterns

The si-4(13): analyze traffic and event patterns requirement is a detection engineering and security monitoring expectation framed at the requirement level: analyze communications traffic and event patterns for the system ¹. For a Compliance Officer, CCO, or GRC lead, the fastest path to “real” compliance is to translate that short sentence into a repeatable operating rhythm that creates artifacts.

This control enhancement sits in the System and Information Integrity family and typically gets assessed alongside broader monitoring, logging, and incident response capabilities. Assessors rarely accept “we have a SIEM” as proof. They look for: (1) what telemetry you analyze, (2) what “patterns” mean in your environment, (3) how often you review outputs, and (4) what you do when you find anomalies.

This page gives you requirement-level implementation guidance you can hand to security operations, network, and cloud teams, then track through a control owner and evidence register. The emphasis is speed to operationalization: define scope, define patterns, implement analytics, and retain artifacts that show analysis occurred and drove action.

Regulatory text

Control requirement (verbatim): “Analyze communications traffic and event patterns for the system;” ¹

What the operator must do:
You must run analysis over (a) communications traffic and (b) security-relevant events to identify meaningful patterns that indicate compromise, misuse, data movement, or control failures. “Analyze” implies an active process with outputs (findings, alerts, investigations), not passive data retention. The phrase “for the system” means your defined system boundary: production environments, supporting infrastructure, and any components included in your authorization or compliance scope.

Plain-English interpretation

SI-4(13) expects you to catch threats that don’t show up as a single obvious alert. Instead, you look for relationships over time: spikes, rare connections, lateral movement sequences, repeated failed authentication followed by success, unusual data transfer volumes, new external destinations, or atypical admin activity. If you can’t explain what “normal” looks like and how you detect deviation, you will struggle in assessment.

Who it applies to

Entity types (typical applicability):

• Federal information systems
• Contractor systems handling federal data ¹

Operational contexts where SI-4(13) becomes real work:

• Cloud-first systems with distributed logs across cloud provider services, SaaS, and endpoints.
• High-change environments (CI/CD, ephemeral workloads) where static allowlists break quickly.
• Data-rich systems (PII, FCI/CUI, financial data, healthcare data) where exfiltration and misuse monitoring matter.
• Third-party integrated systems (APIs, managed service providers) where traffic patterns may be the earliest signal of abuse.

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

Use this as your minimum viable implementation for the si-4(13): analyze traffic and event patterns requirement.

1) Assign ownership and define the system boundary

• Name a control owner (usually SecOps/Detection Engineering) and a GRC control steward accountable for evidence quality.
• Document the system scope: environments, VPC/VNETs, subnets, identity providers, endpoints, and logging platforms included.
• Decide where analysis happens: SIEM, XDR, NDR, cloud-native detection, or a combination.

Output artifact: Control implementation procedure with owner, scope, tools, and review cadence.

2) Define “communications traffic” and ensure telemetry exists

Pick telemetry sources that represent real communications paths. Common sources include:

• Network flow logs (cloud flow logs, firewall logs, proxy logs)
• DNS query logs
• Web gateway logs
• Email security logs (if in-scope)
• API gateway logs (for externally exposed services)

Your job is not to enumerate every possible log. Your job is to show that the traffic you care about is observed and analyzable.

Output artifacts: Logging/telemetry inventory; data flow diagram annotated with telemetry points.

3) Define “event patterns” and ensure event coverage

Patterns depend on events. Ensure you collect events across:

• Identity and access (SSO, IAM, privilege changes)
• Authentication (success/failure, MFA challenges)
• Endpoint security events (process creation, detections)
• Cloud control plane events (create keys, modify security groups, bucket policy changes)
• Application/security events (authz failures, admin actions, critical error patterns)

Output artifacts: Event taxonomy and mapping to sources; log source onboarding records.

4) Establish baseline behavior and “expected patterns”

You need a documented baseline approach. Keep it simple and defensible:

• Define what “normal” means for key dimensions: typical geo, typical external destinations, typical service-to-service paths, typical admin actions, typical data transfer volumes.
• Identify “known rare but legitimate” patterns (batch jobs, backups, vulnerability scans) and document suppressions or exceptions.

Output artifacts: Baseline documentation; exception register tied to change management.

5) Build and run analytics that look for deviation and sequences

Implement a small set of detections that clearly qualify as pattern analysis. Examples:

• Beaconing-like behavior: repeated outbound connections at regular intervals to a new domain.
• Credential abuse sequence: multiple failures → success → privilege escalation → new access key creation.
• Lateral movement indicators: unusual east-west connections between workloads that don’t normally communicate.
• Data movement anomaly: spikes in egress to unsanctioned destinations; repeated downloads from sensitive stores.

This can be rule-based, behavior-based, or ML-assisted. What matters is that you can explain logic, inputs, thresholds, and tuning.

Output artifacts: Detection catalog with logic and data sources; change history for tuning.

6) Create a recurring review and triage workflow

Pattern analysis generates noise until tuned. Put a workflow in place:

• Daily/regular monitoring by SecOps for triggered detections.
• A scheduled pattern review meeting or async review (for trend and anomaly summaries).
• A clear escalation path to Incident Response when thresholds are met.

Tie reviews to tickets. A ticketing trail is the easiest proof that analysis happened and was acted on.

Output artifacts: Runbook; sample tickets; investigation notes; incident linkage.

7) Close the loop: tune, document, and feed risk decisions

Assessors look for operational maturity: you learned something and improved.

• Track false positives and tuning decisions.
• Record gaps: missing logs, blind spots, and remediation tickets.
• Update baseline when business or architecture changes.

Output artifacts: Tuning log; backlog of monitoring gaps; post-incident improvements.

8) Map the requirement to evidence artifacts (assessment readiness)

This is the most common gap: teams “do the work” but can’t produce it on demand. Maintain an evidence register that names:

• What evidence exists
• Where it lives
• Who can retrieve it
• The period it covers

Daydream can help here by turning SI-4(13) into an owned control record with an implementation procedure and a recurring evidence checklist, so collection is routine rather than a scramble.

Required evidence and artifacts to retain

Keep artifacts that prove both coverage (you can observe traffic/events) and analysis (you review patterns and act).

Minimum evidence set (practical):

• Control narrative / procedure for SI-4(13), including owner and scope ¹
• Telemetry inventory (traffic + event sources) and onboarding confirmation
• Baseline and exceptions documentation
• Detection/analytics catalog (what patterns you analyze; inputs; tuning history)
• Samples of outputs: SIEM/NDR dashboards, scheduled reports, alert summaries
• Tickets/cases showing triage and disposition (including false positive rationale)
• Escalations to incident response and post-incident improvements where applicable

Common exam/audit questions and hangups

Expect these lines of questioning:

1. “Show me what patterns you analyze.” Auditors want specific detections, not a generic platform description.
2. “How do you know what ‘normal’ is?” Baselines can be lightweight, but they must exist and be current.
3. “What is in scope?” If your boundary is fuzzy, you will be asked to defend exclusions.
4. “Prove it’s operating.” They will ask for time-bounded evidence: a slice of alerts, reviews, and tickets over a period.
5. “What happens when you find an anomaly?” They will look for a runbook and escalation criteria.

Frequent implementation mistakes and how to avoid them

Mistake 1: Treating log collection as compliance.
Fix: Require proof of analysis outputs (detections, reviews, tickets) tied to those logs.

Mistake 2: No definition of “patterns.”
Fix: Publish a short pattern list aligned to your threat model (beaconing, rare destinations, auth sequences, data movement).

Mistake 3: Blind spots in communications traffic.
Fix: Map traffic flows and confirm telemetry at choke points (egress, DNS, proxy, firewall, cloud flow logs).

Mistake 4: Detections exist but are never tuned.
Fix: Maintain a tuning log and link tuning decisions to cases.

Mistake 5: Evidence is scattered and can’t be reproduced.
Fix: Centralize an evidence register and assign a retrieval owner. Daydream fits naturally as the control-to-evidence system of record.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement, so this page does not cite specific enforcement actions. Practically, the risk is straightforward: if you do not analyze traffic and event patterns, you will miss early indicators of compromise and policy violations, and you will have weak defensibility during incident investigations because you cannot show a consistent monitoring practice ¹.

Practical 30/60/90-day execution plan

Use this as an operator’s rollout plan. Adjust scope to your system boundary and staffing.

First 30 days (stand up and define)

• Assign SI-4(13) control owner and document system boundary.
• Inventory existing traffic and event telemetry; identify gaps.
• Write the SI-4(13) procedure: what data is analyzed, by whom, how findings are handled.
• Implement a starter detection set for a handful of high-signal patterns (auth sequences, rare egress, control plane anomalies).
• Create an evidence register and start capturing artifacts.

Days 31–60 (operationalize and tune)

• Baseline normal traffic and key event patterns; document exceptions tied to known business processes.
• Establish recurring pattern review with outputs (report or case summary).
• Tune detections based on early false positives; document changes.
• Connect detections to incident response thresholds and escalation workflows.

Days 61–90 (scale and prove durability)

• Expand pattern coverage to additional flows and assets in scope (endpoints, workloads, SaaS).
• Run at least one tabletop or simulation focused on a pattern-driven detection leading to triage.
• Audit your own evidence: can you produce a complete packet for an assessor without ad hoc effort?
• Formalize metrics qualitatively (trend summaries) and feed lessons learned into backlog.

Frequently Asked Questions

What counts as “communications traffic” for SI-4(13)?

Any telemetry that describes system communications paths: inbound/outbound network connections, DNS, proxy, firewall, and cloud flow logs are common examples. The key is that you can analyze it for anomalies and patterns, not just store it ¹.

Do we need machine learning to satisfy the si-4(13): analyze traffic and event patterns requirement?

No. Rule-based correlation and sequence detections can satisfy the requirement if they analyze patterns and you can show outputs, tuning, and response actions ¹.

How do we prove “pattern analysis” to an auditor?

Provide a detection catalog (what patterns, what data sources), examples of alerts/findings, and tickets showing triage and disposition. Add baseline documentation and a record of tuning decisions to show the process is operating ¹.

Our environment changes constantly. How can we maintain baselines?

Use baselines that track categories (expected egress destinations, expected service-to-service paths, normal admin actions) and maintain an exception process tied to change management. Update baseline notes when major architecture or business process changes occur.

Can third-party systems be in scope for SI-4(13)?

Yes, if they are part of your system boundary or handle federal data as part of the system. Treat third party integrations as traffic sources and event producers where feasible, and document exclusions with rationale ¹.

What is the most common reason teams fail this requirement in assessments?

They cannot produce evidence that analysis happened over time. They may have tools and logs, but no documented procedure, no recurring review outputs, and no case/ticket trail tying detections to action.

Footnotes

1. NIST SP 800-53 Rev. 5 OSCAL JSON