AU-3(1): Additional Audit Information

AU-3(1): Additional Audit Information requires you to configure audit logging so each relevant audit record includes specific extra fields your organization defines (the AU-3(1) organization-defined parameter), then prove those fields are consistently captured across in-scope systems. Operationalize it by standardizing an “audit record schema,” enforcing it in log sources, and validating in your SIEM.

Key takeaways:

• Define the exact “additional audit information” fields (by event type and system tier) and treat them as a required log schema.
• Configure log sources and your SIEM pipeline to capture, preserve, and make those fields searchable and reportable.
• Keep repeatable evidence: configuration screenshots/exports, sample events showing the extra fields, and test results proving coverage.

A common failure mode in audit logging programs is collecting “some logs” without collecting the fields needed to reconstruct events quickly and defensibly. AU-3(1): additional audit information requirement exists to close that gap. It pushes you past generic event logging and into a defined set of extra details that make audit records actionable for investigations, forensics, and incident response.

The tricky part is that AU-3(1) is parameterized. NIST does not prescribe a universal list of extra fields; you must decide what “additional information” means for your environment and document it. For a CCO, GRC lead, or Compliance Officer, the fastest path is to treat AU-3(1) as a logging data standard: specify the required fields, map them to each log source, enforce collection centrally, and validate with real events.

This page gives requirement-level implementation guidance: who it applies to, what to configure, how to test, what evidence to retain, and the audit questions you will get. It also includes a practical execution plan and common mistakes to avoid.

Regulatory text

Requirement (excerpt): “Generate audit records containing the following additional information: {{ insert: param, au-03.01_odp }}.” ¹

What the operator must do:

1. Choose and document the organization-defined “additional information” fields required in audit records (the AU-3(1) parameter). 2) Implement logging so in-scope systems actually emit those fields for the events you audit. 3) Verify and retain evidence that those fields appear consistently in collected logs, end-to-end, in your central logging/SIEM platform. ²

Plain-English interpretation

AU-3(1) means your audit logs must include more than a timestamp and event name. You must specify extra details that make an event reconstructable: enough context to answer “who did what, to which resource, from where, using what privilege or session, and what happened as a result.”

Because the “additional information” is organization-defined, auditors will judge you on whether:

• the fields you picked make sense for your risk and system type,
• the fields are consistently captured (not aspirational),
• you can retrieve and use those fields during an investigation.

Who it applies to

Entity scope

• Federal information systems implementing NIST SP 800-53 controls. ²
• Contractors and third parties handling federal data (for example, in regulated federal programs or where 800-53 is flowed down contractually). ²

Operational context

• Any environment where audit logs support security monitoring, incident response, privileged access oversight, or compliance attestations.
• Systems that generate security-relevant events: identity systems, endpoints, servers, network devices, cloud control planes, SaaS admin consoles, databases, and key applications.

Control ownership (typical)

• Security Engineering / Platform Engineering: logging configuration and pipelines
• IAM team: identity and authentication event completeness
• GRC/Compliance: parameter definition, governance, testing cadence, evidence packaging
• SOC: detection content that depends on the additional fields

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

Step 1 — Define the AU-3(1) “additional audit information” parameter as a log schema

Create a short standard called “Audit Record Additional Fields (AU-3(1))” with:

• Field name
• Definition
• Format (string, GUID, IP, JSON object)
• Required vs. conditional (for specific event types)
• Authoritative source (IdP, OS audit subsystem, cloud provider log)
• Example value

Practical field candidates (choose what fits your environment and document your rationale):

• User/actor context: user ID, role, group, service account name
• Session context: session ID, authentication method, MFA result, token ID (or trace ID)
• Resource context: application name, resource ID, database/schema/table, object path
• Network/device context: source IP, device ID, user agent, host name
• Privilege context: elevation method, privileged role assumed, approval/ticket reference (if applicable)
• Outcome context: success/failure reason, error code, policy decision (allow/deny), bytes/records affected (when available)

Decision rule to keep you honest: if an incident responder would still need to “go ask another system” for basic context, add that context to the audit record schema where feasible.

Step 2 — Map the schema to each in-scope log source (coverage matrix)

Build a matrix with columns:

• System / log source
• Event categories produced (auth, admin actions, data access, config changes)
• Required AU-3(1) fields supported natively
• Gaps (missing fields)
• Compensating approach (enrichment, correlation, or “not feasible” with justification)
• Owner and target date

This turns AU-3(1) from an abstract requirement into a trackable engineering backlog.

Step 3 — Configure log generation and collection to include the fields

For each log source:

• Enable the right log types (for example, admin audit logs vs. basic access logs).
• Turn on “include” options that add actor/resource context (cloud control planes and SaaS tools often have toggles or API fields).
• Normalize fields in your SIEM (common field names) so investigators can query consistently.
• Preserve raw logs alongside normalized events when possible; normalization sometimes drops nested context.

If you rely on enrichment (for example, mapping user IDs to emails), document:

• enrichment source of truth,
• how often enrichment data refreshes,
• how you handle failures (do logs drop, or do they ingest with a null enrichment field?).

Step 4 — Validate end-to-end with test events (not screenshots alone)

Run a controlled test per major source:

• Generate an event (login success, login failure, admin change, privileged role assumption, data export).
• Verify the event arrives in the SIEM.
• Confirm the AU-3(1) fields are present, populated, searchable, and retained.

Record the exact query used and save the event JSON as evidence (redact secrets carefully; do not redact the fields you are trying to prove).

Step 5 — Put governance around drift (ongoing control operation)

AU-3(1) breaks quietly when:

• an application team changes logging libraries,
• a SaaS vendor changes an API payload,
• a SIEM parser update drops fields,
• a new system launches without the schema.

Set an operating rhythm:

• Require the schema in logging onboarding for new systems.
• Add a parser regression check for critical sources.
• Track exceptions with an expiration date and owner.

Step 6 — Assign a control owner and recurring evidence artifacts

AU-3(1) needs a single owner responsible for:

• maintaining the parameter schema,
• ensuring coverage updates,
• producing evidence on request.

If you use Daydream, treat AU-3(1) as a requirement with an owner, a written procedure, and a recurring evidence checklist so audit readiness becomes routine rather than a scramble.

Required evidence and artifacts to retain

Keep evidence that proves both design (you defined the additional fields) and operation (systems actually generate them):

Design evidence

• AU-3(1) “additional fields” standard (the parameter definition) with version history
• Logging architecture diagram showing sources → collectors → SIEM → archive
• Coverage matrix with gap remediation or approved exceptions

Operational evidence

• Configuration exports or screenshots showing audit settings enabled per key source
• SIEM field extraction rules / parsers (or equivalent) and change history
• Sample raw audit events (JSON/text) showing required additional fields populated
• Test scripts or runbooks for generating validation events
• Investigation playbooks or queries that explicitly use the additional fields

Exception handling

• Risk acceptance for fields not available, with compensating controls (correlation, alternate logs, or monitoring)

Common exam/audit questions and hangups

Expect assessors to ask:

1. “What is your AU-3(1) organization-defined parameter?” They want your exact field list, not a narrative.
2. “Show me an event from System X with those fields.” They will pick a high-risk system (IdP, privileged access, cloud control plane).
3. “How do you know logging hasn’t drifted?” They want a validation method and a change control tie-in.
4. “What happens if the SIEM parser fails?” They want to see raw retention or alerting on ingestion/parsing failures.
5. “Do contractors/third parties’ systems meet this when they handle federal data?” They want flow-down and verification where applicable.

Hangups that slow audits:

• No written parameter definition (only “we log a lot of stuff”).
• Fields exist in raw logs but are not searchable in the SIEM.
• Inconsistent fields across similar systems (two apps, two different “userId” meanings).

Frequent implementation mistakes and how to avoid them

Mistake	Why it fails	How to avoid it
Defining extra fields too broadly (“all relevant context”)	Un-testable and impossible to evidence	Publish a finite schema with required/conditional fields
Relying on screenshots of settings	Doesn’t prove fields appear in real events	Keep sample events plus the query used to retrieve them
Normalization drops context	Raw JSON has fields, SIEM view doesn’t	Preserve raw events and validate parsing for required fields
No exception process	Gaps linger forever	Track gaps with an owner, rationale, and expiration
Logging onboarding is optional	New systems ship without required fields	Make schema a release gate for production

Enforcement context and risk implications

No public enforcement cases were provided in the supplied source catalog for this requirement, so this page does not cite specific actions or settlements.

Risk-wise, AU-3(1) gaps usually show up as investigation failure: you cannot attribute actions to a specific actor/session or cannot prove what was touched. That increases incident response time, complicates legal/regulatory reporting, and weakens your position in disputes over unauthorized activity. For federal programs, it can also become a contractual compliance issue when assessors test auditability against NIST SP 800-53 requirements. ²

Practical execution plan (30/60/90-day)

You asked for speed. Use this phased plan as a control rollout pattern; adjust scope to your system boundaries and assessment date.

First 30 days (define and baseline)

• Name a control owner and publish the AU-3(1) additional fields schema (versioned).
• Inventory top log sources: IdP, cloud control plane, PAM, EDR, key apps, databases.
• Build the coverage matrix and identify the highest-risk gaps.
• Implement validation queries for each major source; save one sample event per source as a baseline evidence pack.

Days 31–60 (close gaps and harden pipelines)

• Remediate the biggest field gaps in top-tier sources (admin actions, auth, privilege changes).
• Fix SIEM parsing/normalization so required fields are searchable.
• Add alerting for ingestion failures and parser failures for critical sources.
• Establish an exception workflow for fields that are not feasible to capture.

Days 61–90 (operationalize and prove repeatability)

• Add AU-3(1) checks to change management for logging parsers and log source configurations.
• Create a recurring validation runbook (SOC or GRC-run) and store results in your evidence repository.
• Expand coverage to remaining systems and third-party managed components in scope.
• Package an “audit-ready” evidence set (schema, matrix, configs, sample events, test results).

Frequently Asked Questions

What counts as “additional audit information” for AU-3(1)?

It’s organization-defined, so you decide the fields, but they must add investigatory value beyond basic event details. Pick fields that support attribution (who), object targeting (what), path/context (where), and privilege/session context (how).

Do we have to add the extra fields to every single log event?

Treat it as required for the event categories you rely on for security oversight (auth, admin actions, privilege use, sensitive data access). Document “required vs. conditional” so you can prove intent and testability.

Our SaaS provider doesn’t expose the field we want. Are we automatically noncompliant?

Not automatically, but you need documented gap handling: capture the closest available fields, add correlation where possible, and record an exception with compensating monitoring. Auditors will test whether you identified the gap and reduced the risk.

Is SIEM normalization required to meet AU-3(1)?

The requirement is to generate audit records with the additional information, but audits usually hinge on your ability to retrieve and use those fields. If the data exists only in raw logs and is not searchable, expect a finding during operational effectiveness testing.

How do we prove AU-3(1) without exposing sensitive logs to auditors?

Prepare a redacted evidence pack that preserves the AU-3(1) fields while masking unrelated sensitive content. Provide sample events, queries, and configuration exports; keep full raw logs available under controlled viewing if required.

What’s the fastest way to operationalize this across many teams?

Publish a single schema, turn it into a logging onboarding checklist, and track coverage in a system-by-system matrix with owners and exceptions. In Daydream, model AU-3(1) as a control with recurring evidence tasks so validation stays on a schedule.

Footnotes

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

2. NIST SP 800-53 Rev. 5