MA-6(3): Automated Support for Predictive Maintenance

MA-6(3) requires you to automatically transfer predictive maintenance data into your maintenance management system using an organization-defined mechanism (the {{ insert: param, ma-06.03_odp }} parameter). Operationally, you must define the transfer method, integrate telemetry and condition data sources with your maintenance platform, and keep evidence that transfers are complete, secure, and support maintenance decisions. (NIST SP 800-53 Rev. 5 OSCAL JSON)

Key takeaways:

• Define and document the approved mechanism for transferring predictive maintenance data (the organization-defined parameter). (NIST SP 800-53 Rev. 5 OSCAL JSON)
• Implement an automated pipeline from asset data sources to a maintenance management system, with monitoring and exception handling.
• Retain defensible evidence: configuration, logs, sample records, and reconciliations that show the transfer works as designed.

The ma-6(3): automated support for predictive maintenance requirement is easy to misread as a “nice to have” reliability feature. Assessors tend to treat it as an operational control with security implications: if predictive maintenance data does not reliably reach the system of record, your organization can miss early warnings, create unsafe operating conditions, or rely on incomplete records during incident response and audits.

MA-6(3) is narrow in wording and broad in execution. The text focuses on “transfer” and on using an organization-defined approach, which means your first job is governance: choose and approve the mechanism (interfaces, protocols, connectors, middleware, or managed integrations) and set clear ownership. Your second job is operational: make the transfer automatic, measurable, and resilient. Your third job is auditability: show that the pipeline is consistently moving the right data to the right place, and that gaps are detected and handled.

This page gives requirement-level implementation guidance you can apply quickly: applicability, practical build steps, evidence to keep, exam questions, and common pitfalls.

Regulatory text

Requirement (excerpt): “Transfer predictive maintenance data to a maintenance management system using {{ insert: param, ma-06.03_odp }}.” (NIST SP 800-53 Rev. 5 OSCAL JSON)

Operator interpretation of the text

• “Transfer predictive maintenance data” means condition-based or predictive signals (telemetry, sensor readings, device health, error codes, vibration/temperature trends, SMART metrics, application performance indicators, or diagnostics) must move from the data source into a place where maintenance work is managed.
• “to a maintenance management system” means a system of record for maintenance activities (commonly CMMS/EAM/ITSM maintenance modules). Spreadsheets and ad hoc dashboards usually fail the “systematic management” test unless governed as the system of record.
• “using {{ insert: param, ma-06.03_odp }}” means you must specify the mechanism. In practice, assessors expect to see that you defined the method (e.g., API integration, message bus, secure file transfer, agent-based connector) and implemented it consistently. (NIST SP 800-53 Rev. 5 OSCAL JSON)

Plain-English meaning (what the control is trying to achieve)

You need an automated, repeatable way to get predictive maintenance signals into the maintenance platform where tickets/work orders, prioritization, approvals, and maintenance history live. If the transfer depends on people exporting CSVs or copying alerts manually, you will struggle to prove the control is operating.

Who it applies to

Entity types

• Federal information systems implementing NIST SP 800-53 controls. (NIST SP 800-53 Rev. 5)
• Contractor systems handling federal data where NIST SP 800-53 is flowed down contractually or via an assessment baseline. (NIST SP 800-53 Rev. 5)

Operational contexts where MA-6(3) is most relevant

• OT/ICS and facilities environments with sensor-rich equipment.
• Data centers and end-user compute fleets where device health telemetry exists.
• Cloud and on-prem platforms with APM/infra monitoring that can trigger maintenance actions.
• Any environment where predictive maintenance alerts should become work orders, not just notifications.

Systems and teams typically involved

• Asset owners (IT, OT engineering, facilities).
• Maintenance operations (work order execution).
• Platform owners for CMMS/EAM/ITSM.
• Security and compliance (access control, logging, evidence).
• Third parties that supply sensors, monitoring tools, or the maintenance management platform.

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

Use this as an implementation checklist for the ma-6(3): automated support for predictive maintenance requirement.

1) Define the organization parameter (your approved transfer mechanism)

MA-6(3) is explicit that the mechanism is organization-defined. Write it down and get it approved.

• Specify source systems (monitoring platforms, sensors, OEM tooling, endpoint management, OT historians).
• Specify the target maintenance management system (CMMS/EAM/ITSM module) as the system of record.
• Specify transfer mechanism(s): direct API, integration platform, message queue, secure file exchange, agent/connector, or managed integration.
• Specify data handling rules: required fields, asset identity keys, severity/priority mapping, timestamps, and retention in the destination system.
• Specify security controls for the transfer: authentication method, authorized service accounts, encryption requirements, and network path approval.

Deliverable: a short “MA-6(3) Transfer Mechanism Standard” that fills the {{ insert: param, ma-06.03_odp }} slot for your environment. (NIST SP 800-53 Rev. 5 OSCAL JSON)

2) Establish ownership and RACI tied to operational outcomes

Assign a control owner who can answer: “Does the data arrive, and do we act on it?”

• Control owner: often maintenance platform owner or reliability engineering lead.
• Technical owners: integration engineer, monitoring platform admin.
• Data owner: asset management team.
• Compliance oversight: GRC lead validates evidence cadence.

3) Build the automated transfer pipeline with explicit mappings

Treat this as an interface specification, not a “best effort” integration.

• Map asset identifiers end-to-end. If the sensor calls it “DeviceID” but CMMS uses “AssetTag,” define translation logic.
• Map predictive signals to maintenance objects: alert → case; condition threshold breach → work request; degradation trend → planned work order.
• Include deduplication rules to avoid alert storms creating duplicate work.
• Capture minimum required context in the maintenance record: source, timestamp, reading/metric, threshold, recommended action, and link back to raw telemetry.

4) Implement monitoring, exception handling, and reconciliation

Automation without detection fails quietly.

• Add integration health monitoring (queue depth, API error rates, failed deliveries, authentication failures).
• Implement an exception queue/workflow: failed transfers create an actionable incident for the integration owner.
• Reconcile counts: confirm that predictive maintenance events generated by the source system appear in the maintenance management system, and investigate mismatches.

Evidence goal: show that you can detect missing transfers and correct them, not just that you built an API.

5) Control access and protect the integrity of maintenance data

Even though MA-6(3) is in the Maintenance family, assessors will look for basic integrity controls:

• Use dedicated service accounts for integrations with least privilege in the maintenance platform.
• Restrict who can modify integration mappings and routing rules.
• Keep logs that prove what was transferred, when, and by which identity.

6) Operationalize: make predictive maintenance data drive maintenance activity

Assessors often ask whether the transferred data is actually used.

• Define decision criteria: which predictive alerts must generate work orders versus informational tickets.
• Define SLAs/OLAs for triage by maintenance planners.
• Confirm that closed work orders reference the originating predictive record when relevant.

7) Put it on an evidence cadence

Set a recurring evidence routine that a GRC team can run without heroics:

• Configuration snapshots (integration settings, mappings, service account permissions).
• Samples of created work orders that show predictive fields populated.
• Reconciliation outputs and exception handling tickets.

Required evidence and artifacts to retain

Keep artifacts that prove design, implementation, and operation:

Governance

• Approved definition of {{ insert: param, ma-06.03_odp }} transfer mechanism(s). (NIST SP 800-53 Rev. 5 OSCAL JSON)
• Data flow diagram: source → integration layer → maintenance management system.
• RACI and control ownership record.

Technical configuration

• Integration configuration exports (API endpoints, connector settings, routing rules).
• Field mapping specification (asset IDs, severities, thresholds).
• Service account inventory and access grants for the integration.

Operational evidence

• Transfer logs (success/failure), with timestamps and correlation IDs.
• Exception tickets and resolution notes for failed transfers.
• Periodic reconciliation report showing completeness between source events and destination records.
• Representative sample of maintenance records created from predictive signals (screenshots or exported records).

Third-party evidence (if applicable)

• Contracts/SOWs defining integration responsibilities.
• Third-party attestation or documentation describing the connector behavior (only if you actually rely on it).

Common exam/audit questions and hangups

Assessors commonly probe these areas for MA-6(3):

1. “What is your organization-defined mechanism?” If you cannot name the mechanism and show it is approved, you will get a finding. (NIST SP 800-53 Rev. 5 OSCAL JSON)
2. “Which maintenance management system is the system of record?” Confusion between ITSM, CMMS, and custom tools creates gaps.
3. “Show me evidence the transfer is automated.” Manual exports are a red flag.
4. “How do you know data isn’t missing?” Lack of reconciliation/monitoring is a common hangup.
5. “How is access controlled for the integration?” Over-privileged tokens and shared accounts are frequent findings.
6. “Do you have examples of predictive signals turning into work orders?” They want to see the control driving operations.

Frequent implementation mistakes (and how to avoid them)

Mistake	Why it fails	How to avoid it
Defining the mechanism informally (tribal knowledge)	You cannot evidence the {{ insert: param, ma-06.03_odp }} requirement	Publish a short standard and link it to system boundary docs. (NIST SP 800-53 Rev. 5 OSCAL JSON)
“Automated” means “emails to a mailbox”	Email alerts don’t equal transferred maintenance data	Require creation of a record in the maintenance system with required fields.
Asset identity mismatch	Alerts land in CMMS but cannot be tied to the right asset	Establish a master asset ID and translation layer with governance.
No failure handling	Transfers break and nobody notices	Monitor integration health and create exception tickets automatically.
Overbroad integration permissions	Integrity risk and audit concern	Use least privilege service accounts and change control on mappings.
Evidence is improvised at audit time	Causes delays and inconsistent proof	Set a recurring evidence package and store it centrally.

Enforcement context and risk implications

No public enforcement cases were provided in the source material for this requirement, so you should treat MA-6(3) primarily as an assessment and operational resilience risk rather than a cited enforcement trend. (NIST SP 800-53 Rev. 5 OSCAL JSON)

Risk implications you can explain to leadership without stretching the text:

• Operational risk: missed early warnings can increase downtime and safety exposure.
• Security and integrity risk: if predictive maintenance data can be altered or lost in transit, your maintenance decisions and audit trails degrade.
• Assessment risk: inability to show an organization-defined mechanism and operating evidence often results in control deficiencies during NIST-based assessments.

A practical 30/60/90-day execution plan

You asked for speed; here’s a plan a CCO/GRC lead can run with, without pretending every environment is identical.

Day 0–30: Define, assign, and scope

• Name the maintenance management system(s) in scope and confirm the system of record.
• Document the {{ insert: param, ma-06.03_odp }} mechanism selection and get approval. (NIST SP 800-53 Rev. 5 OSCAL JSON)
• Identify predictive data sources and classify which ones must feed the maintenance system.
• Assign control owner and technical owners; agree on evidence cadence.

Day 31–60: Implement automation and minimum viable evidence

• Build or configure the integration for at least one high-value asset class.
• Implement basic monitoring and an exception workflow.
• Produce initial evidence: configuration export, sample transferred records, initial log set.

Day 61–90: Scale, harden, and make it auditable

• Expand coverage to additional asset classes and data sources.
• Add reconciliation between source events and destination records.
• Tighten access controls for integration identities and mappings.
• Package an “audit-ready” evidence bundle and store it in your GRC repository.

Where Daydream fits naturally If your pain point is evidence sprawl, Daydream can track MA-6(3) ownership, link the implementation procedure to the approved {{ insert: param, ma-06.03_odp }} mechanism, and schedule recurring evidence collection so you are not rebuilding proof each assessment cycle.

Frequently Asked Questions

What counts as “predictive maintenance data” for MA-6(3)?

Treat it as condition-based signals that indicate impending failure or degraded performance, such as sensor readings, diagnostics, and health metrics that should drive maintenance actions. The key test is whether the data is intended to inform maintenance decisions in your maintenance management system. (NIST SP 800-53 Rev. 5 OSCAL JSON)

Does MA-6(3) require a specific tool like a CMMS or EAM?

The text requires transfer to a “maintenance management system,” but it does not name a brand or category beyond that. Document which system is your system of record and show that predictive data lands there through the organization-defined mechanism. (NIST SP 800-53 Rev. 5 OSCAL JSON)

Can we meet MA-6(3) with manual uploads or spreadsheets?

Manual steps undermine the “automated support” intent and create audit fragility. If you must start manually, treat it as a temporary gap and implement an automated transfer with monitoring as the target state.

What should the {{ insert: param, ma-06.03_odp }} mechanism look like in practice?

It should be a clearly defined and approved method (e.g., API integration, connector, message bus, or secure file transfer) that your teams can implement consistently. Write it down, scope it, and keep configuration evidence that proves it is in use. (NIST SP 800-53 Rev. 5 OSCAL JSON)

How do we prove completeness to an auditor?

Keep transfer logs plus a reconciliation that compares source predictive events to destination maintenance records, and document how exceptions are handled. Auditors want to see you can detect and correct missing transfers, not just that an integration exists.

What if a third party operates the monitoring platform or the maintenance system?

You still own the requirement outcome. Put integration responsibilities, logging access, and evidence delivery expectations into the third party contract and operating procedures, then retain artifacts that show the transfer works end-to-end. (NIST SP 800-53 Rev. 5 OSCAL JSON)