MA-6(2): Predictive Maintenance

MA-6(2) requires you to run predictive maintenance for defined system components at defined locations, using condition data and analytics to prevent failures before they occur. To operationalize it, scope the assets, define predictive triggers and actions, assign ownership, and retain repeatable evidence that the monitoring ran and maintenance actions were executed and tracked. ¹

Key takeaways:

• Scope “what” (components) and “where” (locations/environments) for predictive maintenance, then document it.
• Implement condition monitoring with thresholds, alert routing, and a work-order process that closes the loop.
• Keep assessor-ready evidence: telemetry/alerts, tickets, maintenance logs, and trend reports tied to assets. ¹

The ma-6(2): predictive maintenance requirement is a maintenance operations control, but assessors treat it like an evidence discipline. You need a defined population of components, a defined operating context (for example, specific facilities, enclaves, or cloud environments), and a repeatable mechanism that detects precursors to failure and triggers maintenance before the component fails. That “before failure” element is what distinguishes predictive maintenance from reactive break/fix and from time-based preventive maintenance.

For a CCO or GRC lead, the fastest path is to treat MA-6(2) as a closed-loop workflow: instrument → detect → triage → approve → execute → verify → record. Your objective is operational resilience (fewer outages, fewer emergency changes) and auditability (clear, reviewable artifacts). This page translates the requirement into concrete steps, deliverables, and audit-ready evidence you can implement without waiting for a multi-quarter reliability program.

The control text includes organization-defined parameters for the components and locations. Your first operational decision is what you will define those parameters to be, and how you will prove you consistently met them. ²

Regulatory text

Requirement (verbatim): “Perform predictive maintenance on {{ insert: param, ma-06.02_odp.01 }} at {{ insert: param, ma-06.02_odp.02 }}.” ¹

What the operator must do

1. Define the parameters:

   • ma-06.02_odp.01 (what): the system components subject to predictive maintenance (for example, UPS batteries, SAN controllers, hypervisor clusters, network core switches, HSMs, HVAC supporting a server room).
   • ma-06.02_odp.02 (where): the locations or environments where predictive maintenance must occur (for example, “primary data center and DR site,” “all production cloud accounts,” “all facilities processing federal data”). ¹

2. Perform predictive maintenance on that defined scope:

   • “Perform” means you can show it actually ran: monitoring occurred, signals were reviewed, and maintenance actions were initiated and completed based on predicted issues, not only after failures.

3. Be prepared to evidence repeatability:

   • An assessor will expect you to show ongoing operation, not a one-time project.

Plain-English interpretation (what MA-6(2) expects)

Predictive maintenance means you watch condition indicators (telemetry, diagnostics, health checks, error rates, capacity and performance trends, environmental readings) and act when patterns suggest a component is likely to fail or degrade soon. You are allowed to decide the scope and location, but once you define them, you need a reliable process that produces maintenance actions and records.

A practical compliance framing: MA-6(2) is “condition-based monitoring + a work-order process + proof.” If you can’t produce the proof on demand, you will struggle in an assessment even if the ops team “does this informally.”

Who it applies to (entity and operational context)

MA-6(2) is relevant for:

• Federal information systems, including agency-operated infrastructure and systems. ³
• Contractor systems handling federal data, where NIST SP 800-53 controls are flowed down contractually or required via an authorization boundary. ³

Operationally, it most often applies to:

• Data center and facilities-supported IT: power, cooling, environmental sensors, physical infrastructure supporting system availability.
• Enterprise infrastructure: network, storage, virtualization, directory services, identity infrastructure components, PKI/HSM.
• Cloud operations: platform metrics and managed service health signals, with incident/problem management and change control integration.

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

Use this as your build checklist. Keep each step tied to an artifact.

Step 1: Define scope and ownership (make the ODPs real)

Create a one-page “MA-6(2) Scope Statement” that specifies:

• In-scope components (ODP.01): asset classes and critical instances.
• In-scope locations/environments (ODP.02): facilities, network zones, cloud accounts/subscriptions, or authorization boundary.
• Exclusions: what is explicitly out of scope and why (for example, end-user devices if handled under a different maintenance standard).
• Control owner: usually Infrastructure Ops, Facilities, or SRE; GRC owns oversight.
• Ticketing system of record: where maintenance work is tracked.

Output artifact: MA-6(2) Scope Statement + RACI.

Step 2: Establish condition monitoring signals (the “predictive” part)

For each in-scope component class, identify:

• Signals: SMART disk errors, ECC memory rates, PSU voltage variance, temperature/humidity, fan speed anomalies, interface error rates, battery internal resistance, capacity thresholds, certificate/key-store health, etc.
• Collection method: NMS/APM tooling, cloud monitoring, OEM management interfaces, sensor platforms.
• Trigger thresholds: what conditions generate an alert or create a work item.
• Sampling and retention approach: align to your logging/monitoring standards; keep enough history to show trend-based decisions.

Output artifacts: Monitoring/telemetry mapping table; alert rules list; dashboard screenshots (controlled evidence).

Step 3: Define triage and decisioning (avoid noisy-alert failure)

Write a short “Predictive Maintenance Runbook” for each major component group:

• Alert severity mapping (informational vs action-required).
• Triage steps (confirm signal, rule out false positives, check correlated indicators).
• Decision criteria (repair/replace, firmware update, capacity expansion, vendor RMA).
• Escalation path (on-call, facilities, third party support).

Output artifacts: Runbooks; on-call/escalation matrix.

Step 4: Integrate with work management and change control

Predictive maintenance must result in controlled maintenance actions:

• Auto-create tickets for defined triggers, or require documented manual creation with evidence of review.
• Link to change control when maintenance impacts production (patching firmware, replacing core components, failover tests).
• Close-the-loop verification: ticket cannot close without validation steps (post-maintenance health checks, updated baselines).

Output artifacts: Ticket templates; workflow configuration; sample tickets showing alert → ticket → change → completion.

Step 5: Execute and document recurring review

Predictive maintenance fails in audits when it’s “set and forget.” Establish:

• A review cadence for trend reports and recurring signals (document what “recurring” means in your environment).
• A KPI set that is qualitative if you can’t cite numbers (for example, “repeat alerts reduced,” “fewer emergency repairs,” “aging components identified before outage”).
• Periodic tuning of thresholds and rules with change records.

Output artifacts: Review meeting notes; monthly/quarterly trend report; threshold change records.

Step 6: Prove coverage across components and locations

Assessors will sample. Prepare a coverage map:

• Asset inventory list of in-scope components tagged “MA-6(2) Applicable.”
• Monitoring coverage indicator (monitored/not monitored) and rationale.
• Location/environment mapping (which facility or cloud account each asset belongs to).

Output artifacts: CMDB export or inventory report; monitoring coverage matrix.

Required evidence and artifacts to retain (assessment-ready)

Retain evidence that shows design and operation:

Design evidence

• MA-6(2) Scope Statement (ODP definitions). ¹
• Predictive Maintenance Policy/Standard (can be a section in your maintenance policy).
• Runbooks and alert rule definitions.
• RACI and escalation matrix.

Operating evidence

• Monitoring dashboards or reports showing condition signals for in-scope assets.
• Alert history for sampled components.
• Tickets/work orders created from predictive indicators, with timestamps and assignment.
• Change records where maintenance required controlled implementation.
• Maintenance logs, vendor RMA records, and post-maintenance validation results.
• Periodic review artifacts: meeting notes, trend reports, tuning approvals.

Tip for fast audit response: keep an “MA-6(2) evidence binder” folder keyed by quarter and by asset class.

Common exam/audit questions and hangups

Use these as pre-audit prompts.

1. “What are the organization-defined components and locations?”
   Hangup: teams say “all critical systems” without an explicit list or boundary.

2. “Show me predictive maintenance actually occurring.”
   Hangup: only preventive schedules exist (calendar-based maintenance) with no condition-based triggers.

3. “How do you ensure alerts lead to action?”
   Hangup: alerts exist in a tool, but tickets are not created or not linked to resolution evidence.

4. “How do you handle third-party-maintained components?”
   Hangup: contracts exist, but no proof of performed predictive maintenance or shared reporting.

5. “How do you ensure consistent coverage across environments?”
   Hangup: production is monitored; DR or remote sites are not, despite being in-scope.

Frequent implementation mistakes and how to avoid them

Mistake	Why it fails	Fix that works in practice
Defining scope too broadly (“everything everywhere”)	You can’t prove consistent execution	Start with truly critical components and named environments; expand deliberately
Confusing preventive with predictive	Time-based tasks don’t show prediction	Add condition signals + thresholds + action workflow
Monitoring without ticketing	No closed-loop evidence	Auto-ticket or require documented review and work initiation
No documentation of ODPs	Assessors can’t test	Publish ODP.01/ODP.02 in a scope statement and SSP/control narrative
Third party “handles it” with no artifacts	You still own oversight	Add contract/reporting requirements; collect monthly health summaries and RMAs

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement, so you should treat MA-6(2) primarily as an authorization and auditability risk rather than an enforcement headline risk. ¹

Operational risk is straightforward:

• Missed degradation signals increase the chance of unplanned downtime, emergency maintenance, and rushed changes.
• Weak evidence increases assessment findings, which can affect ATO outcomes and contractual obligations tied to NIST SP 800-53. ³

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

First 30 days (stabilize scope and evidence)

• Assign control owner and write the MA-6(2) Scope Statement (ODP.01 and ODP.02).
• Pick top component classes that are most likely to be sampled (core network, storage, identity, power/cooling where applicable).
• Inventory in-scope assets and tag them in CMDB/inventory.
• Identify what monitoring already exists and where gaps are.
• Create the evidence folder structure and start collecting sample alerts + tickets.

Days 31–60 (close the loop)

• Define thresholds and alert rules for each component class.
• Publish runbooks and escalation paths.
• Connect monitoring to ticketing (or establish a documented manual review workflow).
• Require post-maintenance validation steps and attach results to tickets/changes.
• Begin recurring trend reviews and record decisions (tuning, replacements, RMAs).

Days 61–90 (make it assessable and sustainable)

• Produce a coverage matrix: assets → monitoring → ticket workflow → review cadence.
• Run a tabletop assessment: pick sampled assets, pull evidence, and verify traceability end-to-end.
• Extend scope to remaining in-scope locations/environments, especially DR and remote sites.
• Add third-party reporting requirements where predictive maintenance is outsourced.
• Implement continuous evidence collection in Daydream so the control stays audit-ready without scrambling at assessment time.

Where Daydream fits: map MA-6(2) to a named owner, a documented procedure, and a recurring evidence set, then keep evidence current so you can answer sampling requests quickly.

Frequently Asked Questions

What counts as “predictive” maintenance for MA-6(2)?

Maintenance triggered by condition indicators and trends, such as rising error rates or degrading component health, rather than a fixed calendar schedule. You should be able to show the signal, the decision, and the resulting work order. ¹

How do I fill in the organization-defined parameters (ODP.01 and ODP.02)?

Define ODP.01 as a concrete list of component types and critical instances, and define ODP.02 as the environments or facilities where those components operate. Write both into a scope statement so auditors can test against it. ¹

If we outsource hardware support to a third party, are we still responsible?

You still need oversight evidence. Get maintenance reports, health summaries, and RMA records from the third party and link them to your asset inventory and ticketing records.

Do cloud-native services require predictive maintenance?

For managed services, you can’t replace components, but you can monitor service health metrics, quotas, error rates, and capacity trends, then take preventive actions (scaling, configuration changes, failover testing) with documented work items.

What’s the minimum evidence an assessor will ask for?

A defined scope (components and locations), proof that monitoring is running, and sampled examples where a predictive signal created a maintenance action with closure evidence. Keep alert history and tickets linked to assets.

How should we handle “noisy” alerts so teams don’t ignore them?

Treat tuning as part of the control. Document threshold changes through your change process, and keep a short record of why the tuning occurred and what evidence supports the revised settings.

Footnotes

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

2. NIST SP 800-53 Rev. 5; Source: NIST SP 800-53 Rev. 5 OSCAL JSON

3. NIST SP 800-53 Rev. 5