MANAGE-2.1: Resources required to manage AI risks are taken into account – along with viable non-AI alternative systems, approaches, or methods – to reduce the magnitude or likelihood of potential impacts.

To meet MANAGE-2.1, you must resource AI risk management realistically (people, tools, time, testing, monitoring, incident response) and document when a non-AI option would reduce impact likelihood or severity. Operationalize it by embedding a “resource + alternative analysis” gate into AI intake, risk assessment, and change approval. ¹

Key takeaways:

• Budget and staffing are controls: under-resourced AI risk management is a predictable failure mode you must address and evidence. ¹
• You need a repeatable, documented process to evaluate viable non-AI alternatives for high-impact use cases. ¹
• Auditors will look for artifacts that show you made tradeoffs explicitly, not assumptions. ¹
• Assign an accountable owner and collect recurring evidence, not one-time memos. ¹

MANAGE-2.1 is a pragmatic requirement disguised as a sentence: if you cannot fund, staff, and operate the controls needed to manage an AI system’s risks, you should not deploy it as designed. The requirement also forces an explicit decision point many organizations skip: sometimes the safer choice is a non-AI method (rules, thresholds, human review, process redesign, or a simpler statistical model) because it reduces the magnitude or likelihood of harm. ¹

For a Compliance Officer, CCO, or GRC lead, the fastest path to operationalizing MANAGE-2.1 is to convert it into an intake-and-approval checklist that asks two questions: (1) Do we have the resources to manage this AI risk profile throughout the lifecycle? (2) Is there a viable non-AI alternative that reduces risk while meeting business needs? The control becomes enforceable once you define who approves exceptions, what “viable alternative” means in your environment, and what evidence must be retained for each material AI system. ¹

This page gives you requirement-level implementation guidance with artifacts, step-by-step workflow, audit questions, and a practical execution plan aligned to the NIST AI Risk Management Framework. ²

Regulatory text

Requirement (excerpt): “Resources required to manage AI risks are taken into account – along with viable non-AI alternative systems, approaches, or methods – to reduce the magnitude or likelihood of potential impacts.” ¹

What the operator must do:

1. Identify the risk management work an AI system demands (governance, testing, monitoring, human oversight, security, privacy, incident handling, third-party oversight).
2. Confirm resourcing exists (named owners, time allocation, tooling, budget, vendor support) to perform that work continuously, not just pre-launch.
3. Evaluate non-AI alternatives as part of decisioning for build/buy/deploy and major changes, documenting why AI is still the right choice or why a non-AI approach is safer.
4. Choose the option that reduces harm (lower likelihood or magnitude of impact) when alternatives are viable and business requirements can still be met. ¹

Plain-English interpretation

MANAGE-2.1 means your AI program must be able to answer, with documentation:

• “Who is doing the risk work, using what tools, on what schedule, with what escalation path?”
• “If we didn’t use AI here, what else could we do, and would it reduce the chance or severity of harm?” ¹

This is less about abstract “responsible AI” statements and more about operational capacity and decision discipline. If a model requires monitoring for drift, bias, jailbreaks, prompt injection, data leakage, or explainability disputes, you need the staff and tooling to run those controls. If you do not, the compliant choice may be to pause deployment, narrow scope, add human review, or select a non-AI approach. ¹

Who it applies to

Entities: Any organization developing, procuring, or deploying AI systems, including those relying on third parties for models, platforms, data, or monitoring services. ¹

Operational contexts where auditors will focus:

• Customer-impacting decisions (eligibility, pricing, prioritization, fraud, complaints routing).
• HR and workplace decisions (screening, performance analytics).
• Safety-relevant use cases (operations, critical services, physical environments).
• Generative AI in workflows that can create compliance, privacy, or IP exposure (support, marketing, engineering, legal ops). ¹

Why it matters in third-party risk management: If a third party supplies the model or key components, you still need resources to manage residual risk: contract controls, monitoring, change notices, incident response coordination, and exit plans. MANAGE-2.1 will fail if you outsource the system and forget to staff the oversight. ¹

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

Step 1: Define a “Resource & Alternatives” gate in your AI lifecycle

Add a mandatory gate to:

• AI intake/request
• Pre-production risk assessment
• Material change approval (model updates, new data sources, new use case, new user group)
• Annual (or event-driven) reassessment (major incidents, drift signals, regulatory changes)

Gate output must be one of: Approve / Approve with conditions / Defer pending resources / Reject in favor of non-AI. ¹

Step 2: Create a resourcing checklist tied to the system’s risk profile

Build a checklist that maps risk to concrete capacity, for example:

• Accountable owner (business) and control owner (GRC/Model Risk/Engineering).
• Monitoring plan: performance, drift, input anomalies, harmful output, security abuse cases.
• Testing capacity: pre-release evaluation, regression tests, red teaming for misuse scenarios where relevant.
• Human oversight: review queues, escalation, override authority, training for reviewers.
• Incident response: playbooks, on-call, rollback/kill switch, customer communications path.
• Third-party management: SLAs for incident notice, audit rights, model change notifications, subcontractor transparency.
• Data governance: access controls, retention, provenance checks, privacy reviews. ¹

Operational tip: force each checklist line to have a named role, not “the team.” Unowned controls become non-controls in an exam. ¹

Step 3: Run a structured “viable non-AI alternative” analysis

Require requesters to identify at least one alternative in a short decision record. Common alternatives:

• Rules/thresholds with periodic tuning
• Human-only workflow
• Human-in-the-loop triage with AI suggestions
• Simpler non-AI analytics (descriptive stats, deterministic scoring)
• Process redesign (remove the decision point entirely)
• Narrower AI scope (use AI only for low-impact steps)

Use a simple decision matrix:

Criterion	AI approach	Non-AI alternative
Meets business requirement	Yes/No + notes	Yes/No + notes
Expected impact magnitude	Low/Med/High + rationale	Low/Med/High + rationale
Expected impact likelihood	Low/Med/High + rationale	Low/Med/High + rationale
Control complexity	Low/Med/High	Low/Med/High
Resources required to operate controls	Low/Med/High	Low/Med/High
Residual risk after controls	Accept/Not accept	Accept/Not accept

Your approval memo should explicitly state why the chosen approach reduces magnitude or likelihood of harm compared with viable alternatives. ¹

Step 4: Quantify “resources required” in operational terms (not budget dollars)

MANAGE-2.1 does not require you to publish budget numbers. It does require you to demonstrate that resourcing was “taken into account.” Do it with operational commitments:

• Assigned roles and backups
• Time commitments in a RACI or operating model
• Tooling approvals (ticketing, logging, eval harness, monitoring)
• Vendor support commitments
• Training completion for reviewers and operators

If resourcing is not available, document the decision: delay launch, narrow scope, add controls, or select non-AI. ¹

Step 5: Map the requirement to policy, procedure, owner, and recurring evidence

Turn MANAGE-2.1 into a control with:

• Policy statement: AI deployments require documented resourcing and alternatives analysis.
• Procedure: steps above with templates and approvals.
• Control owner: typically GRC + AI governance lead + system owner.
• Evidence cadence: collected per system per material change. ¹

Daydream fit: many programs fail on “recurring evidence” because it’s scattered across tickets, docs, and emails. A GRC workflow in Daydream can standardize the gate, require the decision matrix, assign owners, and collect artifacts on schedule without chasing teams.

Required evidence and artifacts to retain

Retain artifacts per AI system (and per major change):

• AI intake/request form with intended use, users, impact domain, and decision authority
• Risk assessment that references lifecycle controls and required resources ¹
• “Resources plan” (RACI, operating model, monitoring schedule, escalation path)
• Alternatives analysis decision record (decision matrix + rationale) ¹
• Approval/exception record (who approved, conditions, expiry/review triggers)
• Monitoring runbooks and sample outputs (dashboards, alerts, tickets)
• Incident response playbook references and tabletop notes (if performed)
• Third-party due diligence package if components are externally sourced (contracts, SLAs, change notice terms, security documentation)
• Training attestations for reviewers/operators, where oversight depends on humans

Artifact quality test: can a third party reviewer understand what you decided, why, and who is responsible, without interviewing the team.

Common exam/audit questions and hangups

Expect questions like:

• “Show me how you decided AI was necessary here. What alternatives were considered?” ¹
• “Who owns ongoing monitoring, and how do you know it’s happening?”
• “What happens when the model drifts or produces harmful outputs? Who can stop it?”
• “If a third party updates the model, how do you detect and respond?”
• “Where is the evidence that resourcing was evaluated before launch and at change time?” ¹

Hangup: teams provide a risk assessment but no resourcing proof. Auditors treat that as an incomplete control because the risk response is not operationally feasible.

Frequent implementation mistakes and how to avoid them

1. Mistake: Treating resourcing as a one-time project plan.
   Fix: require an ongoing operating model and evidence of recurring monitoring/ticketing. ¹

2. Mistake: “No non-AI alternative exists” with no analysis.
   Fix: require at least one documented alternative and explain why it fails requirements or increases risk. ¹

3. Mistake: Assuming the third party manages the risk.
   Fix: document your retained responsibilities (oversight, incident handling, customer impact management) and contract for change notifications. ¹

4. Mistake: Approving AI before identifying who will do human oversight.
   Fix: make reviewer staffing a precondition, with queue management and escalation rules.

5. Mistake: Resource plans that ignore “last mile” compliance work.
   Fix: include privacy reviews, records retention, customer communication workflows, and complaint handling in the resource checklist.

Enforcement context and risk implications

NIST AI RMF is a framework, not a regulator, so the requirement itself is not “enforced” the way a statute is. Your risk is indirect but real: if an AI system causes harm and you cannot show that you assessed resourcing and safer alternatives, you will struggle to defend governance decisions to regulators, customers, auditors, and internal oversight bodies. MANAGE-2.1 is also a control maturity signal; weak evidence often correlates with weak ongoing monitoring and poor incident readiness. ¹

Practical 30/60/90-day execution plan

First 30 days (stabilize the control design)

• Appoint a control owner and define approval authority for exceptions. ¹
• Draft the “Resource & Alternatives” gate checklist and decision matrix template.
• Identify the initial in-scope AI inventory (start with highest-impact systems and GenAI tools used in regulated processes).
• Choose your evidence system of record (GRC tool, ticketing, or Daydream workflow) and define required fields.

Days 31–60 (pilot and collect evidence)

• Pilot the gate on a small set of AI systems or new requests.
• Run alternatives analysis for each pilot system; document approval outcomes and conditions. ¹
• Define minimum monitoring and incident requirements by risk tier (qualitative tiers are acceptable if consistent).
• Add third-party contract requirements for model changes and incident notifications where applicable.

Days 61–90 (operationalize and audit-proof)

• Embed the gate into SDLC/procurement change management so it cannot be bypassed.
• Establish recurring evidence collection (monitoring outputs, approval renewals, exception reviews). ¹
• Train approvers and reviewers on the decision matrix and documentation expectations.
• Conduct an internal audit-style walkthrough: pick one system and verify the full evidence trail from intake to monitoring.

Frequently Asked Questions

Do we have to prove the non-AI option is “better,” or just consider it?

MANAGE-2.1 requires that viable non-AI alternatives are taken into account and that you aim to reduce impact magnitude or likelihood. Document the alternatives considered and why the chosen approach is the safer feasible option for the business need. ¹

What counts as a “viable” non-AI alternative?

Viable means it can meet the core business requirement within your constraints (timeliness, cost, accuracy, staffing) while reducing risk. If it fails requirements, say so and keep the evidence. ¹

We buy AI from a third party. Can we rely on their documentation for MANAGE-2.1?

You can use third-party materials as inputs, but you still need your own resourcing and alternatives decision record for your deployment context. Your obligations include oversight, incident handling, and customer impact management. ¹

How do we operationalize “resources required” without sharing budget numbers?

Tie resources to named roles, tooling, monitoring schedules, and escalation paths. Auditors usually accept operational evidence over financial disclosure if it proves controls can run consistently. ¹

What triggers a refresh of the alternatives analysis?

Refresh on material changes: new use case, new user population, model/provider changes, new data sources, major incidents, or a meaningful risk reassessment outcome. Align the trigger to your change management process. ¹

What is the minimum evidence set for a low-risk internal GenAI tool?

Keep an intake record, a brief resourcing note (who monitors and handles incidents), and a short alternatives rationale (for example, why a template-based process is insufficient). Low-risk does not mean “no documentation”; it means “scaled documentation.” ¹

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF program page